eChapters

Abstract

A Mathematical Method for Artificial Neural Networks offers an original, broad and integrated approach that explains each tool in a manner that is independent of specific ANN systems. Artificial Neural Networks are abstract mathematical models of brain structure and functions. They are nothing but a mathematical model that relies on varying the strength of connections between the internal processing elements to interpret data. They can be applied to many pattern classification problems. In this paper, it has been used to predict the level of amino acid in protein sequences. Amino acids are the building blocks of proteins. It is any molecule that contains both amine and carboxylic acid functional groups. Here, known protein sequences of ingredients are taken as input patterns. The network is trained using back propagation algorithm with sigmoid function as activation function by generating random weights. It is tested again with unknown protein sequence. Error is calculated by the difference between actual network output and desired output. If the training is successful the patterns are correctly classified. The network is trained until the error is minimum. The amino acid level is thus predicted in a particular protein sequence.

The dream of having genomes completely sequenced is now a reality. The complete sequence of many genomes including the human one is known. However, the understanding of probably half a million human proteins encoded by less than 30’000 genes is still a long way away and the hard work to unravel the complexity of biological systems is yet to come. A new fundamental concept called proteome (PROTEin complement to a genOME) has recently emerged that should drastically help to unravel biochemical and physiological mechanisms of complex multivariate diseases at the functional molecular level. Proteomics involves the systematic study of proteins in order to provide a comprehensive view of the structure, function and regulation of biological systems. Coupled with advances in bioinformatics, this approach to comprehensively describing biological systems will undoubtedly have a major impact on our understanding of the phenotypes of both normal and diseased cells. Initially, proteomics focused on the generation of protein maps using two-dimensional polyacrylamide gel electrophoresis.

If the 1990s were the decade of genomics, the first ten years of the new century are set to become the decade of proteomics. For the first time, the technologies of proteomics make it possible to generate quantitative protein expression data on a scale and sensitivity comparable to that achieved at the genetic level. This advance has major implications for our understanding of cellular organisation in health and disease and for pharmaceutical and agricultural biotechnology. Indeed, proteomics is already yielding important findings, across a wide range of applications. Most proteomics research, however, is directed towards the more proximal goal of investigating protein expression and function under specified physiological conditions.

The rapid evolution of proteomics has continued during the past years with a series of innovations in the core technologies of two-dimensional electrophoresis and mass spectrometry, and a diversity of productive research programmes. Well annotated proteomics databases are now emerging in a number of fields to provide a platform for systematic research, with particularly promising progress in clinical applications such as cardiology and oncology. Large-scale quantitative research, comparable in power and sensitivity to that achieved for gene expression, is thus becoming a reality at the protein level.

1. Introduction
The era of genome sequencing was exploded by the upcoming of Human Genome Project (1989), coordinated by the U.S. Department of Energy and the National Institute of Health to identify all the approximately 20,000-25,000 genes, 3 billion chemical base pairs, store this information in databases, improve the tools for data analysis, transfer related technologies to the private sector, and address the ethical, legal, and social issues (ELSI) that may arise from the project. The dawn of genome sequencing began with Fredric Sanger’s sequencing almost twenty-five years ago. The time consuming and labor intensive gel preparation and their running, as well as the cost of such experiments replaced the conventional sequencing by “shotgun” sequencing.

The importance of Plant Pathology has been realized over the years owing to number of crop failures in different parts of the world. Plant disease control is indispensable for qualitative production and to compete in global agricultural trade. There is great need of sensitization of our research in various aspects which shall guide the generation to come for managing plant diseases not only for enhancing crop production but also in facilitating trade by conserving the biodiversity and environment.

Research Trend in Plant Pathology

Like in other sciences, there has been a change in the trend of research in Plant Pathology with passage of time. Up to the late 1960s most of the interest in plant pathology was concentrated in crop diseases mostly caused by fungi. Physiology of microorganisms and physiology of parasitism became increasingly popular fields of interest in 1960s.

Sequence Alignment

Sequence Alignment is the procedure of comparing two (Pair-wise Alignment) or more (Multiple Sequence Alignment) sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences.

Significance: Discovering functional, structural and evolutionary information in biological sequences.

