Computational biology / bioinformatics Books

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Book SynopsisDevoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis.Trade ReviewFrom the reviews: "...After the introduction, written according to the authors in ordinary language, and well readable even for laymen, follows a nicely written Chapter 2 on bifurcations in neuron dynamics which must be read. Here also spiking and bursting phenomena are clearly described. Chapter 3 contains a short sketch of nonhyperbolic (when the Jacobian matrix of (1) has at least one eigenvalue with zero real part) neural networks. The remaining part of the book is mainly devoted to canonical models (Chapter 4), their derivation (Chapters 6--9), and their analysis (Chapters 10--12). The term canonical model is not precisely defined here. The authors say that a model is canonical if there is a continuous change of variables that transforms any other model from a given class into this one. As the method of deriving the canonical models, the authors exploit the normal form theory. Canonical models treated in the book have only restricted value: They provide information about local behavior of (1) when there is an exponentially stable limit cycle but they say nothing about global behavior of (1), including the transients. The last Chapter 13 describes the relationship between synaptic organizations and dynamical properties of networks of neural oscillators. In other words, the problem of learning and memorization of phase information in the weakly connected network of oscillators corresponding to multiple Andronov-Hopf bifurcation is treated analytically. Surprisingly the book ends without any conclusions. Also there are no appendices to the book. The references are representative and sufficiently cover the problematics treated in the book." (Ladislav Andrey, Mathematical Reviews) Table of Contents1 Introduction.- 2 Bifurcations in Neuron Dynamics.- 3 Neural Networks.- 4 Introduction to Canonical Models.- 5 Local Analysis of WCNNs.- 6 Local Analysis of Singularly Perturbed WCNNs.- 7 Local Analysis of Weakly Connected Maps.- 8 Saddle-Node on a Limit Cycle.- 9 Weakly Connected Oscillators.- 10 Multiple Andronov-Hopf Bifurcation.- 11 Multiple Cusp Bifurcation.- 12 Quasi-Static Bifurcations.- 13 Synaptic Organizations of the Brain.- References.

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Book SynopsisThis book is the first to approach the fast developing field of wildlife forensics with a focus on the application of DNA profiling and analysis. Case studies throughout link theory and practice and highlight the use of DNA testing in species testing.Table of ContentsForeword ix Preface xi About the Authors xiii Acknowledgements xv 1 Introduction 1 1.1 Importance of wildlife forensic science investigations 1 1.2 Role of forensic science in wildlife crimes 3 1.3 Legislation covering wildlife crime 4 1.4 Role of non-human DNA in forensic science 8 1.5 Development of wildlife DNA testing 9 1.5.1 History and current state of wildlife DNA forensic science 10 1.5.2 Wildlife forensic science testing 11 1.5.3 Performing DNA typing in wildlife investigations 13 1.6 Accreditation and certification 14 1.7 Standardisation and validation 20 1.8 Collection of evidential material, continuity of evidence and transportation to the laboratory 24 1.9 Note taking and maintenance of a casefile 29 1.10 Case assessment and initial testing 30 1.11 Scope of book 32 Useful websites 32 References 33 2 DNA, Genomes and Genetic Variation 37 2.1 Introduction 37 2.2 The DNA molecule 37 2.3 Chromosomes and nuclear DNA 39 2.4 Genomes 41 2.4.1 Nuclear DNA 41 2.4.2 Mitochondrial and chloroplast DNA 44 2.5 DNA mutation and genetic variation 47 2.5.1 Genetic variation of repetitive DNA 48 2.5.2 Single base changes leading to genetic variation 48 2.5.3 Genetic loci used in species testing 50 2.6 DNA polymorphisms leading to speciation 53 2.6.1 Genetic isolation 54 2.6.2 Other processes leading to speciation 56 2.7 What is a species? 56 2.7.1 Subspecies 60 2.7.2 Genus to Kingdom 61 2.8 Summary 63 References 64 3 Methods in Wildlife Forensic DNA Analysis 69 3.1 Introduction 69 3.2 Protein polymorphisms 69 3.3 DNA isolation, purification and concentration 70 3.3.1 Generic aspects of DNA isolation 70 3.3.2 Lysis step 71 3.3.3 DNA purification: silica-based extraction 72 3.3.4 DNA purification: Chelex R 100 resin 73 3.3.5 DNA purification: organic extraction 74 3.3.6 Microconcentration 76 3.4 DNA quantification 76 3.5 Restriction fragment length polymorphisms (RFLP) 78 3.6 Methods based on the polymerase chain reaction 81 3.6.1 Factors affecting PCR efficiency and optimisation of PCR 84 3.6.2 PCR-based methods of DNA quantification 88 3.6.3 Random amplification of polymorphic DNA 91 3.6.4 Amplification of fragment length polymorphisms (AFLP) 93 3.7 PCR set-up 95 3.8 PCR clean-up 98 3.9 DNA sequencing 99 3.10 SNP typing 100 3.11 New generation of DNA sequence methods 102 Suggested reading 104 4 Species Testing 105 4.1 Introduction 105 4.2 Species 106 4.2.1 Genetic variation and correspondence with taxonomy 106 4.3 Attributes of a species testing locus 106 4.4 Application of a locus to a species 110 4.5 Tests available and how they are performed 110 4.5.1 Sequencing 111 4.5.2 Species-specific primers 124 4.6 Developing a species test 127 4.6.1 Use of data on GenBank and sequence alignment 128 4.6.2 Designing primers 135 4.6.3 Validation 156 4.7 Interpretation and reporting of results 159 4.7.1 Interpretation and reporting sequencing results 160 4.7.2 Interpretation and reporting species-specific testing results 169 4.8 Other limitations: hybrids and wild/captive bred 171 4.9 Future methodologies 173 References 173 5 Genetic Linkage 177 5.1 Introduction 177 5.2 Whole genome testing 177 5.3 Types of individualisation testing 178 5.3.1 Short Tandem Repeats 179 5.4 Identifying STR loci 182 5.4.1 DNA libraries 183 5.4.2 Locating novel microsatellite motifs using Next Generation Sequencing 184 5.5 Allele databases 190 5.5.1 Number of theoretical genotypes 192 5.5.2 Allelic ladders 192 5.6 Hardy–Weinberg equilibrium 193 5.7 Kinship factors and accounting for shared alleles 199 5.7.1 Rare or absent alleles on the database 202 5.8 Assessing the suitability of STR loci 203 5.8.1 The Genetic Data Analysis software (GDA) 205 5.8.2 The Excel Microsatellite Toolkit 214 5.8.3 Arlequin 220 5.8.4 API-Calc 228 5.8.5 Genepop 230 5.8.6 FSTAT 235 5.8.7 Structure 236 5.8.8 Summary 242 5.9 Genetic assignment: paternity testing 244 5.9.1 Genetic assignment: paternity testing if one parent is not available 249 5.9.2 Genetic assignment in paternity testing, incorporating kinship factor 251 5.10 Concluding comments 253 References 254 6 Interpretation, Evaluation and Reporting of Results 259 6.1 Introduction 259 6.2 Case assessment 260 6.3 Hierarchies of propositions 261 6.4 DNA evidence evaluation 262 6.4.1 The frequentist approach 263 6.4.2 Likelihood ratios 264 6.4.3 The Bayesian approach 266 6.4.4 Comparison of the three approaches 267 6.5 Evaluation of DNA evidence in wildlife cases 269 6.5.1 Case scenario 1 269 6.5.2 Case scenario 2 271 6.5.3 Case scenario 3 272 6.6 Role of the expert witness 273 6.7 Report writing 275 6.8 Summary and comments 277 Statement of witness 278 References 299 Measurements 303 Glossary 305 Appendix A Simulated Sample Populations 311 Appendix B Useful websites 323 Index 325

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Book SynopsisLet''s face it: From adenines to zygotes, from cytokinesis to parthenogenesis, even the basics of genetics can sound utterly alien. So who better than an alien to explain it all? Enter Bloort 183, a scientist from an asexual alien race threatened by disease, who''s been charged with researching the fundamentals of human DNA and evolution and laying it all out in clear, simple language so that even his slow-to-grasp-the-point leader can get it. In the hands of the award-winning writer Mark Schultz, Bloort''s predicament becomes the means of giving even the most science-phobic reader a complete introduction to the history and science of genetics that''s as easy to understand as it is entertaining to read.

