eChapters

Meta-analyses has been used extensively to combine the results of existing studies and obtain powerful estimates of a parameter. In animal breeding, attempts have been made to obtain meta-analysis estimates for genetic parameters. This tool is more pertinent to animal breeding on account of heterogeneity in terms of location, sample size and other factors in the estimation of genetic parameters. This chapter explains the background, methodology, precautions and implications of meta-analysis with respect to genetic parameters.

A meta-analysis is a powerful tool of statistics that could combine several estimates from different studies for a parameter and the resulting weighted estimate could be used as a representative value for the particular parameter. However, not all the studies are similar with respect to location, sample size, and methodology used and thus various precautions are to be taken right from the selection of studies to incorporating of between-study heterogeneity in the models. In animal breeding, met-analysis is very much useful for obtaining meaningful estimates of genetic parameters and this chapter explains the methodology of meta-analysis in animal breeding.

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained.

Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations.

Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

The aim of the breeder is to improve on various economic traits through selection. However, due to the lesser number of parents used, over a period of time, there creeps in the effect of inbreeding and loss of genetic variability which in turn causes depression and loss of fitness. This is more prominent in finite endangered populations and evaluation for genetic variability also forms an integral part of the animal breeding analysis.

Genetic variability is an integral part of domestic animal populations as fitness is directly related to the variability.

The large volume, type and nature of animal breeding data and the complex nature of analytical requirement warrants additional tools for handling, storage, retrieval and solving complex equations. Accuracy, speed, computational requirement, applicability, etc., has resulted in the development of numerous software exclusive for animal breeding data analysis. The database management system is already explained in the chapter on ‘Nature of animal breeding data and database management’. This chapter gives a note on the commonly used animal breeding software.

Abstract

Quercus species of family Fagaceae are very important from both economic and ecological perspectives. These exhibit irregular fruiting, and produce seeds recalcitrant to storage, thereby difficult to propagate through conventional procedures. Again because of their very difficult to root nature, extremely limited success has been observed in vegetative propagation. Application of new technological approaches like in vitro propagation may be of interest for accelerating tree improvement programs to these hardwood species. This review provides a summary of the advancement in the application of in vitro propagation to oak species of Kumaun West Himalaya India.

Principle

Orchid seeds are produced in large numbers but are exceedingly small, hence are also called “dust seeds” may be as small as 0.01 by 0.05 mm in dimension and weigh as little as 10-30 ug. Seedling development is generally a slow process in orchids, with the seedlings passing through an initial stage, termed the protocorm stage, prior to the production of distinct vegetative organs (leaves, roots, etc). The protocorms of terrestrial orchids are subterranean and entirely heterotrophic, with all of the nutritional needs of the orchid being supplied by the mycorrhizae (the symbiotic fungus). The length of the protocorm stage is variable, however, in many cases, months, or even years, elapse before leaves and roots are produced.

The combination of a small seed size and fungal symbiont requirement largely precludes the propagation via conventional seed propagation protocols. Accordingly, most of the early research on the development of orchid seed germination protocols focused on in vitro germination with the seeds and fungal symbiont being sown on the same petri plate. Successful protocols were developed for several species; however, the process was complicated by the diversity of fungal symbionts required (across orchid taxa) and the difficulty in isolating and culturing these fungi.

In the 1920’s the process of orchid seed propagation was revolutionized by Lewis Knudson with the demonstration that nutrient media that could be used as a substitute for fungal infection. The isolation and culture of multiple fungal strains was no longer required, and improvements in germination frequency, germination speed, and seedling vigor were generally observed. To date, successful asymbiotic germination techniques, termed ‘flasking’, have been developed for many epiphytic orchids, with several species being saved from extinction via the application of these techniques.

The knowledge and principles from interconnected disciplines of bioengineering, material science and life sciences are combined in tissue engineering with the aim of developing a construct that can wholly or partly restore/maintain/augment the function of a damaged tissue or organ. Tissue engineering construct generally comprises cells on a suitable scaffold. The ultimate functionality of a tissue engineering construct is mainly dependent on the cell behavior on scaffolds. The proliferation and cell viability on scaffolds varies from one cell type to another and from one species to another. So it is important to investigate individual cell-scaffold combinations.

The similarity in structure and composition to bone mineral, osteoconductive properties, ability to integrate with the bony tissue and absence of immune response makes hydroxyapatite [Ca10 (PO4)6(OH)2] the choice of calcium phosphate biomaterial with only disadvantage of slow rate of resorption. On contrary, silica–calcium phosphate composite in comparison to calcium phosphate is rich biomaterials having a faster resorption rate. Studies have shown that coating of hydroxyapatite with a calcium silicate containing layer encourages cell proliferation and osteogenic differentiation of human bone marrow-derived stromal cells.

