ON THE PRESENCE OF MEASUREMENT ERROR IN THE REGRESSION ...dbrau.org.in/Journals/Agra University Journal Scie.pdf · ON THE PRESENCE OF MEASUREMENT ERROR IN THE REGRESSION METHOD OF

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ON THE PRESENCE OF MEASUREMENT ERROR IN THEREGRESSION METHOD OF ESTIMATION

MEENAKSHI SRIVASTAVA AND NEHA GARG

Abstract : In survey sampling, data collection undervarious kinds of sampling schemes and various waysof estimation procedure are generally based on theassumption that the observation has been recordedwithout any measurement error. But practically, it isnot true because every data may contain observationaland measurement errors.In the present article, we have examined the effect ofmeasurement error on the regression method ofestimation of population mean.Key words : regression method, measure-ment error.1. IntroductionIn survey sampling usually we study the properties ofthe estimators which are based on different samplingschemes and different estimation procedures assumingthat observation on y, on ith unit is the correct valuefor that unit. These assumptions hold reasonably wellin simpler types of surveys in which measuring devicesare accurate and quality of work is high. But incomplex surveys, particularly when difficult problemsof measurement are involved, the assumptions maybe far from true. The data may contain observationalor measurement errors due to various reasons. In thisregard, Shalabh (1997) has considered the estimationof population mean arising from a ratio method ofestimation and has analyzed its properties in thepresence of measurement errors. Sahoo et. al. (2006)have conducted an empirical study to examine themagnitude of imprecision introduced in the ratio andregression estimators in the presence of measurementerrors. Recently, Baxter et. al. (2010) have analyzedthe effects of exposure measurement error on healthfrom traffic related air pollution.

Meenakshi SrivastavaDepartment of Statistics, Institute of Social Sciences,Dr. B. R. Ambedkar University, Agra, India.Neha GargSchool of Sciences, Indira Gandhi National OpenUniversity, New Delhi, India.

*[email protected]**[email protected].

The accuracy of an estimate is affected by errorsarising from causes such as incomplete coverage andfaulty procedures of estimation together withobservational errors. The result of sample surveys arealways subject to some uncertainty firstly because onlya part of the population has been measured andsecondly because of error of measurement.This uncertainty can be reduced by taking larger sampleand by using superior instruments of measurement.The error of estimate arises solely from the randomsampling variation that is present when n units aremeasured instead of the complete population of N units.Three additional sources of error that may be presentare as follows :

a. Failure to measure some of the units in the chosensample. This may occur by oversight or withhuman population, because of failure to locatesome individuals or their refusal to answer thequestions when located.

b. Errors of measurement on a unit. The measuringdevice may be biased or imprecise. With humanpopulations the respondents may not possessaccurate information or they may give biasedanswers.

c. Errors introduced in editing, coding and tabulatingthe results.

These sources of error necessitate a modification ofthe standard theory of sampling.In this paper, an attempt has been made to examinethe effect of measurement error in the estimation ofpopulation mean.The motivation has been derived from the procedureof Shalabh (1997). For this purpose, two estimatorshave been considered first one is the traditionalunbiased estimator of the population mean y and the

second one is the linear regression estimator .lry

2. Notations And Main ResultsLet y and x denote the study variable and the auxiliaryvariable taking values Xi and Y i (1 < i < N) respectivelyfor the unit of a population of size N. It is assumed

Agra University Journal of Research : ScienceVol. 1. Issue. 1 (January–April, 2017), pp 1–3

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that y1 and x1 (1 < i < n) respectively are the valuesfor the ith sampling unit obtained through simple randomsampling procedure, which are recorded instead oftrue values Xi and Yi The difference between thereported value and the true value is called the error ofobservation. The observational or measurement errorsare defined asui = (yi – Yi)vi = (xi – Xi)Which are assumed to be stochastic with mean zerobut possibly different variances σu

2 and σv2.

For the sake of simplicity in exposition, we assumethat ui’s and vi’s are uncorrelated although Xi’s andYi’s are correlated. Thus, Cov(u,v) = 0. Such aspecification can , however be, relaxed at the cost ofsome algebraic complexity. It is also assumed thatfinite population correction can be ignored i.e .( – )

1≅N n

NLet µx and µy be the population means and σx

2 andσy

2 be the population variances of X and Ycharacteristics. Further, let ρ be the populationcorrelation coefficient between X and Y.We generally use (sample mean of Y variable) as thetraditional unbiased estimator of population mean µy.Another way of estimating population mean is ratiomethod, which utilize the prior information and hasfound to be more precise. The ratio method, assumingthat µx is known and is different from zero is given by

= R xy

t µx

Where denotes the sample mean of X.Like the ratio estimate, the linear regression estimateis designed to increase the precision of the estimateby the use of auxiliary variate xi that is correlated withyi. It has been examined that if the relation betweenxi and yi is approximately linear and the line does notpass through the origin then the estimate based onlinear regression is more efficient than the ratio oftwo variables.The linear regression estimate of µy, the populationmean of yi is

iry = – ( – )xy b x µ

Where b is the linear regression coefficient of y on xin the finite population

i.e. b = 2

σ

σyx

x

Where σyx = population covariance between X and Y.

2.1 Some Derived Results For Analyzing TheProperties Of The Estimators y And lry In ThePresence Of Measurement Errors

In order to study the efficiency properties of the lry inthe presence of measurement error, let us denote

Cy =

σ y

yµ Coefficient of variation of YY

Wu =–1/2 ,∑ in u

Wy =( )–1/2 –∑ i yn Y µ

Cx =

σ y

yµ Coefficient of variation of X

Wv =–1/2 ,∑ in v

Wx =–1/2 ( – )∑ i xn X µ

2.2.1 The Bias Of y In The Presence OfMeasurement Errors

( )– yy µ = ( )1– + ∑ i y iY µ u

n

= –1/2( )x un W W+

Thus, we find that it is unbiased.

2.2.2 The Variance Of y In The Presence OfMeasurement Errors

( )V y = 2 21 σ + σ y un2.2.3 Linear Regression Estimate In ThePresence Of Measurement ErrorWe can express the linear regression estimate as

–lr yy µ = ( – ) – ( – )y xy µ b x µ

= 1/2

1( ) – ( )y u x vW W b W W

n + +

2.2.3 (a) Bias Of Linear Regression Estimate InThe Presence Of Measurement Error

Thus the biasness of lry up to order O (n–1) is given by

( )B lry = E ( – µ )lr yy

= 0Hence we observe that it is unbiased.

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2.2.3 (b) The Variance Of lry In The PresenceOf Measurement Errors

Mean square error of lry up to order O(n–1)∴ Bias = 0,∴ MSE = Variance

( )lrV y = 2( – )lr yE y µ

( )lrV y = 2 2 21[ y u b

nσ + σ +

2 2( ) – 2 ]x v x ybσ + σ σ σρ

It is clear from the above expressions that themeasurement errors have no influence at all on theunbiasedness of both y and .lry

Also the sampling variability in each case increaseswhen measurement errors are present and the increasein variability attributable to measurement errors is lessin case of y than .lry

2.2.4 Efficiency Comparison

Regression estimator lry will be superior than y upto order O(n–1) when

2

212

x v

y x

b σ σρ < + σ σ

if b < 0

2

212

x v

y x

b σ σρ > + σ σ

if b > 0

It is evident from the above expressions that both ofthese conditions will be satisfied only if σv

2 does notexceed σx

2 . One can infer that, if the auxiliarycharacteristic is poorly measured such that errorvariance σv

2 is larger than σx2, then y will be more

precise than .lry

This result shows that regression estimator will bebetter than sample mean in the absence of anymeasurement error.

ReferencesBaxter, L.K., Wright, R.J., Paciorek, C.J., Laden, F.,Suh, H.H. and Levy, J.I. (2010). Effects of exposuremeasurement error in the analysis of health effectsfrom traffic related air pollution, Journal of Expo-sure Science and Environmental Epidemiology, 20,101-111.

Sahoo, L. N., Singh, G. N. and Das, B. C. (2006). Anote on an IPPS Sampling Scheme, Advances inStatistical Analysis, 90 (3), 385-393.

Shalabh (1997). Ratio Method of Estimation in thePresence of Measurement Errors, Journal of IndianSociety of Agricultural Statistics, 50 (2), 150-155.

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Meenakshi Srivastava & Neha Garg : On the Presence of Measurement Error in the Regression Method of Estimation

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AN EFFECTIVE CRYPTOGRAPHY ALGORITHM FOR LOSSLESSIMAGE TRANSMISSION

TRIBODH TRIPATHI AND DR. DIPAK SUDHALWAR

Abstract : In this research work a unique algorithmis proposed for Digital images. A new symmetric keygeneration method for colour images is proposed forsecured images. The image is encrypted with the helpof one time password. Two public key of matrix size4*4 and 8*8 is used to encrypt the image. A colourimage of JPG format with resolution of 168*128 hasbeen used. To make transmission lossless, perfectsquare matrix approach has been followed.IntroductionSecurity is the basic need for multimedia datatransmission. In daily life we use internet to shareimportant data with our friends .The multimedia dataneeds to be protected from unauthorised person. Toprotect data from unauthorised person data protectiontechniques are required. Data encryption is one of theimportant techniques used for data protection. (Tribodhetal. 2015) Two fundamental technologies are used toprotect the image, these are water marking andencryption, a lot of research work has been completedin Water marking, however image encryption is stillan emerging area of research.The experimental result has been compared with otheralgorithm by implementing proposed algorithm inMATLAB software. Result indicates that proposedwork provides satisfactory outcome. Due to thisbenefits algorithm has a wide range of applicationKeywords : Encryption, Decryption, Symmetric key,Cipher ImageLiterature ReviewKaladharan (2014) has proposed new approach ofcryptography algorithm. Algorithm gives fine resultswith some draw backs. Whenever image size isincreased, it

Tribodh [email protected]. Dipak SudhalwarAssociate Professor (CSE)Department of Engineering and TechnologyPSS Central Institute of Vocational Education, NCERT,Bhopal

[email protected]

takes more time to encrypt. So further analysis ofvarious techniques of encryption and decryption ofimage are very much needs to achieve satisfaction.Kumar, and Agrawal, (2013) provided symmetric keycryptography algorithm for image transmission theydrew certain conclusion :

1. There is a need of new version of VideoEncryption Algorithm (VEA) is developed, whichrequired less computation than the old versionand achieve the same encryption results. Thatalgorithm can be used to secure many MPEGvideo applications.

2. Some algorithm can achieve an acceptablequality of service and suitable for differentsecurity level of the video

3. Some encryption model based on the orthogonaltransforms for images. Symmetric encryptionmethod use Malakooti Raeisi (M-R) transformalgorithm for key generation of DCT, HT andMT.

4. Cryptography algorithm for multimedia (that isimages and video) is not so easy. DES, AES,RES are not suitable for colour images and video,which have 3D arrays of data

Mohammad (2012) has proposed a new algorithm forimages based on the orthogonal transforms. Thismethod is based on the block cipher symmetric keycryptography. In this paper Author emphasis ondevelopment of a novel lossless digital encryptionsystem for multimedia. (Pucch et al. 2012)Francesco, 2011, provided a fast generation procedureof authentication codes for images contentcryptography, whose length and computationalcomplexity can be tuned accordingly to the specificmobile service and application. Authors suggested adigital algorithm to generate a pair of long(asymmetric) keys from one short primitive key.Mazloom and Moghadam, (2011) proposed a novelimage cryptographic algorithm based on confusion–diffusion architecture that is specifically designed forcolor images encryption, which are 3D arrays of datastreams. An image encryption is somehow differentfrom text data encrypted due to some inherent features


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of the images. Bhowmik and Achrya (2011) and willet al. (2010).Marwaha and Marwaha (2010) has described thatCryptography and steganography area unit theforemost wide used techniques to beat this threat.Cryptography involves changing a text message intoan unreadable cipher. On the opposite hand,steganography embeds message into a canopy mediaand hides its existence. Each these techniques givesome security of information neither of them alone issecure enough for sharing data over an unsecurecommunicating and area unit susceptible to trespasserattacks. (Marwaha and Marwaha worha, (2010).Proposed AlgorithmStep 1 : Take an original imageStep 2 : Make a divisible image matrixStep 3 : Take three value p, q, r from senderStep 4 : Generate keys by using these valuesStep 5 : Extract red colour component from original

imageStep 6 : Extract green colour component from

original imageStep 7 : Extract blue colour component from

original imageStep 8 : Reshape RGB separately by using keysStep 9 : Combine all encrypted RGB component

into a single matrixStep 10 : EndResultResults were found after implementing experimentalsetup in MATLAB software.

Fig.1.1 Original image Fig.1.2 Cipher imageFig.1.3 Decrypted image

A colour image of JPG format with resolution of168*128 has been used. Here flower.jpg image is usedfor encryption purpose Figure 1.1 represents originalimage, Figure 1.2 represents cipher image Figure 1.3represents decrypted image.Histogram Representation of ImagesHistogram shows the intensity of image. Result showsthat both the histogram (either original image ordecrypted image are identical. This indicates that wehave reconstructed lossless image at receiving end.In plotted histogram x axis represents the grey colour

level of given image and y axis represents number ofpixels in each grey level

Fig.1.4 Histogram of original image

Histogram Representation of ImagesHistogram shows the intensity of image. Result showsthat both the histogram (either original image ordecrypted image are identical. This indicates that wehave reconstructed lossless image at receiving end.In plotted histogram x axis represents the grey colourlevel of given image and y axis represents number ofpixels in each grey level. Here both histogram isidentical that maeans transmit image is similar toreceived image.Mean Square Error of Proposed WorkMean Square error was calculated by comparingoriginal image matrix to decrypted image matrix. HereN represents the num ber of block of image matrix

Transform/ N = 32 N = 64 N = 128Mean SquareErrorDiscrete Cosine 5.079 5.640 6.249Transform E-9 E-9 E-9(DCT)Malakooti 0 0 5.749Transform (MT) E-17Hadamard 0 0 0Transform (HT)Proposedalgorithm using 2 2 1DCTProposedalgorithm without 0 0 0using anytransform

Table 1.1 Mean Square error for various transform

Tribodh Tripathi & Dr. Dipaka Sudhalwar : An Effective Cryptography Algorithm for Lossless Image Transmission

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ConclusionIn this paper, we proposed a lossless image encryptiontechnique based on square matrix approach of images.Our encryption method is based on symmetric keycryptography in which key is shared in receiving andtransmitting end. The encrypted image is obtained bychanging the pixel value of original image. We testedproposed algorithm with several images and result werecompared with other encrypted techniques, resultshows the satisfactory quality of outcome. Proposedalgorithm is secured for image transmission. Meansquare error of reconstructed image indicates thatproposed work provides lossless image transmission.Dissertation work is limited only for image data.Proposed algorithm may not be suitable for video dataas video has more size as compared to image andrequires more real time for execution, hence there arepossibilities for future development. The futuredirection of this work is to develop suitable algorithmfor multimedia data like video and sound. Another majorarea for extending the work is by providing losslessand secure video and sound transmission in wire andwireless mode.

ReferencesAmitava Nag, Jyoti Prakash Singh, Srabani Khan,Saswati Ghosh Sushanta Biswas, D. Sarkar, ParthaPratim Sarkar “Image encoding exploitation Affineremodel and XOR Operation” Proceedings of 2011International Conference on Signal process,Communication, Computing and NetworkingTechnologies (ICSCCN 2011)].

Fan Wu, Chung-han bird genus, and Hira Narang “ANeconomical Acceleration of bilaterally symmetrical KeyCryptography victimization General Purpose Graphicsprocess Unit” technology Department TuskegeeUniversity 2010 Fourth International Conference onrising Security data, Systems and Technologies]

Francesco, Benedetto, Gaetano Giunta “An EffectiveCode Generator for Frequent Authentication of

Multimedia Contentsin Mobile Applications andServices” IEEE; 2011 Digital Signal Processing,Multimedia, and Optical Communications Lab. Dept.of Applied Electronics, University of ROMATRE©2011978-1-4244-8331-0/11].

Niraj kumar, Prof Sanjay Agrawal “Issues andChallenges in Symmetric Key based CryptographicAlgorithm for Videos and Images”; May 2013;IGARCSSE, volume 3

Mohammad V. Malakooti, Mojtaba Raeisi NejadDobuneh “A Lossless Digital Encryption System forMultimedia Using Orthogonal Transforms”; 2012;IEEE; 978-1-4673-0734-5/12/2012 IEEE.

Piyush Marwaha, Paresh Marwaha “VisualCryptographic Steganography In Images” IEEEInfosys Technologies Limited, India, @2010, 978-1-4244-6589-7/10/2010].

S a h a r M a z l o o m , A m i r - M a s u E f t e k h a rMoghadam,“ColorImage Cryptosystem using ChaoticMaps”; 2011 IEEE; Faculty of electricacomputer andIT Engineering, 978-1-4244-9915-1.

Sandeep Bhowmik Sriyankar Acharyya “ImageCryptography: The Genetic Algorithm Approach”IEEE; 2011, 978-1-4244-8728-8/11]

Tribodh Tripathi, Anshuj Jain and Bharti Chourasia“Study on generating a cryptography algorithm forimage transmission with no losses”; October 2015;IJSETR, Volume 4, Issue10

Vincy.J, Gowtham.K “Design of New CryptosystemUsing Menezes Vanstone Cryptosystem ” ; February2014 ; IGARCSSE ,Volume 4, Issue 2

W. Puech, Z. Erkin, M. Barni, S. Rane, and R. L.Lagendijk “Emerging Cryptographic Challenges InImage And Video Processing Mitsubishi ElectricResearch Laboratories”, TR2012-067 September 2012.

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FUZZY INFERENCE SYSTEM TO TAKE THE DECISION FORTHE PREMIUM OF CAR INSURANCE

SANJEEV KUMAR, SANJAY CHAUDHARY AND ANURAG SISODIA

Abstract : Now days the insurance companies arestill working on the traditional market system and thatis based on the previous experiences of company aswell as the survey made by that company. There maybe some other factors also, but this traditional way isalways helpful for the owner having negative creditwhile not helpful for the owner having positive credit.Therefore it is important that there may be a properinsurance premium so that owner having positive creditshould get bonus while owner having negative creditshould have malus. A model is designed here for thepremium of car insurance which is based on the fuzzyinference system. The input factors used in this modelare: age of car, colour of car, driving record from RTO(Regional Transport Office) and geographical conditionof area where car has driven, while the output is thepremium of car insurance.Keywords : Fuzzy logic, insurance, inferencesystem, claim validation, MATLAB, soft computing.Introduction :During the past few decades, fuzzy logic has used asan attractive tool for various applications ranging fromhousehold goods, finance, pricing decision trafficcontrol, automobile speed control, nuclear reactor, andearthquake detections etc. Fuzzy inference system isthe best tool to solve not only for the problems of controlsystems but can also handle the problems of real worklike medicine, economy, finance etc. As far as theapplications of fuzzy logic are concerned thanBuckley (1987) used the concept of fuzzymathematics in finance and again Buckley (1992)solved the fuzzy equations of economics and finance.After having applications in economics and finance,Cummins et al (1993) gave an idea about fuzzytrends in property–liability insurance claim costs. Inthe same direction Derring et al (1995)

Sanjeev KumarDept. of MathematicsIBS Campus, Dr. B. R. Ambedkar University, Agra

[email protected] ChaudharyDept. of MathematicsIBS Campus, Dr. B. R. Ambedkar University, AgraAnurag SisodiaDept. of Mathematics

used fuzzy techniques of pattern recognition in the riskand claim classification.In this paper we studied insurance of car dependingupon various factors. Vehicle driven up to 10,000 kmhave the safest records while vehicle drivenbetween10,000-50,000 km have less chance ofaccidents and vehicle driven more than 50,000 km areunder at greater risk of being in an accident. Furtherliving in areas with little or no traffic are likely to spendless on insurance than those living in congested citiessuburbs because areas with a lot of traffic may tendto see more accidents.Introduction to problem and significanceFor the purpose of illustration, we consider three inputs-age of the car (Φ1) (in km. driven), colour of car (Φ2),and driving record from RTO (Φ3). These indices arerepresentative of the risk value for premium calculation[Kumar 2010].

1. Basic rate = Rs. 5,000.2. Evaluate the authenticity of claim settlement. The

values of the inputs of the claims have to beevaluated, Φ1 = 36,000 Km., Φ2 = 0.65 (0 forwhite and 1 for black) and Φ3 = 0.42 (say)

3. Fuzzification of the crisp values of inputs.Through the use of membership functionsdefined for each fuzzy set for each linguisticvariable, the degree of membership of a crispvalue in each fuzzy set is determined as follows :

Normally the insurance premium has two componentsbasic rate and an increment or decrement. Basic ratecan be calculated according to the age of the car andthe current price of the car, while increment ordecrement is to adjust the premium, based on the riskassociated with a particular client. A risk value between0 and 1 suffices to set a net rate.Premium = Basic Rate + ((risk value/0.5)–1) xbasic rateRISK FACTOR FOR A CAR INSURANCE PREMIUM :Three risk factors for a car insurance premium (andtheir inputs) :


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1. Age of Car. (Input: Short, Moderate, High)2. Color of car. (Input: Very good, Good, Bad)3. Driving record from RTO. (Input: Good,

Average, Bad)And there are two outputs :

1. Malus. (Ma) 2. Bonus. (Bo)

Methodology :In this methodology the model is divided into four mainparts [Zimmerman and Zysno (1980)]

1. Fuzzification :The process of translating the measured numericalvalue into fuzzy linguistic values is called fuzzification.In another words, fuzzification is the process ofchanging a real scalar value into a fuzzy value (or intoa degree of belongingness).

2. Fuzzy Inference Engine :Once all crisp input have been fuzzified into theirrespective linguistic values, the inference engine willaccess the fuzzy expert system to device linguisticvalues for the intermediate as well as the outputlinguistic variables. It has a number of rules thattransform a number of variables into a fuzzy result,i.e. the result is described in terms of membership infuzzy sets.

3. Rule Base :The fuzzy rule base is characterized by constructing aset of linguistic rules based on expert knowledge. Theexpert knowledge is usually in the form of if-thenrules. These are two steps in the rule base process,viz: aggregation which is the process of computing forthe values of the if (antecedent) part of the rules whilecomposition is the process of computing for the valuesof then (consequent) part of the rules.4. Defuzzification :This is the last stage of fuzzy inference system, whichis used to convert the fuzzy output set to a crispnumber. The method we often use is the centre ofgravity defuzzification method (COG method). It isthe basic general defuzzification method that computesthe centre of gravity of the area under the membershipfunction.Algorithm :Following are the steps of the expert system :1. Input Functions :The crisp value of the age of vehicle, colour of car,driving record from RTO, insurance claim settlementand other information are obtained.(a) Age of the Car (in Km. Travelled) (Φ 1)First input variable is taken as the age of vehicle onbasis of km. driven, not related to the number of year(as taken by automobile insurance company).

Table 1: Linguistic Rating Conversion Tablefor Age of Car Criteria

Linguistic Trapezoidal crisp valuerating (x103 Km) (p, r , s, q)

Short (S) (0, 0, 6, 10)Moderate (M) (6, 15, 30, 50)High (H) (30, 55, 80, 80)

The fuzzy set related to the age of vehicle is characterizedby a trapezoidal membership function, that is :

1 1

1

1

1

– ifmax. 0,–

( ) 1 if–

max. 0, if–

p rr p

µ r sq

sq s

φ φ <

φ = ≤ φ < φ

... (1)

Fig.1. Age of car in Km travelled (Φ1)


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(b) Colour of Car (Φ 2)Here we take second input variable as colour of car. For this we take triangular membership function as givenbelow :

Fig.2. Colour of car (towards to darkness in %)

Table 2: Linguistic Rating Conversion Table for Colour of Car Criteria.

