eChapters

Q.1.    In what sense the analysis of covariance is an extension of ANOVA ?

    Answer
    
    The analysis of covariance is an extension of ANOVA in the sense that it covers the case where observations are taken on more than one variable from each experimental unit. The interest centers on the study variable y which depends upon on auxillary variable x.

The male genital system consists of:

1. The pair gonads, the testes
2. Epididymis
3. Vas deference or ductus deference
4. Accessory glands (Seminal vesicles, prostate glands, & bulbo-urethral glands)

1.     Tissue Collection and Culture
    
2.     Trypsinization of Cells
    
3.     Cell Separation by Density Gradient Method
    
4.     Low Speed Separation of Cells
    
5.     Isolation of Lymphocyte
    
6.     Cryo Preservation
    
7.     Isolation of Spleen Cells
    
8.     Isolation of Peritoneal Macrophage
    
9.     Isolation of Blood Dna
    
10.     Differential Count of Leucocytes
    
11.     Slide Gel Electrophoresis
    
12.     Haemagglutination Assay
    
13.     Elisa
    
14.     Western Blotting
    
15.     Widal Test
    
16.     Identification of Y-chromosomes in Cattle using Pcr

Analysis of Variance
    
Completely Randomised Design (C.R.D.)
    
Randomised Block Design (R. B. D.)
    
Latin Square Design

Appendix A: Diagrammatic Presentation of Processes Involved in Obtaining an Indian Patent

Abstract

Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. The aim of the study is to investigate the ability of neural network for yield prediction of rice in Andaman and Nicobar Islands. Field experiments were conducted at the various regions of Andaman and Nicobar Islands during 1980 to 2002 and data collected was subjected to Artificial Neural Networks (ANN) approach for developing a meaningful yield prediction for four varieties of rice C14-8, Quing Livan No.1, Taichung , Sen Yu, Jaya under island conditions with the objective to estimate the crop production a few days or months before harvesting of crop. The observations of all experiments viz., Varietal spectrum, Weather parameters , Yield components, Soil, Abiotic stressors, Biotic stressors for the period of twenty years from 1980 to 2002 were collected from the basic records of experimental field. All the data were used for training data set. Prediction of crop yield, mainly strategic plant of rice has been an interesting research area to agronomists since long for its importance in national and international economic programming. Application of ANN was found to be significant tool for prediction of yield in rice.

Introduction

Conventional practises of raising livestock had been practised for several years on a worldwide basis to satisfy the protein needs. Increasing human population, income growth and increase of urbanization, pressurizes serious demand on the quantity and attribute of livestock products. The feed stuffs such as maize, soya, groundnut, etc. used in livestock especially for poultry possesses a great competition for human food.

Introduction 
Plant disease has been highly impacted by the development of new technology. The degree of the impact depends on how much the technology affects the component steps of the system. Many of the steps in plant disease diagnosis and management depend heavily on accurate and timely information and so can greatly benefit from the development of improved methods of accessing and disseminating information. Computers provide a wide range of tools which are extensively used in plant disease diagnosis and management. In fact, they will be a key to improve farm practices in the coming decades as it is scientifically pertinent to organize farm information in spatial databases because agricultural systems are inherently spatial. Biological and physical aspects of agricultural system create spatial heterogeneity, and as a result, patchiness is the rule in the occurrence and distribution of plant pathogens and disease (Campbell and Madden, 1990). In the area of plant diseases computers are useful

Biotechnology in the food processing sector targets the selection and improvement of microorganisms, including bacteria, yeasts and moulds with the objectives of improving process control, yields and efficiency as well as the quality, safety and consistency of bioprocessed products. They perform a series of processes such as fermentation, hydrolysis, etc. and even spoilage of food, depending upon the species of organisms, nature of raw material (food), environmental factors (temperature, pH, salt concentration, etc.), duration, etc.

Fermentation
Is the process of bioconversion of organic substances by microorganisms and/or enzymes (complex proteins) of microbial, plant or animal origin. It is one of the oldest forms of food preservation which is applied globally.

In recent years, a new branch of biological science, called as bio-informatics receives more attention among its users and calls for application of computational statistical algorithms to a greater extent and needs employment of more sophisticated mathematical/statistical tools. Though biological science and information technology forms a major part in bio-informatics research as applied to agriculture, statistical tools act as their backbone. Its utility is more felt starting from the stage of framing of hypothesis till analyzing/interpreting results of a scientific experiment.  It can also be viewed as a three way multi-disciplinary approach with biotechnology, information technology and statistical technology as its three faces, with the principles of statistics applied to biological science through an interface information technology.

The goats play a vital role in the economy of the poor and marginal farmers of rural India. Notwithstanding the fact that the goat population has shown a steady growth over the year’s very little effort has been taken for genetic improvement at farm level. As per 20th Livestock Census, 27.74% of the livestock population is Goats. Out of 148.88 million Indigenous Goats (Including Non-descript), 27.4% are pure breed, 9.1% are graded breeds and the remaining 63.5% are non-descript breeds. So, to increase the productivity per goat, we need to improve the breed quality scientifically. Artificial insemination (AI) using frozen semen is practiced to produce superior progeny and to accelerate the up gradation of stock. The conception rate with AI is lower but it has great potential to multiply superior quality of goat with faster rate. This technique also spread elite genetic material throughout a population with increased rate of genetic improvement. This technique is also important for the breed conservation process and has paved the way for other reproductive biotechnologies. The sufficient elite germplasm of male buck is not available throughout the country to cover breeding programmed by natural mating. AI is the best solution to improve the quality and productivity per goat.