In this chapter a composite scaffold (calcium silicate, hydroxyapatite and tricalcium phosphate), namely HASi with elements in the following percentages: 66.36 % - calcium, 25.35 % - phosphorus and silicon - 8.29 % and porosity of 50 – 500 µm is undertaken to evaluate the rBMSC proliferation and differentiation properties in vitro with the aim of assessing cytocompatibility of HASi and rBMSC in terms of cell attachment, cell morphology, cell proliferation and osteogenic differentiation.

Abstract
In-silico analysis of nucleic acids and proteins from fishes has enriched our understanding to several fundamental problems of classical fish biology and genetics. Mining of data which is analogous to information discovery is the process of automating knowledge discovery. Analysis of data in all possible manner and recognition of a pattern which has a scientific basis by an efficient algorithm is the fundamental principle of Bioinformatics. Ever increasing number of Bioinformatics tools as computational packages are now a days used in tandem to analyze sequence of a loci and to model a novel gene product. High efficiency algorithms with excellent homology searching functions have lead to this advancement. Cat fishes, a group of diverse bony fishes have been studied with respect to genetic analysis in several species and several peptides. Tracking of the evolutionary foot prints using genomic libraries and between expressed sequences has been generating much interest.

Introduction

Spatial planning tools brings together multiple users of the earth to make informed and coordinated decisions about how to use of resources  and helps to organize, analyze and present information from a number of different sources.  Thus, viewing from single interest or multiple use viewpoints is enhanced and can make a very important contribution to environment. Use of Remote Sensing (RS) and Geographical Information Systems (GIS) tools has the capacity to bring together experts from a variety of disciplines to address complex spatial problems.

Introduction
Science and its development have had a great impact on the biological research. The main problem arises to understand and interpret the data which are the results of the higher dimensional research in the above mentioned field, and it is extremely difficult some times to give answers for complex and tedious biological questions generated through the large datasets. Parallel developments of statistical methods provide relaxation over these issues. The statistical method by bioinformatics presents many new and logical problems for researchers, scientists, and scholars related to these communities. Thus the knowledge of statistical concepts and techniques are prime important in this chain. The effort is made to cover stochastic theory, finite Markov chains in learning models to diffusion processes in population genetics, advanced statistical methods with Hidden Markov model and provide some literature on Bayesian computation like MCMC and their resources. Fortunately the applied aspect of probability theory and theory of stochastic processes seem to have been extensively used in the research and development level of biological field but the lacking of required material is a very problematic aspect in the system with proper and systematic ways.

Introduction

The proteome is often described as the set of all expressed proteins in a cell, tissue or organism. It is the systematic analysis of cellular proteins, that is, their identity, quantity and function. One advantage of proteomics over transcriptome is that mRNA represents an early stage of gene expression whereas the study at the proteome level describes the state of protein which is the final functional molecule of the cell. Thus, it gives a clear picture of the cell function that may not be concluded from mRNA study. In fact we can say that proteins are more than just information carriers so it gives more information with respect to only nucleic acid based studies.  Proteomics covers two-dimensional gel electrophoresis (2-DE), image analysis, mass spectrometry, amino acid sequencing, and bio-informatics to resolve comprehensively, to quantify, and to characterize proteins. Biomarker discovery as an important aspect of proteomics technology is highly appreciable when it comes to diseases. Today,  a better understanding of the disease mechanisms leading to discovery of new disease specific biomarkers are very much possible due to the sequencing of the human genome. It has accelerated the proteomic technologies for accepting the challenges so as to meet the diseased related investigations.

The analysis of chromosome behaviour at meiosis can provide valuable information about the genomic composition of both species and hybrids. The basis for this analysis is the idea that chromosomes that pair and undergo chiasma formation are homologous. Where two genomes are present, as in diploids, the expectation is that only bivalents will be observed and where there are multiple genomes, as in polyploids, two general patterns of pairing are possible. In autopolyploids, configurations of multiple chromosomes are to be expected since all the component genomes are homologous and have the possibility of pairing with each other, whereas in allopolyploids, with different genomes, bivalent formation is the expectation. Thus, meiotic analysis can provide an oversight for genome analysis to investigate genetic relationships between the species. Further, chromosomal rearrangements are correlated with pollen fertility in species and F₁ hybrids.  Observations on pollen formation, size and viability can provide useful information, as low pollen viability is often indicative of meiotic irregularities and large sized pollen can indicate the formation of unreduced gametes. In addition, the occurrence of nucleolar dominance has been recorded in many interspecific hybrids.

Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays that require increasingly complex analysis tools. The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Kowledge on software  and web-based interfaces for tools all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience.However, making tools accessible does not necessarily address the crucial problem of reproducibility.

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.

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.