A genome is an organism’s complete genetic signature and the collection of information that an organism can pass on to its offspring. Genomics, the study of the structure, function, and evolution of entire genomes is principally dealing with genetic information which helps us to understand how organisms and ecosystems work, how theymalfunction, or how they might be melded to human needs. The model organisms used in genomics have, in general, small genomes and short generation times. To meaningfully analyze the gene-to-phenotype relationships, a large amount of sequencing information is necessary. Progress in DNA sequencing technologies leads to accumulate Information about structures of genomes (Mohan Kumar, 2007).

HISTORY OF INSECT GENOMICS

Commencing from the publication of the entire nucleotide sequence of the genome of Haemophilus influenzae in 1995, a complete genome sequence of many organisms, including lower and higher eukaryotes, have been reported. The cost-effective way to get sufficient sequence information to address the questions is to sequence a high-quality reference genome and then map sequences from other genomes onto the reference sequence. Insects constitute the vast majority of known species with their importance including biodiversity, agricultural, and human health concerns. Hence, a study of Insect genomics is imperative to elucidate information to other organisms.

Biology is in the middle of a major paradigm shift driven by computing technology. Due to the impact of information technology, biological sciences have been rapidly becoming much more computational and analytical. Rapid progress in research in the field of genetics and related field combined with the tools provided by modern biotechnology has generated massive volumes of genetic and protein sequence data over last two decades. Compilation, storage, analysis for extraction of information and knowledge from this data becomes a challenging task as usual analytical procedures are not directly applicable to these data sets.
 
Bioinformatics has been defined as a means for analysing, comparing, graphically displaying, modeling, storing, systemising, searching, and ultimately distributing biological information, which includes sequences, structures, functions, and phylogeny. Thus, bioinformatics may be defined as a discipline that generates computational tools, databases, and methods to support genomic and post-genomic research. It comprises the study of DNA structure and function, gene and protein expression, protein production, structure and function, genetic regulatory systems, and clinical applications. Bioinformatics applications need knowledge from computer science, mathematics, statistics, medicine, chemistry and biology.

Soil is formed by disintegration and decomposition of rocks, minerals and organic matter, Soil is a habitant for plant growth and serves as a storehouse of water and food nutrients for plants, Soil is not same everywhere and varies morphologically, physically, chemically and mineralogically from one point to another both in horizontal and vertical directions. Development of soil informatics is necessary to classify the soils, to assess the productivity of soils for various crop productions and to use the soil for various purposes such as land capability classification, soil irrigability classification, soil rating and soil mapping.

Morphologically soil may appear red, yellow, black, brown or ashy in colour. Soil may vary from sandy to clayey in texture. Depending upon the nutrient reserve capacity, the soil may be fertile or infertile. On the basis of climate, a soil may be classified as laterite soil. Red earth and red loam soil, podzol soil or desert soil.

Different kinds of soil contain minerals of various types and amounts and so exhibit distinct physio co-chemical properties. Although various methods have been used in the past to classify the soils, the most modern and scientific methods is based on dominants pedogenic processes acting on a place to from a soil. The new method of soil classification has two important characteristics-Firstly soil classification is based on certain diagnostic parameters which could be estimated quantatively, and secondly depending on diagnostic parameters, a scientific method is being followed to classify a soil from higher to lower levels such as order, suborder, great group, subgroup, family and series.

Today, when the collection of biological data has been increasing at explosive rate, the processing of these data is needed. It would be extremely valuable if the specialists who know what the data mean were also able to imagine ways to collect, process and exploit these data. Biological and agricultural scientists should note that concepts from computer science, discrete mathematics and statistics are being used increasingly to study and describe biological systems. The specialists, who know the subjects of mathematics, statistics and computer programming, are needed for solving the computational problems in biology. Statistical data mining approaches seem ideally suited for bioinformatics, since it is data rich, but lacks a comprehensive theory of life organization at molecular level, or in the words of I.E. Alcamo “ Keeping up with the directions and application of DNA is a never ending job.” Starting with possible definitions of statistical data mining and bioinformatics, this chapter will give a general side view of both the fields on the statistical data mining part and its technologies and discuss how statistical data mining may be used in bioinformatics.

ANALYSIS OF CODON USAGE VARIATION

Multivariate analysis methods such as Correspondence Analysis and Principle Component Analysis have often been used to identify major trends of variation in synonymous codon usage among inter or intra specific genes. Genetic codes are degenerate meaning most of amino acids can be encoded by more than one codon (triplet of nucleotides); such codons are synonymous and usually differ by one nucleotide in the third position. The alternative synonymous codons are not used with similar frequency and their usage varies among different genes. Hence there is the presence of Relative Synonymous Codon Usage values. 

