15 research outputs found

    A Method for Bio-Sequence Analysis Algorithm Development Based on the PAR Platform

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    The problems of biological sequence analysis have great theoretical and practical value in modern bioinformatics. Numerous solving algorithms are used for these problems, and complex similarities and differences exist among these algorithms for the same problem, causing difficulty for researchers to select the appropriate one. To address this situation, combined with the formal partition-and-recur method, component technology, domain engineering, and generic programming, the paper presents a method for the development of a family of biological sequence analysis algorithms. It designs highly trustworthy reusable domain algorithm components and further assembles them to generate specifific biological sequence analysis algorithms. The experiment of the development of a dynamic programming based LCS algorithm family shows the proposed method enables the improvement of the reliability, understandability, and development efficiency of particular algorithms

    A highly condensed genome without heterochromatin : orchestration of gene expression and epigenomics in Paramecium tetraurelia

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    Epigenetic regulation in unicellular ciliates can be as complex as in metazoans and is well described regarding small RNA (sRNA) mediated effects. The ciliate Paramecium harbors several copies of sRNA-biogenesis related proteins involved in genome rearrangements resulting in chromatin alterations. The global chromatin organization thereby is poorly understood, and unusual characteristics of the somatic nucleus, like high polyploidy, high genome coding density, and absence of heterochromatin, ought to call for complex regulation to orchestrate gene expression. The present study characterized the nucleosomal organization required for gene regulation and proper Polymerase II activity. Histone marks reveal broad domains in gene bodies, whereas intergenic regions are nucleosome free. Low occupancy in silent genes suggests that gene inactivation does not involve nucleosome recruitment. Thus, Paramecium gene regulation counteracts the current understanding of chromatin biology. Apart from global nucleosome studies, two sRNA binding proteins (Ptiwis) classically associated with transposon silencing were investigated in the background of transgene-induced silencing. Surprisingly, both Ptiwis also load sRNAs from endogenous loci in vegetative growth, revealing a broad diversity of Ptiwi functions. Together, the studies enlighten epigenetic mechanisms that regulate gene expression in a condensed genome, with Ptiwis contributing to transcriptome and chromatin dynamics.Epigenetische Regulation kann in einzelligen Ciliaten so komplex sein wie in Vielzellern und wurde umfassend angesichts kleiner RNA (sRNA)-vermittelter Effekte untersucht. Der Ciliat Paramecium besitzt mehrere Kopien sRNA-Biogenese assoziierter Proteine, die an Genomprozessierungen und resultierenden Chromatinänderungen beteiligt sind. Die globale Organisation des Chromatins ist dabei kaum verstanden und obskure Eigenschaften des somatischen Kerns, wie hohe Polyploidie, Kodierungsdichte und Fehlen von Heterochromatin, sollten eine komplexe Regulation zur Steuerung der Genexpression erfordern. Die vorliegende Studie charakterisiert die Chromatinorganisation, die für die Genregulation und Polymerase II Aktivität notwendig ist. Histonmodifikationen zeigen breite Verteilungen in Genen, während intergenische Regionen Nukleosomen-frei sind. Ein Stilllegen von Genen scheint ohne die Rekrutierung von Nukleosomen zu erfolgen, womit die Genregulation in Paramecium dem aktuellen Verständnis der Chromatinbiologie widerspricht. Neben Nukleosomenstudien wurden zwei sRNA-bindende Proteine (Ptiwis), die klassisch mit Transposon-Silencing assoziiert sind, im Hintergrund des Transgeninduzierten Silencings untersucht. Überraschenderweise laden Ptiwis sRNAs von endogenen Loci im vegetativen Wachstum, was vielfältige Ptiwi-Funktionen offenbart. Die Studien zeigen epigenetische Mechanismen zur Genregulation in einem kompakten Genom, wobei Ptiwis zur Transkriptom- und Chromatindynamik beitragen

    Proteomics

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    Biomedical research has entered a new era of characterizing a disease or a protein on a global scale. In the post-genomic era, Proteomics now plays an increasingly important role in dissecting molecular functions of proteins and discovering biomarkers in human diseases. Mass spectrometry, two-dimensional gel electrophoresis, and high-density antibody and protein arrays are some of the most commonly used methods in the Proteomics field. This book covers four important and diverse areas of current proteomic research: Proteomic Discovery of Disease Biomarkers, Proteomic Analysis of Protein Functions, Proteomic Approaches to Dissecting Disease Processes, and Organelles and Secretome Proteomics. We believe that clinicians, students and laboratory researchers who are interested in Proteomics and its applications in the biomedical field will find this book useful and enlightening. The use of proteomic methods in studying proteins in various human diseases has become an essential part of biomedical research

    Architectural Techniques for Disturbance Mitigation in Future Memory Systems

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    With the recent advancements of CMOS technology, scaling down the feature size has improved memory capacity, power, performance and cost. However, such dramatic progress in memory technology has increasingly made the precise control of the manufacturing process below 22nm more difficult. In spite of all these virtues, the technology scaling road map predicts significant process variation from cell-to-cell. It also predicts electromagnetic disturbances among memory cells that easily deviate their circuit characterizations from design goals and pose threats to the reliability, energy efficiency and security. This dissertation proposes simple, energy-efficient and low-overhead techniques that combat the challenges resulting from technology scaling in future memory systems. Specifically, this dissertation investigates solutions tuned to particular types of disturbance challenges, such as inter-cell or intra-cell disturbance, that are energy efficient while guaranteeing memory reliability. The contribution of this dissertation will be threefold. First, it uses a deterministic counter-based approach to target the root of inter-cell disturbances in Dynamic random access memory (DRAM) and provide further benefits to overall energy consumption while deterministically mitigating inter-cell disturbances. Second, it uses Markov chains to reason about the reliability of Spin-Transfer Torque Magnetic Random-Access Memory (STT-RAM) that suffers from intra-cell disturbances and then investigates on-demand refresh policies to recover from the persistent effect of such disturbances. Third, It leverages an encoding technique integrated with a novel word level compression scheme to reduce the vulnerability of cells to inter-cell write disturbances in Phase Change Memory (PCM). However, mitigating inter-cell write disturbances and also minimizing the write energy may increase the number of updated PCM cells and result in degraded endurance. Hence, It uses multi-objective optimization to balance the write energy and endurance in PCM cells while mitigating intercell disturbances. The work in this dissertation provides important insights into how to tackle the critical reliability challenges that high-density memory systems confront in deep scaled technology nodes. It advocates for various memory technologies to guarantee reliability of future memory systems while incurring nominal costs in terms of energy, area and performance

    A Survey on Metric Learning for Feature Vectors and Structured Data

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    The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method
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