The selection of breeding bucks in relation to maximizing the reproductive efficiency depends upon males. AI permits intense selection of sires with exceptional merits and provides opportunity to exploit the value of superior sires. Young males can be trained for exhibiting sexual behaviors and semen collection through the uses of male, anoestrus doe or oestrus doe as dummy. Successful AI depends on precise management of semen collection, its frequency and use. The semen collection implies mounting a teaser doe or a dummy. Artificial vagina (AV) and electro-ejaculator (EE) are the two methods for semen collection in bucks. AV method is being used as universal method for routine semen collection whereas electro-ejaculation is generally used for collecting semen from untrained and valuable sires incapable of service. Moreover, this method of collection is painful for the animals. Semen evaluation should be rapid and effective so that collected semen samples can be processed for initial quality which ultimately affects the fertility. There are several tests for the assessment of semen quality but no single test is reliable to give definite prediction of the fertility.

Natural Gene Flow in Plants

More than 70% of plant species, for example potato, oat, rape-seed, wheat, and kiwifruit have come from spontaneous interspecific or intergeneric hybridization although the extent of natural hybridization differs among the plant genera and families. It creates genetic variation and produce recombinant genotypes that may exhibit heterosis.The stabilization of these hybrids often through polyploidy (autopolyploidy or allopolyploidy) can result in the formation of new biological species (Masterson, 1994), thereby, having impact on both ecological and evolutionary processes (Martinsen et al., 2001). In addition, species genomes are often differentially permeable to introgression if they are under simple genetic control (unlike many genes contribute to hybrid fitness with linkages) and certain portion of the genome can readily incorporate alien alleles. Introgression is a selective process of gene flow that filters deleterious genes and takes only beneficial genes (incorporating the alleles from one taxon into other).

A gene microarray is a (mxn) matrix, containing n-experimental conditions, also called attributes for m-genes. Gene clustering refers to partitioning a microarray of genes based on the commonality of attributes. Genes belonging to a cluster often do not coexpress or coregulate. This calls for biclustering that segregates the (mxn) matrix into small windows. In other words biclustering refers to simultaneous clustering of rows and columns of the microarray. This chapter reviews some of the well known biclustering algorithms, and proposes an alternative solution to the problem using evolutionary multi-objective optimization technique.

Introduction

Clustering is process to automatically partition a set of p-dimensional data points (p³1) into classes, where the data in each class have similarity with respect to a given distance metric. Each class usually has an ideal member that possesses the necessary characteristics of the class.

1.     Determination of lmax
    
2.     Verification of Beer-Lambert Law
    
3.     Estimation of Carbohydrate by Anthrone Method
    
4.     Estimation of Reducing Sugar by Di Nitro Salicylic (DNS) Acid
    
5.     Estimation of Protein by Biuret Method
    
6.     Estimation of Protein by Lowry’s Method
    
7.     Estimation of Protein by Bradford Method
    
8.     Estimation of Cholesterol
    
9.     Estimation of DNA
    
10.     Estimation of RNA
    
11.     Determination of iso Electric Point of Amino Acid

1.     Common Solvents
    
2.     Common Buffer Compositions

1.     Chromatography
    
2.     Identification of Sugar in Fruit Juice by Thin Layer Chromatography
    
3.     SDS-PAGE
    
4.     Composition for Native PAGE
    
5.     Silver Staining of Protein Gels

The different biochemical values serve as an indicator of the correct health status, viz important organ functions,  the alteration in values if any, helps in identifying the specific organ damage and exact diagnosis of the  abnormal health condition or disease. Some of the important biochemical values of the animals, which may assist in diagnosing the specific disease  conditions are listed in this chapter.

Introduction

Saffron is a naturally derived plant product from the dried stigma of the Crocus sativus flower  (Gresta et al., 2009).The saffron (Crocus sativus L.) is an Iridaceae family member, herbal perennial, sterile (2n=3x=24) geophyte, traditionally propagated by vegetative corms. Its cultivation dates back to late Bronze Age in the Mediterranean region (Negbi, 1999) in Anatolia since the ancient times (Arslan et al., 2007) and for at least 3500 years in Egypt and Middle East. Saffron contains more than 150 volatile and aroma yielding compounds. The chemical components mainly include carotenoids, crocin, picrocrocin, safranal etc (Abdullaev, 1993, Tarantalis et al., 1995, Karaoglu 
et al., 2007). Saffron is mostly used as spice, food colorant, dye or perfume. The dried stigmas introduce unique color, taste and fragrance and thus making it the world’s most expensive spice (Baghlian et al., 2010). Saffron has been used in folklore herbal medicines for centuries (Basker and Negbi, 1983).