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DNA sequencing has become increasingly efficient over the years, resulting in an enormous increase in the amount of data gen- ated. In recent years, the focus of sequencing has shifted, from being the endpoint of a project, to being a starting point. This is especially true for such major initiatives as the human genome project, where vast tracts of DNA of unknown function are sequenced. This sheer volume of available data makes advanced computer methods ess- tial to analysis, and a familiarity with computers and sequence ana- sis software a vital requirement for the researcher involved with DNA sequencing. Even for nonsequencers, a familiarity with sequence analysis software can be important. For instance, gene sequences already present in the databases can be extremely useful in the design of cloning and genetic manipulation experiments. This two-part work on Analysis of Data is designed to be a practical aid to the researcher who uses computers for the acquisition, storage, or analysis of nucleic acid (and/or p- tein) sequences. Each chapter is written such that a competent sci- tist with basic computer literacy can carry out the procedure successfully at the first attempt by simply following the detailed pr- tical instructions that have been described by the author. A Notes section, which is included at the end of each chapter, provides advice on overcoming the common problems and pitfalls sometimes enco- tered by users of the sequence analysis software. Software packages for both the mainframe and personal computers are described.
Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
This textbook presents an algorithmic approach to mathematical analysis, with a focus on modelling and on the applications of analysis. Fully integrating mathematical software into the text as an important component of analysis, the book makes thorough use of examples and explanations using MATLAB, Maple, and Java applets. Mathematical theory is described alongside the basic concepts and methods of numerical analysis, supported by computer experiments and programming exercises, and an extensive use of figure illustrations. Features: thoroughly describes the essential concepts of analysis; provides summaries and exercises in each chapter, as well as computer experiments; discusses important applications and advanced topics; presents tools from vector and matrix algebra in the appendices, together with further information on continuity; includes definitions, propositions and examples throughout the text; supplementary software can be downloaded from the book’s webpage.
DNA sequencing has become increasingly efficient over the years, resulting in an enormous increase in the amount of data gener ated. In recent years, the focus of sequencing has shifted, from being the endpoint of a project, to being a starting point. This is especially true for such major initiatives as the human genome project, where vast tracts of DNA of unknown function are sequenced. This sheer volume of available data makes advanced computer methods essen tial to analysis, and a familiarity with computers and sequence analy sis software a vital requirement for the researcher involved with DNA sequencing. Even for nonsequencers, a familiarity with sequence analysis software can be important. For instance, gene sequences already present in the databases can be extremely useful in the design of cloning and genetic manipulation experiments. This two-part work on Computer Analysis of Sequence Data is designed to be a practical aid to the researcher who uses computers for the acquisition, storage, or analysis of nucleic acid (and/or pro tein) sequences. Each chapter is written such that a competent scien tist with basic computer literacy can carry out the procedure successfully at the first attempt by simply following the detailed prac tical instructions that have been described by the author. A Notes section, which is included at the end of each chapter, provides advice on overcoming the common problems and pitfalls sometimes encoun tered by users of the sequence analysis software.
The refereed proceedings of the 12th International Conference on Computer Analysis of Images and Patterns are presented in this volume. The papers cover motion detection and tracking, medical imaging, biometrics, color, curves and surfaces beyond two dimensions, reading characters, words and lines, image segmentation, shape, image registration and matching, signal decomposition and invariants, and features and classification.
Sequencing is often associated with the Human Genome Project and celebrated achievements concerning the DNA molecule. However, the history of this practice comprises not only academic biology, but also the world of computer-assisted information management. The book uncovers this history, qualifying the hype and expectations around genomics.
CHAPTER 17: Respiratory Pattern, Invested Effort, and Variability in Heart Rate and Blood Pressure During the Performance of Mental Tasks -- CHAPTER 18: Power Spectra of Blood Pressure in Normotensive and Spontaneously Hypertensive Rats: Relationship with Sympathetic Cardiovascular Control -- CHAPTER 19: Sympathectomy, Sinoaortic Denervation and Spectral Powers of Blood Pressure and Heart Rate in Lyon Rats -- CHAPTER 20: Heart Rate Variability in Chronic Heart Failure -- CHAPTER 21: Spectral Analysis of Blood Pressure and Heart Rate in Patients with Myocardial Infarction -- CHAPTER 22: Heart Rate Variability and Sudden Death: What's the Connection? -- CHAPTER 23: Power Spectrum Analysis of Heart Rate in Diabetic. Patients: A Marker of Autonomic Dysfunction -- References -- Author Index
The two volume set LNCS 11678 and 11679 constitutes the refereed proceedings of the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019. The 106 papers presented were carefully reviewed and selected from 176 submissions The papers are organized in the following topical sections: Intelligent Systems; Real-time and GPU Processing; Image Segmentation; Image and Texture Analysis; Machine Learning for Image and Pattern Analysis; Data Sets and Benchmarks; Structural and Computational Pattern Recognition; Posters.