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Book SynopsisComputer technology has impacted the practice of medicine in dramatic ways. In many cases, these appli­ cations are limited to administrative functions, e.g., office practice man­ agement, location of hospital patients, appointments, and scheduling.Table of Contentsfor Volume 1.- I. Introduction.- 1. An Overview of Methods for Computer-Assisted Medical Decision Making.- 2. Reasoning Foundations of Medical Diagnosis.- II. Algorithmic Approaches.- 3. Computer-Based Consultation: Electrolyte and Acid-Base Disorders.- 4. Reduction of Digitalis Toxicity by Computer-Assisted Glycoside Dosage Regimens.- 5. A Consultant-Extender System for Breast Cancer Adjuvant Chemotherapy.- III. Statistically Oriented Approaches.- 6. A Mathematical Approach to Medical Diagnosis: Application to Congenital Heart Disease.- 7. Computer-Aided Diagnosis of Acute Abdominal Pain.- 8. Computer-Assisted Diagnosis of Abdominal Pain Using “Estimates” Provided by Clinicians.- 9. Bayes’ Theorem and Conditional Nonindependence of Data in Medical Diagnosis.- 10. Transferability of Medical Decision Support Systems Based on Bayesian Classification.- 11. Experience with a Model of Sequential Diagnosis.- 12. Pattern-Based Interactive Diagnosis of Multiple Disorders: The MEDAS System.- 13. Enhancement of Clinical Predictive Ability by Computer Consultation.- 14. Decision Analysis and Clinical Judgment.

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Book SynopsisThis book offers an overview of some recent advances in the Computational Bioacoustics methods and technology. In the focus of discussion is the pursuit of scalability, which would facilitate real-world applications of different scope and purpose, such as wildlife monitoring, biodiversity assessment, pest population control, and monitoring the spread of disease transmitting mosquitoes. The various tasks of Computational Bioacoustics are described and a wide range of audio parameterization and recognition tasks related to the automated recognition of species and sound events is discussed. Many of the Computational Bioacoustics methods were originally developed for the needs of speech, audio, or image processing, and afterwards were adapted to the requirements of automated acoustic recognition of species, or were elaborated further to address the challenges of real-world operation in 24/7 mode. The interested reader is encouraged to follow the numerous references and links to web resources for further information and insights. This book is addressed to Software Engineers, IT experts, Computer Science researchers, Bioacousticians, and other practitioners concerned with the creation of new tools and services, aimed at enhancing the technological support to Computational Bioacoustics applications. STTM, Speech Technology and Text Mining in Medicine and Health Care This series demonstrates how the latest advances in speech technology and text mining positively affect patient healthcare and, in a much broader sense, public health at large. New developments in text mining methods have allowed health care providers to monitor a large population of patients at any time and from any location. Employing advanced summarization techniques, patient data can be readily extracted from extensive clinical documents in electronic health records and immediately made available to the physician. These same summarization techniques can also aid the healthcare provider in extracting from the large corpora of medical literature the relevant information for treating the patient. The series topics include the design and acceptance of speech-enabled robots that assist in the operating room, studies of signal processing and acoustic modeling for speech and communication disorders, advanced statistical speech enhancement methods for creating synthetic voice, and technologies for addressing speech and language impairments. Titles in the Series consist of both authored books and edited contributions. All authored books and contributed works are peer-reviewed. The Series is for speech scientists and speech engineers, machine learning experts, biomedical engineers, medical speech pathologists, linguists, and healthcare professionals Table of Contents1. Introduction 2. Why Computational Bioacoustics? 3. Getting to know the problems of automated species recognition 4. Preparation of data 5. Sound production mechanisms 6. Parameterization of bioacoustic signals 7. Machine learning methods 8. Application examples 9. Useful resources 10. Conclusion and outlook

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Book SynopsisThis open access volume presents a novel computational framework for understanding how collections of excitable cells work. The key approach in the text is to model excitable tissue by representing the individual cells constituting the tissue. This is in stark contrast to the common approach where homogenization is used to develop models where the cells are not explicitly present. The approach allows for very detailed analysis of small collections of excitable cells, but computational challenges limit the applicability in the presence of large collections of cells.Table of ContentsDerivation of a cell-based mathematical model of excitable cells.- A cell-based model for ionic electrodiffusion in excitable tissue.- Modeling cardiac mechanics on a subcellular scale.- Operator splitting and finite difference schemes for solving the EMI model.- Solving the EMI equations using finite element methods.- Iterative solvers for EMI models.- Improving neural simulations with the EMI model.- Index.

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Book SynopsisThe aim of this book is to provide a well-structured and coherent overview of existing mathematical modeling approaches for biochemical reaction systems, investigating relations between both the conventional models and several types of deterministic-stochastic hybrid model recombinations. Another main objective is to illustrate and compare diverse numerical simulation schemes and their computational effort. Unlike related works, this book presents a broad scope in its applications, from offering a detailed introduction to hybrid approaches for the case of multiple population scales to discussing the setting of time-scale separation resulting from widely varying firing rates of reaction channels. Additionally, it also addresses modeling approaches for non well-mixed reaction-diffusion dynamics, including deterministic and stochastic PDEs and spatiotemporal master equations. Finally, by translating and incorporating complex theory to a level accessible to non-mathematicians, this book effectively bridges the gap between mathematical research in computational biology and its practical use in biological, biochemical, and biomedical systems.Trade Review“The primary audience is graduate students and researchers. … content would have to be selected and formatted for lectures carefully. It is most suitable for independent study, and is especially useful as a reference text for the foundations of techniques used in current research. The choice of references are excellent and very current: it is a good survey of both the relevant foundations and current state of research … and would be a useful starting point for a literature review.” (Chay Paterson, zbMATH 1467.92005, 2021)Table of ContentsIntroduction.- Well-mixed stochastic reaction kinetics.- Population scaling.- Temporal scaling.- Spatial scaling.- Summary and Outlook.- Mathematical background.

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Book SynopsisThis textbook provides step-by-step protocols and detailed explanations for RNA Sequencing, ChIP-Sequencing and Epigenetic Sequencing applications. The reader learns how to perform Next Generation Sequencing data analysis, how to interpret and visualize the data, and acquires knowledge on the statistical background of the used software tools. Written for biomedical scientists and medical students, this textbook enables the end user to perform and comprehend various Next Generation Sequencing applications and their analytics without prior understanding in bioinformatics or computer sciences.Table of Contents

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Book SynopsisThis monograph considers the mathematical modeling of cellular adhesion, a key interaction force in cell biology. While deeply grounded in the biological application of cell adhesion and tissue formation, this monograph focuses on the mathematical analysis of non-local adhesion models. The novel aspect is the non-local term (an integral operator), which accounts for forces generated by long ranged cell interactions. The analysis of non-local models has started only recently, and it has become a vibrant area of applied mathematics. This monograph contributes a systematic analysis of steady states and their bifurcation structure, combining global bifurcation results pioneered by Rabinowitz, equivariant bifurcation theory, and the symmetries of the non-local term. These methods allow readers to analyze and understand cell adhesion on a deep level.Trade Review“Modelers who wish to use similar approaches in their modeling will find this a good source of base information, as well as a valuable guide for initiating similar analyses for their own models. Analysts wishing to expand our understanding … will find this book a fine building block. It could also prove a useful resource for graduate students looking for potential projects … . this monograph is an admirable attempt … and hopefully will inspire significant further study.” (Kevin Painter, SIAM Review, Vol. 64 (1), March, 2022)“The detailed analysis, as presented here, shows a stimulating interaction between model symmetries, mathematical analysis, and biological reality, which probably are inspired the authors and hopefully the readers of this book too.” (Andrey Zahariev, zbMATH 1473.92001, 2021)Table of ContentsIntroduction.- Preliminaries.- The Periodic Problem.- Basic Properties.- Local Bifurcation.- Global Bifurcation.- Non-local Equations with Boundary Conditions.- No-flux Boundary Conditions.- Discussion and future directions.