Linguistic rating Colour of car Triangular crisp value (p, r, q)

Very good (VG) White colour (0, 0.30, 0.40)

Good (G) Light colour (0.30, 0.55, 0.65)

Bad (B) Dark colour (0.55, 0.70, 1)The fuzzy set related colour of car: characterized by a triangular member function such that :

2 2

2

2

2

– ifmax. 0,–

( ) 1–

max. 0, if–

p rr p

µq

rq r

φ φ <

φ = φ φ≤

... (2)

(c) Driving Record from RTO (Φ 3)Here we take that third input variable as driving record from RTO. For this we take triangular membershipfunction as given below :

Fig.3. Driving record from RTO (for 5 years)

Sanjeev Kumar, Sanjay Chaudhary and Anurag Sisodia : Fuzzy Inference System to take the Decision for the....

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Table 3: Linguistic Rating ConversionTable for driving record from RTO

Linguistic rating Triangular crisp value(p,r,q)

Good(G) (0, 0.20, 0.40)Average(AV) (0.20, 0.45, 0.70)Bad(B) (0.45, 0.72, 1)

The fuzzy set related to the driving record from theRTO is characterized by a triangular membershipfunction such that :

3 3

3

3

3

– ifmax. 0,–

( ) 1–

max. 0, if–

p rr p

µq

rq s

φ φ <

φ = φ φ≤

... (3)

2. Fuzzify the crisp values of inputs :Through the use of the membership functions definedabove for each fuzzy set for each linguistic variable,determine the degree of membership of a crisp valuein each fuzzy set.3. Fire the rule bases that correspond to these

inputs :All expert systems which are based on fuzzy logic if-then rules. The “if ” part is known as antecedent orpremise, whereas the “then” part is termed as aconsequence or conclusion. Three inputs have threefuzzy sets. Therefore 27 (3 × 3 × 3) fuzzy decisions areto be fired with two outputs: bonus (Bo) and malus (Ma).4. Execute the inference engine :The two main steps in the inference process areaggregation and composition. Aggregation is theprocess of computing the value of the if part of therules while composition is the process of computingthe value of the then part of the rules. Duringaggregation, each condition in the if part of a rule isassigned a degree of truth based on the degree ofmembership of the corresponding linguistic term. Thenext step in the inference process is to determine thedegrees of truth for each linguistic, term of the outputlinguistic variable. Usually, either the max or sum ofthe degrees of truth of the rules with the same linguisticterm in the then parts is computed to determine thedegrees of truth of each linguistic term of the outputlinguistic variable.5. Defuzzification :Defuzzification is interpreting the membership degreesof the fuzzy sets into a specific decision or real value.A common and useful defuzzification technique iscentre of gravity. The centre of gravity (COG) is themost popular defuzzification technique and is widely

utilized in most of the applications. The defuzzificationof the data into a crisp output is accomplished bycombining the result of the inference process and thencomputing the “fuzzy centroid” of the area. Theweighted strengths of each output member functionare multiplied by their respective output membershipfunction center points and summed. Final output, thisarea is divided by the sum of the weighted membershipfunction strength and the result is taken as the crispoutput.

Table 4 : Sample rule base for the fuzzy logicbased expert system

Input OutputRule Age of Colour of Driving YNo. Car (f1) Car (f2) Record

fromRTO (f3)

1. S VG G Bo2. M VG G Bo3. H VG G Bo4. S VG AV Bo5. M VG AV Bo6. H VG AV Bo7. S VG B Bo8. M VG B Bo9. H VG B Ma

10. S G G Bo11. M G G Bo12. H G G Bo13. S G AV Bo14. M G AV Bo15. H G AV Bo16. S G B Bo17. M G B Bo18. H G B Ma19. S B G Bo20. M B G Bo21. H B G Ma22. S B AV Bo23. M B AV Bo24. H B AV Ma25. S B B Ma26. M B B Ma27. H B B Ma


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6. Output of the decision of the expert system :As discussed in the previous chapter the types ofoutput are: bonus or malus on the premium. Thespecific features of each controller depend on themodel and performance measure. However, inprincipal, in all the fuzzy logic based expert system,we explore the implicit and explicit relationship withinthe system by mimicking human thinking andsubsequently develop the optimal fuzzy control rulesas well knowledge base.

Fig.4. Output of the decision of the expert (Y)

Case Study : For the purpose of illustration, we consider threeinputs- age of car (f1), colour of car (f2), and drivingrecord from RTO (f3). These indices arerepresentatives of the risk value for premiumcalculation.

1. Let basic rate = Rs. 5,0002. Evaluate the risk factor. The values of the inputs

for the premium have to be evaluated.f1 = 36,000Km, f2 = 0.65 and f3 = 0.42

3. Fuzzification of the crisp value of inputs. Throughthe use of membership function defined for eachfuzzy set for each linguistic variable the degreeof membership of a crisp value is each fuzzy setis determined as follows :

µ (f1) S = 0 µ (f2) V = 0 µ (f3) G = 0µ (f1) M = 0.70 µ (f2) G = 0 µ (f3) AV = 0.88µ (f1) H = 0.24 µ (f2) B = 0.66 µ (f3) B = 04. Fire bases that correspond to these inputs based

on the value of the fuzzy membership functionvalues for the examples under consideration, thefollowing values apply :

Rule : 23 If f1 is Moderate, f2 is bad and f3 is Average,so Y is Bonus (Bo).Rule : 24 If f1 is high, f2 is bad and f3 is Average, soY is Malus (Ma).5. Execute the inference engine :We use minimum (min) method to combine the effectsof all applicable rules, so;

For Rule : 23 Y is 0.66 i.e. fuzzy value for Bonus =0.66 andFor Rule : 24 Y is 0.24 i.e. fuzzy value for Malus =0.24

6. Defuzzification :We use weighted average method for defuzzification.The defuzzification of the data into crisp output isaccomplished combining the results of the inferenceprocess. The weighted average method informed byweighting each membership function in the output byits respective max membership value and the result istaken as the crisp output. He crisp output (risk value) is0.456.

Z = ( )( )

c

c

µ z zµ z

ΣΣ

The crisp output belongs to the set of Bonus (Bo) ismore than the Malus (Ma). Hence the decision in thiscase is to provide bonus to the car owner.Premium = basic rate + ((risk value/0.5)–1) x basicrate

= 5,000 + ((0.456/0.5)–1) × 5,000= 4,560.

So a customer gets ` 440 as bonus

Conclusion :The development of a fuzzy based expert system forBonus and Malus for car premium in insurance isreported in this paper. By considering a case studywe observed that the customer gets a bonus, so thecustomer get a benefit and similarly if the customergets a malus then in this situation the insurancecompany get a benefit. Our future efforts will be onthe improvement of the performance of the systemby adjusting the membership function of the inputs. Itwould be interesting to tune the rule base using datafrom real life problems so that the performance of thesystem is optimized. Other factors that may help todetermine the insurance premiums such as drivingdistance to work, years of driving experience, businessuse of the vehicle, theft protection devices, andtopological condition of driving area.ReferencesArora, N. and Arora, P. (2014) “Insurance premiumoptimization : Perspective of insurance seeker andinsurance provider” Journal of management andScience ,4,43-53.

Beekman, J.A. and Fuelling, C.P. (1990) :“Interestand mortality randomness in some annuities”,Insurance: Mathematics and Economics, 9, 185-196.

Sanjeev Kumar, Sanjay Chaudhary and Anurag Sisodia : Fuzzy Inference System to take the Decision for the....

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Berkan, R.C. and Trubatch, S.L. (1997) : “FuzzySystem Design Principles: Building Fuzzy if-then RuleBases”, Wiley-IEEE Press.

Buckley, J.J. (1992) : “Solving fuzzy equations ineconomics and finance”, Fuzzy Sets and System”, 48,289-296.

Buckley, J.J. (1987) : “The fuzzy mathematics offinance”, Fuzzy Sets and System”, 21, 257-273.

Cummins, J.D. and Derring, R.A. (1993) : “ Fuzzytrends in property-liability insurance claim costs”, TheJournal of Risk and Insurance, 60, 429-465.

Derring, R. and Ostaszewski, K. (1995) : “Fuzzytechniques of pattern recognition in the risk and claimclassification,” The Journal of the Risk and Insurance,62, 3, 447-482.

Dewit, G.W.(2003): “ Underwriting and Uncertainty”,Insurance Mathematics and Economics, 1, 277-285.

Horgby, P. J. (1998) :”Risk classification by fuzzyinference”, The Geneva Paper on Risk and InsuranceTheory, 23, 63-82

Kumar, S. and Jain, H. (2011) : “Indicative results onthe risk of cancellation of policies; A fuzzy approach”,Workshop on Opt. and Info. Theory with their Appl;24th -26th March, 60-66.

Kumar, S. and Pathak, P. (2010) : “Fuzzy basedbonus-malus system for premium decision in carinsurance”, Int. Rev. of Pure & Applied Math, 1, 77-81.

Kumar, S. and Pathak, P. (2009) : “Premiumallocation- fuzzy approach in insurance business”,Proceeding of the 3rd National Conference;INDIACom, 703-705.

Zadeh, L.A. (1965) : “Fuzzy sets”, Information andControl, 8(3), 338-353.Zimmermann, H. J. and Zysno, P. (1980): “Latentconnectives in human decision making”, Fuzzy Setsand Systems, 4, 37-51.

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NEURAL NETWORK TECHNIQUES FOR PARSING NATURALLANGUAGE SENTENCES

SUKRATI CHATURVEDI AND MANU PRATAP SINGH

Abstract : In the paper we are presenting an approachfor parsing natural language sentences using neuralnetworks. Here we are applying pre-processingtechniques to code the sentences into string of bitsthen after training process is started for the patternavailable in the form of coded information. Themultilayer feed forward networks are used here fortraining the neural networks to classify the words intoappropriate syntactical categories. The classifiedwords are representing the parse information of thegiven sentences. The main function of the network isto assign the respective syntactical categories to eachword of a sentence with a minimal error rate. Besidethis we are also presenting the comparison betweenthe two popular neural network approaches i.e. feedforward neural network and radial basis neuralnetwork.Key Words : Neural Networks, Parsing, Multilayerfeed forward network, Radial Basis Function.1. Introduction :Natural Language Processing (NLP) is the field ofstudy that deals with the interactions between humanlanguage and computers understanding and forinterpreting the natural language English sentences.NLP systems have useful roles, such as convertingspeech to text, grammar correction and automaticallytranslating between languages. NLP is a way forcomputers to analyze, understand, and derive meaningfrom human language in a smart and useful way[1].Natural language processing systems take stringsof words (sentences) as their input and producestructured representations capturing the meaning ofthose strings as their output. The nature of this outputdepends heavily on the task at hand [2]. The naturalsentences are mostly acquired by the machine asparsing tree. Therefore the parsing of NLP is a majorarea in the field of NLP. The parsing step is dividedinto many steps: lexical and morphological analysis

Sukrati Chaturvedi and Manu Pratap SinghDepartment of Computer Science, Dr. B. R. AmbedkarUniversity Khandari, Agra, India.

[email protected][email protected]

which highlights the basic constituents of the phrase,syntactic analysis which finds out the syntacticcategories (noun, verb, adjective etc.) of suchconstituents, and semantic analysis which tries to catchthe meaning of the phrase often contributing to itsdisambiguation [3].In this paper we are applying multilayer feed forwardnetworks with variant of optimize back propagationalgorithm (Levenberg-Marquardt backpropagationalgorithm) and radial basis function network forgenerating the parsing of selected words/phrases fromthe constructed vocabulary. In the vocabulary we arealso assigning respective syntactical categories (verb,noun, adjective etc.) of the collected words. Theconstruction of vocabulary is considered as a pre-processing step. Further in the pre-processing the words/phrases are coded in binary string to construct the patternvector for training set. Then these pattern vectors arepresented to Feed Forward Network for capturing thegeneralized classification for these words/phrases intothe respective pre defined syntactical categories.The paper is organized as follows: section 2 presentsthe pre-processing steps, section 3 introduce neuralnetwork model, section 4 presents the coding part,section 5 represents the simulated results and section6 presents the conclusion followed by references.

2. Pre-processing :The system is consisted of a pre-processor, a neuralparser, a vocabulary and a phrase parser. An overallview of the system is shown in figure 1. The input tothe pre processor will be a phrase and the phraseparser will produce the output by assigning the eachword of the phrase its syntactical category.

Figure 1: An overview of the system


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The first task of the pre processor is to furtherdecompose the paragraph (or sentence) into stand-alone phrases by detecting punctuation marks likecommas, full stops, half colons, sentences insideparenthesis etc. After detecting punctuation marks westore phrases for further processing. Then wedecompose these stored phrases into individual words.Now the following pre-processing steps have beentaken in the further pre-processing :(i) First, the system detects the articles in the phrases

and gets rid of the articles.(ii) Then mark the word which follows the article as

noun.(iii) And then, the words are categorized.(iv) The last step of pre-processing is to code the

words according to their syntactical categories.After getting input codes from the pre-processor, theneural parser performs the further computation.3. Introduction to NN modelWe will discuss both the neural network models: themultilayer feed forward neural network and Radialbasis function network.3.1 Multi Layer Feed Forward Network :Let us describe more about the neural parser, whichis the core of the syntactical analyzer. It is composedof a multi layer feed forward network with one hiddenlayer.The feed forward neural network architectureoften usesfor the back propagation-learning algorithmfor the determination of the weights between thedifferent interconnected layers. This learningprocedure uses gradient decent technique, applied toa sum of square error function for the given pattern.It evolves the iterative procedure for minimization ofan error function, with adjustments to the weights beingmade in a sequence of steps [4].

Figure 2: Three layer feed forward networkSince the input pattern vector al is given at the inputlayer i.e. al

1, al2,…….., al

1, and the desired outputvector bl for the output layer i.e bl

1, bl2………., bl

K

should available only at the output layer, the errorbetween the desired output layer bl

k and the actualoutput vector Sl

k, where k = 1,…, K is available onlyat the output layer. Now, the error for the lth patternfrom each output unit can be defined as :

El =2

1

1[ – ]

2=

∑k

k kl l

k

b s (3.1.1)

The descent gradient along the error surface for thelth pattern to determine the increment in the weightconnecting unit j and k is :

∆Wjk = –∂

η∂

l

jk

EW (3.1.2)

Where η > 0 is a learning rate parameter The weightmodification between the hidden and the output layerand input and hidden layer for the lth pattern at the (t+ 1)th iteration can show as :

Wkj(t + 1) = Wkj (t) + ∆Wkj (t)

=1

( ) [ – ]=

+ η ∑k

l l l lkj k k k j

kW t b s s s (3.1.3)

and Wji (t + 1) = Wji (t) + ∆Wji (t)

=1

( ) [ – ]=

+ η ∑k

l l l l lji k k k kj j i

kW t b s s W s a (3.1.4)

where l = 1, 2,…………..L.3.2 Radial Basis Function Network :A Radial basis function network (RBFN) is a threelayer feed forward network that consists of one inputlayer, one hidden layer and one output layer as shownin Figure 3, each input neuron corresponds to acomponent of an input vector x. The hidden layerconsists of K neurons and one bias neuron. Each nodein the hidden layer uses an RBF denoted with φ(r), asits non-linear activation function [5].

Figure 3 : Architecture of the RBFN. The input layerhas N nodes; the hidden and the output layer have K


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and M neurons, respectively. φ0 (x) = 1, correspondsto the bias.The hidden layer performs a non-linear transform ofthe input and the output layer this layer is a linearcombiner which maps the nonlinearity into a new space.The biases of the output layer neurons can be modeledby an additional neuron in the hidden layer, which has aconstant activation function φ0 (r) = 1. The RBFN canachieve a global optimal solution to the adjustableweights in the minimum MSE range by using the linearoptimization method. Thus, for an input pattern x, theoutput of the jth node of the output layer can define as :

yj(x) = 01

(|| – ||)=

φ +∑k

kj k i k jk

w x µ w (3.2.1)

for j = (1, 2, ..... , , M) where y1 (x) is the output ofthe jth processing element of the output layer for theRBFN, wkj is the connection weight from the kth hiddenunit to the jth output unit, µkis the prototype or centreof the kth hidden unit. The Radial Basis Function φ (.)is typically selected as the Gaussian function that canbe represented as :

φk(xl) =2

2|| – ||

exp(– )2σ

i k

k

x µ for

k = (1, 2, ..... , , K) (3.2.2)and 1 for k = 0 (bias neuron)Where x is the N- dimensional input vector, µk is thevector determining the centre of the basis functionandφk and σk represents the width of the neuron. Theweight vector between the input layer and the kt h

hidden layer neuron can consider as the centre µk forthe feed forward RBF neural network.Hence, for a set of L pattern pairs {xl, yl}, (2.1) canbe expressed in the matrix form as :

Y = wTφ (3.2.3)where W = [w1............wm] is a KxM weight matrix,wj = (w0j............wkj)

T, φ = [φp.............φk] is a K xL matrix, φ l,k........[φ l,t.........φ l,k]

T is the output of thehidden layer for the lth sample, φ l,k = φ(||xl – ck||), Y =[y1, y2..........ym ] is a M x L matrix and y lj =(yl1..........ylm)T.

4. Coding of Syntactical Categories :To classify the word following article as noun (pre-processing step ii), we have used 9 bit coding as shownin Table 1. For classification of rest of the words of aphrase we have used 26 bit input coding for each wordof the phrase while output syntactical categories havebeen coded using 4 bits, as there should be no ambiguity.Input pattern vector has size 26X1 and in total wehave 5,411 sample patterns. Therefore, the inputpattern vector (say P) is of size 26X5411. Outputpattern vector has size 4X1, therefore the output patternvector (say T) is of size 4X5411. The possiblesyntactical categories are shown in Table 1, togetherwith their coding.Each bit in an 26 input represents the presence ofalphabets (A-Z). For instance, let our input string is“man”, then its input code will be :10000000000011000000000000Which represents that in the given input string one“a”, one “m”, and one “n” is present. F And if thecount of an alphabet present in an string is more thanone, the input code will show the total number of analphabet occurs in that string. For instance, let say“book” is the input string where “o” is occurring 2times, so its input code will be :01000000001000200000000000

Table1: Coding Of Syntactical Categories

Syntactical Input Coding OutputCategory (for 9 bit input N.N) Coding

Noun 000000001 0001Adjective 000000010 0010Pronoun 000000100 0011Personal 000001000 0100PronounAdverb 000010000 0101Conjunction 000100000 0110Verb 001000000 0111Aux. Verb 010000000 1000Preposition 100000000 1001

Table 2: An Example for Parsing a PhraseInput Phrase This book belongs To HerPossible Adj. Pro. Noun, Verb Verb Prep. Pers. Pron.Syn. Cat.Inputs 000000110 001000001 001000000 100000000 000001000Desired Adj. Noun Verb Prep. Pers. Pron.Syn. Cat.Outputs 0010 0001 0111 100000000 000001000

Sukrati Chaturvedi and Manu Pratap Singh : Neural Network Techniques for Parsing Natural Language Sentences

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5. Simulated Results :After following the pre-processing steps, we have beenconstructed the training set. The training set isconsisted of input pattern vector (P) and output patternvector (T).We have constructed the feed forward neural networkarchitecture as specified in figure 4.

Figure 4: MLFFN ArchitectureWe have trained our neural network using Levenberg-Marquardt algorithm. Training occurs according totraining parameters, with default values as shown inTable 3. Beside this we have also trained a RBFN forcomparing the two networks. RBFN architecture isshown in figure 5. Training parameters for RBFN areshown in Table 4.

Figure 5 : RBFN architectureTable 3 : Parameters used for LM algorithm

Parameter ValueEpochs between displays 25generate command line output 0show training GUI 1Maximum number of epochs to train 100Performance goal 0Performance goal 5Factor to use for memory/speed 1trade offMinimum performance gradient 1e-10Initial Mu 0.001Mu decrease factor 0.1Mu increase factor 10Maximum Mu 1e10Maximum time to train in seconds Inf

Table 4: Parameters used for RBF

Parameter ValueRxQ matrix of Q input PvectorsSxQ matrix of Q target class Tvectors

Mean squared error goal 0(default)Spread of radial basis 1.0(default)functionsMaximum number of neurons Q(default)Number of neurons to add 25 (default)between displays

6. Results and Discussion :Whole system was mainly divided into 2 parts: trainingand testing. During training the following results forboth of the networks are drawn:

Table 6 : Results drawn during training

Paramet MLFFN RBFNTraining time 00 : 02 : 16 00 : 02 : 51Number of epochs 20 16Accuracy 87.95% 99.45%Error 12.05% 0.55%

During training, ina. MLFFN, 4791 patterns were classified correctly

while 620 patters were misclassified.b. RBFN, 5381 patterns were classified correctly

while 30 patters were misclassified.c. 55 patterns were given to the system for testing.

The results for those testing patterns are:d. for MLFFN, 18 patterns were correctly classified

whilee. for RBFN, 38 patterns were correctly classified

7. Conclusion :In the proposed approach for parsing the neuralnetwork sentences we considered a multi layer feedforward neural network and also a radial basis functionnetwork. The training sets are composed of phraseswith known outputs. The vocabulary is composed of5411 words. Observations drawn from the abovementioned approaches are :MLFFN :a. Accuracy for training patterns : 99.45%b. Correct classification for testing patterns = 32.72%c. Misclassification for testing patterns = 67.28%RBFN :a. Accuracy for training patterns : 87.95%b. Correct classification for testing patterns = 69.09%c. Misclassification for testing patterns = 30.91%


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ReferencesC. Beardon, Lumsden and G. Holmes, “NaturalLanguage and Computational Linguistics”, EllisHerwood, Chichester, UK, 1991.

D. Archambault, J-C Bassano , “A Neural Networkfor Supervised Learning of Natural LanguageGrammar”, France, 1994.

M. Marchesi, G. Barabino, L. Benedicenti, “NeuralNetworks for Parsing Natural Language Sentences,IEEE Xplore,pg.1476-1479(1996).

Miao Kang, Dominic Palmer-Brown, “An AdoptiveFunction Neural Network (ADFUNN) for PhraseRecognition, United Kingdom, 2005.

Naveen Kumar Sharma, Dr. Manu Pratap Singh,Sanjeev Kumar “Mathematical Formulation for theSecond Derivative of Back Propagation Error withNon-Linear Output Function in Feed Forward NeuralNetworks”.

Naveen Kumar Sharma, S R Pande, “PerformanceEvaluation Analysis of MLP & DG-RBF FeedForward Neural Networks for pattern Classificationof Handwritten English curve scripts”.

