The biological function of clusterin (CLU, also known as ApoJ, SGP2, TRPM2, CLI) has been puzzling researchers since its discovery and characterization in the early 1980s. Approaches such as cloning, expression and functional characterization of the different protein products generated by the CLU gene have now produced a critical mass of information of tremendous biological importance that are teaching us an important lesson in molecular biology of gene expression regulation. This volume brings together the contributions of top researchers in the field, providing an overview and synthesis of the latest thought and findings relating to CLU.
Recognition of carbohydrates in biological systems has been gaining more and more attention in recent years. Although methodology for studying recognition has been developing, there is no volume that covers the wide area of methodology of carbohydrate recognition. This volume, Recognition of Carbohydrates in Biological Systems, Part B: Specific Applications, and its companion, Volume 362, present state-of-the-art methodologies, as well as the most recent biological observations in this area. - Covers carbohydrate-binding proteins - Discusses glycoproteins and glycolipids - Polysaccharides, enzymes and cells are also covered
The AACR Annual Meeting is the focal point of the cancer research community, where scientists, clinicians, other health care professionals, survivors, patients, and advocates gather to share the latest advances in cancer science and medicine. From population science and prevention; to cancer biology, translational, and clinical studies; to survivorship and advocacy; the AACR Annual Meeting highlights the work of the best minds in cancer research from institutions all over the world.
With the advent of digital computers more than half a century ago, - searchers working in a wide range of scienti?c disciplines have obtained an extremely powerful tool to pursue deep understanding of natural processes in physical, chemical, and biological systems. Computers pose a great ch- lenge to mathematical sciences, as the range of phenomena available for rigorous mathematical analysis has been enormously expanded, demanding the development of a new generation of mathematical tools. There is an explosive growth of new mathematical disciplines to satisfy this demand, in particular related to discrete mathematics. However, it can be argued that at large mathematics is yet to provide the essential breakthrough to meet the challenge. The required paradigm shift in our view should be compa- ble to the shift in scienti?c thinking provided by the Newtonian revolution over 300 years ago. Studies of large-scale random graphs and networks are critical for the progress, using methods of discrete mathematics, probabil- tic combinatorics, graph theory, and statistical physics. Recent advances in large scale random network studies are described in this handbook, which provides a signi?cant update and extension - yond the materials presented in the “Handbook of Graphs and Networks” published in 2003 by Wiley. The present volume puts special emphasis on large-scale networks and random processes, which deemed as crucial for - tureprogressinthe?eld. Theissuesrelatedtorandomgraphsandnetworks pose very di?cult mathematical questions.
The biological function of clusterin (CLU, also known as ApoJ, SGP2, TRPM2, CLI) has been puzzling researchers since its discovery and characterization in the early 1980s. Approaches such as cloning, expression and functional characterization of the different protein products generated by the CLU gene have now produced a critical mass of information of tremendous biological importance that are teaching us an important lesson in molecular biology of gene expression regulation. This volume brings together the contributions of top researchers in the field, providing an overview and synthesis of the latest thought and findings relating to CLU.
There is now considerable genetic evidence that the type 4 allele of the apolipoprotein E gene is a major susceptibility factor associated with late-onset Alzheimer's disease, the common form of the disease defined as starting after sixty years of age. The role of apolipoprotein E in normal brain metabolism and in the pathogenesis of Alzheimer's disease are new and exciting avenues of research. This book, written by the most outstanding scientists in this new filed, is the first presentation of results concerning the implications of apolipoprotein E on the genetics, cell biology, neuropathology, biochemistry, and therapeutic management of Alzheimer's disease.
Enabling technologies - An overview of cluster computing / Thomas Sterling / - Node Hardware / Thomas Sterling / - Linux / Peter H. Beckman / - Network Hardware / Thomas Sterling / - Network Software / Thomas Sterling / - Setting Up clusters : installation and configuration - How fast is my beowulf? / David Bailey / - Parallel programming / - Parallel programming with MPI / William Gropp / - Advanced topics in MPI programming / William Gropp / - Parallel programming with PVM / Al Geist / - Fault-tolerant and adaptive programs with PVM / Al Geist / - Managing clusters / - Cluster workload management / James Patton Jones / - Condor : a distributed job scheduler / - Maui scheduler : A multifunction cluster scheduler / David B. Jackson / - PBS : portable batch system / James Patton Jones / - PVFS : parallel virtual file system / Walt Ligon / - Chiba city : the Argonne scalable cluster.
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
In this Handbook of Experimental Pharmacology on “High Density Lipoproteins – from biological understanding to clinical exploitation” contributing authors (members of COST Action BM0904/HDLnet) summarize in more than 20 chapters our current knowledge on the structure, function, metabolism and regulation of HDL in health and several diseases as well as the status of past and ongoing attempts of therapeutic exploitation. The book is of interest to researchers in academia and industry focusing on lipoprotein metabolism, cardiovascular diseases and immunology as well as clinical pharmacologists, cardiologists, diabetologists, nephrologists and other clinicians interested in metabolic or inflammatory diseases.
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