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Book SynopsisThis textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.Table of ContentsPart I: Introduction1 Introduction and Objectives1 Introduction 1.2 Nonrandomized Epidemiologic Research 1.3 The Treatment of Uncertainty in Nonrandomized Research 1.4 Objective 1.5 Conclusion 2 A Guide to Implementing Quantitative Bias Analysis 2.1 Introduction 2.2 Reducing Error 2.3 Reducing Error by Design 2.4 Reducing Error in the Analysis 2.5 Quantifying Error 2.6 Evaluating the Potential Value of Quantitative Bias Analysis2.7 Planning for Bias Analysis 2.8 Creating a Data Collection Plan for Bias Analysis 2.9 Creating an Analytic Plan for a Bias Analysis 2.10 Bias Analysis Techniques 2.11 Introduction to Inference 2.12 Conclusion 3 Data Sources for Bias Analysis 3.1 Bias Parameters 3.2 Internal Data Sources 3.3 Selection Bias 3.4 Uncontrolled Confounder 3.5 Information Bias 3.6 Limitations of Internal Validation Studies 3.7 External Data Sources 3.8 Selection Bias 3.9 Uncontrolled Confounder 3.10 Information Bias 3.11 SummaryPart II: Preliminary Methods to Adjust for Systematic Errors 4 Selection Bias 4.1 Introduction 4.2 Definitions and Terms4.3 Motivation for Bias Analysis 4.4 Sources of Data 4.5 Simple Correction for Differential Initial Participation 4.6 Simple Correction for Differential Loss-to-Follow-up4.7 Sensitivity Analysis of the Bias Analysis 4.7 Signed Directed Acyclic Graphs to Estimate the Direction of Bias 5 Uncontrolled Confounders 5.1 Introduction 5.2 Definitions and Terms5.3 Motivation for Bias Analysis 5.4 Sources of Data5.5 Introduction to Simple Bias Analysis 5.6 Implementation of Simple Bias Analysis5.7 Sensitivity Analysis of the Bias Analysis 5.8 Uncontrolled Confounder in the Presence of Effect Modification 5.9 Polytomous Confounders 5.10 Bounding the Bias Limits of Uncontrolled Confounding5.10 Signed Directed Acyclic Graphs to Estimate the Direction of Bias5.11 Uncontrolled Confounding with Continuous Outcome, Exposure, or Confounder 6 Misclassification 6.1 Introduction 6.2 Definitions and Terms6.3 Motivation for Bias Analysis6.4 Sources of Data6.5 Calculating Classification Bias Parameters from Validation Data6.6 Exposure Misclassification for Dichotomous Exposures6.7 Exposure Misclassification for Polytomous Exposures6.8 Disease Misclassification 6.9 Covariate Misclassification 6.10 Dependent Misclassification6.11 Sensitivity Analysis of the Bias Analysis6.12 Adjusting Standard Errors for Corrections 7 Measurement Error for Continuous Variables7.1 Introduction7.2 Definition and Terms7.3 Motivation for Bias Analysis7.4 Exposure Measurement error7.5 Outcome Measurement error7.6 Covariate Measurement Error7.7 Correlated errors 8 Multiple Bias Modeling 8.1 Introduction 8.2 Order of Bias Analyses8.3 Multiple Bias Analysis, Simple MethodsPart III: Methods to Incorporate Systematic and Random Errors 9 Bias Analysis by Simulation for Summary Level Data9.1 Introduction 9.2 Probability Distributions 9.3 Correlated Distributions 9.4 Analytic Approach 9.5 Exposure Misclassification Implementation9.6 Exposure Measurement Error Implementation 9.7 Uncontrolled Confounding Implementation 9.8 Selection Bias Implementation 10 Bias Analysis by Simulation for Record Level Data10.1 Introduction 10.2 Analytic Approach 10.3 Exposure Misclassification Implementation10.4 Exposure Measurement Error Implementation 10.5 Uncontrolled Confounding Implementation 10.6 Selection Bias Implementation 11 Combining Systematic and Random Error11.1 Analytic approximation11.2 Resampling approximation11.3 Bootstrapping 12 Bias Analysis by Missing Data Methods12.1 Introduction 12.2 Analytic Approach 12.3 Exposure Misclassification Implementation12.4 Exposure Measurement Error Implementation 12.5 Uncontrolled Confounding Implementation 12.6 Selection Bias Implementation 12.7 Combining Systematic and Random Error 13 Bias Analysis by Empirical Methods13.1 Introduction 13.2 Analytic Approach 13.3 Exposure Misclassification Implementation 13.4 Exposure Measurement Error Implementation13.5 Uncontrolled Confounding Implementation 13.6 Selection Bias Implementation 13.7 Combining Systematic and Random Error 14 Bias Analysis by Bayesian Methods14.1 Introduction 14.2 Analytic Approach 14.3 Exposure Misclassification Implementation 14.4 Exposure Measurement Error Implementation 14.5 Uncontrolled Confounding Implementation 14.6 Selection Bias Implementation 14.7 Combining Systematic and Random Error 15 Multiple Bias Modeling15.1 Multiple Bias Analysis, Probabilistic Methods15.2 Multiple Bias Analysis, Missing Data Methods15.3 Multiple Bias Analysis, Empirical Methods15.4 Multiple Bias Analysis, Bayesian Methods Part IV: Good Practices16 Good Practices for Quantitative Bias Analysis16.1 Selection of bias sources16.2 Selection of analytic strategies16.3 Selection of values to assign to bias parameters17 Presentation and Inference 17.1 Presentation of simple and multidimensional bias analyses17.2 Presentation of advanced bias analyses 17.3 Inference 17.4 Caveats and Cautions 18 References 19 Index

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Book SynopsisThis book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.DART 2021 accepted 13 papers from the 21 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning. Table of ContentsDomain Adaptation and Representation Transfer.- A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis.- Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning.- FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation.- Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation.- Self-Supervised Multimodal Generalized Zero Shot Learning For Gleason Grading.- Self-Supervised Learning of Inter-Label Geometric Relationships For Gleason Grade Segmentation.- Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training.- Transductive image segmentation: Self-training and effect of uncertainty estimation.- Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation.- Cohort Bias Adaptation in Federated Datasets for Lesion Segmentation.- Exploring Deep Registration Latent Spaces.- Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift.- Unsupervised Domain Adaption via Similarity-based Prototypes for Cross-Modality Segmentation.- A ordable AI and Healthcare.- Classification and Generation of Microscopy Images with Plasmodium Falciparum via Arti cial Neural Networks using Low Cost Settings.- Contrast and Resolution Improvement of POCUS Using Self-Consistent CycleGAN.- Low-Dose Dynamic CT Perfusion Denoising without Training Data.- Recurrent Brain Graph Mapper for Predicting Time-Dependent Brain Graph Evaluation Trajectory.- COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19Patient Cases from Point-of-care Ultrasound Imaging.- Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning.- Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks.- Continual Domain Incremental Learning for Chest X-ray Classification in Low-Resource Clinical Settings.- Deep learning based Automatic detection of adequately positioned mammograms.- Can non-specialists provide high quality Gold standard labels in challenging modalities.