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Sukrati Chaturvedi and Manu Pratap Singh : Neural Network Techniques for Parsing Natural Language Sentences

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ASSEMENT OF EXPOSURE OF MIXTURE OF AGRICULTURALTOXICANTS ON BLUE ROCK PIGEON (COLUMBA LIVIAGMELIN)

VIJAY KR. SINGH AND SOMA BHOWMICK

Abstract : With the increased use of pesticides, therisk of their adverse effects on the ecological systemincreases. These compounds are not only toxic totargeted organisms but also to the non-targetedindividuals inhabiting the pesticide treated area. Birdsare one of the most important non targeted speciescoming in direct or indirect contact of these syntheticchemicals. Haematological (Serum total protein,albumin, globulin, DNA and RNA) analysis of Columbalivia was done after oral exposure of mixture of threepesticides Fenvalerate, Isoproturon and Ziram. Serumtotal protein was found to be significantly decreased(P < 0.01) for acute and chronic exposure. Serumalbumin also showed significant decrease (P < 0.05).Globulin was non-significantly increased whenassessed for acute and chronic exposure. DNA andRNA was found to be increased significantly (P < 0.01).Key words : Fenvalerate, Isoproturon, Ziram, Proteins,Nucleic acids, Blue rock pigeon.INTRODUCTION :Indian agronomy is largely based on syntheticchemicals like pesticide and fertilizers. In the presentscenario, approximately 1,500 active ingredients andaround 900 mixed formulations of pesticides are usedin one form or another. Generally, study of singlepesticide and its effect on various organisms arecarried out but in environment most of the chemicalsexists in mixture. This mixture imparts a different modeof action of, when compared to a single activeingredient. Synthetic pyrethroids belong tocyclopropanic ester insecticide containing phenoxymoiety. They possess greater insecticidal activity andlower mammalian toxicity. Synthetic pyrethroidsattended approximately 20% of the commercialmarket, (Xpao-Dan et al. 2011). Fenvalerate is a classII synthetic pyrethroid, widely used due to its strongneurotoxic activity for insects and low toxicity for

Vijay Kr. Singh and Soma BhowmickToxicology Laboratory, Dept. of Zoology, Faculty ofLife Sciences, Agra College, Agra

[email protected]

mammals, birds and plants. Isoproturon is a selectiveherbicide, belonging to the chemical class of substitutedphenyl urea. Low acute toxicity for mammals and birdsresult in higher demand of Isoproturon.Dithiocarbamate compounds have low persistency inenvironment, low mammalian and avian toxicity. Ziram,used widely for plant fungal diseases is a class IIIdithiocarbamate fungicide. Ziram is also used as a birdand rodent repellant. It is a potent aneugen andendocrine disruptor. Though pesticides are unabidingin nature but there longer exposure is creating threatof harmful effects on non targeted organisms. Birdsare very important factor in any ecosystem and anychange in their metabolism may hinder their activitiesrequired for ecosystem food chain. In the presentstudy, haematobiochemical parameters of Blue rockpigeon were investigated to evaluate the toxic effectsof mixture of pesticides.MATERIALS AND METHODSExperimental AnimalBlue rock pigeon of almost equal size and weight (200± 20 gm) were selected randomly irrespective of sex.They were maintained in clean polypropylene cagesat temperature 30° ± 5°c, relative humidity 60° ± 5°and photoperiod 12 hours/day. They were providedmillets and pulses. Water was provided ad libitumthroughout the experimental period.Experimental Compound and DosingFenvalerate was purchased from Bharat InsecticideLtd., Isoproturon from Riedel (India) Chemical Pvt.Ltd and Ziram was obtained from FIL Industries Ltd.For dosing, a mixture of all the three compounds wasprepared in the ratio of (1:1:1) and LD50 wasdetermined by Log/ Dose Probit method (Finney, 1971).The LD50 of mixture was found to be 93.43 mg/kg.b.wt.Experimental ProtocolBirds were taken into seven groups, each consistingof five birds for Acute (1 and 2 day) and Sub-chronic(15, 30, 60 days) and 7 days Recovery for acute and45 days Recovery for subchronic dosing. Birds weretreated orally with a dose of 54.65 mg/kg.b.wt for


19 AUJR-S

acute treatment and 22.44 mg/kg.b.wt for sub-chronictreatment. Control groups received equal amount ofground nut oil only. Birds were etherized and blood ofexperimental birds of all the treated groups, wascollected from ventricle of heart at the end of thestipulated period. The collected blood samples werecentrifuged at 3000 rpm for 30 mins for the estimationof haematobiochemical parameters namely serum totalprotein, albumin, globulin, DNA and RNA by usingErba diagnostic kit.Statistical AnalysisThe data were analyzed statistically using Student’s‘t’test for significance level.RESULT AND DISCUSSIONSerum total protein level when compared with controlwas significantly decreased (P < 0.01) on day 1, 15,30 and 60 respectively. During recovery period (7 and45 day) changes returned to non-significant level incomparison to control values. Serum albumin level wasfound to be decreased significantly (P < 0.05) on day1 and significant (P < 0.01) decrease was noted after2, 15, 30 and 60 days treatment respectively. Recoverygroup showed non-significant changes. It was foundthat globulin constituents are mostly unaffected ofpesticidal combination except for day 1 and 60 whichshows significant decrease (P < 0.01) (Table I, II, III).Significant decrease in the levels of protein and albuminand non significant increase in globulin levels wereobserved following daily oral dosing of pesticidemixture. The decrease in the level of albumin mayresult from the poorer liver function as well as

proteinuria due to kidney damage as reported byUyanik et al. (2001). The cause for serum proteinlevel may be due to rapid break down of protein tomeet energy demands during toxic stress andmobilization of protein as reported by Latha andRajyasree (2012). Similar decrease in total protein dueto stressogenic effect was also reported by Sankhalaet al. (2012). Similarly, Manna et al. (2004) reportedthe decreased level of serum protein and globulin butnonsignificant decrease in albumin.Table iv. and v. shows the changes in DNA and RNAlevel of Blue rock pigeon. DNA of treated group showssignificant increase (P < 0.01) on all the respectivetreatment days when compared wit2h that of controlgroup. A non significant increase was found on 7thday recovery whereas 45th day recovery showed (P< 0.05) increment. Similar results were obtained inRNA assessment except for nonsignificant change inrecovery group (7 and 45 day) .The increase in DNAcontent may be correlated with increased number ofleucocyts as observed by Siroki et al.(1994) and Salehet al. (1998). The increase in RNA content may bedue to increase in RNA polymerase activity in cellularcomponent.DNA mediates the synthesis of nucleic acid in thecells that would likely affect the protein content ofcells in the body. Alteration in protein synthesis resultsin change of protein concentration. The change inprotein content further changes DNA concentration,(Sharma, 2004) and increase in DNA increases RNAconcentration as shown by Shivandappa andKrishnakumari (1981).

Table-I: Serum Total Protein of Blue rock pigeon treated with pesticide mixture (1:1:1).

TreatmentTime in days

1 2 7(R) 15 30 60 45(R)6.18 ± 0.37 6.05 ± 0.37 5.70 ± 0.35 6.65 ± 2.31 5.97 ± 0.36 6.02 ± 0.35 5.89 ± 0.36

ControlMixture (1:1:1)

3.84 ± 0.23a 4.79 ± 0.19b 5.25 ± 0.36° 4.64 ± 0.44° 4.75 ± 0.17a 3.88 ± 0.28a 5.64 ± 0.35c

Each value is a mean ± SE, n = 5, Statistical difference from control : a = highly significant at P < 0.01, b =significant at P < 0.05, c = non significant at P > 0.05.R = Recovery

Table-II : Serum Albumin of Blue rock pigeon treated with pesticide mixture (1:1:1).


1 2 7(R) 15 30 60 45(R)3.47 ± 0.22 2.07 ± 0.21b 4.06 ± 0.27 2.57 ± 0.16a 3.20 ± 0.23 3.30 ± 0.22c 3.57 ± 0.18

ControlMixture (1:1:1)

2.15 ± 0.22a 3.66 ± 0.20 2.18 ± 0.22a 3.45 ± 0.22 2.07 ± 0.19a 3.42 ± 0.24 3.49±0.19cEach value is a mean ± SE, n = 5, Statistical difference from control : a = highly significant at P < 0.01, b =significant at P < 0.05, c = non significant at P > 0.05. R = Recovery

Vijay Kr. Singh and Soma Bhowmick : Assement of Exposure of Mixture of Agricultural Toxicants on Blue Rock......

AUJR-S 20

Table-III: Serum Globulin of Blue rock pigeon treated with pesticide mixture (1:1:1).


1 2 7(R) 15 30 60 45(R)

Control 2.70 ± 0.16 2.77 ± 0.08a 1.99 ± 0.15 2.22 ± 0.05c 2.43 ± 0.17 2.53 ± 0.15c 3.08 ± 0.16

Mixture (1:1:1) 2.49 ± 0.32c 2.31 ± 0.20 2.57 ± 0.09c 2.57 ± 0.15 2.81 ± 0.07c 2.47 ± 0.19 2.30 ± 0.17a

Each value is a mean ± SE, n = 5, Statistical difference from control: a= highly significant at P < 0.01, b =significant at P < 0.05, c = non significant at P > 0.05. R = Recovery

Table-IV : DNA estim ation of Blue rock pigeon treated with pesticide mixture (1:1:1).


1 2 7(R) 15 30 60 45(R)

Control 27.82±1.24 80.04±3.25a 26.72±1.05 83.27±0.91a 25.16±1.01 26.17±1.06c 37.16±2.80

Mixture (1:1:1) 78.63±3.43a 30.73±2.70 75.03±3.52a 28.74±2.01 79.87±1.35a 31.09±2.18 40.10±1.29b

Each value is a mean ± SE, n = 5, Statistical difference from control : a = highly significant at P < 0.01, b =significant at P < 0.05, c = non significant at P > 0.05. R = Recovery

Table-V : RNA estimation of Blue rock pigeon treated with pesticide mixture (1:1:1).


1 2 7(R) 15 30 60 45(R)

Control 31.56±2.32 69.12±2.37a 34.10±4.97 63.21±3.49a 29.32±2.31 31.20±2.00c 32.84±3.06

Mixture (1:1:1) 69.02±3.63a 30.42±3.23 58.68±4.29a 32.69±2.42 68.51±2.71a 35.81±3.87 44.67±1.81c

Each value is a mean ± SE, n = 5, Statistical difference from control: a= highly significant at P < 0.01, b =significant at P < 0.05, c = non significant at P > 0.05. R = Recovery

REFERENCESa. Finney DJ. 1971.Probit analysis. Cambridge

University Press; PP 303.b. Latha, V.U.S and M.Rajyasree.2012. Effect of

carbamide on serum biochemical aspects of chick.Bioscan.7 :695-696.

c. Manna, S., D. Bhattacharyya, T.K. Mandal andS. Das. 2004. Repeated dose toxicity of alfa-cypermethrin in rats.J.Vet.Sci. 5 : 241-245.

d. Sankhala, L.N., S.M.Tripathi, S.K.Bhavsar,A.M.Thaker and P.Sharma. 2012. Hematologicaland immunological changes due to short term oraladministration of acephate.ToxicologyInternational.19: 162-166.

e. Saleh, A.T.,S.A.Sakr, Z.Y. Al-Sahhaf,O.M.Baharoth and O.M. Sarhan.1998. Toxicity ofpyrethroid insecticide tetramethin in albino rats:hematological and biochemical effects. J.Egypt.Ger.Soc. Zool.25 :35-52.

f . Sharma, D.C. 2004. Cytogenetic and biochemicalalterations in blood of albino rat after synthetic

pyrethroid intoxication.Phd thesis. D.B.R.A.Univ.Agra.

g. Shivanandappa.I. and M. K. Krishnakumari. 1981.Histochemical and biochemical changes in rats feddietary benzene hexacloride.Ind.J.OfExperi.Biol.19 : 163-168.

h. Siroki, O., L.Institoris,E.Tator and I.Desi.1994.Immunotoxicological investigation of SCMF, a newpyrethroid pesticide in mice. Hum. Exp.Toxicol.13:337-343.

i . Uyanik, F., M. Even, A. Atasever, G. Tuncoku andA.H. Kolsuz. 2001. Changes in some biochemicalparameters and organs of broilers exposed tocadmium and effect of zinc on cadmium-inducedalterations. Israel Jvct Med. 56 : 128-34.

j. Xpao-dan, S.H.I., B.I.Huan-jing, L.I. Liang-Yun,L.I.U., De-Kang and L.I. Jian-min. 2011. Effectof low dose fenvalerate on Semen qualitycapacitation in adult mice. Chinese MedicalJournal. 124: 1529-1533.

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CONTRIBUTION OF ENVIRONMENTAL POLLUTION OF PRE-TERM BIRTHS

MADHU ANAND AND AJAY TANEJA

Abstract : There are number of environmentalpollutants that have been explored for their potentialto increase the risk for preterm birth. Manyenvironmental chemicals deserve investigation in thisframework because of (a) prevalent exposures atpresent or in past, (b) proven reproductive toxicities inanimal studies, (c) ability to cross the placental barrierand (d) association with other adverse birth outcomessuch as low birth weight and intra uterine growthretardation (IUGR) that may result from relatedmechanisms. Several epidemiological studies haveshown significant association between differentpollutants and preterm birth, particularly for pesticides,metals and polycyclic aromatic hydrocarbons that mayincrease the risk of preterm birth. In conclusion, theepidemiologic studies estimating relationships betweenenvironmental exposures and preterm birth are veryfew, inconsistent and have limited statistical power.Keywords : Pre-term birth, Placenta, Pesticides,Polycyclic Aromatic Hydrocarbon, MetalsIntroduction : The prevalence of pre-term birth inIndia is a matter of serious concern because the rateof pre-term birth has increased in the last few yearsand reaches top of the countries, with highest numberof pre-term deliveries i.e., 3,341,000 every year. About361,600 children under five die due to direct pre-termcomplications (WHO, 2012). Pre-term birth is definedas babies born before 37 weeks of pregnancy, subcategorized into extremely pre-term (<28 weeks) verypre-term (28 to <32 weeks) and moderate to late pre-term (32 to <37 weeks) based on gestational age. Theetiology of pre-term birth is multifactorial and intricateand remains poorly understood, in fact, an exactmechanism cannot be recognized in most of the cases.There are various characteristics that have been

Madhu Anand and Ajay TanejaDepartment of Chemistry, Dr. B.R. AmbedkarUniversity, Agra, India.

[email protected]

associated with pre-term birth including demographicfactors (socio-economic strata, age, occupation, race),nutritional status, pregnancy history, present pregnancycharacteristics (diabetes mellitus and pre-eclampsia),infections, cervical dysfunction, foetal physiologicalstress (malformation, intrauterine growth retardation)and placental abruption etc. Several studies indicatethe role of environmental factors in detrimental effectson pregnancy outcomes (Ashton et al, 2009, Kumaret al, 2006 &Anand et al, 2015) because modern worldis supported by variety of chemicals and having thesechemicals in our bodies is unavoidable in recent time.Many of the chemicals that have been developed andare playing or once played important roles in our dailylife, some have toxicities as intentional (such aspharmaceuticals, pesticides and lead) or unintentional(such as polychlorinated biphenyls, some of flameretardants, environmental tobacco smoke etc.), forwhich the weight of evidence suggests that maternalexposure to these pollutants increases the risk of pre-term birth. Many of those environmental pollutantscross the placental barrier, therefore, may increasethe risk to infants.Organochlorine pesticides :To increase agriculture production, huge amount ofsynthetic fertilizers and pesticides are used by farmers.At a period when the world’s intensive hi-techagricultural system is being criticized for its health andenvironmental impacts, pesticides are gradually underscrutiny. Green revolution heavily relies on chemicalfertilizers, pesticides and herbicides which no doubtincrease the production rate but also give a scary byproduct in terms of various health disorder such ascancers of gall bladder, lung, breast, corpus uteri, ovary,thyroid, brain, non-Hodkin’s lymphoma and myeloidleukemia which have shown an increase (Siddiqqui etal, 2005, Farhang et al., 2005& McDuffie., 1994). Thelinks between pesticides and pre-term births aresupported by many studies that analyzed an associationbetween DDT exposure and preterm birth (Table 1).


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Table-1 Comparison of organochlorine pesticides in placenta/blood/milk samples worldwide.

Area Samples Pesticides References Size Specimen DDT HCH

LucknowIndia 100 Placenta 56.2 ppb 454.4 ppb Saxena et al., 1981Agra India 90 Placenta 19.68 ppb 110.85 ppb Anand et al., 2015Canada 2000 Maternal blood 48.33 ppm 2.31ppm Fisher et al., 2016Spain 2122 Maternal blood 115.90 ppb NA Basterrechea et al., 2014Norway 1070 Mother’s milk 65.48 ppb 4.26 ppb Forns et al., 2016Spain 320 Umbilical cord 10.16 ppb NA Monteaguda et al., 2016

serumFrance 556 Mother’s milk 63.4 ppb 11.21 ppb Antignac et al., 2016Delhi India 60 Maternal/ 3.8 ppb 19.8 ppb Dewan et al., 2013

cord blood 18.9 ppb 5.8 ppbDelhi India 70 Maternal blood 3.9 ppb 8.4 ppb Kalra et al., 2016Zagreb Croatia 33 Mother’s milk 23.2 ppb 4.7 ppb Klincic et al., 2016Spain 1117 Maternal blood 2.11 ppb NA Vafeiadiet al., 2014China 71 Umbilical cord 226.8 ppb 73.96 ppb Guo et al., 2014

serumShanghai 1438 Cord blood 1960 ppb 450 ppb Cao et al. 2011ChinaMexico 188 Blood serum 189 ppb 51 ppb Torres et al., 2003

A variety of agricultural chemicals are manufacturedand widely applied in the United States and worldwideto control pests and enhanced agricultural productivity.Exposures to human may result from production oruse or may be incidental because of contamination ofenvironmental media, like water, air, and food. Amongthe agricultural chemicals, pesticides have been themost intensively considered for their association withpreterm birth. Dichloro di phenyl trichloro ethane(DDT)is the most notorious pesticides because of itspersistent nature, poorly excreted, its property to mimicestrogen hormone and biomagnified in the food chain,thereby increasing potential for human exposure.Studies to date suggest that exposures to agriculturalchemicals deserve greater attention as potential riskfactors for preterm birth. Thefirst report byLongnecker et al. (2001) provides the strongestevidence for an association of DDT exposure withpreterm birth, although it should be noted that theexposure levels were substantially higher for thesamples used in that study compared with the currentlevels of DDT exposure in the United States. The firstIndian study to examine this relationship bySaxena etal 1981, observed that placental tissue and maternal

blood of the mothers who delivered preterm had higherlevels of DDT metabolites compared to controls i.e.,full-term delivery. In comparison with studiesconducted in other countries and cities, data found thatconcentrations of organochlorine pesticides were foundrelatively high in the population of China, although avirtual ban of organochlorine pesticides had been putinto effect in China since 1980s.Although prohibitions on the uses of organochlorinepesticides (OCPs) have been implemented indeveloping nations, several of them are still in use inSouth Asian countries. For instance, recent input ofDDTs to the environment has been monitoredregularly in Pakistan and India (Chakraborty et al.,2010; Syed and Malik, 2011; Eqani et al., 2011;Bergman et al., 2013; Syed et al., 2014, Alamdar etal., 2014;). India is involved in the manufacturing, useand export of OCPs on large scale, considered as thesecond largest pesticides producing country in Asiaafter China and fourth largest in the world after theUSA, Japan and China with 90,000 tons of annualproduction of pesticides like DDTs and HCHs (Khan,2010a &Pozo et al., 2011).


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Table-2Comparison of metals in placenta/bloodsamples worldwide.

Area Samples Metals References Size Specimen Pb Cd As Hg

Beijing 3254 Serum NA < 0.65, 0.65– NA NA Wang et al 2016China 0.94,

> 0.95µg/LAgraIndia 80 Placenta 2.93 µg/dl 0.40 µg/dl NA NA Singh etal 2016Canada 1835 Maternal 0.17- 4.04 <LOD- 4.65 <LOD <LOD- Thomas et al. 2015

blood µg/L µg/L 33.0 6.80µg/L µg/L

Sudan 97 Maternal 9.09µg/dL 1.76 NA NA Adam et al 2015blood µg/dL

Finland 130 Placenta 13.1 ng/g 3.70 5.68 2.31 Leino et al 2013ng/g ng/g ng/g

Tehran Iran 348 Maternal 3.8 µg/dL NA NA NA Vigeh et al. 2011blood

New York 43, Maternal 2.1µg/dL NA NA NA Zhu et al. 2010288 blood

Lucknow 60 Placenta 0.33 µg/g NA NA NA Ahamed et al. 2009IndiaChina 44 Placenta NA 0.084 -3.97 NA NA Zhang 2004

µg/gMurcia, 89 Placenta 103 ng/g NA NA NA Falcon et al 2003SpainCamdem 705 Maternal 1.18 µg/dL NA NA NA Sowers et al. 2002NewJersy bloodSweden 30 Maternal 11.2 µg/L 0.1 µg/g NA NA Fagher et al. 1993

blood/Placenta

LOD = limit of detection

MetalsThere are many studies on metal and metalloidexposure and preterm birth provide evidence for effectof Pb, Cd, As, and Hg at higher levels. Increasedlevelof lead affects the essential metal level and its levelincreased with parity whereas, there was a reductionof essential metals (Zn, Cu, Fe and Ca) present inmaternal body with increasing parity (Singh et al 2010).Elevated levels of Mn and Fe in drinking water arelinked with a reduction in birth weight in full term babies(Grazuleviciene et al. 2009). Maternal exposed to WF(welding fumes) and MD/F (metal dust/ fumes) during

pregnancy have reduce fetal growth; increase the riskof preterm delivery and small-for-gestational age(Quansah; 2008). Reduced iron levels and increasedconcentrations of cadmium, lead and arsenic inplacentas of mothers delivering low birth weightneonates. Among the metals, lead exposure has beenstudied the most intensively for an association withpreterm birth. Lead is the well-recognized toxicant forwhich such actions have been taken to protect childrenfrom neurotoxicity by eradicating lead from gasolineand paint. Elements such as copper (Cu), zinc (Zn),iron (Fe), and magnesium (Mg) are essential for

Madhu Anand and Ajay Taneja : Contribution of Environmental Pollution of Pre-term Births

AUJR-S 24

essential nutrients for normal pregnancy and growthof the foetus; excess or deficiency of these metalsduring pregnancy is related to mortality and morbidityin the infants [Srivastava et al. 2002]. Embryotoxicand fetotoxic effects associated with exposure tocadmium (Cd) and some other metals (Pb, Cd, Asand Cr) are known to cause birth defects andassociated with low birth weight have been describedby several researchers (Shen et al. 2001, Rahman etal. 2009, Lianos et al. 2009& Singh et al, 2016).Exposures of pregnant women with both As and Cdassociated to higher risk of oxidative damagewhichmay cause vascular damage and trigger reduction ofplacental blood flow, ultimately leads to death of thefoetus. (Tabocova, 1994, Iyengar and Rapp,2001;Falcon et al., 2003 &Ahamed et al, 2009). Metalexposures have long been studied in association withadverse reproductive outcomes, and the results ofmany studies indicate associations with preterm birth.Although many early studies focused on females withoccupational exposures, some studies examined theassociation in populations exposed throughenvironment only (Table 2).Polycyclic aromatic hydrocarbonsPolycyclic aromatic hydrocarbons (PAHs) areomnipresent environmental contaminants formed from

combustion products of coal, oil and gas, and otherorganic matter, cigarette smoking and consumption ofcharbroiled foods. (ATSDR 1995). Studies on micehave demonstrated that ingestion of high levels ofbenzo(a)pyrene during pregnancy lead tobirth defectsand decreased body weight in the progeny (Kristensenet al, 1995). It is not known whether these effects canoccur in humans. Nevertheless, it was demonstratedthat exposure to PAHs during pregnancy is associatedto adverse birth outcomes including low birth weightand premature delivery (Perera et al, 2005).They arereported to alter trophoblast proliferation in placenta,in addition to disturbing its endocrine functions, whichmay be able to increase the risk of preterm delivery inpregnant women. Studies using various biomonitoringapproaches have similarly observed a positiveassociation between PAH exposure and preterm birth.(Singh et al, 2008 &Madhavan et al, 1995)Singh et al performed a case-control study with smallsample size in the women of Lucknow and foundsignificantly higher levels of fluoranthene andbenzo(b)fluoranthene in pre-term group. An anotherstudy from China measured PAHs with large numberof samples in different matrices (milk, placenta andumbilical cord blood)and found highest concentrationsin placenta followed by human milk.