SEQUENCE DATA

The gene sequences downloaded from Genbank of National Center for Biotechnology Information that is the nucleotide sequence repository known worldwide, serves as the secondary data used for analysis. Relative synonymous codon usage value of each gene is extracted separately using the Codon Usage software of Sequence Manipulation Suite. These are the raw data which needs to be normalized to be used before application of various multivariate analysis.

Bioinformatics can be defined as “the collection, archiving, organization and interpretation of biological data” (Orengo et al., 2003). By extension, structural bioinformatics deals with the collection, archiving, organization and interpretation of 3-D structural data of bio macromolecules. Structural bioinformatics emerged with the first crystal structures of haemoglobin and myoglobin. These structures were analyzed and contributed significantly to our understanding of the principles of life at a molecular level. A repository of all publicly accessible protein crystal structures, the Protein Data Bank (PDB) was established by Walter Hamilton et al., at Brookhaven National Laboratory in 1971. Until the late 1980s, structural biologists often knew all the available structures of bio macromolecules and much of the analysis, such as structure comparison and hydrogen-bonding analysis was done manually without much computer intervention. As the number of available structures increased, so did the need for software tools for structure analysis. Structural bioinformatics encompasses a wide variety of applications, including structure visualization, validation, classification, structure-based function prediction, identification of targets for structural genomics projects and prediction of the structures of individual bio macromolecules and complexes as well the binding modes of small molecules inside proteins of pharmaceutical or biotechnological interestStructural and theoretical analyses of proteins are central to the understanding of complex molecular mechanisms and are fundamental to the drug discovery process.

Introduction

Zymomonas mobilis, a gram negative, anaerobic, micro aerotolerant, ethanologenic bacterium metabolizes glucose, fructose, and sucrose. One of the major factor that limits the usage of Z. mobilis is the narrow substrate range. Moreover, there are no obvious genes reported for using lactose, maltose or cellobiose. Our preliminary sequence analysis of the Zymomonas mobilis ZM4 genome indicated the presence of a putative beta-galactosidase (bga) gene. However, the mechanism of beta-galactosidase (Bga) function is still unknown. Hence, Bioinformatics analysis was made to predict the physicochemical, biochemical and biophysical properties, domains, secondary and tertiary structures of the putative beta-galactosidase. These studies suggested that the putative beta-galactosidase gene was poorly expressed from its native promoter in Z. mobilis. The absence of beta-galactoside transacetylase and permease genes in Z. mobilis led to its inability to metabolize lactose. Hence, function predictions of Bga are performed because of the fact that it has a homologous partner.

A fundamental problem facing researchers today is how to make effective use of available wealth of online background domain knowledge to improve their understanding of complex biological system. Effective use of such online background domain knowledge plays a crucial role in all stages of integrated biological studies.

In the past years, research in molecular biology and molecular medicine has accumulated enormous amounts of data. This includes genomic sequences gathered by the Human Genome Project, gene expression data from micro array experiments, protein identification and quantification data from proteomics experiments, and SNP data from high-throughput SNP arrays. However, the understanding of the biological processes underlying these data lags behind. There is a strong interest in employing methods of knowledge discovery and data mining to generate models of biological systems. Mining biological databases put forward challenges which knowledge discovery and data mining have to address in the future. Important background knowledge in bioinformatics is often contained in documents, such as scientific publications or database annotations. Analyzing data from biological databases often requires the consideration of data from multiple relations rather than from one single table. Recently, approaches (such as propositionalization algorithms) are being studied that utilize multi-relational data and yet meet the efficiency requirements of large-scale data mining problems.

Abstract
This paper presents a lucid and application-oriented delineation of the theory of probability which has proved itself as a basic tool in the theoretical and applied researches in almost all disciplines of science, specially, in the problems encompassing the real-life issues. Bio-informatics has recently emerged as a potential discipline of science and, needless to mention that the application of the probability theory has delved deep to elucidate the laws under its ambit. The inference procedures based on biochemical systems require the formulation of stochastic models (owing to the prevalence of low to moderate levels of stochasticity and noise in their dynamics) which, in turn, are developed using the probability laws in abundance. In Systems Biology, the estimation of parameters poses a prime task in biokinetic models developed on experimental data, and such estimation procedures have been possible only by applying appropriate tools derived using probability. Likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty is an off-shoot of the celebrated, Bayes' theorem of probability.