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Book Synopsis1. Wiederholungen und Ergänzungen aus der Schulmathematik.- 1.1 Wiederholungen und Ergänzungen aus der Algebra.- 1.2 Wiederholungen und Ergänzungen aus der Geometrie.- 1.3 Wiederholungen und Ergänzungen aus der analytischen Geometrie.- 2. Das Rechnen mit Veränderlichen und Funktionen.- 2.1 Veränderliche und Funktion.- 2.2 Ableitung und Integral.- 2.3 Die Technik des Differenzierens I (Grundlagen).- 2.4 Die Technik des Differenzierens II (Erweiterungen und Anwendungen).- 2.5 Die Technik des Integrierens I (Grundlagen).- 2.6 Die Technik des Integrierens II (Ergänzungen).- 2.7 Die Potenzreihen und ihre Anwendung.- 2.8 Die gewöhnliche Differentialgleichung erster Ordnung.- 2.9 Die lineare Differentialgleichung II. Ordnung mit konstanten Koeffizienten.- 2.10 Eine partielle Differentialgleichung II. Ordnung.- 3. Zur Anwendung mathematischer Methoden in der Physiologie.- 3.1 Die Aufgabe und mathematische Hilfsmittel zu ihrer Lösung.- 3.2 Wichtige Typen empirischer Funktionen.- 3.3 Das Auffinden eines formalen Verknüpfungsgesetzes.- 3.4 Das Auffinden eines realen (kausalen) Verknüpfungsgesetzes.- 4. Zur Anwendung mathematischer Methoden in der medizinischen Statistik.- 4.0 Ein Gedankenversuch zur Einführung.- 4.1 Kennzeichnung einer Stichprobe durch Maßzahlen.- 4.2 Kennzeichnung einer Grundgesamtheit durch ein Verteilungsgesetz..- 4.3 Verbindung von Stichprobe und Grundgesamtheit durch die Schätz-Ungleichung.- 4.4 Die Prüfgleichung zum t-Test und ihre Anwendung.- 4.5 Verteilungsfreie Tests als Ersatz für den t-Test. Zwei Beispiele.- 4.6 Beurteilung von Häufigkeitsziffern.- 4.7 Beschreibung und Prüfung von Zusammenhängen.- 5. Zur Anwendung mathematischer Methoden in der medizinischen Datenverarbeitung.- 5.0 Vorbemerkung.- 5.1 Mathematische Grundlagen deselektronischen Rechnens.- 5.2 Mathematische Grundlagen der elektronischen Verarbeitung allgemeiner Daten.- Ratschläge zur Weiterbildung des Lesers.Table of Contents1. Wiederholungen und Ergänzungen aus der Schulmathematik.- 1.1 Wiederholungen und Ergänzungen aus der Algebra.- 1.2 Wiederholungen und Ergänzungen aus der Geometrie.- 1.3 Wiederholungen und Ergänzungen aus der analytischen Geometrie.- 2. Das Rechnen mit Veränderlichen und Funktionen.- 2.1 Veränderliche und Funktion.- 2.2 Ableitung und Integral.- 2.3 Die Technik des Differenzierens I (Grundlagen).- 2.4 Die Technik des Differenzierens II (Erweiterungen und Anwendungen).- 2.5 Die Technik des Integrierens I (Grundlagen).- 2.6 Die Technik des Integrierens II (Ergänzungen).- 2.7 Die Potenzreihen und ihre Anwendung.- 2.8 Die gewöhnliche Differentialgleichung erster Ordnung.- 2.9 Die lineare Differentialgleichung II. Ordnung mit konstanten Koeffizienten.- 2.10 Eine partielle Differentialgleichung II. Ordnung.- 3. Zur Anwendung mathematischer Methoden in der Physiologie.- 3.1 Die Aufgabe und mathematische Hilfsmittel zu ihrer Lösung.- 3.2 Wichtige Typen empirischer Funktionen.- 3.3 Das Auffinden eines formalen Verknüpfungsgesetzes.- 3.4 Das Auffinden eines realen (kausalen) Verknüpfungsgesetzes.- 4. Zur Anwendung mathematischer Methoden in der medizinischen Statistik.- 4.0 Ein Gedankenversuch zur Einführung.- 4.1 Kennzeichnung einer Stichprobe durch Maßzahlen.- 4.2 Kennzeichnung einer Grundgesamtheit durch ein Verteilungsgesetz..- 4.3 Verbindung von Stichprobe und Grundgesamtheit durch die Schätz-Ungleichung.- 4.4 Die Prüfgleichung zum t-Test und ihre Anwendung.- 4.5 Verteilungsfreie Tests als Ersatz für den t-Test. Zwei Beispiele.- 4.6 Beurteilung von Häufigkeitsziffern.- 4.7 Beschreibung und Prüfung von Zusammenhängen.- 5. Zur Anwendung mathematischer Methoden in der medizinischen Datenverarbeitung.- 5.0 Vorbemerkung.- 5.1 Mathematische Grundlagen des elektronischen Rechnens.- 5.2 Mathematische Grundlagen der elektronischen Verarbeitung allgemeiner Daten.- Ratschläge zur Weiterbildung des Lesers.

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Book SynopsisTable of Contents1. Einleitung und Fragestellung.- 2. Allgemeines.- 2.1 Typische Beispiele aus der Literatur.- 2.1.1 University Group Diabetes Program (UGDP)-Studie.- 2.1.2 Coronary Drug Project (CDP) — Studie.- 2.1.3 Clofibrat Studie.- 2.2 Generelle Probleme.- 2.2.1 Inhalt von Zwischenauswertungen.- 2.2.2 Gründe für einen vorzeitigen Studienabbruch.- 2.2.3 Allgemeine methodische statistische Aspekte bei Zwischenauswertungen und Studienabbruch.- 3. Verfahrensübersicht.- 3.1 Offene Sequentialpläne.- 3.2 Geschlossene Sequentialpläne.- 3.3 Gruppensequentielle Pläne.- 3.4 Testverfahren für Lebensdauerdaten.- 3.5 Multivariate Methoden und multiple Vergleiche.- 3.6 Sonstige Verfahren.- 4. Gemischte Strategien.- 4.1 Formale Ableitung.- 4.2 Praktische Berechnung.- 4.3 Ergebnisse.- 4.4 Vergleiche mit den bisherigen Verfahren.- 5. Verfahrensvergleiche bei Lebensdauerdaten durch Simulation.- 5.1 Simulationsmodell.- 5.2 Simulation und Ergebnisse.- 6. Schlußfolgerungen.- 7. Zusammenfassung.- 8. Literatur.- 9. Anhang.

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Book SynopsisThe free-radical chemistry of DNA had been discussed in some detail in 1987 in my book The Chemical Basis of Radiation Biology. Obviously, the more recent developments and the concomitant higher level of understanding of mechanistic details are missing. Moreover, in the living cell, free-radical DNA damage is not only induced by ionizing radiation, but free-radical-induced DNA damage is a much more general phenomenon. It was, therefore, felt that it is now timely to review our present knowledge of free-radical-induced DNA damage induced by all conceivable free-radical-generating sources. Originally, it had been thought to include also a very important aspect, the repair of DNA damage by the cell’s various repair enzymes. Kevin Prise (Cancer Campaign, Gray Laboratory, L- don) was so kind to agree to write this part. However, an adequate description of this strongly expanding area would have exceeded the allocated space by much, and this section had to be omitted. The directors of the Max-Planck-Institut für Strahlenchemie (now MPI für Bioanorganische Chemie), Karl Wieghardt and Wolfgang Lubitz, kindly allowed me to continue to use its facilities after my retirement in 2001. Notably, our - brarian, Mrs. Jutta Theurich, and her right-hand help, Mrs. Rosemarie Schr- er, were most helpful in getting hold of the literature. I thank them very much. Without their constant help, this would have been very difficult indeed.Table of ContentsFormation of Reactive Free Radicals in an Aqueous Environment.- The Hydroxyl Radical.- Hydrogen Atom and Hydrated Electron.- Inorganic Radicals.- Carbon-Centered Radicals.- Heteroatom-Centered Radicals.- Peroxyl Radicals.- Polymer Radicals.- Nucleobases, Nucleosides and Nucleotides.- Polynucleotides and Single-Stranded DNA Fragments.- DNA and Double-Stranded Oligonucleotides.- Methods.