The purpose of this paper is to provide a brief reviewof the status and results of biomonitoring studies onsome environmental chemicals (specificallyorg-anochlorine pesticides, metals and PAHs) and pre-term birth. Since females are frequently exposed tocomplex mixtures of environmental chemicals, it isdifficult to attribute the risk of pre-term birth to anyparticular chemical or compound. Studies investigatingthe relationship between environmental chemicals andpre-term births have revealed conflicting results asshown in Table 1, 2 & 3, in part, because of differencesin the selection of matrices (maternal blood/ cord blood/breast milk/ placenta/ maternal serum), sample size,

exposure level, location and confounders included.Most of the studies reviewed here evaluate commonexposures in the general population (not in highexposure population), this accumulating body ofevidence suggests that the foetus and young childrequire more protection than is currently provided.Conclusion :Environmental chemicals mainly organochlorinepesticides, metals and PAHs are plausible contributorto oxidative stress; toxicology studies have shownincreases in lipid peroxidation products, decreasedglutathione levels and disrupt thyroid hormone levelsand inflammation. Increased level of oxidative stress

Table : 3 Comparison of Polycyclic aromatic hydrocarbons in placenta samples worldwide.

Area Samples Polycyclic aromatic hydrocarbons References

Size SpecimenBeijing, China 3254 Placenta 8.53 ppb Yanxin et al., 2011Lucknow, India 60 Placenta 1590.79 ppb Singh et al., 2008Chennai, India 24 Placenta 240 ppb Madhavan et al., 1995Texas 200 Placenta 8.04 ppb Gladen et al., 2000Riyadh, Saudi 1543 Placenta 32.016 ppb Iman., 2013Arabia


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trigger premature placental aging, which can result inpreterm birth. The link between environmentalchemicals and preterm birth shows an evidence forthe effects of some exposures, although, results areinconclusive, there are too many unknown chemicals,some chemicals have strong evidence for anassociation between maternal exposure to somepersistent pesticides, metals and PAHs, but there isno data on other environmental contaminants. Furtherrobust multicentric studies investigating multipleexposures to conclude the link between various otherenvironmental chemicals and pre-term births arerequired.

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MOLECULAR ASSESSMENT OF INTER-BREED GENETICVARIABILITY OF NATIVE BREED; KADAKNATH AND LONGTERM SELECTED WHITE LEGHORN CHICKENS BYMICROSATELLITE MARKERS

RUCHI GAUR , S. O. PRATAP, J. L. BHAT, PRAVAL PRATAP,A. A. KHAN AND SK MISHRA ITM UNIVERSITY, GWALIOR

Abstract : Efficacy of Molecular advances hasopened a new horizon for the identification andutilization of genetic variations and foremost genes aswell for the improvement of our livestock. Moleculartools have greatly facilitated the analysis of variouspopulation parameters among the inter-breed geneticvariability of chickens. The current study has beencarried out for the establishment of inter-breed geneticdifferences between a native chicken breed:Kadaknath (KN) and a long term selected layer breed:White Leghorn (WLH) maintained with differentbreeding regimen at CARI, Izatnagar, Bareilly, rearingas a closed flock for various purposes. Though, theseselected breed having phenotypic variants but thesevariations should also confirm on the basis of geneticanalysis for the reliable molecular estimates. In thisregard, 10 informative microsatellites have beenselected out of a panel of 25, screened on these twopopulations. Results showed distinct moleculardifferences in the population parameters; PIC, Na, Ne,HO, Nei, Fis and Shannon’s Information Index(0.59, 5.29, 1.7, 0.69, 3.5, 0.22, 1.39) for KN in contrastof WLH (0.29, 2.5, 1.6, 1.7, 0.31, 0.95, 0.58),respectively among the chosen STRs. Un-rooteddendrograms generated diversified pattern ofevolutionary relation-ship among the KN and WLHvia neighbor-joining method. It was concluded thatgenomic analysis through ten or more microsatellitemarkers can efficiently delineate population-differences in closed-flocks, arisen due to uniquebreeding histories and evolutionary forces.

Ruchi Gaur , S. O. Pratap, J. L. Bhat, PravalPratap, A. A. Khan and SK Mishra ITM Univer-sity, GwaliorDepartment of Biotechnology, Dr. MPS Group ofInstitutions, Agra Central Avian Research Institute,Izatnagar, Bareilly-243122

Key Words : Kadaknath, microsatellites, selection,genetic variability.INTRODUCTION:Advancements of Biotechnological tools andtechniques have simplified the understanding ofinherent-variability between various breeds ofeconomic importance in Poultry for the good qualityof protein in term of egg and meat to overcome themalnutrition of our society (Mishra et al., 2008).Traditionally, the Inter-breed genetic differencesbetween organized chicken populations have beencarried out earlier upon the basis of qualitative andquantitative phenotypic trait analytical system.However, quantitative genetic approaches are fraughtwith various limitations like: need for structured-pedigrees, information from sibs and robust statisticaldesigns, manual inputs, which reduce their applicationsinto finer breeding strategies. Use of genetic markerscan potentially ameliorate bulk of these problems, sincethey can be measured at any age, in both sexes andcan be free of phenotypic trait measurements, beingapplicable to any number of individuals over differentbreeds and species (Pratap et al., 2013).The microsatellites are reputed amongst the availableDNA-marker system of the day, they qualify well asa polymorphic and robust marker system, have provedto be more versatile, particularly for population analysis(Pratap et al., 2014). Microsatellites have beensuccessfully used in many chicken studies (Fulton etal., 2006 and Chatterjee et al., 2008, Pratap et al., 2013)because of their co-dominant nature and availabilityin different allelic forms across the genome. As mostof the CARI-bred stocks are accompanied withdefined breeding histories and quantitative-parameters,it provides opportunity to analyze the coherence ofpopulation parameters derived from both molecularand quantitative sources.


AUJR-S 30

The current study has been carried out to analyze theinter-breed genetic differences between a prominentnative chicken breed: Kadaknath (KN) and a popularegg-type chicken breed: White Leghorn (IWH line)maintained with distinctly-different breeding-history atCARI, using microsatellite markers. The KN ismaintained as an undiluted germplasm, under ex-situconservation for 29 generations, with the history ofno-deliberate selection but, laying more emphasis ongenetic diversity (Mishra et al., 2008). In contrast, IWH(WLH) has been bred for productivity, throughcontinued selection (Osborne-index) for high eggnumber (upto 64th weeks), for more than 29generations.MATERIAL AND METHODSFor the current study, randomly selected chickens ofnative breed; KN and long term selected IWH (36each) were picked up from the respective lines whichhave maintained as a closed flocks at CARI, for morethan 29 generations, under similar managemental andhealth-care conditions. DNA was extractedindividually, from their blood, using standard Phenol-chloroform protocol followed by their amplificationthrough standard microsatellite-PCR conditions(Pratap et al., 2013). The STRs for this study included10 informative markers chosen out of a panel of 25,screened on these 2 populations from theMicrosatellites, Kit 7(Kit#7, genome-mapping lab,MSU, Lansing, USA), found uniformly disseminatedacross the chicken genome. The genotyping was doneusing high resolution Metaphor agarose gelelectrophoresis, followed by their proper geldocumentation and analysis, incorporation of 10 and20 bp molecular-weight standards for efficient allelicsizing. The statistical analysis was carried out by POP-Gene, Gene-Alex and MS-Tools softwares (Peakalland Smouse, 2005) for arriving at the populationparameters like as; heterozygosity (He), polymorphicinformative content (PIC) (Botstein et al., 1980), Nei’sIndex, Shannon Information Index and Fis estimatesfor the KN and WLH, respectively.RESULTS AND DISCUSSION :Molecular indices of KN and WLH breeds were quiteevident for most of the genotypic traits premeditatedin the current study, are depicted in Table 1.The Shannon’s informative index (I) generally indicatesspecies diversity of a species was observed higher

(1.39) for KN as compare to WLH (0.58) the currentstudy. Comparable ‘I’ values like that of our KN havebeen reported in various chicken breeds around theworld which included, Isfahan native chickens (0.97)by Nasiri et al., (2007); non-descript Indian chickenpopulations (1.67) by Pirany et al., (2007).Nei’s Index which have their own relevance indiversity estimation and higher value (3.53) wasrecorded in case of native breed KN as compare toselected WLH (1.76) in our study. Chatterjee et al..(2010) observed Nei’s index of (4.12) between anothernative breed Aseel, Kadaknath and both breeds havemore similarity as a native breed as like our breedKN. Similarly, Rajkumar et al., (2008) reported widerdistance between Babcock, a commercial layer andDahlem Red (1.52) a purebred layer strain like ourWLH.Polymorphism Information Content (PIC) is a realindicator of efficient molecular markers to notoriousgenetic variability within and between species to aparticular locus. The relation between PIC andheterozygosity is not direct but they are closely relatedsince they both depend upon the recorded number ofalleles in each locus. The higher PIC values wererecorded for KN (0.59) as compare to WLH (0.29)which were in accordance to Rajkumar et al., (2008),Chatterjee et al., (2010), Pratap et al., (2013). Themolecular indices like Na, Ne and HO and Fis wereremained as (5.29, 3.53, 0.699, 0.222) for the KN incontrast of WLH ( 2.57, 1.68, 0.31 0.956) and ourfindings were supported by Rajkumar et al., (2008),Chatterjee et al., (2010), Pratap et al., (2013).Interestingly value of Fis remained lower in KN whichis rearing without any selection while long termselection WLH attained comparable higher value,which clearly reflects the fact that selection force haseroded the genetic diversity of long term selected breedWLH.The locus : ADL 102 and LEI 074 remained highlypolymorphic by producing more number of alleles 6each for KN and lower to WLH (4 &2) while ADL202 and MCW 005 produced least number of allelesfor KN (3) as compared to higher alleles for WLH(5), respectively. The Figures 1 and 2 (A & B presentedbelow) exhibit the representative-alleles detected atlocus: MCW0029 and Lei 0074 for some samples ofKN and WLH. Phylogeny was carried out for these


31 AUJR-S

populations individually, through dendrograms in shapeof unrooted trees, via neighbor-joining method, whichexhibited distinctly-different spreads of samples andvariability patterns. The comparison also showed thatWLH shaped less number of clusters as compared toKN and the distance between two adjacent sampleswas lower in IWH than KN. As the history of IWH issuggestive of reduced inter-sample variation due tolong-term selection (29 generations) in contrast to theKN which is synonymous with undiluted variability,these patterns were not surprising. Formation of lessnumber of clusters in WLH coupled with lower numberof alleles per locus than KN highlights the internalmolecular disposition of the former due to narrowingof genetic-variability over time (generations). Thedistinct breed-specific molecular grouping of KN asdistinct from IWH via use of multiple STRs as realizedhere gains support from few other reports on native

germplasm including Ankaleswar (Pandey et al., 2005)and another KN study involving Chittagong, Ghagus,Kalasthi, Tellichery (Ahlawat et al., 2008).CONCLUSION :The result of this study confirmed the efficiency ofmicrosatellite markers for evaluation of geneticvariation and divergence between two definedpopulations. Considering the small number ofmicrosatellites used for this study, these results couldserve as indicators of molecular variability, though notthe ultimate estimates of this inter-breed genomiccomparison. Therefore, it was concluded that:microsatellite-based genomic analyses using ten ormore polymorphic STR markers can efficientlydelineate population-differences in closed-flockchicken populations, arisen due to unique breedinghistories and evolutionary forces (selection).

Figure-2 : Comparison between KN (left) and IWH (right) for locus MCW0029

Figure-3: Genomic Comparison between KN (left) and IWH (right) for STR- LEI 074

Ruchi Gaur, S. O. Pratap, J. L. Bhat, Praval Pratap, Ruchi Gaur, S. O. Pratap,...........................

AUJR-S 32

Tab

le. 1

: P

opul

atio

n pa

ram

eter

s fo

r K

N a

nd I

WH

pop

ulat

ions

est

imat

ed f

rom

ST

R-T

ypin

g

Cite

dPr

oduc

t.O

bser

ved

Shan

onS.

No

Locu

sSi

zesi

zePI

CN

o of

alle

les

Na

Ne

Nei

Inde

xH

oFI

SIn

dex

(I)

KN

WLH

KN

WLH

KN

WLH

KH

WLH

KN

WLH

KN

WLH

KN

WLH

KN

WLH

1.A

DL0

278

119

110-

120

0.37

50.

294

15

3.7

3.25

1.9

3.25

20.

710.

350.

81.

091.

290.

99

2.A

DL1

0212

294

-135

0.86

0.6

64

73

5.43

2.5

5.43

2.87

0.84

0.51

0.05

1.03

1.77

1.1

3.A

DL1

8513

412

7-16

10.

70.

42

25

42.

851.

72.

851.

720.

650.

390.

120.

951.

340.

67

4.LE

I120

290

270-

319

0.67

0.42

32

50.

991.

91.

41.

971.

70.

520.

290.

011

0.69

0.42

5.M

CW

212

160

190-

220

0.62

0.19

32

53.

12.

471.

012.

471.

010.

630.

270.

420.

931.

30.

27

6.M

CW

152-

209

157-

210

0.5

0.22

42

5.2

32.

481.

312.

481.

440.

670.

270.

31

1.31

0.5

0029

7.A

DL2

0224

523

8-25

70.

40

34

41.

22.

90.

992.

950.

990.

730.

150.

190.

831.

20.

13

8.M

CW

220

208-

257

0.63

0.45

35

52.

43.

561.

713.

691.

780.

820.

340.

150.

81.

420.

79

005

9.M

CW

200

181-

241

0.63

0.31

52

63.

45

2.31

52.

30.

720.

340.

130.

931.

780.

69

104

10.

Lei0

7430

3-31

530

6-30

30.

60.

096

25.

71

5.5

1.99

5.25

1.88

0.7

0.19

0.05

11.

810.

26

Ave

rage

0.59

850.

297

3.9

2.6

5.29

2.57

93.

534

1.68

23.

534

1.76

90.

699

0.31

0.22

20.

956

1.39

10.

582

Na

= O

bser

ved

num

ber o

f alle

les;

* n

e =

Eff

ectiv

e nu

mbe

r of a

llele

s ; #

I =

Shan

non’

s Inf

orm

atio

n in

dex;

Wri

ght’

s fix

atio

n in

dex

(Fis

) is a

mea

sure

of h

eter

ozyg

ote

defi

cien

cyor

exc

ess

; **

Nei

’s e

xpec

ted

hete

rozy

gosi

ty.

Figu

re-1

: Unr

oote

d de

ndro

gram

(Tre

e) fo

r K

N (A

) an

d IW

H p

opul

atio

n (B

)

AUJR-S


33 AUJR-S

ReferencesAhlawat, S.P.S, Vijh, R.K., Mishra, B., Bharani Kumar,S. T. and Tantia M. S. (2008). Asian-AustralasianJournal of Animal Science, 21: 6-10.

Botstein, D., White, R.L., Skolnick, M. and Davis. R.W.(1980). American Journal of Human Genetics, 32:314-331.

Chatterjee, R.N., Sharma, R. P., Mishra, A., Dange,M. and Bhattacharya, T. K. (2008). The Journal ofPoultry Science, 45:186-191

Chatterjee, R.N., Niranjan, M., Sharma, R. P., Dange,M. and Bhattacharya, T. K. (2010). Estimation ofgenetic heterogeneity of chicken germplasm beingused for development of rural varieties utilizing DNAmarkers. Journal of Genetics, 89, 33–37.

Fulton, J.E., Juul-Madsen, H.R., Ashwell, C.M.,McCarron, A. M., Arthur, J.A., O’Sullivan, N.P.,Taylor, J. R. L. (2006). Immunog-enetics, 58:407-21.

Mishra, S. K., Arora, G., Pratap, S.O., Singh, D.P.,Narayan, R., Beura, C. K. (2008). Interaction offibromelanosis gene with various genetic backgroundsaffecting carcass pigmentation in crossbredKadakanath chicken.Indian Journal of Poultry Science,43: 267-271.

Nasiri, M. T. B., Shoari, F., Esmaeil Khanian, S.,Tavakoli, S. (2007). Study on polymorphism of Isfahannative Chickens populations using Microsatellite

markers. International Journal of Poultry Science, 6:835-837.

Pandey, A. K., Kumar, D., Sharma, R, Sharma, U.,Vijh, R. K and Ahlawat, S. P. S. (2005). Asian-Australasian Journal of Animal Science, 18: 955.

Peakall, R., Smouse, P. E. (2005). GenAlEx6: Geneticanalysis in excel. Population genetic software forteaching and research. Molecular Ecology Notes.

Pirany, N., Romanov, M. N., Ganpule, S. P.,Devegowda, G., Prasad, D. T. (2007). Microsatelliteanalysis of genetic diversity in Indian chickenpopulations. The Journal of Poultry Science, 44: 19-28.

Pratap, S.O., Mishra, S.K., Khan A.A., Gaur, R., AroraG. and D.P. Singh (2014). “Short Tandem RepeatProfiling of Long Term Selected White LeghornPopulation”, SKUAST J. Res. 16 (2): 141-144.

Pratap, S.O., Mishra, S.K., Khan A.A., Gaur, R., AroraG. and D.P. Singh and Mishra A. K. (2013). “STR-based genetic appraisal in two distinct chicken breedswith contrasting- breeding regimen”, Indian Journalof Poultry Science: 48 (2):137-147.

Rajkumar, U., Gupta, R. B., Reddy, R.A.(2008).Genomic heterogeneity of chicken populationsin India. Asian-Australasian Journal of Animal Science,21: 1710-1720.

l

Ruchi Gaur, S. O. Pratap, J. L. Bhat, Praval Pratap, A.A.khan and S.K.Mishra

AUJR-S 34

USE OF PLEASANT SOUNDS (MUSIC THERAPY) IN THETREATMENT OF CARDIOVASCULAR DISORDERS.

DINESH C. SHARMA

Abstract : The purpose of this study was to examinethat why we feel cool and calm when we listenspecific music. How the music therapies affect us?Is pleasant sound have the power to effectphysiology of livings? This study setup a relationbetween sound and hemato-biochemistry, that canbe used to control cardiovascular disordersthrough neuro-endocrine stimulation up to a limit,but still need more work to be done to say withsurety that specific music target specific bio-molecules, which effect the physiology andultimately behaviour of humans. The fast pace ofchanging our life styles makes adjustment to itdifficult. The blood, the blood vessels and the hearttogether form the cardiovascular system. Theseblood vessels are of a certain calibre, however theymay constrict for a long period of time, thus causingthe blood to flow through them under increasedtension giving rise to an entity called‘Hypertension’one of the major manifestations ofmental stress and CVD’s.Music therapy is based on the associative andcognitive powers of the mind. Sound creates certainvibrations which are picked up and amplified bythe human ear. Thesewaves are then picked up by the sensory nervegoing into the middle of the brain and redistributedthroughout the neuron network to other parts ofthe brain to distinguish the pitch, tone, andfrequency of that sound. During my study I usedthe sounds of acoustical environment which is thecombination of natural sounds (wind, water,wildlife, vegetation) and cultural and historicsounds (battle re-enactments, tribal ceremonies).

Dinesh C. SharmaDept. of ZoologyKm. M. Government Girls P. G. College,Badalpur, G. B. Nagar, NCR, India

[email protected](+91-9211119972).

In this study conducted over human volunteers, Iobserved that the music have their impact over thehypertension and CVD’s by altering the levels ofbiochemical and haematological parameters suchas significant decrease of serum total cholesterol,non-significant decrease of triglycerides, non-significant decrease of VLDL and LDL, significantincrease of HDL in most cases, significant increaseof adrenalin by sound A and B in human,significant decrease of cortisol in most cases. Thedecreases of LDL, VLDL, TG, Cortisol and increaseof HDL and adrenalin are indicative of good healthand the sounds which are responsible for suchchanges will be used to cure the patient of CVD,hypertension. On the basis of my finding, I canconclude that music therapy is helpful to controland treatment of hypertension, arteriosclerosis andother CVD’s and make us stress free healthy life.Key Words : CVD, Music therapy, AlternativeMedicine :I. INTRODUCTION :Today we are living in the sea of chemicals. We areusing them in most of our activity for making our lifeeasy. They can do this but on the other side they arealso responsible for creating various type of diseaseor disorder to cure them we are again using chemicalsin form of drug. There is no debut that drugs have theability to cure them, but on the other side it is a wellestablish fact that each drug have some short of sideeffects on the body of livings including human bodyand sometime side effects are converting in to seriousproblems leading up to death. On the other side today’shectic life style and working stress creating varioustypes of health problems such unusual B.P.,Cardiovascular problems, headache, depression,sleeplessness, nervous problems etc. which aresometime converted in to serious problems.Now a daysin place to fight these problems most of us andteenagers are starting use of alcohol, smoking and otherunhealthy things to overcome the stress, tension,depression but they are helpful for time being andbecome emerged itself as a new problem after a timeperiod and this will effects the working efficiency of


35 AUJR-S

your employee; ultimately the growth of a nationMusic therapy using since time immemorial and it isan established fact that brain is controlling entiremetabolism, biochemistry, haematology, physiology etc.of the body, so anything which is capable to influencethe brain also influences the entire metabolism andphysiology of body. No debut this can be done by soundvibrations in form of music. Music can be used asnon-pharmacological aids to the patients of variousdisorders which are related with the imbalance ofvarious biochemical, studied during the present study.Indian classical ‘Ragas’ have been acclaimed by Vedicscience to have healing effects. Music has frequentlybeen used as a therapeutic agent from the ancienttimes. Melody is the keynote of Indian Music. Thereare countless ‘Ragas’ of course with countlesscharacteristic peculiarities of their own. That is whywe cannot establish a particular Raga for a particulardisease. Different types of Ragas are applied in eachdifferent case. When the term Music Therapy is used,we think world-wide system of therapy. Literature ofVocal part of Indian Classical Music is not sufficientin that case. Classical music with its unique swara/note structure ensures calm and cozy mind byexposure and subdues the emotion provokingsituations. Music plays an effective role in subduingthe so-called emotional imbalance. Present study isplan to evaluate the effect of different type of soundon hemato-biochemical parameters related with thehypertension and cardiovascular disorders.Shakespeare once wrote: “If music be the food oflove, play on.” Profound words, true, but the Bardfailed to mention that music is not just nourishment forthe heart, but also for the soul.Music therapy is the prescribed use of music to effectpositive changes in the psychological, physical, cognitiveor social functioning of individual with individual.Individuals facing cardiac and hypertensive problemscan use music control them up to a limit. As we knowour most of the behavioral and functional aspects ofbody are the result of various types of metabolicreactions which are directly or indirectly controlled bythe neuro-endocrine system. So music therapy is aneffective tool in case of all type of disorders but it isnot effective in case of emergency and where surgeryis the only cure. The conclusion is based on our studiesover albino rats and human volunteers in which weobserved that the specific sounds are helpful to controlthe hypertension and CVD’s by altering the levels ofvarious biochemical and haematological parameters.Findings of present work are so surprising. The

biochemical parameters altered significantly in rats,while in humans most parameters altered nonesignificant to very highly significant. The difference insignificance level of humans and rats shows that thereis factor which is responsible to effect the result in laband field condition, but it is clear from the presentfindings that music has the power to effect the hemato-biochemistry of livings and it should be need moreinvestigation from different aspectsII. MATERIALS AND METHODSSelection of Natural Sound (Test compound)Music are selected on the basis of their property.Specific Indian ragas are selected for the treatmentof Albino rat, where as for human volunteers specificsongs and music based on ragas are selected fromthe list they have been provided in questionnaire. Threesets of pre-recorded sounds are selected on the basisof trial and error methods. They are given to theexperimental animals for a period of 90 days. Thebiochemical analysis of blood samples are carried at30 days, 60 days and 90 days. The results wereanalyzed and after that the similar sound treatmentare given to volunteers for a period of 90 days. Thebiochemical analysis of blood samples are carried at30 days, 60 days and 90 days interval. The bloodsamples of volunteers are collected by a physician hiredfor the purpose, where as the blood of albino rats wastaken in lab from treated and control groups. Sound ofspecific Indian ragas at a 60-80 db (controlled by soundmeter) are given to albino rat for two hours (9-10 AMand 3-4 PM) daily by speakers attached to the wall oftheir cage for 30, 60, 90 days, whereas humanvolunteers are allowed to listen a specific soundsthrough head phones provided them at home (for thesame time period as to rats) after training them inworkshops organized in department on Sundays andholidays. Control groups of both rats and humans arealso assigned to listen to taped “white noise” (“Whitenoise” or “synthetic silence” is an attempt to blockout environmental noise. In this case it was a prenature sound such as sea sounds, which themselvesare rhythmic) through headphones, or to a controlgroup.Maintenance and Feeding of ExperimentalAlbino Rats :The experimental albino rats (Rattus norvegicus[Berkenhout]), procured from inbred colony wereacclimated for one month to the laboratory conditions(temperature. 25±0.50C, relative humidity 60±5% andphotoperiod 12 hr/day) before using them for theexperiment. Adult male and female rats of almost equal

Dinesh C. Sharma : Use of Pleasant sounds (Music therapy) in the treatment of cardiovascular disorders.