Advancement of both biological sciences and computer technology gave birth today’s one of the hottest area known as bioinformatics. Gradually bioinformatics analysis has become an essential component to the continued improvement and development of any kind of biological research work. It has various applications in medicine, biotechnology, agriculture etc. Some of the applications of bioinformatics related to biological information analysis are as follows [1]
    
·    Information related to bio-molecules can be mapped (e.g.), the sequences can be parsed to find sites where so-called “restriction enzymes” will cut them.
    
·    Sequences can be compared, usually by aligning corresponding segments and looking for matching and mismatching letters in their sequences. Genes or proteins that are sufficiently similar are likely to be related and are therefore said to be “homologous” to each other.
    
·    If a homologous exists then a newly discovered protein may be modeled - that is 3D structure of the gene product can be predicted without doing laboratory experiments.
    
·    Bioinformatics is used to attempt to predict the function of actual gene products.

The advances in science and technology are deeply intertwined. Researchers can use the semiconductor manufacturing techniques that underlie miniaturization to build radios and exceptionally small mechanical structures that sense fields and forces in the physical world. These inexpensive, low-power communication devices can be deployed throughout a physical space, providing dense sensing close to physical phenomena, processing and communicating this information, and coordinating actions with other nodes. These multifunction devices are battery operated and are connected wirelessly with sensing, processing and communicating capabilities and are known as the wireless sensors. If we use these sensors with biological issues or Biotechnology, then we can find the new concepts biosensors. If we really implement these concepts in real life we can get rid of many diseases like heart attack, blood sugar level, gene reaction and other changes that happens in our body much before its shows to outside.

Two-dimensional nanomaterials have expanded the possibilities for designing advanced green composites with tailored multifunctional properties. This chapter focuses on MXenes and nanosilicates, including laponite, montmorillonite, and halloysite, as important alternatives and complements to graphene-based nanomaterials. Their integration into green composite matrices can influence mechanical strength, thermal stability, barrier characteristics, surface functionality, and other performance parameters. The chapter examines the development of advanced green composites based on these nanomaterials and considers their potential for sustainable material design. Particular attention is given to the relationship between nanoscale structure, matrix interaction, and composite performance, highlighting opportunities for multifunctional and environmentally responsible material systems.

Accurate crop-yield forecasting supports food-security planning, resource allocation and farm decision-making. Yield is influenced by interacting factors such as weather, soil, irrigation, fertilizer and pesticide use, making reliable prediction challenging. This chapter presents an algorithmic framework using historical agricultural and environmental data. It covers cleaning, missing-value treatment, categorical encoding, normalization and feature extraction. Decision Tree, Gradient Boosting, AdaBoost and XGBoost regression models are evaluated individually and combined through soft voting. Performance is measured using standard prediction-error metrics. The case study demonstrates that ensemble modelling can improve accuracy and robustness, helping farmers and planners optimize inputs, anticipate production and strengthen agricultural productivity.

Punjab, known as the “breadbasket of India,” relies heavily on livestock, particularly cattle and buffalo, for its agricultural prosperity. These animals play a crucial role in the region’s dairy industry, contributing significantly to milk production and rural livelihoods. However, like all livestock, cattle and buffalo in Punjab are susceptible to a range of diseases that can impact their health, productivity, and overall well-being. Timely and accurate diagnosis is essential for effective treatment and prevention. This article explores some of the most common diseases affecting cattle and buffalo in Punjab, along with the diagnostic procedures used to detect and manage these health issues, ensuring the sustainability and growth of the dairy sector.

Hydatidosis

Hydatidosis is a zoonotic disease caused by metacestode stages of parasite Echinococcus granulosus and is recognized as a major public health problem worldwide. This tapeworm is found in small intestine of the canid (definitive host) where it produce proglottids containing eggs. Upon shedding through faeces the later are ingested by the cattle, buffaloes and other intermediate hosts including man. These hatch in their intestine and the oncosphere invade circulatory system and lodge in various organs (liver, lung, brain etc.) where they develop into large thick walled unilocular cavity called hydatid cyst, which expands and can reach very large size and buds hundreds and thousands of protoscoleces endogenously. In a survey on hydatid cysts, a prevalence rate of around 5% has been recorded in more than 4000 meat samples of different animals in Punjab.