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Book SynopsisValuation is a hot topic among life sciences professionals. There is no clear understanding on how to use the different valuation approaches and how to determine input parameters. Some do not value at all, arguing that it is not possible to get realistic and objective numbers out of it. Some claim it to be an art. In the following chapters we will provide the user with a concise val- tion manual, providing transparency and practical insight for all dealing with valuation in life sciences: project and portfolio managers, licensing executives, business developers, technology transfer managers, entrep- neurs, investors, and analysts. The purpose of the book is to explain how to apply discounted cash flow and real options valuation to life sciences p- jects, i.e. to license contracts, patents, and firms. We explain the fun- mentals and the pitfalls with case studies so that the reader is capable of performing the valuations on his own and repeat the theory in the exercises and case studies. The book is structured in five parts: In the first part, the introduction, we discuss the role of the players in the life sciences industry and their p- ticular interests. We describe why valuation is important to them, where they need it, and the current problems to it. The second part deals with the input parameters required for valuation in life sciences, i.e. success rates, costs, peak sales, and timelines.Trade ReviewFrom the reviews:"‘Valuation in Life Sciences: A Practical Guide’ leads readers step by step through the theory of life sciences valuation. … it is an important read for biotech investors. … It is the first book of its kind that combines industry data and valuation theory together with practical advice. It includes valuation techniques and tools that allow investors to pick undervalued biotech stocks." (GEN – genetic engineering news, February, 2007)"Valuation in Life Sciences: A Practical Guide opens up the black box and describes, step by step, a relatively simple procedure for quantitative valuation in life sciences. The book guides the reader through the recommended procedure of quantitative valuation … . This book is recommended to those who would like to acquire a profound understanding of quantitative valuation and use a simple spreadsheet approach of their own cases." (Rudolf Gygax, Nature Biotechnology, Vol. 25 (9), 2007)"This book provides some interesting examples – for business development and licensing executives – of where ROV may be used. … For the general reader and business development executive there is a good explanation of ROV and its applications in life science … . The book may also be of interest to biotechnology company business development executives and university technology transfer officers … . this book is essential reading as it not only provides the mathematical theory but also the applications in different types of deals." (Roger Davies, Business Development & Licensing Journal, Issue 4, 2007)From the reviews of the third edition:“The content and discussion contained in ‘Valuation in Life Sciences’ is excellent. The Swiss authors provide a practical guide to what mathematical tools gives meaningful valuations … . Recommended for anyone planning to be involved in the development of a biotech venture.” (C. Mathews, Amazon, December, 2010)“The authors always keep the necessary rigour to make the book a valuable reference to professionals such as business developers, investment bankers, or analysts. … it an excellent textbook for students as well. … ‘Valuation in Life Sciences’ addresses also the financial beginner with guided and easy-to-understand examples in Excel. I highly recommend the book to everyone in financial biotech.” (Amazon, November, 2009)“This book is an excellent overview of business development valuation and great for those who want to catch up on some techniques and tools. It gives an accurate picture of BD in pharma. For those in the business … it remains a very valuable tool.” (J. Serres, Amazon, March, 2009)Table of ContentsBasics of Valuation.- Valuation in Life Sciences.- Exercises.- Case Studies.

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Book SynopsisThis textbook is aimed at students of non-specialist courses with computer science components. Special emphasis is placed on the so-called life sciences, such as medical technology, rescue engineering, biotechnology, environmental engineering or process engineering. The textbook is suitable for readers in study and practice who want to get an introduction to computer science. The special feature of this book is the problem-based approach, as well as the exercises designed according to different taxonomy levels.Table of ContentsIntroduction to computer science.- Basics of programming in C++.- Arrays and pointers.- File operations.- Object orientation and inheritance.

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Book SynopsisThis book offers a gripping introduction to the fastest growing field of biology with easy-to-follow examples and a well-prepared appendix for the reader to cook up and experience everything right away.The book gets the reader started with the basics, such as how to easily find sequence information and then analyze it. In further chapters, the authors go into the various analysis options from RNA, DNA and proteins to entire metabolic pathways. Exciting examples from biology are chosen in each chapter to illustrate the analysis. Each chapter concludes with an exercise section that immediately puts what has been learned to use.The subject of this book is a must for any biology student, whether undergraduate or graduate, as bioinformatics is now unearthing amazing insights into the molecular basis of all living things. Computer science students and other students from related sciences will get a good introduction to bioinformatics, as biology and current topics (e.g. AI) are systematically introduced step by step alongside the software.Discover the key to life together with the authors and learn to understand the language of life. This book is a translation of the original German 2nd edition Bioinformatik by Thomas Dandekar and Meik Kunz, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.Table of ContentsI How does bioinformatics work?.- 1 Sequence analysis: deciphering the language of life.- 2 Magic RNA.- 3 Genomes - molecular maps of living organisms.- 4 Modeling metabolism and finding new antibiotics.- 5 Systems biology and uncovering the causes of disease. II How do I understand bioinformatics?"- 6 Superfast sequence comparisons identify which molecules are present.- 7 Better understand signaling cascades by measuring encoded information.- 8 When does a computer stop computing?"- 9 Complex systems behave in fundamentally similar ways.- 10 Better compare evolution with a computer.- 11 Design principles of a cell. III What is the magical, fascinating thing about bioinformatics?.- 12 Life is always developing new information in dialogue with the environment.- 13 Life is always inventing new levels of language.- 14 We can think about ourselves - the computer cannot.- 15 How is our own, extremely powerful brain built?.- 16 Bioinformatics connects life with the universe and all the rest.- 17 Conclusion. 17 Concluding remarks and summary.- IV Glossary, tutorial, solutions and web links.- 18 Glossary.- 19 Tutorial: an overview of important databases and programs.- 20 Solutions to the exercises.- Appendix: overview of important databases and programs and their general use.- Index.

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Book SynopsisA compact guide to DNA: who we are, how we're wired, and how we repair ourselves. Table of ContentsMeet your genome • How do genes work? • Our genetic journey • Under attack! • Who do you think you are? • People are not peas • Genetic superheroes • Turn me on • Epigenetics • The RNA world • Building a baby • Wiring the brain • Compatibility genes • Why women are stripy • The viruses that made us human • When things go wrong • Human 2.0.