AUJR-S 36

size and weight were kept in the polypropylene cagesand cleaned regularly to avoid any infection orundesirable odour in the laboratory. Each cage wasequipped with a metallic food plate and water bottle.The albino rats were offered fresh feed dailythroughout the experimentation on Gold Mohar rat andmice feed at regular interval and water was providedad libitum.Selection of Individuals :Albino Rats: For the experimentation individualsselected randomly irrespective of sex. Five healthyadult albino rats (6¬8 weeks of age, with average bodyweight of 150¬200 g) were selected randomly for testand control studies their blood was collected after 30,60 and 90 days for the present investigation. Each ratwas assigned a number for convenience prior toexperimentation.Volunteers-The Volunteers were selected through awide publicity (News paper, SMS, TV Programmes)from Agra, Noida, Delhi, Ghaziabad, Gurgaon region.They are provided to fill a questionnaire. On the basisof a questionnaire they are provide a recorded CD ofselected songs and sounds.Collection of Blood SamplesThe blood from rats collected in the early morninghours (7-8 AM) in lab on the scheduled date. The bloodsamples were obtained with the help of 2.0 mldisposable syringe from the tail of albino rats, whereasthe blood samples of human were collected by aphysician hired for the purpose. The variousbiochemical parameters of rats were analyzed withthe help of a standard kit methods in departmental lab,while human blood tests were conducted in authorizedlabs of a respective city.

What is Sound ?l Noise, which is often referred to as unwantedsound, is typically,

characterized by the intensity frequency, periodicity(continuous or intermittent) and duration of sound.

Unwanted sound to some may be considered wantedsound by others, as in the case of loud music(Talbott, 1995).l Sound Pollution, which is often referred to as

greater than normal frequency.l Music Therapy, which is often referred to Rhythmic

desired sound of specific frequency and pressureof choice at specific type & time.

III-RESULTS :Table : I

ParametersAlbino Rat (In Lab)

Sound A Sound B Sound C

Adrenalin ↑NS ↑NS ↑NSCholesterol ↓VHS ↓NS ↓VHS

Cortisol ↓S ↓NS ↓SHDL ↑HS ↑S ↑NSLDL ↑NS ↑NS ↑STG ↑NS ↑NS ↑S

VLDL ↑NS ↑NS ↑S

Human Volunteers

ParametersAlbino Rat (In Lab)

Sound A Sound B Sound C

Adrenalin ↑S ↓S ↑SCholesterol ↓HS ↓NS ↓S

Cortisol ↓S ↓NS ↓SHDL ↑S ↑S ↑NSLDL ↓NS ↓NS ↓STG ↓NS ↓NS ↓S

VLDL ↓NS ↓NS ↓S

Significant Level : P < 0.01 (HS = Highly Significantincrease ↑ or decrease ↓), P < 0.05 (↑ = Significantincrease or decrease ↓), P > 0.05 (↑ = Non significantincrease or decrease ↓).IV-DISCUSSIONLipid bound proteins are called lipoproteins.Lipoproteins are found in plasma and their function is


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to transport lipids. Lipoprotein includes VLDL, LDLand HDL. In the present study VLDL and LDL aredecrease non-significantly except in case of sound “C”.The HDL significantly increased in most cases. Thedecrease of serum LDL and VLDL will lead to thedecrease of triglycerides and cholesterol which meansfavourable lipid concentration in body and a healthyheart. In the present study sound of nature are foundto increase the adrenalin level in both humans andalbino rats while decreasing the level of cortisol. Thedecreases of various lipids are indicative of good healthand support the view that sound can be used as a drugto control various lipid parameters. The findings canbe correlated with the metabolism and used for thetreatment of hypertension and CVDs.Most of the above biochemical findings of presentinvestigation are helpful to establish the doctrine thatmusic can be used cure or control for variouscardiovascular disorders an hypertension. The effectof music on the cardiovascular disorders has beeninitially evident in “Lancet”(medical journal), In whichVincent and Thompson (1929) made an attempt todiscover the influence of listening to gramophone, andradio, music on blood pressure and he observed thatlistening to music was accompanied by a slight rise inblood pressure in the listener. Bason and Celler (1972)observed that the human heart rate could be variedover a certain range by entrainment of the sinus rhythmwith external auditory stimulus. Bason`s paper isimportant for supporting the proposition often madeby music therapists that meeting the tempo of thepatient influences their musical playing and is the initialkey to therapeutic change. An extension of thispremise, that musical rhythm is a pacemaker, wasinvestigated by Haas et al. (1986) in terms of theeffects of perceived rhythm on respiratory pattern, apattern that serves both metabolic and behaviouralfunctions. He hypothesized an external rhythmicalmusical activity, in this case listening to taped music.Several authors have investigated this relationship inthe setting of hospital care (Bonny1983; Davis et al.1987; Zimmerman et al. 1988; Guzzetta 1989; Philip1989; Elliott 1994) often with the intent of reducinganxiety in chronically ill patients (Gross and Swartz1982; Standley 1986), for treating anxiety in general(Robb 2000), or specifically in musicians (Brodsky andSloboda 1997). Bonny (1978,1983) has suggested aseries of musical selections for tape recordings whichcan be chosen for their sedative effects and accordingto other mood criteria, associative imagery andrelaxation potential, none of which have been

empirically confirmed. For this Updike (1990)conducted an experiment and confirms Bonny‘simpression that there is a decreased systolic bloodpressure, and a beneficial mood change from anxietyto relaxed calm, when sedative music is played. Rider(1985a,b) explained that disease related stress wascaused by the desynchronization of circadian oscillatorsand that listening to sedative music, with a guidedimagery induction, would promote the entrainment ofcircadian rhythms as expressed in temperature andcorticosteroid levels of nursing staff. This study foundno conclusive results, mainly because there was nocontrol group. Guzzetta (1989) conducted a study todetermine whether relaxation and music therapy wereeffective in reducing stress in patients admitted to acoronary care unit with the presumptive diagnosis ofacute myocardial infarction. In this experimental study,80 patients were randomly assigned to a relaxation,music therapy, or control group. Music therapy wascomprised of a relaxation induction and listening to a20 minute musical cassette tape selected from threealternative musical styles; soothing classical music,soothing popular music and non-traditional music.Stress was evaluated by apical heart rates, peripheraltemperatures, cardiac complications, and qualitativepatient evaluative data. Data analysis revealed thatlowering apical heart rates and raising peripheraltemperatures were more successful in the relaxationand music therapy groups than in the control group.The incidence of cardiac complications was found tobe lower in the intervention groups, and mostintervention subjects believed that such therapy washelpful. Both relaxation and music therapy were foundto be effective modalities of reducing stress in thesepatients, and music listening was more effective thanrelaxation alone. Furthermore, apical heart rates werelowered in response to music over a series of sessionsthus supporting the argument that the assessment ofmusic therapy on physiological parameters isdependent upon adaptation over time. Further researchstrategies may wish to make longitudinal studies ofthe influence of music on physiological parameters.Bason‘s (1972) study could influence heart rate bymatching the heart rate of the patient, then we mustconclude that studies of the influence of music on heartrate must match the music to the individual patient.This also makes psychological sense as different peoplehave varied reactions to the same music. Furthermore,improvised music playing which takes meeting thetempo of the patient as one of its main principles mayhave an impact other than the passive listening to

Dinesh C. Sharma : Use of Pleasant sounds (Music thrapy) in the treatment of cardiovascular disorders.

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music. In addition, the work of Haas (Haas et al. 1986)mentioned above showed that listening, coupled withtapping, synchronizes respiration pattern with musicalrhythm, further emphasizing that active music playingcan be used to influence physiological parameters andthat this synchronization can be learned.

ReferencesAPI - Textbook of Medicine, 4th edition, 1986, 460.

Bason, B and Celler, B. (1972). Control of the heartrate by external stimuli. Nature 4, 279- 280.

Bonny, H (1983). Music listening for intensive coronarycare units: a pilot project. Music Therapy 3 (1): 4-16.

Bonny, H. (1978). GIM Monograph -2. The role oftaped music programs in the GIM process. Baltimore:ICM Press.

Brodsky, W. and Sloboda, J. A. (1997). Clinical trial ofa music generated vibrotactile therapeutic environmentfor musicians: Main effects and outcome differencesbetween therapy subgroups. J Music Therapy 34 (1):2-32.

Delong,D.M., E.R.Delong, P.D.Wood, K.Lippleand B.M. Rifkind. (1986). A comparison of methods for theestimation of plasma low and very low-densitylipoprotein cholesterol. The lipid Res. ClinicsPrevalence Study. JAMA, 256 : 2372.

Elliott, D. (1994). The effects of music and musclerelaxation on patient anxiety in a coronary care unit.Heart and Lung 23 (1): 27-35.

Friedewald. W.T., R.I. Levy and D.S. Fredrickson.(1972). Estimation of the concentration of low-densitylipoprotein cholesterol in plasma without use of thepreparative Ultracentrifuge. Clin. Chem. 18 : 499.

Gross, J-L and Swartz, R. (1982). The effects of musictherapy on anxiety in chronically ill patients. MusicTherapy. 2 (1): 43-52.Haas, F , Distenfeld, S and Axen, K. (1986). Effectsof perceived musical rhythm on respiratory pattern. J.of Applied Physiology. 61 (3): 1185-91.

Philip, Y. T. (1989) Effects of music on patient anxietyin coronary care units letter. Heart and Lung. 18 (3):322.

Rider, M. S. (1985a). The effects of music imageryand relaxation on adrenal corticosteroids and the reentrainment of circadian rhythms. J. of Music Therapy.22 (1): 46-56.

Rider, M. S. (1985b). Entrainment mechanisms areinvolved in pain reduction, muscle relaxation, andmusic-mediated imagery. J. of Music Therapy .22 (4):183-192.

Robb, S. L. (2000). Music assisted progressive musclerelaxation, progressive muscle relaxation, musiclistening, and silence: A comparison of relaxationtechniques. J. of Music Therapy. 37 (1): 2-21.

Schettler, G. and E. Nussel. (1975). Determination oftriglyceride ARB. Med. Med. Prav. Med. 10 : 25.

Standley, J. M. (1986). Music research in medical/dental treatment: meta analysis and clinicalapplications. J. of Music Therapy 23 (2): 56-122.

Traux B. (2001). Acoustic Communication (2nd ed.),Ablex Publishing, Westport, ConnecticutVincent, S and Thompson, J (1929). The effects ofmusic on the human blood pressure. Lancet 1(9): 534-537.

Wybenga, D.R. and Pileggi. (1970). In vitrodetermination of cholesterol and HDL cholesterol inserum/plasma. Clin. Chem. 16 : 980.

Zimmerman, L. M, Pierson, M. A and Marker, J.(1988). Effects of music on patient anxiety in coronarycare units. Heart and Lung. 17 (5): 560-66.

Zollner, N. and K. Kirsch. (1962). Estimation of totallipid Gess. Exp. Med. 135 : 545.

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SINGLE CRYSTAL CRYSTALLOGRAPHY : CRYSTAL GROWTHAND DESIGN

NAZIA SIDDIQUI, SANGEETA AND SALEEM JAVED

Abstract : Determination of structure is a key goalin the determination of the connectivity of the atomsin a compound, the way the molecule (or molecules)pack to form a solid crystalline material, positiveidentification of a single crystal, exact connectivity,bond distances and angles, complete identificationof the compound, intermolecular interactions, andIntramolecular interactions. Information gained israrely incorrect. It provides positive identificationand answers of basic questions regarding bonding.This technique provide confirm structure, confirmlattice arrangement, confirm solution and solidstate results. Comparison of SCXRD and PXRD canconfirm that crystal is representative of bulk.Keywords : Single crystal; crystal growth; datacollection; structure refinementIntroduction :In 1895 scientist Wilhelm Conrad Röngten [1] usedX-ray techniques and in 1913 William Lawrence Braggand William Henry Bragg [2] discovered the Bragg’slaw of X-ray diffraction. Single crystal analysis wasfirst used in the field of physics and chemistry in the1910.In 1969, Dorothy Hodgkin [3] solved the structure ofinsulin. Biological substance such as Vitamin B12 [4],Penicillin and Cholesterol has been studied by X-raycrystallography in the 1940s [5]. Structure of DNAhas been discovered by Watson and Crick [6] by usingthis technique. Structure of the Influenza C virushaemagglutinin-esterase-fusion (HEF) glycoproteinwas investigated by Zhang et.al [7].Single crystal X-ray crystallography is one of the mostpopular analytical methods. The determination of acrystal structure consists of several steps: crystal

Nazia Siddiqui, Sangeeta, and Saleem Javed*

1*Department of Chemistry, Institute of H. Science,Khandari, Dr. B. R. Ambedkar University, Agra,282002, India 2 Department of Chemistry, AgraCollege, Agra, India. 3Department of Chemistry, IndiaInstitute of Technology Kanpur, Kanpur 208016, India.

[email protected]

growth, unit cell determination, data collection, datareduction, space group determination and structuresolution, following these steps the crystallographerobtain atomic coordinates for some or all non-hydrogenatoms. These steps from unit cell determination tostructure solution are collectively called refinement[8-14]. This technique is very powerful and popularbecause it provides structure of the compound,Information gained is rarely incorrect, provides positiveidentification and answers basic questions regardingbonding. To gain a crystal structure we need to growfine quality single crystals.In this paper we are focusing mainly on: Importanceof the single crystal crystallography, methods ofgrowing crystals and providing some idea of steps ofgetting crystal structure and discussing its impact.Importance of the X-ray single crystalcrystallographyAll materials or substances present around us areconsist of atoms. The three dimensional arrangementand type of atom define the structure of the substance.Its structure is directly related with the properties andfunction of materials. Many of materials used inelectronics, pharmaceutical, food or in life science.Many researchers involves in the synthesis ofcompounds and determination of structure is a key goal,for that they used many techniques like IR (Infraredspectra), NMR (Nuclear magnetic resonance) andMass spectroscopy and different spectroscopytechniques but these techniques do not provide the solidinformation about the structure of the compound.Therefore to know the systematic structure we usesingle crystal crystallography. Crystal structureprovides, 1. Confirm Structure, 2. Positive identificationof a single crystal, 3. Exact connectivity, 4. Bonddistances and angles, 5. Complete identification of thecompound, 6. Intermolecular interactions, 7.Intramolecular interactions, 8. Total geometry of thecompound, 9. Packing of crystal in the unit cell, 10.Idea of cooperativity present in the compound, 11.Confirm solution and solid state results, 12. ConfirmLattice arrangement, 13. Comparison of SCXRD andPXRD to confirm that crystal is representative of bulk.


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Till to date 29 Nobel prizes have been given in thefield of X-ray crystallography and related field [15].

Fig 1 : Pictorial presentation of crystal structure

Crystal structure (Fig 1 and 2) showing the connectivityof the atoms in a compound and the way molecule (ormolecules) pack to form a solid crystalline material.

Fig 2: Showing the connectivity and packing ofmolecules in the crystal

Techniques to obtain single crystals [16]Single crystal is produced by the transport of crystalconstituent in solid, liquid or vapour phase, that’s whysingle crystal growth can be divided into threecategories as follow :I. Solid Growth–Solid to solid phase transformationII. Liquid Growth–Liquid to solid phase transformationIII. Vapour Growth–Vapour to solid phasetransformationTherefore the main goal is to create a single crystalswhich diffract on the instrument such that an analysiscan be accomplished. Generally this means trying toget the material to go from solution to a solid veryslowly and create an environment that slowly changesover time to cause crystallization.

Although there are many techniques to grow the singlecrystals but scientists mainly use the following threestrategies :(i) Vapour diffusion (ii) Layering (iii) Slow evaporationBefore using any of the above methods to grow singlecrystals we need to have pure solid material thereforesolid materials must be recrystallize properly (Table1) and other important things to keep in mind are:glassware should be clean, consider location andconsider volume needed to grow the crystals. Thentry to grow fine single crystals.Vapour diffusion:This method is most popular and wildly use across theglobe by synthesis community and crystallographers.To grow the crystal by this method we need milligramamounts of compound and solvents. For diffusion,slowly create a less desirable solvent and there is needto be aware of vapor pressures of solvents. Haveavailable a chart of physical properties of solvents (asgiven in Table 1 and 2)Take diffusion chamber as shown in fig 3

Fig 3: Showing vapour diffusion method

Filtered less volatile solvent solution of compound istaken inside the tube and more volatile solvent outsidein the chamber.Compound should not be soluble or less soluble in themore volatile solvent that is outside solvent. Whenvapours of more volatile solvent enters in the tubecontaining compound solution then compound startsto precipitate slowly. This slow precipitation leads togrow crystals inside the tube.


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Mostly chamber placed in cool place where nobodydisturbs the chamber. Grown single crystals can beseen by using white torch light or laser light withoutdisturbing or touching the chamber.

Fig 4 : Showing crystal structure obtained byvapour-diffusion method

LayeringMost of the time layering is done in the NMR tube.Layering must be very careful. Place a Solvent isplaced between the two layers.

Fig 5 : Showing layering tube and crystal structureobtained by layering method.

The vessel is not disturb Set it so you can view it withoutmoving it.

For layering the following care has to be taken careoff-Polar- polar solvent layered with a non-polarsolvent.Non-polar-Non-polar solvent, evaporation or layer withpolar solvent, harder. Consider densities, works bestwith solution as bottom layerThe bottom solvent must be of higher density (Table2) and compound can be soluble in one of the solventand partially or insoluble in other solvent and thesetwo solvent must be fully miscible with each other.When these solvent, one containing compound,carefully layered with each other and placed in coolplace then they starts to mix with each other and inthis process the compound also starts to precipitateslowly. This slow precipitation like previous methodgrow the crystals on the walls of layering tube [Fig 5].Slow evaporationThis method is the easiest method among the threeand for the chemists. First step is-filter to remove anyparticles and allow the material to crystallize out asthe solvent evaporates. Keep the solution clean andcovered to avoid dust particles. Place the setup in coolplace without any disturbance for longer time,sometimes it takes more than a month

Fig 6: Representation of slow evaporation method

Fig 7: Crystal structure obtained by slow evaporation

Nazia Siddiqui, Sangeeta, and Saleem Javed : Single Crystal Crystallography : Crystal Growth and Design

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Tabe 1. Combination of solvent for slowevaporation method

Method Solvent and Solvent and

Combination Combination

CH3CN (37.5) CH2Cl2 (9.02)

Recrystallization CH3CN + EtOH CH2Cl2 – Hexane(1:5)CH3CN +Hexane CH2Cl2 – Et2O(2:1)CH3CN + Et2O CH2Cl2 – MeOHCH3CN + DMF CH2Cl2 – Toluene(2:1)CH3CN+ Toluene(2:1)

Slow Evaporation CH3CN + EtOHCH3CN + CH2Cl2CH3CN + THF

Vapour Diffusion CH3CN – Et2OCH3CN – EtOAcCH3CN + MeOH(1:1) – Et2OCH3CN + MeOH(1:1) –MeOHCH3CN + Toluene/Et2OCH3CN + THF/Et2O

Layering CH3CN – Toluene CH2Cl2– HexaneCH3CN – Benzene CH2Cl2 – PentaneCH3CN – THF CH2Cl2 – HeptaneCH3CN + CH2Cl2 CH2Cl2 + Toluene– THF – Hexane

CH2Cl2 – Et2OCH2Cl2 + Acetone– HexaneCH2Cl2 – Pet ether

CH3OH (32.6) Acetone (21.0)

Recrystallization MeOH + CH2Cl2 Acetone + H2O(1:2)MeOH + H2O Acetone + 2-(1:1) propanol (1:1)MeOH + MeCN(2:1)

Slow Evaporation MeOH + THFVapour Diffusion MeOH – Et2O Acetone /Et2O

MeOH + H2O– Et2OMeOH +CH3COCH3/Et2O

Layering MeOH – Toluene Acetone – HexaneMeOH – Benzene Acetone – Heptane

Acetone – PentaneAcetone – CH2Cl2

Table 1. Combination of solvent for slowevaporation method

Solvent Density Boiling PointsCH2Cl2 1.32 41Acetone 0.78 56CHCl3 1.49 61MeOH 0.79 65THF 0.88 66Hexane 0.65 69EtOH 0.78 78EtOAc 0.89 78Benzene 0.87 80CH3CN 0.78 82Toluene 0.86 111DMF 0.94 155

Result and discussionBy using any of the above methods we can get finecrystals (Fig 8) which can diffract and crystal datacan be collected using single crystal X-raydiffractometers

Fig 8 : Showing single crystals obtained bydifferent methods

The crystal data can be solve by using differentsoftware like WingX [17], Olex2, and SHELXTL etc.freely available on internet. After solving the data inthe proper manner one can secure his data in TheCambridge Crystallographic Data Centre (CCDC)which is a data base of crystal structures.While publishing the crystal structure author have togive CCDC number in the paper so that referee canobtain the data to check it.


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On the other hand we can determine the secondaryinteractions like hydrogen bonding, H-π interaction,π-π interaction etc.ConclusionsThe most salient features of the present study are asfollows :(i) This study showed how important are the singlecrystals for any synthetic chemist. (ii) It includesdifferent methods of crystallization. (iii) Most importantthree methods, vapour diffusion, layering and slowevaporation, have been described. (iv) Crystalstructures and its packing have been shown. (v)Secondary interaction have been shown (vi) Differentphysical parameter of solvent described and shown.(vii) Combination of solvent for crystallization havebeen explained and tabled.AcknowledgmentsWe acknowledge Indian Institute of Technology,Kanpur for permitting us to perform data collectionby Bruker single crystal X-ray diffractometer.Laboratory facility of chemistry department, IITKanpur and Aligarh Muslim University, Aligarh.Glassblowing section of IIT Kanpur for makingapparatus used in growing single crystals and Dr. B.R. Ambedkar University, Agra for necessary fundingand infrastructure.

ReferencesC. Brink, D.C. Hodgkin, J. Lindsey, L. Pickworth, J.R.Robertson and J.G. White, Nature, 1954, 174, 1169-1171.

D.C. Hodgkin, Adv. Sci., 1949, 6, 85-89.

D.J. Watkin, Structure Refinement: Some BackgroundTheory and Practical Strategies. J. Appl. Crystallogr.2008, 41, 491–522.

http://www.xtal.iqfr.csic.es/Cristalografia/parte_10_1- en.html

D.J. Watkin, The Control of Difficult Refinements.Acta Crystallogr; 1994, A50, 411–437.

G. J. Peter; Crystal Growing, Chemistry in Britain,1981, 17, 222-225

G.M. Sheldrick, A Short History of SHELX. ActaCrystallogr. 2008, A64, 112–122.

J.D. Watson and F.H.C. Crick, Nature, 1960, 171, 416-422.

L.J. Farrugia, WinGX ver. 1.64, An Integrated Systemsof Windows Programs for the Solution, Refinementand Analysis of Single-Crystal X-ray Diffraction Data,Department of Chemistry, University of Glasgow, 2003.

M.J. Adams, T.L. Blundell, E.J. Dodson, G.G. Dodson,M. vijayan, E.N. Baker, M.M. Harding, D.C. Hodgkin,B. Rimmer and S. Sheat, Nature, 1969, 224, 491-495.

P. Müller, R. Herbst-Irmer, A.L. Spek, T.R. Schneider,M.R. Sawaya, Crystal Structure Refinement ACrystallographer’s Guide to SHELXL; OxfordUniversity Press: Oxford, UK, 2006.

R. Herbst-Irmer, G.M. Sheldrick, Refinement ofTwinned Structures with SHELXL97. Acta Crystallogr.1998, B54, 443–449.

R. Herbst-Irmer, G.M Sheldrick, Refinement ofObverse/Reverse Twins. Acta Crystallogr; 2002, B58,477–481.

W. Clegg, A. J. Blake, R.O. Gould, P. Main, CrystalStructure Analysis Principles and Practice; OxfordUniversity Press: Oxford, UK, 2001.

W.C. Röngton, Nature, 1895, 53, 274-276.

W.L. Bragg, Prov. Cambridge Phil. Soc., 1913, 17,43- 57.

X. Zhang, P.B. Rosenthal, F. Formanowski, W. Fitz,C.H. Wong, H.M. Ewert, J.J. Skehel and C. Wiley,Acta Cryst., 1999, D55, 945-961.

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ANALYSIS OF THE FACTORS AFFECTING THE OBESITYAMONG ADOLESCENTS

ARCHANA SINGH AND ABHILASHA SINGH

Abstract : Good health not only implies freedom fromdisease but physical, mental and emotional fitness aswell. Dietary and lifestyle behaviors amongadolescents are risk factors for several chronicdiseases in adulthood. The objective of the study toevaluate the factors affecting the obesity amongadolescents. Selected 100 adolescents of age group13-20 years in Agra district. Sedentary life style, lackof exercise watching television, hereditary was thefactors which negatively affected the health ofadolescents. Prevalence of obesity in the present studywas found to be more among females as compared tomales.KEYWORDS : Life Style, Adolescents, Dietaryhabits, ObesityIntroductionIn recent years, obesity is the most common nutritionaldisorder in the western countries and among the higherincome groups in the developing countries9. Theprevalence of overweight and obesity among childrenand adolescents has widely increased worldwidemaking it one of the most common chronic disordersin this age group and in adulthood1. However recentexpert committee recommendations suggested use ofthe term ‘obesity’ for children with BMI-95th percentileto reflect the correlation of high BMI with excess bodyfat among children and to emphasize the clinical riskof such weight status7. Optimum and good nutrition isused to indicate that the supply of the essential nutrientsis correct in amount and proportion1,6. There iscompiling evidence that dietary habits and lifestyleduring adolescence are risk factors for several nutrition

Archana Singh and Abhilasha SinghDepartment of Food & Nutrition (Biochemistry)Institute of Home Science Dr. B.R. AmbedkarUniversity Agra-282 002 (U.P.) INDIACorresponding Author: Archana Singh

[email protected]

related on-communicable diseases in adulthood2 .Understanding the dietary patterns and lifestylebehaviors of both children and adults is an essentialstep in constructing an effective interventionprogramme to prevent diet-related diseases. Factorsassociated with obesity among15–18 year-oldschoolchildren12 showed that carbohydrate andsaturated fatty acid intakes were significantly higheramong obese than non-obese children, and thecontribution of bread, meat and sugar to daily intakewas significantly higher among boys than girls Thereis growing evidence that in present conditions, perhapsdue to decreased physical activities, sedentary lifestyle, altered eating and increased fat content of thediet. Children and adolescents are overweight ascompared to their contemporaries in the past. Thepresent study also throws some light on the importanceand ill effect of sedentary life style which is mostresponsible factor to create several problems relatedto respiration circulation, metabolism and complicationrelated to health problems i.e. obesity etc. The currentstudy aimed to to evaluate the factors affecting theobesity among adolescents.Materials and MethodA survey was conducted on adolescents of Agra cityin Uttar Pradesh, India. Multistage stratified randomsampling technique was used for selecting 100 samplesof age group 13-20 years from two coaching institutein urban area of Agra district. Information wascollected regarding general information, parentalinformation, and anthropometric measurement, specificinformation related to associated factors,environmental factors, life style and dietary patternamong the adolescents. The lifestyle section includedquestions on the hours spent watching television, usingthe Internet, sleeping and physical activity. Theobjective of the study and information in thequestionnaire were explained to the students by


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qualified nutritionists, who also supervised the collectionof the data. The 24 hours recall method was used inthe present study. A previously pretested validatedquestionnaire was used to collect the data. The

questionnaire consisted of two sections :(A) Anthropometric measurements of the selectedadolescents,(B) Analysis the factors associated with obesity.

Result and DiscussionA : Study of the anthropometric measurements of the selected adolescents

Table-1 : Distribution of adolescents according to their Height

Height (in cms) Sex of Adolescents Total

Male Female

No. % No. % No. %140–150 8 16.0 5 10.0 13 13.0150–160 20 40.0 33 66.0 53 53.0160–170 22 44.0 12 24.0 34 34.0

Total 50 50.0 50 50.0 100 100.0Mean 156.98 153.44 155.21

SD 6.84 4.11 5.91t 3.137p < 0.05

Above Table reveals the distribution of adolescentsaccording to their height. Out of total respondents,majority of them 53% were from 150-160 cm in height,followed by 34.0% were from 160-170 cm in height.Out of male respondents 44% were from 160-170cms in height followed by 40% who were from 150-160 cm in height. Out of the female respondents 66%

were from 160-170 cm in height and minimum 10%were from 140-150 cm in height. Further the Tableshows that average height of adolescents was 155.21cm which was more in males (156.98 cm) comparedto female (153.44cms). Statistically, significantdifference in mean height was observed between sexof adolescents (t = 3.137, p < 0.05).

Above Table depicts the distribution of adolescentsaccording to their weight. Out of the total respondentsmajority of them (44%) weighed from 50-60 kgs, 4%who weighed from 60-70 kgs and the minimum (22%)weighed from 40-50 kgs. Out of the male respondents

majority of them 46% weighed from 60-70 kgs followedby 34% who weighed from 50-60 kgs and the minimum20% weighed from 40-50 kgs. Statically, no significantdifference in mean weight was observed between sexof adolescents4,5 (t = 0.511, p > 0.05).

Table-2 : Distribution of adolescents according to their weight

Weight (in kg) Sex of Adolescents TotalMale Female

No. % No. % No. %40 – 50 10 20.0 12 24.0 22 22.050 – 60 17 34.0 27 54.0 44 44.060 –70 23 46.0 11 22.0 34 34.0Total 50 50.0 50 50.0 100 100.0Mean 55.88 55.06 55.46SD 8.03 7.63 7.84

t 0.511p >0.05

Archana Singh And Abhilasha Singh : Analysis of the Factors Affecting the Obesity among adolescents

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Table-3: Distribution of adolescents according to their Body Mass Index

Body Mass Index Sex of Adolescents TotalMale Female

No. % No. % No. %15-20 5 10.0 3 6.0 8 8.020-25 37 74.0 31 62.0 68 68.025-30 8 16.0 16 32.0 24 24.0Total 50 50.0 50 50.0 100 100.0Mean 22.58 23.32 22.95SD 2.35 2.46 2.43

t 1.538p >0.05

Above Table highlights the distribution of adolescents according to their BMI. Out of total respondents, majority(68%) of them having BMI from 20-25 followed by 24% having BMI from 25-30 and the minimum (8%) wasfrom 15-20. Out of the total male respondents majority of them 74% had BMI from 20-25 followed by 16%having BMI from 25-30 and the minimum (10%) with BMI from 15-20. 5Fauzia (2007) found that mean BMI ofsports and athletes girls was 21.97. Further the table shows the average BMI of the respondents (23.32) ascompared to male respondents (22.58). Statistically, no significant difference regarding average BMI betweenmale and female respondents was observed even at 5% level of significance.1,13,14B: Analysis of the factors associated with obesity

Table-1: Distribution of adolescents according to obesity status of parent

Obesity Status of Sex of Adolescents TotalParent Male Female

No. % No. % No. %Mother 10 20.0 7 14.0 17 17.0Father 8 16.0 8 16.0 16 16.0Both 16 32.0 22 44.0 36 36.0None 16 32.0 13 26.0 29 29.0Total 50 50.0 50 50.0 100 100.0

X2 = 1.787, df= 3, p > 0.05Table express the distribution of adolescents according to the obesity status of their parents. Out of the totalrespondents parents, majority of them (36%) both were obese, followed by 29% who were not obese and theminimum (16%) father were obese. Out of male respondents, parent’s majority of them (32%) both were obeseand none of them obese, followed by 20% mothers were obese and the minimum 16% fathers were obese. Outof female respondents parents, majority (44%) of them both mother & father were obese, followed by 20%mother were obese and the minimum 16% father were obese. Statistically, no significant difference regardingobesity status of parents (both mothers and fathers) between male and female respondents was observed (x2 =1.787, df = 3, p>0.05)3,15

Table 2: Distribution of adolescent according to their food habitsFood Habit Sex of Adolescents Total

Male FemaleNo. % No. % No. %

Sweet 17 34 16 32 33 33Spicy 21 42 21 42 42 42

Simple 12 24 13 26 25 25Total 50 50 50 50 100 100

X2 = 0.070, df = 2,p > 0.05


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Above Table shows the distribution of adolescents according to their food habits. Out of the total respondents,majority of them (42%) preferred spicy food, followed by 33% who preferred sweet food and minimum (25%)preferred simple food. Out of male respondents majority of them (42%) preferred spicy food followed by 34%who preferred sweet food and the minimum (24%), who preferred simple food. Out of the female respondentsmajority of them (42%) preferred spicy food, followed by 32% who preferred sweet food and the minimum(26%) preferred simple food. Statistically, no significant association was observed between food habit17 (x2 =0.070,df = 1, p > 0.05)

Table 3 : Distribution of adolescents according to outside meals takenOutside meals taken Sex of Adolescents Total


Regular 3 6 3 6 6 6Occasionally 39 78 43 86 82 82

Never 8 16 4 8 12 12Total 50 50 50 50 100 100

Table reveals the distribution of adolescents according to the outside meal taken by them i.e. food taken inrestaurant, hotels etc. Out of the total respondents majority of them (82%) occasionally take their meal outside,followed by 12% never take meal outside and the minimum (6%) regularly take outside meal. Out of malerespondents majority of them (78%) occasionally take their meal outside, followed by 16% never take mealoutside, followed by 6% take regularly meal outside. Out of the female respondents majority of them 86% takeoccasionally their meal outside, followed by 8% never take meal outside and the minimum 6% take regularly mealoutside.8Table 4 : Distribution of adolescents according to how often they include different foods in their diet.

Food Frequency of food in the diet TotalDaily Occasionally Never

No. % No. % No. % No.Fast Food – – 97 97.0 3 3.0 100

Sweet 19 19.0 81 81.0 – – 100Chocolate/Toffee 35 35.0 62 62.0 3 3.0 100

Salad 64 64.0 36 36.0 – – 100Ice-cream 4 4.0 96 96.0 – – 100

Fruits 60 60.0 40 40.0 – – 100Vegetables 100 100.0 – – – – 100

Banana 24 24.0 73 73.0 3 3.0 100Potato 89 89.0 10 10.0 1 1.0 100Milk 86 86.0 12 12.0 2 2.0 100

Sugar 85 85.0 14 14.0 1 1.0 100Nuts 15 15.0 79 79.0 6 6.0 100

Above Table depicts the distribution of adolescents according to how often they include different foods in theirdiet. Out of total respondents, majority of the respondents who include occasionally, the following in their foodnamely fast food (97%), ice cream (96%), sweets (81%), nuts (79%), banana (73%) and chocolate (62%),followed by daily, while majority of the respondents who includes daily the following in their food namely vegetable(100%), potato (89%), milk (86%), salad (64%) and fruits (60%) in their diet10,11,12.


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Table 5: Distribution of adolescents according to how they take their mealsTake Meal With Sex of Adolescents Total


Watching 25 50 39 78 64 64TelevisionReading 1 2 7 14 8 8

MagazineNothing 24 48 4 8 28 28Total 50 50 50 50 100 100

Table depicts the distribution of adolescents according to how they take their meals. Out of total respondentsmajority of them (64%) take their meal while watching television followed by 28% who take their meal by doingnothing and the minimum (8%) take their meal by reading magazines. Out of the male respondents majority ofthem (50%) take their meal while watching television, followed by 48% take their meal by doing nothing and theminimum 2% take their meal by reading magazine. Out of the female respondents majority of them (78%) taketheir meal while watching television, followed by 14% who take their meal while reading magazine and theminimum 8% take their meal by doing nothing.3,7

Table 6: Distribution of adolescents according to their sleeping habit

Sleeping Habit Sex of Adolescents TotalMale Female

No. % No. % No. %More than 17 34 10 20 27 27

8 hoursLess than 31 62 25 50 56 568 hoursWith fix 2 4 15 30 17 17routineTotal 50 50 50 50 100 100

Above Table suggest distribution of adolescentsaccording to their sleeping habits. Out of the totalrespondents majority of them i.e. 56% of them sleepless than 8 hours, followed by 54% who sleep morethan 8 hours and the minimum 17% sleep with fixroutine. Out of the total respondents majority of themi.e. 62% sleep less than 8 hours followed by 34%sleep more than 8 hours and the minimum 4% sleepwithout fix routine. Out of the female respondentsmajority of them (50%) sleep less than 8 hours followedby 30% sleep without any fixed routine and theminimum 20% sleep more than 8 hours.ConclusionFrom our study the use of body mass index (BMI) forage to define being overweight and obese inadolescents is well established for both clinical andpublic health applications, because of their feasibilityunder clinical settings. In our study the prevalence ofobesity was more in adolescent females as comparedto males. Most of the obese adolescents have obese

parents too. Adolescents who engaged in light workas compared to those who were engaged in moderatework, market made food, fast food addiction, sweets,ice-cream were observed on obesity. Sedentary lifestyle, lack of exercise, watching television was thefactors which negatively affected the health ofadolescents. Furthermore, these data could be usedas base-line information for the comparison of foodhabits and lifestyles, before and after the conflict.Primary prevention of obesity by promoting activelifestyles and healthy diets should be a national publichealth priority.

ReferencesBin Zaal AA, Musaiger AO, D’Souza R (2009): Dietaryhabits associated with obesity among adolescents inDubai, United Arab Emirates. Nutrition Hospitalities.24(4): 437–444

C. B. Ebbeling, D. B. Pawlak, and D. S. Ludwig,(2002) “Childhood obesity: public-health crisis, common


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sense cure,” The Lancet, vol. 360, no. 9331, pp. 473–482.

Hazzaa M Al-Hazzaa, Nada A Abahussain,Hana I Al-Sobayel et. al. (2012) Lifestyle factorsassociated with overweight and obesity among Saudiadolescents BMC Public Health 12:354

HIMES, J.H. (2006): Prevalence of overweight andobesity in American and Indian school children inAberdeen area, A population study, 23: pp 243.

I. Lissau, M. D. Overpeck, W. J. Ruan, P. Due, B. E.Holstein, and M. L. Hediger, (2004) “Body mass indexand overweight in adolescents in 13 EuropeanCountries, Israel, and the United States,” Archives ofPediatrics and Adolescent Medicine, vol. 158, no. 1,pp. 27–33,.

Khosla, Anju and Manocha, Ruchi, (2012): HomeScience, Danika Publishing Co. (Publisher ofTrueman’s specific series;2.

KANERIA, Y. 2006: Prevalence of overweight andobesity in relation to socio-economic condition of twogroups of school age children of Udaipur city, IndianAcademy of Clinical Medicine, 7: pp 79-83.

Korean Nutrition Health and Nutrition ExaminationSurvey (2005): To explore dietary pattern clarify theirinfluence on obesity among adolescents Dissertationabstracts International, 26,2353-A

Lin Yang, Graham A. Colditz (2015). Prevalence ofOverweight and Obesity in the United States, 2007-2012. JAMA Internal Medicine

Musaiger AO, Bader Z, AL-Roomi K, D’SouzaR(2011). Dietary and lifestyle habits amongstadolescents in Bahrain. Food and Nutrition Research;55: 7122.

Maria del Mar Bibiloni, Antoni Pons, and Josep A.Tur(2013), Prevalence of Overweight and Obesityin Adolescents: A Systematic ReviewVolume 2013 Article ID 392747, 14

Nasreddine L, Mehio-Sibai A, Maryati M, Adra N,Hwalla N. (2009) Adolescent obesity in Syria.Prevalence and associated factors. Childcare, Healthand Development,.p.1365–2214.

Rama chandran, R (2002): Adolescents children in M.P.International Journal of obesity, 20,395-403.

Rama Chandran, R (2007): Prevalence of adolescentsobesity in affluent school girls. International Journalof obesity, 24,430-448.

Skeleton, J (2005): childhood obesity, Urriaturepublication Toronto 23-42.

World Health Organization (WHO) (2002): Diet,Nutrition and the Prevention of Chronic Diseases.Technical Report Series Geneva, Switzerland,; 916.

Wierzbicaand, E. and Rozkowski, W. (2005): Analysisof food intake including fast food meals by groups ofadolescents, Bromat Chem. Toxkykol. Suppl.:38: pp561-566

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FUZZY MODEL TO DIAGNOSE THE FACTORS AFFECTING E-TOURISM ADOPTABILITY

GUNJAN GUPTA AND MANISH KUMAR SHRIVASTAVA*

Abstract : Tourism industry is a consumer of adiverse range of information and the main user ofits related technologies. Information Technologyhas affected the way tourism organizations conducttheir business and in particular, the wayorganizations distribute their tourism products inthe marketplace. Information technologies haveundoubtedly become one of the most importantelements of the tourism industry, since thegeneration, gathering, processing, application andcommunication of information are very importantfor day-to-day operations in tourism industry. Inthis paper we emphasize the factors affecting thee-tourism industries.Key words : e-tourism, fuzzy model, membershipfunction, fuzzy logic, e-commerce1. Introduction and SignificanceE-tourism, takes into account when, traditional travelagents, tour operators, national tourist offices, airlines,car hire firms, hotels and other accommodationproviders offer their services online which enable thetourists to schedule their trip online. Travel servicesfit extremely well with the new interactive media, asthe products are natural candidates for multimediadescriptions. In this paper we will introduce thefollowing sections which are the primary steps forhaving successful adoption in e-business in tourismindustry. It has been pointed out that the developmentof information and Communication Technologies blursthe traditional boundaries between distribution andinformation. For example- interactive informationscreens at tourist information centers enables touriststo obtain information and make bookings at the sametime. On the other hand, tourism organizations canuse Information and Communication Technologies foraddressing individual needs and wants of their

Gunjan Gupta and Manish Kumar Shrivastava*Mewar University, Mewar, Rajasthan*Lecturer of Maths, Boy’s Anglo Bengali I/C,Sunderbagh, Lucknow

[email protected]

consumers. Saeed Rouhani, Ahad Zare ravasan,Homa Hamidi and Sherlie Vosough pointed out thefactors affecting e-tourism adoptability and mentionedkey factors in successful implementation of e-tourismservices.2. The input factor of the e-tourism adoptability2.1 Customer expectation in e-tourism:Although too many people use e-tourism recently andthe demand side of e-tourism is having a great growth,but note that the supply side of the e-tourism is alsohaving a massive growth and there is a hugecompetition in this market. Due to this massivecompetition in the global market the expectations ofthe customers have increased. The Scottish parliament(2002) has mentioned the expectations of tourists thatcan be created after implementing e-tourism :a. Quick response to enquiries.b. More detailed and tailored information on tourism

destinations.c. Able to check out competition easily at the click

of a button.d. Importance of destination management and

marketing.e. Branding-collection of tourism products and

services.f. Tourism marketing as a means of facilitating

regional development.g. Marketing should be used as a strategic

mechanism in co-ordination with planning andmanagement rather than as a sales tool.

h. Must acknowledge travel motivations (movestowards personal service through customerprofiling).

2.2 Threat of tourism websites :Perhaps the most powerful competitors emerging inInternet-based tourism services are those with theresources to invest. They seem to be the portal sites,reservation technology providers and Microsoft.Portal sites are approaching the sector throughstructuring their vast data warehouses into the mastic


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sites (e.g. tourism). CRS providers see the internet asanother distribution channel that compliments theirexisting arrangements, while Microsoft recognizes thepotential of the sector for exploiting their ownconsiderable technological and financial strengths.Scottish parliament, 2002, has mentioned that thereare some other possible technological innovations thatmay impact on tourism include :a. Interactive Digital television (IDTV).b. Mobile and m-commerce distribution,These two can either be the competitor of e-tourismor can be combined with that in order to increase itsefficiency.1.3 lmpact of internet on tourism industry :Since the emergence of e-commerce is very crucialfor tourism firms, all of them including small to mediumsized, SMEs, are eager to apply that in their business.It has been argued that the challenges and issues facedby the industry include :a. Low/varied level of ICT literacy amongst some

micro businesses and SMEs.b. Limited access to technology.c. Extent to which SMEs feel Government should

be responsible for providing a national website withlinks to information about their business.

d. Level of responsibility each SME should have forits own marketing and the development of theirown website.

e. Collapse of dot comes in 1999-2000 led tosignificant losses for venture capitalists.