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Book SynopsisNeuropathology of Transmissible Spongiform Encephalopathies (Prion Diseases).- Central Pathogenesis of Prion Diseases.- Hereditary Prion Protein Amyloidoses.- Mouse Behavioural Studies and What They Can Teach Us about Prion Diseases.- Electrophysiological Approaches to the Study of Prion Diseases.- Prion Protein, Prion Protein-Like Protein, and Neurodegeneration.- Oxidative Stress and Mitochondrial Dysfunction in Neurodegeneration of Transmissible Spongiform Encephalopathies (TSEs).- Mechanisms of Prion Toxicity and Their Relationship to Prion Infectivity.- A Stone Guest on the Brain: Death as a Prion.- Molecular Mechanisms Mediating Neuronal Cell Death in Experimental Models of Prion Diseases, in vitro.- Processing and Mis-Processing of the Prion Protein: Insights into the Pathogenesis of Familial Prion Disorders.- Signaling Pathways Controling Prion Neurotoxicity: Role of Endoplasmic Reticulum Stress-Mediated Apoptosis.- Cell Culture Models to Unravel Prion Protein Function and AberrTable of ContentsNeuropathology of transmissible spongiform encephalopathies (prion diseases).- Central pathogenesis of prion diseases.- Hereditory prion protein Amyloidoses.- Mouse behavioural studies and what they can teach us about prion diseases.- Electrophysiological approaches to the study of prion diseases.- Prion protein, prion protein-like protein, and neurodegeneration.- Oxidative stress and mitochondrial dysfunction in neurodegeneration of transmissible spongiform encephalopathies (TSEs).- Mechanisms of prion toxicity and their relationship to prion infectivity.- A stone guest on the brain: Death as a prion.- Molecular mechanisms mediating neuronal cell death in experimental models of prion diseases, in vitro.- Processing and mis-processing of the prion protein: Insights into the pathogenesis of familial prion disorders.- Signaling pathways controling prion protein neurotoxicity: Role of endoplasmic reticulum stress-mediated apoptosis.- Cell culture models to unravel prion protein function and aberrancies in TSE.- Insights into the cellular trafficking of prion proteins.- The molecular basis of prion protein-mediated neuronal damage.

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Book SynopsisIn situ hybridization is a technique that allows for the visualization of specific DNA and RNA sequences in individual cells, and is an especially important method for studying nucleic acids in heterogeneous cell populations. in situ Hybridization in Electron Microscopy reviews the three main methods developed for the ultrastructural visualization of genes: hybridization on ultrathin sections of tissue embedded in hydrophilic resin (post-embedding method) hybridization prior to embedding (pre-embedding) hybridization on ultrathin sections of frozen tissue (frozen tissue method). For each technique, the different stages are described in detail: the preparation of tissue, pretreatment, hybridization, and visualization of the hybridization products. The book combines theory and practice, starting with the basic principles, then breaking down the experimental process into successive steps illustrated by numerous diagrams, detailed protocols, and tables. This is aTable of ContentsGeneral Introduction. Abbreviations. Probes. Principles of Methodology. Sample Preparation. Post-Embedding Technique. Pre-Embedding Technique. Frozen Tissue Technique. Semithin Sections. Controls and Problems. Examples of Results. Appendices. Glossary. Index.

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Book SynopsisA Step-by-Step Guide to Present and Future Uses of Microarray TechnologyMicroarray technology continues to evolve, taking on a variety of forms. From the spotting of cDNA and the in situ synthesis of oligonucleotide arrays now come microarrays comprising proteins, carbohydrates, drugs, tissues, and cells. With contributions from microarray experts in both academia and industry, Microarray Methods and Protocols is a turn-by-turn roadmap through the processes necessary to perform a successful microarray experiment.This easy to use book addresses the fundamental aspects of preparing and processing microarrays and bead arrays, labeling, and detection. It also includes a detailed How it Works section that discusses the underlying principles of a number of techniques. Troubleshooting guides offer additional advice for the successful performance of more than 100 protocols in 10 chapters that cover work iTrade ReviewThe book will be useful for researchers in protein chemistry and biochemistry, molecular biologists, and teachers and students of medical schools and universities specializing in microarray methods.--Doctor of Biological Sciences, G. Ya. Wiederschain. Biochemsitry - Moscow. 2010, VOL 75, No 9,1182Table of ContentsIntroduction to Microarray Technologies. Nucleic Acid Sample Preparation. Solid-Phase Substrates for Nucleic Acid Microarrays. Protein Sample Preparation for Microarrays. Solid-Phase Chemistries for Protein Microarrays. Protein Microarrays: The Link between Genomics and Proteomics. Bead Arrays: An Introduction to Multiplexed Bead-Based Assays for Proteins. Carbohydrate Arrays. Lectin Microarrays. Printing Methods. Appendix A: Microarray Reagent, Materials, and Equipment Sources. Appendix B: Image Analysis. Index.

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Book SynopsisScientists can use molecular profiling microarrays to compare healthy cells with their diseased counterparts and develop gene-specific treatments. Finding the best way to interpret original profiling data into accurate trends, however, continues to drive the development of normalization algorithms and software tools. Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases. They also review useful software, including discussions on key algorithms, comparative data, and download locations. The book discusses the use of early normalization techniques for new profiling methods and includes strategies for assessing the utility of various normalization algorithms. It presents the latest microarray innovations from companies such as Agilent, Affymetrix,Trade Review"Methods in Microarray Normalization compiles the most useful and novel techniques for the first time into a single, organized source. Experts in the field provide a diverse view of the mathematical processes that are important in normalizing data and avoiding inherent systematic biases." – In Anticancer Research, July/August 2008, Vol. 28, No. 4ATable of ContentsA comprehensive analysis of the effect of microarray data preprocessing methods on differentially expressed transcript selection. Differentiation detection in microarray normalization. Preprocessing and normalization for affymetrix genechip expression microarrays. Spatial detrending and normalization methods for two-channel DNA and protein microarray data. A survey of cDNA microarray normalization and a comparison by k-NN classification. Technical variation in modeling the joint expression of several genes. Biological interpretation for microarray normalization selection. Methodology of functional analysis for omics data normalization. Exon array analysis for the detection of alternative splicing. Normalization of array CGH data. SNP array-based analysis for detection of chromosomal aberrations and copy number variations. Index.

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Book SynopsisA Step-by-Step Guide to Describing Biomolecular StructureComputational and Visualization Techniques for Structural Bioinformatics Using Chimera shows how to perform computations with Python scripts in the Chimera environment. It focuses on the three core areas needed to study structural bioinformatics: biochemistry, mathematics, and computation.Understand Important Concepts of Structural BioinformaticsThe book covers topics that deal primarily with protein structure and includes many exercises that are grounded in biological problems at the molecular level. The text encourages mathematical analysis by providing a firm foundation for computations. It analyzes numerous Python scripts for the Chimera environment, with the scripts and other material available on a supplementary website.Build Python Scripts to Extend the Capabilities of ChimeraThrough more than 60 exercises that involve the develTrade Review"This book supports an approach to bioinformatics that balances biological problems, mathematical modeling, and computational tools. It breaks down the mathematical complications into manageable parts and augments programming skills while developing biological insight."—L. Ridgway Scott, Louis Block Professor of Computer Science and Mathematics, Member of the Institute for Biophysical Design, Senior Fellow of the Computation Institute, University of Chicago"This book is a useful resource for experienced and newcomer Chimera users alike. It helps extend it programmatically, allowing users to perform any number of useful types of calculations that are often required in conjunction with visualization. From distances to graphs, any way you measure it, Prof. Burkowski’s book is a must have for Chimera users."—Rafael Najmanovich, PhD, Associate Professor, Université de Sherbrooke"Structural bioinformatics is a young science that lies at the crossroads of several synergizing disciplines, including biophysics, structural biology, mathematical modeling, and computational methods. In this context, the book by Forbes Burkowski constitutes a true landmark, enabling the practitioner to implement and exploit computational techniques to test the mathematical models and frame novel hypothesis. This engrossing book is directed at advanced undergraduates and professionals who seek to represent and explore novel structural concepts to describe biomolecular phenomena. Very useful practical applications of the computational techniques presented in the book are likely to emerge, especially in the fields of molecular engineering and structure-based drug design. Thus, I predict that the book will become mandatory for researchers interested in rational design in the pharmaceutical industry and materials science."—Ariel Fernandez, Ph.D., Former Hasselmann Professor of Bioengineering at Rice University and Pioneer of the Wrapping Technology in Drug Design"Students taught with Computational and Visualization Techniques for Structural Bioinformatics Using Chimera will gain not only a thorough understanding of the important concepts of structural bioinformatics, from the basics of protein structure through advanced topics such as molecular mechanics, but also the ability to perform their own analyses using the programming techniques and libraries offered by this book and its supporting materials. The exercises are engaging and vary from problems that can be solved in a few minutes to mini-projects that might require a week of effort. All concepts are clearly described and accompanied by helpful illustrations where needed. This text does an excellent job of turning students into investigators."—Eric Pettersen, UCSF Department of Pharmaceutical ChemistryTable of ContentsIntroduction: Macromolecules and Chimera. Accessing and Displaying Molecular Data with Chimera. Algorithms Dealing with Distance. Algorithms Dealing with Angles. Structure Overlap and Alignment. Potential Energy Functions. Rotamers and Side‑Chain Conformation. Residue Networks. Appendices. Index.

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Book SynopsisInternationally acclaimed science writer Lone Frank swabs up her DNA to provide the first truly intimate account of the new science of consumer-led genomics. She challenges the business mavericks intent on mapping every baby's genome, ponders the consequences of biological fortune-telling, and prods the psychologists who hope to uncover just how much or how little our environment will matter in the new genetic century - a quest made all the more gripping as Frank considers her family's and her own struggles with depression.Trade Review"A probing biological memoir… Refreshing [and] wonderfully poetic." * Publishers Weekly *"Readers will be intrigued by Frank's witty, engaging account of the possibilities of emerging genetic sciences as well as her thoughtful considerations of the philosophical and ethical dimensions of the widespread use of genetic data." * Library Journal *"My Beautiful Genome covers some of the most interesting controversies in biology today, including designer babies, brain imaging, and even whether or not we have free will. It's an enthralling read." * New Scientist *"The huge research effort to understand the complexity of the genome is throwing up new insights into the nature of humanity, as the Danish science writer Lone Frank shows in My Beautiful Genome, her excellent look into the postgenomic world…. Fascinating." * Financial Times *"A fascinating exploration of a subject that is not going to go away -- the influence of our genes on the way we are both medically and socially… Frank's light style and storytelling verve… make this an enjoyable read." Brian Clegg * PopularScience.co.uk *"It’s not often you can wholeheartedly recommend a book… but this is it." * Daily Mail *"A pin-sharp, lively memoir-cum-investigation… Frank's discoveries make for some truly tingling moments." * The Mail on Sunday *"The story of DNA told through one woman's quest to find out if the secret to her depression lies in her genes… A must-read." * BioNews *"Packed with juicy informative nuggets … weird, wonderful and very funny." * Independent on Sunday *“I thoroughly enjoyed this book and would not hesitate to recommend it as both a personal and a book group read.” * New Books *

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Book SynopsisThe mathematical models in this book are concerned with a variety of approaches to the manner in which the clinical radiologic treatment of human neoplasms can be improved. These improvements comprise ways of delivering radiation to the malignan­ cies so as to create considerable damage to tumor cells while sparing neighboring normal tissues. There is no unique way of dealing with these improvements. Accord­ ingly, in this book a number of different presentations are given. Each presentation has as its goal some aspect of the improvement, or optimization, of radiotherapy. This book is a collection of current ideas concerned with the optimization of human cancer radiotherapy. It is hoped that readers will build on this collection and develop superior approaches for the understanding of the ways to improve therapy. The author owes a special debt of thanks to Kathy Prindle who breezed through the typing of this book with considerable dexterity. TABLE OF CONTENTS Chapter GENERAL INTRODUCTION 1. 1 Introduction 1 1. 2 History of Cancer and its Treatment by Radiotherapy 8 1. 3 Some Mathematical Models of Tumor Growth 12 1. 4 Spatial Distribution of the Radiation Dose 20 Chapter 2 SURVIVAL CURVES FROM STATISTICAL MODELS 24 2. 1 Introduction 24 2. 2 The Target Model 26 2. 3 Single-hit-to-kill Model 27 2. 4 Multitarget, Single-hit Survival 29 2. 5 Multitarget, Multihit Survival 31 2. 6 Single-target, Multihit Survival 31 2.Table of Contents1 General Introduction.- 1.1 Introduction.- 1.2 History of Cancer and its Treatment by Radiotherapy.- 1.3 Some Mathematical Models of Tumor Growth.- 1.4 Spatial Distribution of the Radiation Dose.- 2 Survival Curves from Statistical Models.- 2.1 Introduction.- 2.2 The Target Model.- 2.3 Single-hit-to-kill Model.- 2.4 Multitarget, Single-hit Survival.- 2.5 Multitarget, Multihit Survival.- 2.6 Single-target, Multihit Survival.- 2.7 Properties of In Vitro Survival Curves.- 3 A Molecular Model of cell Survival.- 3.1 Introduction.- 3.2 The Molecular Model.- 3.3 Interpretations of the Molecular Model.- 4 Kinetic Models of Biological Radiation Response.- 4.1 Introduction.- 4.2 Basic Postulates in the Dienes Model.- 4.3 Low LET Kinetic Models with no Recovery.- 4.4 Further Discussion of the Models.- 4.5 Low LET Kinetic Models with Recovery.- 4.6 Low LET and High LET Kinetic Model with No Recovery.- 4.7 Other Kinetic Schemes: Sparsely-ionizing Radiation.- 4.8 Kinetic Schemes with Age-specific Compartments in the Cell Cycle.- 4.9 Thermal Potentiation of Cell Killing.- 5 Cell Survival after Successive Radiation Fractions.- 5.1 Introduction.- 5.2 Some Results in Connection with Instantaneous Cell Kill and Exponential Tumor Growth.- 5.3 Instantaneous Cell Kill Followed by Logistic Growth of Normal Tissue.- 5.4 Cohen’s Cell Population Kinetics Programs.- 5.5 A Model of Radiation Therapy with Resistant and Sensitive Cell Populations.- 5.6 Dose Fractionation and General Survival Curves.- 6 Optimization Models in Solid Tumor Radiotherapy.- 6.1 Introduction.- 6.2 Optimal Radiotherapy of Tumor Cells Based on Cumulative Radiation Effect and a Multitarget, Single-hit Survival Function.- 6.3 Optimal Radiotherapy of Tumor Cells Based on Cumulative Radiation Effect and an Exponential-quadratic Survival Expression.- 6.4 Fractionation Scheme with a Four Level Population Tumor Model.- 6.5 A Dynamic Programming Solution to the Problem of the Determination of Optimal Treatment Schedules.- 6.6 Optimal Treatment Schedules in Fractionated Radiation Therapy for Fischer’s Tumor Model.- 6.7 Optimal Radiation Schedules with Cell Cycle Analysis.- 7 Numerical Solution of Multistage Optimal Control Problems.- 7.1 Introduction.- 7.2 Continuous Time Optimal Control.- 7.3 Optimization of Multistage Systems.- 7.4 Multi-dimensional Optimization by Gradient Methods.- 7.5 Gradient Method with Penalty Function.- 7.6 A Numerical Scheme for a Nonlinear Problem.- 7.7 The Method of Conjugate Gradients.- 7.8 Discrete Dynamic Programming.- 8 Some Optimization Criteria in Radiotherapy.- 8.1 Introduction.- 8.2 Therapeutic Policy, Strategy and Tactics.- 8.3 Optimization and Clinical Trials.- 8.4 Score Functions and Age Response Functions.- 8.5 Comparison of Models Used in Optimization Procedures.- 8.6 The Complication Probability Factor.- 9 The Optimization of External Beam Radiation Therapy.- 9.1 Introduction.- 9.2 Some Approaches for Treatment Plans.- 9.3 Linear Programming.- 9.4 Linear Programming and Radiation Treatment Planning.- 9.5 Optimization of External Beam Radiation Therapy Using Nonlinear Programming.- 9.6 Quantitative Study of Relative Radiation Effects and Isoeffect Patterns.- 10 Reconstructive Tomography.- 10.1 Introduction.- 10.2 Reconstruction Algorithm.- 10.3 Numerical Approximations for the Attenuation Coefficient.- 10.4 Cross-sectional Absorption Density Reconstruction for Treatment Planning.- 10.5 Towards the Optimization of Dose Reduction in Computerized Tomography.- 10.6 Other Imaging Technologies.- Appendix 1.- Appendix 2.- Appendix 3.