As a consequence investors are more cautious ofbusiness plans based on costly marketing campaignsaimed at quickly raising the company’s global profile.Instead it has been suggested that the future of manydot comes lies in the development of ‘clicks andmortar’, ‘bricks and clicks’ businesses, which benefitfrom the advantage of both internet and traditionalbusiness.3. MethodologyFactors Affecting e-tourism Adoptability

Fig.1: Model Structure

The process starts by information from the person andfrom various other sources which are obtained from astandard form that is used by Tourism Company. Thisinformation contains different sections such asCustomer expectations, Threat of tourism and Impactof Internet. Now the expert system generates thevarious measures. These qualitative measures arequantified and converted into linguistic variables withcorresponding membership functions. For example-the customer section ith is given by :

Xi = 1

jl

ij iji j i

W

I= =

∆∑ ∑

where wij is the weightage or impact factor given tothe jth information of the ith section, and ∆ij is a 0-1variable (where ∆ij = l, if there is any deviation/difference in the information furnished by the claimantand the one obtained by the auditor, 0 otherwise). All

the weights for a set of information ith, 1

Jijj

w=∑ add

to unity. Similarly, the values of the other inputs canbe determined. The normalized values of thesemeasures are used as inputs to the expert system.The degree of membership corresponding to a valueof input is determined by the use of trapezoidalmembership functions because of their simplicity andgood result obtained by simulation. These informationfunctions are designed on the basis of availableinformation.The figure 2 shows the definition of the fuzzy sets ofthe input and the output functions. A rule-base is thenconstructed which will be based on all the applicableinput parameters and for each decision several rulesare fired. Table 1 shows a sample rule base for thesystem under consideration. These rules result in anaggregate fuzzy set that represent a particular decisionregarding the processing of adoptability. This fuzzyset is then converted into a crisp number, which depictsthe degree of suitability of the decision regarding thee-Tourism adoptability. The rules aggregation is doneusing fuzzy centroid algorithm. Mamdani implicationis used to represent meaning of ‘if-then’ rules. In thiscontext, statement “if X is A then Y is B” or A→Bresult in a relation R such that µX(x, y) = min (µA(x),µB(y)).

Gunjan Gupta and Manish Kumar Shrivastava* : Fuzzy Model to Diagnose the Factors Affecting E-Tourism Adoptability

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Figure 2: Membership function of input and output functionsThis implication is precise, computationally simple and fits various practical applications. The min operator is anatural choice for the logical AND. Bellman and Giertz (1973) have designed a set of axioms that should besatisfied by the AND operator and have proved that min operator satisfies them.

Table 1: Sample rule base for the Fuzzy Logic based Expert system.

Rule No. Customer Threat of Impact of OutputExpectation tourism factor internet factor

factor

1 G G G LR2 G G N LR3 G G B LR4 G N G NR5 G N N NR6 G N B NR7 G B G HR8 G B N HR9 G B B HR10 N G G LR11 N G N LR12 N G B NR


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13 N N G NR14 N N N NR15 N N B NR16 N B G HR17 N B N HR18 N B B HR19 B G G NR20 B G N NR21 B G B HR22 B N G HR23 B N N HR24 B N B HR25 B N G HR26 B N N HR27 B N B HR

4. Algorithm (using fuzzy approach)The steps of expert system are summarized below :

a. Input-The crisp values of the Customerexpectation factor, Threat of tourism factor andImpact of internet factor are obtained or byinvestors term. In the process of designing afuzzy system important task analyzer, the firstand probably most important task is to identitythose factors that contribute primarily to theassociated services for e-tourism adoptability.

b. Evaluate the inputs-Determine the Customerexpectation factor x1, Threat of tourism factorx2 and Impact of internet factor x3.

c. Fuzzify the crisp values of inputs -Throughthe use of membership function defined variablefor each fuzzy set and for each linguistic variable,determines the degree of membership of a crispvalue in each fuzzy set. Each of these threeindices has been divided into three fuzzy sets(good-G, normal-N and bad-B). The equationfor computing membership are :

µ(x1) =

11

1

11

–max 0,

1–

max 0,–

x aif x c

c aif c x d

b xif d x

b d

< − ≤ ≤ <

µ(x2) =

22

2

22

–max 0,

1–

max 0,–

x aif x c

c aif c x d

b xif d x

b d

< − ≤ ≤ <

µ(x3) =

33

3

33

–max 0,

1–

max 0,–

< − ≤ ≤ <

x aif x c

c aif c x d

b xif d x

b d

where a, b, c, d are the vertices of the trapezoidalmembership function, G, N and B represent the fuzzyset for good, normal and bad respectively while LR,MR, and HR represent the fuzzy set for low risk,medium risk and high risk respectively.

d. Fire the rule bases that correspond to theseinputs – All expert system which are based on fuzzyuses if-then rules. Since all the 3 inputs have 3 fuzzysets (GOOD-G, NORMAL-N and BAD-B), therefore27 (3x3x3) fuzzy decisions are to be fired. There arethree outputs: low risk (LR), medium risk (MR) andhigh risk (HR).

e. Execute the inference engine - Once all crispinput values have been fuzzified into their respectivelinguistic values, the inference engine will access thefuzzy rule base of the fuzzy expert system to derivelinguistic values for the intermediate as well as theoutput linguistic variables. The two main steps in theinference process are the aggregation andcomposition. Aggregation is the process of computingthe value of the if (antecedent) part of the rules whilecomposition in the process of computing the value ofthe then (conclusion) part of the rules. Duringaggregation, each condition in the if part of a rule isassigned a degree of truth based on the degree ofmembership of the corresponding linguistic term.


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f. Defuzzification : The last phase in the fuzzyexpert system is the defuzzification of the linguisticvalues of the output linguistic variables into crisp values.The most common techniques for defuzzification areCentre-of-Maximum (COM) and Centre-of-Area(COA). COM first determines the most typical valuefor each linguistic term for an output linguistic variableand then computes the crisp value as the compromisefor the typical values and the respective degree ofmembership. The other common method is COG orsometimes called Centre-of-Gravity.

g. Output of the decision of the expertsystem–In our case, the types of the outputs are: Lowrisk (LR), Medium risk (MR) and High risk (HR).The specific feature of each controller depends onthe model and performance measure. However, as aprinciple in all the fuzzy logic based expert system,we explore the implicit and explicit relationshipsubsequently to develop the optimal fuzzy control rulesas well as knowledge base.5. Case Study

a. The value of the input have to be evaluated :x1 = 25, x2 = 328 and x3 = 2.5

b. Fuzzification of the crisp values of inputs throughthe use of membership functions defined for eachfuzzy set. For each linguistic variable the degreeof a crisp value in each fuzzy set is determinedas follows :

µG(x1) = 0.33 µN(x1) = 0.66 µB (x1) = 0

µG(x2) = 0 µN(x2) = 0.4 µB (x2) = 0.3

µG(x3) = 0.5 µN(x3) = 0.33 µB (x3) = 03. Fire the rules bases that corresponds to these

inputs based on the value of the fuzzymembership function. For the example underconsideration, the following rules apply :

a. Rule 4 : If x1 is GOOD, x2 is NORMAL and x3is GOOD, then Y is NORMAL RISK (NR).

b. Rule 5: If x1 is GOOD, x2 is NORMAL and x3 isNORMAL, then Y is NORMAL RISK (NR).

c. Rule 7: If x1 is GOOD, x2 is BAD and x3 isGOOD, then Y is HIGH RISK (HR).

d. Rule 8: If x1 is GOOD, x2 is NORMAL and x3 isNORMAL, then Y is NORMAL RISK (HR).

e.Rule 13: If x1 is NORMAL, x2 is NORMAL and x3is GOOD, then Y is NORMAL RISK (NR).

f. Rule 14: If x1 is NORMAL, x2 is NORMAL and

x3 is NORMAL, then Y is NORMAL RISK (NR).g. Rule 16: If x1 is NORMAL, x2 is BAD and x3 is

GOOD, then Y is HIGH RISK (HR).h. Rule 17: If x1 is NORMAL, x2 is BAD and x3 is

NORMAL, then Y is HIGH RISK (HR).4. Execute the inference engine–Once all crisp

inputs value have been fuzzified into theirrespective linguistic values, the inference enginewill access the fuzzy rule base of the fuzzy expertsystem to derive linguistic values for theintermediate as well as the output linguisticvariables. The two main steps in the inferenceprocess are the aggregation and composition.Aggregation is the process of computing the valueof the if (antecedent) part of the rules whilecomposition in the process of computing the valueof the then (conclusion) part of the rules. Duringaggregation, each condition in the if part of arule is assigned a degree of truth based on thedegree of membership of the correspondinglinguistic term.We use the “ROOT SUM SQUARES” (RSS)method to combine the effects of all applicablerules. The respective output membership functionstrengths

(Range : 0–1) from the possible rules (R1 – R27) are :

⇒ RSS = 2HR 1(µR )i∈Σ

“HIGH RISK”

= 2 2 2(.3) (.3) (.3) (.3) 0.6+ + + =

“NORMAL RISK”

= 2 2 2(.33) (.33) (.33) (.4)+ + +

= 0.7078

“LOW RISK” = 2HR (µR ) 0i i∈Σ =

5. Defuzzification – The last phase in the fuzzyexpert system is the defuzzification of thelinguistic values of the output linguistic variablesinto crisp values. The most common techniquesfor defuzzification are Centre-of-Maximum(COM) and centre of area (COA). COM firstdetermines the most typical value for eachlinguistic term for an output linguistic variable,and then computes the crisp value as thecompromise for the typical values and respectivedegrees of membership. The other common


55 AUJR-S

method is COG or sometimes called Centre-of-Gravity.

Fig. 3 shows, the output of the expert system and herethe crisp output is 50%. The crisp output belongs tothe NR more than the set of HR or LR (as evidentfrom its membership function), which shows that thefactors affecting to the e-tourism adoptability is 50%,which indicate that the factors affecting to e-tourismis 50%.

6. Output of the decision of the expert system– In our case, the types of the outputs are : LR,MR and HR. The specific feature of eachcontroller depends on the model and performancemeasure. However in principle, in all the fuzzylogic based expert system, we explore the implicitand explicit relationship subsequently develops

the optimal fuzzy control rules as well asknowledge base.

6. Conclusion : We introduced three factors thatcan affect customer satisfaction in onlineretailing. Among those factors convenience wasthe most important one. E-retailing is promotedwidely as a convenient avenue for shopping.Shopping online can economize time and effortby making it easy to locate merchants, find items,and procure offerings. On the other hand,customer service in e-tourism could not satisfycustomers more than traditional travel agencies.As the result showed, lack of customer serviceis a threat for the e-tourism firms; however itcan be converted to an opportunity for e-tourismorganizations to gain more online customers byoffering better services in comparison to the

traditional travel agencies.

Fig. 3: Output of the Expert System

Present study was based on the e-tourism and aimedto explore the major drivers affecting adoptability ofe-tourism services in India; therefore, it wasconcentrated on the primary data only. The studyrevealed of total three drivers/ factors namelycustomer expectation, threat of tourism and Impactof internet. As the finding indicates that in India, thereis huge scope to improve the percent of e-commerceadoptability, increase in the percent of e-commerce

adoptability would ultimately result positive change inthe e-tourism penetration rate. The study found majorfive drivers based on the primary data, which are asimportant as all other required supports includinginfrastructure, laws, awareness etc. to increase theadoptability e-tourism services in India.In this worke the output of the expert system and thatis 50%. The crisp output belongs to the NR more thanthe set of HR or LR (as evident from its membership


AUJR-S 56

function), which shows that the factors affecting tothe e-tourism adoptability is 50%.

ReferencesBardgett, L., (2000) “The e-tourism industry”,Research Paper 00/66, 7-23 June 2000, House ofCommons Library

Bellman and Giertz, (1973) “On the analyticformalism of the theory of fuzzy sets”. InformationSciences, Volume 5, 149-156

Buhalis, D. and Licata, M., (2002) “The future ofe-tourism intermediaries”. Tourism Management 23,207–220.

Buhalis, D. (2003) “E-tourism InformationTechnology for Strategic Tourism Management”,Prentice Hall, Harlow

Buhalis, D., (1998) “Strategic use of informationtechnologies in the tourism industry”. TourismManagement 19, 5, pp. 409–421.

Cho, V., (1998) “World wide web resources”. Annalsof Tourism Research 25, 2, pp. 518–521.

Chulwon K., (2004) “ E-tourism: an innovativeapproach for the small and medium sized tourismenterprises in Korea”, OCED, 2004.

Flint, J. and Herbert, R (2000), ‘Marketing, TheInternet & Regional Small Business’, Presented atANZMAC 2000 Conference – Visionary Marketingfor the 21st Century, Facing the Challenge, Gold Coast,Australia, 28 Nov.

Hamedi, Z. and Jafari, S. (2011), “Using fuzzydecision-making in e-tourism industry: A case study

of Shiraz city e-tourism”, International Journal ofComputer Science Issues, 8, 3, 1, 1694-0814.

Jowkar, Z. and Samizadeh, R. (2011), “Fuzzy riskanalysis model for e-tourism investment”. Int. J.Mgt.Bus. Res, 1, 2, 69-76

Luc C., (2006) “E-travel and e-tourism Figure andTrend and e-hospitality and e-marketing Analysis”.Accessed on June 2006,

Millar R.J. and Hanna J.R.P., (1997) “Promotingtourism on internet”, Tourism Management, 18, 469-470.

Porter, M., (2001) “Strategy and the Internet”.Harvard Business Review 79 3, 63–78.

Rayman-Bacchus, L. and Molina, A. (2001)“Internet-based tourism services: business issues andtrends”. Futures, 33, 587-605.

Reinders, J. and Baker, M., (1998) “The futurefor direct retailing of travel and tourism products: Theinfluence of information technology. Progress inTourism and Hospitality Research 4 1, pp. 1– 15.

Walle, H., (1996) “Tourism and the Internet:Opportunities for direct marketing”. Journal of TravelResearch 35 1, pp. 72–77.

Werthner, H. and Klein, S. (1999) “InformationTechnology and Tourism- A challenging relationship”,Wiley, New York Springer.

Xiaoqiu Ma, Jennifer, D. B., and Haiyan Song(2003) “ICT and internet adoption in china’s tourismindustry”, International Journal of InformationManagement, 23, 451-467at.http://www.etourismnewsletter.com/index.html.

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CHARACTERIZATION OF SOIL BORNE HETERORHABDITISSPECIES ISOLATES BY PCR AMPLIFICATION OF rDNA GENE

ISTKHAR AND A. K. CHAUBEY

Abstract : A number of nematologists are working onentomopathogenic nematodes (EPNs) in India but theircontribution to taxonomy of these nematodes is still intheir preliminary phase. Several species of EPNsconsidered either as junior synonym speciesincuirendae and nomina nudumdue to providing theinsufficient/inaccurate data. Hence, the identificationof species together with morphological and moleculartools showed necessity of serious accurateidentification of EPNs. The present study deals withthe identification of six isolates of Heterorhabditisviz.CH1, CH2, CH3, CH4, CH5 and CH6 isolated fromdifferent localities of western part of Uttar Pradesh,India. The variations in average body length of 3rdstage juveniles were seen within the isolates withlowest in isolate CH2 (526 µM) followed by 527 µMin CH6, 537 µM in CH5, 545 in CH3 and CH4, andlargest in 552 µM in CH1. The length of excretorypore, nerve ring and pharynx varied slightly within theisolates, While comparing with other described species,the close resemblance was shown with H. indica,however the average body length was greater inisolates CH1, CH3, CH4 and CH5. Body length wasalso compatible with H. noenieputensis, H. baujardiand H. floridensis. The other parameters were closingsimilar to H. indica. High variations in morphometryof 1st generation male of isolates were accounted.Molecular identification based on ITS rDNA displayedthat the highest similarity was shown by H. indica inisolates CH1 , CH2, CH3 , CH4, CH5 and CH6respectively. The nucleotide differences with H.gerrardi were 11, 9, 35, 7, 35, 8 and similarity was98.2%, 98.5%, 94.1%, 98.8%, 94% and 98.7%. In H.noenieputensis these nucleotide differences were of17, 15, 41, 13, 41, 14 and similarity was 97.2%, 97.5%,93%, 97.9%, 93% and 97.7%. The bootstrapconsensus trees placed all six isolates along with H.noenieputensis, H. gerrardi, H. indica and H.

Istkhar and A. K. ChaubeyNematology Laboratory, Department of Zoology, Ch.Charan Singh University, Meerut : 250004, India.

[email protected]

pakistanense and comprise a monophyletic group with100% bootstrap support. The morphological andmolecular characteristics that we found here weresufficient to regard all the nematodes member ofHeterorhabditis species as the isolates of H. indica.

Keywords : Taxonomy, synonymise, morphology,morphometry, ITS rDNA.

Introduction

Improved alertness over the dangers caused by theconstant and undiscriminating use of pesticides forcedus to find safe and eco-friendly means of insect pestmanagement. One such way is to useentomopathogenic nematodes (EPNs). EPNs arerhabditid nematodes belong to familiesSteinernematidae Travassos, 1927 with genusSteinernema and Heterorhabditidae Poinar, 1976having genus Heterorhabditis.These nematodes areobligate parasites of insects and are frequently usedas biological control agents of economically importantinsect pests (Kaya and Gagular, 1993). Thesenematodes have mutualistic association with bacteriawhich provide them an excellent potential to kill insecthosts within a very short duration. These nematodesare free living creature and are found dominantly inthe soil but also reported from the insect’s body. Allthe life stages of EPNs are found within the body ofinsect host which serve as a food reservoir fordeveloping nematodes. As conditions decline Infectivejuveniles (IJ) are produced that leave the insect toseek new hosts (Ciche and Ensign, 2003). The IJ isthe only free living stage and is actively seeks hostsusing a species specific strategy along a continuumfrom ambush to cruise foraging (Campbell & Gaugler,


AUJR-S 58

1993, 1997; Campbell & Kaya, 2002; Grewal et al.,1994). Among the newly produced nematodes the IJsre-associate with the mutualistic bacteria beforeleaving the insect cadaver (Martens et al., 2003). Thenematodes carry their bacterial symbiontsmonoxenically in a special vesicle of the IJs known as“vesicle of Bird and Akhurst” (Bird & Akhurst, 1983)in Steinernematidae and throughout the whole intestineof Heterorhabditidae (Endo & Nickle, 1991). It hasbeen proved that the free living stage of the EPN isthe most infective and operative stage where the free-living infective juveniles can search and move into thesoil in search of insects to infect (Spence et al, 2008).Surveys for EPNs conducted in various parts of theworld reveal that these genera have global distributionsand found under varied ecological conditions (Hominicket al., 1996). In heterorhabditids, H. bacteriophorais the most widespread species common to both torridand temperate zones, originally reported fromAustralian region. H. indica is found in all the tropicaland subtropical areas whereas the H. megidis is onlyin temperate zone of Holoarctic. Interestingly, the H.zealandica, originally described from New Zealand,later found in the northeastern Europe recently reportedfrom northeastern China, Florida and from SouthAfrica (Wang et al., 2014; Nguyen et al., 2007; Malanet al., 2011). Traditional methods such as morphologyand morphometric were always the baseline of thetaxonomy of any organism and in this context fornematodes too, but now days these becomesupplemented with the molecular approaches toconcern the identification of presumed new species(Hominick et al., 1997). It is important to mention thatthe data accoutred on diversity of entomopathogenicnematodes is highly influenced by wrong identificationand doubtful about several species. DNA sequencesof internal transcribed spacer (ITS) regions producecomprehensive information about dissimilarity withinand among nematode species than PCR-RFLPapproaches. These spacer sequences have been usedsuccessfully to diagnose species and populations ofnematodes (Cherry et al. 1997; Szalanski et al., 1997).

The aim of present study was the characterization ofsix EPN isolates at molecular level to ensure moreprecise identification within the genusHeterorhabditis.

Materials and Methods

Isolation and culture maintenance of nematodesand host insect

The soil samples were collected from differentagriculture and non-agriculture fields of Meerut,Muzaffarnagar and Saharanpur districts and processedin laboratory for the isolation of EPNs where larvaeof Galleria mellonella were used as bait material.The processing of soil samples for nematode isolation,maintenance of host insect G. mellonella and isolatednematode was done as previously described (Istkharet al., 2016). The 3rd stage juveniles (IJs) were storedin double distilled water and stored in BOD at 15±1ºC for further investigations.

Processing of nematodes for morphologicalidentification

For morphological and morphometrical studies of theEPN isolates, adult stages and IJs were recovered bydissecting the cadaver of G. mellonella . Thehermaphroditic females were recovered from 2-3days after mortality. For 2nd generation amphimicticmales and females, the dissections were made after4-6 days of death of G. mellonella larvae. Freshlyemerged IJ were collected from White trap (White,1927). All the stages were processed up to slidepreparation for morphological and morphometricalstudies (Istkhar and Chaubey, 2016). Features of 3rd

stage IJs and males were used for identification ofisolated nematodes at morphological level.

Isolation and amplification of rDNA

The genomic DNA was isolated from Fresh IJs cultureusing the DNeasy Blood and Tissue Kit (Qiagen) asper the instruction given by the manufacturer. Theisolated DNA was electrophoresed with 0.7% agarosegel in TAE buffer containing EtBr. The gel wasvisualized under UV light for the presence of DNA.Agarose Gel Electrophoresis (AGE) for the detection


59 AUJR-S

of DNA was performed to detect the presence of DNAin the eluted solution. ITS rDNA (Internal transcribedspacers) was amplified and used as molecular markerto distinguish the present specimen form comparedspecies of Heterorhabditis (Joyce et.al. 1994). Theamplifications of final product were confirmed by 1%AGE.

Molecular and phylogenetic analyses

All the sequence were annotated and submitted toNCBI.The phylogenetic analysis of all the sequenceswas performed using the default parameters (gapopening penalty 15, gap extension penalty 6.66) inClustal W using software MEGA 7.0 (Kumar et al.,2016). Available data of ITS gene sequences of alreadydescribed Heterorhabditis species was retrieved fromthe NCBI database and was utilized for theconstruction of distance matrix and phylogeneticanalysis through maximum parsimony method. ITSsequence of Caenorhabditis elegans was taken andused as out group for generating the trees.

Results and Discussion

During the present investigation, six isolates ofHeterorhabditis species CH1, CH2, CH3, CH4, CH5and CH6 were recovered from the soil samples. Thenatural host of all the isolates are unknown, as thenematodes were isolated by Galleria baiting methodfrom the soils. The isolate CH1 was isolated from soilcollected from the grassland CH3 from Wheat fieldand CH4 from sugarcane field of Meerut district.Isolate CH2 was isolated from the soil collected aroundthe roots of wheat field of Muzaffarnagar district. Twoother isolated, CH5 and CH6 were isolated from openfield and a mango garden of Saharanpur district.

Heterorhabditis isolates CH1, CH2, CH3, CH4, CH5and CH6 were compared from other Heterorhabditisspecies and can be distinguished by a combination ofmorphological and morphometric traits. The indicagroup presently comprises eight species, viz. H. indicaPoinar, Karunakar & David, 1992, H.amazonensisAndaló, Nguyen & Moino, 2007, H.baujardi Phan, Subbotin, Nguyen, and Moens, 2003,

H. floridensis Nguyen, Gozel,Koppenhofer andAdams 2006, H. gerrardi Plichta, Joyce, Clark,Waterfield and Stock, 2009, H. mexicana Nguyen,Shapiro, Stuart, McCoy, James and Adams, 2004, H.noenieputensisMalan, Knoetze and Tiedt, 2014andH. taysearae Shamseldean, Abou El-Sooud, Abd-Elgawad and Saleh, 1996. Recently Shaina et al. (2016)claimed a ninth member of indica group named H.pakistanense but in a reviewing of Heterorhabditisspecies by Hunt & Subbotin (2016) it was synonymizedas junior synonym of H. indica based on molecularevidences and also reducing the number ofHeterorhabditis species from 32 to 19 only. In presentstudy, the present isolates were compared itself andwith eight described species of indica group namelyH. taysearaeShamseldean et al., 1996, H.indicaPoinar et al., 1992, H. baujardiPhan et al.,2003, H. floridensisNguyen et al., 2006, H.mexicanaNguyen et al., 2004, H.bacteriophoraPoinar, 1976, H. pakistanense Shahinaet al., 2016 and H. noenieputensisMalan et al., 2014.