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Book SynopsisThe human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a "competition and cooperation" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers presented at a U. S. -Japan Joint Seminar on "Competition and Cooperation in Neural Nets" which was designed to catalyze better integration of theory and experiment in these areas. It was held in Kyoto, Japan, February 15-19, 1982, under the joint sponsorship of the U. S. National Science Foundation and the Japan Society for the Promotion of Science. Participants included brain theorists, neurophysiologists, mathematicians, computer scientists, and physicists. There are seven papers from the U. S.Table of ContentsI. An Opening Perspective.- 1. Competitive and Cooperative Aspects in Dynamics of Neural Excitation and Self-Organization.- II. Reaction-Diffusion Equations.- 2. Sigmoidal Systems and Layer Analysis.- 3. Asymptotic Behavior of Stationary Homogeneous Neuronal Nets.- 4. Aggregation and Segregation Phenomena in Reaction-Diffusion Equations.- III. Single-Neuron and Stochastic Models.- 5. Nerve Pulse Interactions.- 6. Micronetworks in Nerve Cells.- 7. Role and Use of Noise in Biological Systems.- 8. Stochastic, Quantal Membrane Conductances and Neuronal Function.- 9. Diffusion Approximations and Computational Problems for Single Neurons’ Activity.- 10. Periodic Pulse Sequences Generated by an Analog Neuron Model.- 11. On a Mathematical Neuron Model.- IV. Oscillations in Neural Networks.- 12. Control of Distributed Neural Oscillators.- 13. Characteristics of Neural Network with Uniform Structure.- V. Development and Plasticity of the Visual Systems.- 14. Systems Matching and Topographic Maps: The Branch-Arrow Model (BAM).- 15. Differential Localization of Plastic Synapses in the Visual Cortex of the Young Kitten: Evidence for Guided Development of the Visual Cortical Networks.- 16. Self-Organization of Neural Nets with Competitive and Cooperative Interaction.- 17. A Simple Paradigm for the Self-Organized Formation of Structured Feature Maps.- 18. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition.- 19. On the Spontaneous Emergence of Neuronal Schemata.- 20. Associative and Competìve Principles of Learning and Development.- VI. Sensori-Motor Transformations and Learning.- 21. Modelling Neural Mechanisms of Visuomotor Coordination in Frog and Toad.- 22. Two-Dimensional Model of Retinal-Tectal-Pretectal Interactions for the Control of Prey-Predator Recognition and Size Preference in Amphibia.- 23. Tensor Theory of Brain Function:The Cerebellum as a Space-Time Metric.- 24. Mechanisms of Motor Learning.- 25. Dynamic and Plastic Properties of the Brain Stem Neuronal Networks as the Possible Neuronal Basis of Learning and Memory.

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Book SynopsisClustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.Trade ReviewFrom the reviews:“The book focuses on emerging metaheuristic approaches to unsupervised classification, with an emphasis on a symmetry-based definition of similarity. … I found this book very appealing. I also thought of it as very valuable for my preoccupations towards the real-world application of unsupervised classification to medical imaging. I thus believe that, when reading this book, junior as well as experienced researchers will find many new challenging theoretical and practical ideas.” (Catalin Stoean, zbMATH, Vol. 1276, 2014)“The book views clustering as a (multiobjective) optimization problem and tackles it with metaheuristics algorithms. More interestingly, the authors of this book propose the exploitation of the concepts of point and line symmetry to define new distances to be used in clustering techniques. … researchers in the field will surely appreciate it as a good reference on the use of the symmetry notion in clustering.” (Nicola Di Mauro, Computing Reviews, July, 2013)Table of ContentsChap. 1 Introduction.- Chap. 2 Some Single- and Multiobjective Optimization Techniques.- Chap. 3 SimilarityMeasures.- Chap. 4 Clustering Algorithms.- Chap. 5 Point Symmetry Based Distance Measures and their Applications to Clustering.- Chap. 6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation.- Chap. 7 Symmetry Based Automatic Clustering.- Chap. 8 Some Line Symmetry Distance Based Clustering Techniques.- Chap. 9 Use of Multiobjective Optimization for Data Clustering.- References.- Index.

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Book SynopsisDNA profiling is often heralded as unassailable criminal evidence, a veritable "truth machine" that can overturn convictions based on eyewitness testimony, confessions, and other forms of forensic evidence. This book traces the controversial history of DNA fingerprinting by looking at court cases in the US and UK from the mid-1980s onwards.Trade Review"I could not put it down. This is a must-read for anyone interested in the history of science." (Times Higher Education) "An interesting read.... It illustrates that the controversy of DNA profiling is rooted not in the science, but mainly in the restrictions of the adversarial system." (Nature)"

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Book SynopsisCovers various aspects of contemporary biology including gene therapy, the Human Genome Project, DNA testing, and genetic engineering, and fundamental concepts. This book discusses classical and molecular genetics, quantitative and population genetics including cloning and genetic diseases, and the many applications of genetics to the world.Trade ReviewThe book covers much of the material in a high school textbook...but Omoto and Lurquin write in a way that makes things relevan to any interested adult. I think this is an excellent book that will be of great value in any public library collection...also in university and college libraries. -- Margaret Henderson E-Stream Very useful introduction to genes and genetic applications...Recommended. General readers. -- P. M. Watt Choice As a society we are asked to make informed decisions on complex issues such as stem cell research and the labeling of our food based on its level of genetic modification. We have a lot of homework to do, and this book is a good start. -- Stephen Jones Washington State MagazineTable of ContentsPreface: Why Is Genetics Important? 1. What Are Genes? Try This at Home: Extract DNA from Vegetables in Your Kitchen 2. Inheritance of Single-Gene Traits 3. Mendelian Traits in Humans Try This at Home: Pedigree Game 4. From Genes to Phenotype Try This at Home: DNA Replication, Transcription, and Translation Game 5. Using Bacteria as Protein Factories 6. Genetically Modified Plants 7. When Things Go Wrong 8. Mutagens, Teratogens, and Human Reproduction 9. Linkage and Mapping: Gene Discovery Try This at Home: Independent Assortment of Chromosomes and the Making of a Unique Individual Try This at Home: Explore Genetics Databases 10. Genetics of Populations and Genetic Testing 11. Survival of the Fittest? Try This at Home: Demonstrations of the Effects of Small Population Size 12. Nature Versus Nurture 13. Genetically Modified Animals and the Applications of Gene Technology for Humans Appendix A. Internet Resources Appendix B. Glossary of Scientific Names of Organisms Appendix C. Glossary of Human Genetic Diseases Appendix D. Glossary of Terms

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Book SynopsisTrade ReviewThe book is novel, easy to read and combines excellent cartoons with good personal vignettes and history. I spent many years mastering genetics and yet learned new and valuable things from this book. Take a look, you will not be disappointed. -- Robert Trivers, Rutgers University A unique, richly detailed, and fun biography of DNA grounded in deep historical and philosophical knowledge--Rosenfield, Ziff and Van Loon give us everything we need to know about biology's most important molecule. -- Oliver Sacks Right now, you may not know the difference between a prokaryote and a eukaryote, but read this richly detailed work and that could be your next cocktail party opener. Toronto Globe & Mail A clear summary of the DNA story with a lighthearted approach. CHOICE

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