The variations in average body length of 3rd stagejuveniles were seen within the isolates with lowest inisolate CH2 (526 µM) followed by 527 µM in CH6,537 µM in CH5, 545 in CH3 and CH4, and largest in552 µM in CH1. The values of a, b, c were close toeach other with almost similar body width. The lengthof excretory pore, nerve ring and pharynx variedslightly within the isolates. D% and E% were muchsmaller in isolate CH1 but greater to CH4 and CH5.While comparing with other described species, theclose resemblance was shown with H. indicahowever the average body length was greater inisolates CH1, CH3, CH4 and CH5. Body length wasalso compatible with H. noenieputensis, H. baujardiand H. floridensis. The other parameters given inTable 1 were closing similar to H. indica but alsoshowing resemblance with other species.

High variations within the average body length of 1st

generation male of isolates were accounted. IsolateCH6 showed highest body length (911 µM) within allthe isolates and with compared species. It was 699

Istkhar and A. K. Chaubey : Characterization of Soil Borne Heterorhabditis species Isolates by PCR....................

AUJR-S 60

µM in CH2, 781 µM in CH4, 784 µM in CH3, 850 µMin CH5 and 868 µM in CH1. The other parameterscompared, were close to each other with minorvariations. When compared to described species, theposition of excretory pore was close to H. taysearae,greater to H. baujardi, H. noenieputensis andsmaller to others. Position of nerve ring was variableand showing similitude to the species compared. Theother parameters were also close to the speciescompared except SW which was higher to all otherspecies but shorter to H. noenieputensis (Table 2).

The ITS rDNA o f isolate CH1 (KP994669), CH2(KP994669), CH3 (KP994669), CH4 (KP994669),CH5 (KP994669) and CH6 (KP994669) werecharacterized by the sequence lengths of 777, 717,744, 824, 818 and 792 base pairs respectively by PCRamplification. All the sequences had a complete set ofITS1-5.8S-ITS2 with flanking regions of 18S and 28Sregions except in CH2 which produced partial ITS1-5.8S-ITS2 sequence. The nucleotide compositions aregiven in Table 3. Pairwise distances between closelyrelated species showed that H. indica, H. gerrardi,H. noenieputensis and H. pakistanense were leastdivergent species with the isolates. The highestsimilarity was shown by H. indica which was 98.5%,98.5%, 94.1%, 98.9%, 94% and 98.7% with anucleotide difference of 9, 9, 35, 7, 35 and 8 nucleotidesin isolates CH1, CH2, CH3, CH4, CH5 and CH6respectively. In H. pakistanense it was 98%, 98.3%,93.9%, 98.7%, 93.9%, 98.5% whereas the nucleotidedifferences were of 12, 10, 36, 8, 36 and 9 nucleotides.In the same way the nucleotide differences with H.gerrardi were 11, 9, 35, 7, 35, 8 and similarity was98.2%, 98.5%, 94.1%, 98.8%, 94% and 98.7%. InH. noenieputensis these nucleotide differences wereof 17, 15, 41, 13, 41, 14 and similarity was 97.2%,97.5%, 93%, 97.9%, 93% and 97.7% (Table 4).

The evolutionary history was inferred using theMaximum Parsimony method. A total of 25 nucleotide

sequences were involved in analysis where 18sequences were of described Heterorhabditisspecies. C. elegans was used as as outgroup. Thedata retrieved from NCBI was aligned with the contigsequences of 6 isolates of Heterorhabditis species.Out of total 1219 sites, 409 were conserved, 728 werevariable and 393 were parsimony informative. The MPtree was obtained using the Subtree-Pruning-Regrafting (SPR) algorithm with search level 1 inwhich the initial trees were obtained by the randomaddition of sequences in MEGA 7.0 (Figure 1). Theanalysis produced 5 parsimonious trees with a totalcharacter length of 721. There were a total of 614positions in the final dataset. The consistency index,retention index and the composite index were 0.71,0.89, and 0.7 for all sites). The bootstrap consensustree included two strongly supported monophyleticgroups. In the first, the indica group, all six isolatesalong with H. noenieputensis, H. gerrardi, H. indicaand H. pakistanense formed and comprise amonophyletic group with 100% bootstrap support. Theother monophyletic sister group is formed by H.amazonensis , H. baujardi, H. floridensis , H.mexicana, H. taysearae and H. sonorensis, withstronger bootstrap support (100%). The second group,the megidis group, was formed by eight other speciesnamely H. bacteriophora, H. georgiana, H.zealandica, H. downesi, H. megidis, H. marelatus,H. atacamensis and H. safricana. The applicabilityof traditional data is an important requisite to comparethe species but the applications of molecular analysisprovide more reliable and accurate data for thediagnostic of Heterorhabditis species. Internaltranscribes spacers are useful to resolve the differenceamong the related Heterorhabditis species and thejoining region of 5.8S gene is too preserved to resolveall heterorhabditid connections. The morphological andmolecular characteristics that we found here weresufficient to regard all the nematodes member ofHeterorhabditis species as the isolates of H. indica.


61 AUJR-S


Tab

le 1

. Com

pari

son

tabl

e of

3rd

sta

ge ju

veni

les

of H

eter

orha

bditi

s sp

ecie

s. A

ll m

easu

rem

ents

exc

ept n

(in

num

ber)

are

in µ

M ±

SD

follo

wed

by

rang

e.

Cha

rac-

H.

H.

H.

H.

H.

H.

H.

H.

Isol

ate

Isol

ate

Isol

ate

Isol

ate

Isol

ate

Isol

ate

ters

tays

eara

ein

dica

Bau

jard

ifl

orid

ensi

sm

exic

ana

bact

erio

ph-

paki

stan

e-no

enie

p-C

H1

CH

2C

H3

CH

4C

H5

CH

6

ora

nse

uten

sis

N30

2525

2525

1525

2520

2020

2020

20

L41

852

855

156

257

858

858

153

655

252

654

554

553

752

7

(332

-499

) (

479-

573)

(497

-595

)(5

54-6

09)

(530

-620

) (

512-

617)

(55

8-62

4) (

484-

578)

(51

6-58

4)(4

88-5

69)

(49

3-58

9) (

500-

574)

(520

-560

)(4

91-5

57)

A21

2628

27.6

25.8

2527

.424

2726

2626

2525

(18-

27)

(25

-27)

(26

-30)

(25-

32)

(23

.6-2

8.4)

(17

-30)

(25

-29)

(21

-27)

(26

-30)

(22-

29)

(23

-28)

(23

-27)

(23-

27)

(17-

28)

B3.

84.

54.

84.

34.

64.

54.

84.

94.

94.

94.

84.

74.

74.

6

(3.4

-4.2

) (

4.3-

4.8)

(4.5

-5.1

)(3

.9-4

.9)

(4.

2-5.

1) (

4.0-

5.1)

(4.

7-5.

3) (

4.3-

5.2)

(4.

5-5.

6)(4

.7-5

.5)

(3.

9-5.

7) (

3.7-

5.1)

(4.4

-5)

(4.4

-4.9

)

C7.

75.

36

5.6

5.9

6.2

5.78

6.2

5.3

4.9

5.5

5.4

5.4

5.4

(6.5

-8.7

) (

4.5-

5.6)

(6-6

.7)

(5.3

-6.6

) (

5.5-

6.3)

(5.

5-7.

0) (

5.4-

6.2)

(5.

5-6.

8) (

4.6-

7)(4

.4-5

.8)

(4.

8-6.

4) (

4.6-

6.3)

(4.8

-6.3

)(4

.9-6

.2)

GB

W20

2020

21.2

2323

2123

2020

2121

2121

(17-

23)

(19-

23)

(18-

22)

(19-

23)

(20-

24)

(18-

31)

(19

-23)

(21

-25)

(18

-21)

(18-

23)

(20

-23)

(20

-23)

(20-

23)

(19-

31)

EP

9098

9710

910

210

399

.397

102

106

113

9291

112

(74-

113)

(88-

107)

(91-

103)

(101

-122

)(8

3-10

9) (

87-1

10)

(95

-106

) (

88-1

05)

(91

-115

)(9

1-11

9) (

94-1

40)

(85

-104

)(8

5-95

)(9

8-12

3)

NR

6482

8186

8185

8281

8385

8279

8184

(58-

87)

(72-

85)

(75-

86)

(68-

107)

(74-

88)

(72

-93)

(73

-90)

(69

-96)

(78

-92)

(70-

112)

(71

-89)

(75

-90)

(77-

86)

(79-

95)

ES

110

117

115

135

122

125

117

106

113

108

115

116

114

114

(96-

130)

(109

-123

)(1

07-1

20)

(123

-142

)(1

04-1

42)

(10

0-13

9) (

113-

125)

(79

-115

) (

98-1

22)

(98-

114)

(97

-133

) (

107-

135)

(109

-118

)(1

04-1

20)

Tai

l55

101

9010

399

9899

8610

599

100

101

100

98

(44-

70)

(93-

109)

(83-

97)

(91-

113)

(91-

106)

(83

-112

) (

95-1

10)

(78

-95)

(82

-118

)(8

9-11

2) (

84-1

15)

(87

-121

)(8

5-11

2)(8

5-11

3)

AB

W13

1415

13.7

1414

1315

1415

14

(11-

14)

(12-

16)

(12-

17)

– (

12-1

6) (

12-1

6) (

11-1

6)(1

2-15

) (

11-2

2) (

12-1

6)(1

4-18

)(1

1-16

)

D%

8284

8481

8184

8489

9098

9980

8098

(71-

96)

(79-

90)

(78-

88)

(71-

90)

(72-

86)

(76

-92)

(78

-97)

(81

-95)

(83

-99)

(86-

108)

(88

-109

) (

69-8

8)(7

6-87

)(8

5-11

7)

E%

180

9410

810

510

411

210

011

397

108

115

9292

114

(110

-230

)(8

3-10

3)(9

8-11

4)(9

5-13

4)(8

7-11

1) (

103-

130)

(95-

107)

(99-

125)

(81-

139)

(83-

122)

(82-

163)

(71-

114)

(80-

105)

(97-

136)

AUJR-S 62


Tab

le 2

. Com

pari

son

tabl

e of

1st

gen

erat

ion

mal

es o

f H

eter

orha

bditi

s sp

ecie

s.A

ll m

easu

rem

ents

exc

ept

n (

in n

um

ber

) ar

ein

µM

±SD

fol

low

ed b

y ra

nge.

Cha

rac-

H.

H.

H.

H.

H.

H.

H.

H.

Isol

ate

Isol

ate

Isol

ate

Isol

ate

Isol

ate

Isol

ate

ters

tays

eara

ein

dica

Bau

jard

ifl

orid

ensi

sm

exic

ana

bact

erio

ph-

paki

stan

e-no

enie

p-C

H1

CH

2C

H3

CH

4C

H5

CH

6

ora

nse

uten

sis

N20

1214

2020

1515

1515

1515

15

L70

3 ±

2372

188

986

268

682

081

964

986

869

978

478

185

091

1

(648

-736

)(5

73-7

88)

(818

-970

)(7

85-9

24)

(614

-801

) (

780-

960)

(72

0-10

13)

(53

0-77

5) (

776-

905)

(63

8-78

4) (

717-

820)

(70

4-90

6)(8

18-9

23)

(789

-989

)

GB

W43

.542

4947

.642

4339

.838

4536

4144

±47

51

(38-

48)

(35-

46)

(45-

53)

(43-

50)

(38-

47)

(38-

46)

(38-

43)

(33

-46)

(37

-49)

(33

-41)

(36

-46)

(37

-50)

(42-

52)

(46-

59)

EP95

123

8111

712

412

112

2.8

8699

9996

9394

99

(78-

120)

(109

-138

)(7

1-93

)(1

04-1

28)

(108

-145

) (

114-

130)

(11

2-13

3) (

75-1

02)

(92

-107

) (

91-1

07)

(87

-109

) (

82-1

13)

(86-

103)

(92-

107)

NR

6575

6580

7172

8967

7577

7268

7876

(54-

88)

(72-

85)

(54-

77)

(73-

90)

(61-

83)

(65

-81)

(80

-110

) (

64-7

5) (

70-8

4) (

72-8

3) (

63-8

5) (

60-7

5)(6

4-78

)(6

6-84

)

ES11

210

111

610

596

103

102.

895

9910

396

9398

101

(85-

1230

)(9

3-10

9)(1

05-1

32)

(97-

111)

(89-

108)

(99-

105)

(100

-105

)(8

8-10

6) (

93-1

09)

(99-

110)

(89-

106)

(84

-126

)(9

0-10

5)(9

7-10

7)

TR12

291

9193

9679

8297

9584

100

101

106

(100

-146

)(3

5-14

4)(2

8-38

)(7

8-11

6)(6

5-13

0)(5

9-87

)–

(67-

104)

(61-

118)

(76-

114)

(54-

95)

(79-

111)

(85-

114)

(79-

120)

Tai

l25

2834

2728

3725

3529

3230

3234

(20-

29)

(24-

32)

– (2

9-40

)(2

1-36

) (

30-4

2)(2

1-32

)(3

2-37

)(2

6-33

)(2

8-35

)(2

5-41

)(2

7-37

)(2

9-39

)

AB

W25

2322

2624

2324

.519

2220

2019

2223

(21-

30)

(19-

24)

(20-

24)

(20-

31)

(23-

27)

(22-

25)

(22-

26)

(15-

22)

(19-

26)

(17-

25)

(19-

22)

(15-

22)

(20-

25)

(18-

28)

SP

L39

4340

4241

4038

.543

4642

4146

4444

(30-

42)

(35-

48)

(33-

45)

(36-

46)

(30-

47)

(36-

44)

(35-

42)

(37-

49)

(42-

49)

(36-

48)

(36-

470)

(42-

50)

(40-

50)

(38-

52)

GL

1821

2023

2320

2120

2223

2224

2425

(14-

21)

(18-

23)

(18-

22)

(17-

30)

(18-

32)

(18-

25)

(20-

22)

(17-

24)

(20-

23)

(21-

26)

(19-

26)

(21-

32)

(21-

29)

(20-

27)

D%

8812

1

112

129

117

119.

390

100

9710

010

096

97

(105

-119

)(1

14-1

49)

(110

-126

)(8

1-10

8)(9

5-10

9)(8

6-10

5)(9

2-11

60)

(90-

111)

(88-

106)

(92-

109)

SW

156

187

182

157

167

174

156

231

211

214

202

236

202

196

(138

-208

)(1

33-2

09)

(130

-196

)(1

44-1

91)

(20

2-30

1) (

188-

249)

(175

-286

) (

167-

226)

(19

4-32

5)(1

83-2

30)

(150

-260

)

GS

4649

5053

.856

5058

4749

5655

5455

57

(44-

61)

(47-

65)

(43-

70)

(48-

65)

(38-

56)

(40-

53)

(49-

69)

(45-

66)

(41-

78)

(46-

70)

(49-

67)

63 AUJR-S


Tab

le 3

.Com

para

tive

acc

ount

of

nucl

eoti

de c

ompo

siti

ons

of H

eter

orha

bditi

s sp

ecie

s. D

ata

for

Het

eror

habd

itis

isol

ates

com

pare

d is

in b

old.

S. N

o.SP

EC

IES

Acc

essi

onN

o.(b

p)(b

p)(b

p)G

+C

%A

+T%

AC

GT

Tot

alL

engt

h

1Is

olat

e C

H1

KP

9946

6937

215

421

744

.92

55.0

820

515

519

422

377

7

2Is

olat

e C

H2

KP

2935

8635

015

421

344

.49

55.5

119

014

217

720

871

7

3Is

olat

e C

H3

KP

9946

7032

015

221

444

.76

55.2

419

614

718

621

574

4

4Is

olat

e C

H4

KP

9946

7136

915

924

344

.78

55.2

222

116

520

423

482

4

5Is

olat

e C

H5

KP

9946

7241

915

422

044

.62

55.3

821

616

420

123

781

8

6 I

sola

te C

H6

KP

9946

7336

614

321

944

.44

55.5

621

115

719

522

979

2

7 H

. noe

niep

uten

sis

JN62

0538

371

154

216

46.5

153

.49

270

216

264

282

1032

8 H

. ger

rard

iFJ

1525

4544

.69

55.3

123

517

221

123

985

7

9 H

. ind

ica

AY

3214

8337

015

421

546

.05

53.9

526

020

325

227

398

8

10 H

. am

azon

ensi

sD

Q66

5222

395

154

211

45.9

354

.07

270

214

266

295

1045

11 H

. bau

jard

iA

F548

768

397

153

212

44.6

555

.09

204

156

199

234

795

12 H

. flo

ride

nsis

DQ

3729

2239

315

421

345

.654

.427

321

526

730

210

57

13 H

. mex

ican

aA

Y32

1478

394

154

213

46.2

453

.76

258

209

263

285

1010

14 H

. tay

sear

aeE

F043

443

46.1

53.9

270

217

268

297

1052

15 H

. son

oren

sis

FJ47

7730

44.7

755

.23

235

174

220

251

880

16 H

. bac

teri

opho

raA

Y32

1477

389

154

228

45.6

454

.36

269

204

262

286

1021

17 H

. geo

rgia

naE

U09

9032

389

154

228

45.5

454

.36

269

204

261

286

1021

18 H

. ata

cam

ensi

sH

M23

0723

350

154

211

46.5

853

.42

172

153

187

218

730

19 H

. saf

rica

naE

F488

006

379

154

211

47.2

552

.75

249

220

270

298

1037

20 H

. mar

elat

usA

Y32

1479

379

154

211

48.0

451

.96

233

215

263

284

995

21 H

. zea

land

ica

AY

3214

8138

715

421

249

.15

50.8

522

321

927

428

710

03

22 H

. dow

nesi

AY

3214

8237

415

421

247

.88

52.1

223

621

525

928

099

0

23 H

. meg

idis

AY

3214

8038

415

422

049

.15

50.8

523

922

127

327

710

05

24 H

. pak

ista

nens

eJX

1447

4045

.34

54.6

621

716

220

822

981

6

AUJR-S 64


Tab

le 4

. Pai

rwis

e di

stan

ces

of t

he I

TS

regi

ons

betw

een

Het

eror

habd

itis

isol

ates

an

d c

lose

ly r

elat

ed s

pec

ies

of H

eter

orha

bditi

s. B

elow

dia

gon

al:

% a

ge s

imil

arit

y; a

bove

the

dia

gona

l: n

o. o

f ba

se s

ubst

itut

ions

per

sit

e be

twee

n se

quen

ces,

acc

ordi

ng t

o th

e K

imur

a 2-

para

met

er m

odel

.

SP

EC

IES

CH

1C

H2

CH

3C

H4

CH

5C

H6

NO

EG

ER

IND

AM

AB

AU

FLO

MA

XTA

YSO

NB

AC

GE

OA

TASA

FM

AR

ZEA

DO

WM

EG

PAK

H. n

oeni

eput

ensi

s

Isol

ate

CH

118

3714

3215

1711

971

7378

7879

7814

614

514

415

115

316

915

216

212

Isol

ate

CH

297

4110

4111

159

968

6974

7576

7514

214

113

814

514

716

314

615

610

Isol

ate

CH

393

.793

3834

3941

3535

8486

9191

9291

164

162

164

169

170

183

171

177

36

Isol

ate

CH

497

.798

.493

.538

713

77

6567

7272

7372

141

140

137

144

146

162

145

155

8

Isol

ate

CH

594

.693

94.2

93.5

3941

3535

8688

9292

9392

165

163

168

173

175

183

171

179

36

Isol

ate

CH

697

.598

.293

.398

.993

.314

88

6769

7474

7574

139

140

138

145

147

163

146

154

9

H. n

oeni

eput

ensi

s97

.297

.593

97.9

9397

.78

865

6770

7273

7214

214

114

014

714

916

514

815

89

H. g

erra

rdi

98.2

98.5

94.1

98.8

9498

.798

.72

6062

6767

6867

140

139

136

143

145

161

144

154

1

H. i

ndic

a98

.598

.594

.198

.994

98.7

98.7

99.7

6264

6969

7069

140

139

138

145

147

163

146

156

3

H. a

maz

onen

sis

87.4

87.9

84.8

88.5

84.4

88.1

88.5

89.5

89.1

710

1314

1315

315

414

915

615

417

115

616

259

H. b

auja

rdi

8787

.784

.488

.183

.987

.788

.189

.188

.798

.98

1212

1115

215

314

915

615

417

115

616

261

H. f

lori

dens

is86

86.8

83.4

87.2

83.1

86.8

87.6

88.2

87.8

98.4

98.7

910

915

815

915

416

115

917

616

116

666

H. m

exic

ana

8686

.683

.487

.283

.186

.887

.288

.287

.897

.998

98.5

87

159

160

156

163

159

175

160

165

66

H. t

ayse

arae

85.8

86.4

83.2

8782

.986

.687

8887

.697

.798

98.3

98.7

116

015

915

115

815

817

515

716

267

H. s

onor

ensi

s86

86.6

83.4

87.2

83.1

86.8

87.2

88.2

87.8

97.9

98.2

98.5

98.8

99.8

161

160

152

159

159

176

158

163

66

H. b

acte

riop

hora

71.2

72.1

66.7

72.4

66.3

72.8

72.1

72.6

72.6

69.4

69.6

68.2

67.8

67.6

67.3

1497

103

101

126

102

113

141

H. g

eorg

iana

71.4

72.4

67.2

72.6

66.8

72.6

72.4

72.8

72.9

69.1

69.4

67.9

67.6

67.8

67.6

97.7

101

106

105

130

105

116

140

H. a

taca

men

sis

71.5

72.9

66.3

73.2

65.1

72.8

72.4

73.4

72.9

70.2

70.1

68.9

68.3

69.7

69.4

8281

.211

1965

2140

137

H. s

afri

cana

69.8

71.3

65.1

71.5

63.9

71.2

70.7

71.7

71.2

68.4

68.4

67.2

66.6

67.9

67.7

80.8

80.2

98.2

2270

2543

144

H. m

arel

atus

69.2

70.7

64.8

7163

.370

.670

.271

.270

.769

68.9

67.7

67.7

67.9

67.7

81.2

80.4

96.8

96.3

6529

4614

6

H. z

eala

ndic

a65

.166

.761

.367

61.2

66.6

66.1

67.2

66.7

64.7

64.6

63.3

63.6

63.6

63.3

75.9

7588

.687

.688

.666

8316

2

H. d

owne

si69

.571

64.6

71.3

64.4

70.9

70.5

71.5

7168

.568

.467

.267

.468

.268

8180

.496

.595

.895

.188

.433

145

H. m

egid

is66

.768

.362

.768

.662

68.7

67.7

68.8

68.3

66.7

66.7

65.7

65.9

66.8

66.5

78.6

7893

.192

.692

8594

.415

5

H. p

akis

tane

nse

9898

.393

.998

.793

.998

.598

.599

.899

.589

.789

.388

.388

.388

.288

.372

.472

.673

.171

.570

.966

.971

.268

.5

Dat

a fo

r H

eter

orha

bditi

s co

mpa

red

is in

bol

d.

Init

ial t

hre

e le

tter

s of

sp

ecie

s w

ere

use

d a

bov

e d

iago

nal

.

65 AUJR-S


Figure 1. Phylogenetic relationships by the Maximum Parsimony method ofHeterorhabditis species isolatesand other identified based on ITS-rDNA sequences. Caenorhabditis elegans (X03680) was used as outgroup.The analysis involved 25 nucleotide sequences. All positions containing gaps and missing data were eliminated.Evolutionary analyses were conducted in MEGA7.

AUJR-S 66


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67 AUJR-S

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l

AUJR-S 68

69 AUJR-S

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