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Simplifying Design of Wireless Sensor Networks with programming languages, Compilers, and Synthesis

Simplifying Design of Wireless Sensor Networks with Programm...

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作者： Bai, Lan University of Michigan

学位级别：Ph.D.

Wireless sensor networks have opened opportunities for new applications and attracted users from domains beyond computer system design. Sensor network design is challenging. It is generally an ad hoc process carried out by embedded system experts. In this dissertation, we argue that human efforts necessary to the design of sensor networks can be reduced with the help of high-level specification languages, compilers, and synthesis tools. We designed and implemented a framework to simplify and automate the design of a class of sensor network applications. Our results show that a sensor network novice given only a few pages of instructions, can successfully specify sensing applications within 30 minutes, compared with hours or days required by prior approaches. Within approximately 30 minutes, our modeling and design exploration techniques translate these specifications into implementations, automatically selecting from among 405,790 designs. Moreover, our memory management and compiler-assisted techniques make difficult-to-implement optimizations available to novice programmers, enabling better tolerance of sensor faults and making 39% more usable memory available than would otherwise be the case. We propose a design process that decouples specification from implementation. Application designers specify abstract functionality and design requirements. Compiler and synthesis tools automatically determine implementation details, optimizing design parameter optimization and generating code. First, we develop a design process in which programming novices (e.g., application experts) use high-level, specification languages designed for particular classes of applications. We focus on the class most commonly encountered in sensor network deployment publications. Second, we develop two compiler and runtime techniques to relieve application experts from explicitly dealing with sensor faults and limited memory, two common sources of sensor network design complexity. The first tec

关键词： Wireless Sensor Network Design Automation programming language Synthesis Modeling Thesis

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Automated Conditioning in Larval Zebrafish

Automated Conditioning in Larval Zebrafish

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作者： Ruey-Kuang Cheng Suresh Jesuthasan

A primary goal in neuroscience is to understand how the brain gives rise to innate and learned behavior. One way to expand the contributions of zebrafish in this respect is to use a computerized system that can train larval zebrafish in various behavior assays. This will allow more efficient genetic and chemical screens aimed at investigating mechanisms of brain function. In this chapter, we describe a simple, economical and efficient way to automate avoidance learning in larval zebrafish. A webcam is used to acquire images for custom-built software to identify the position of fish in real-time. Based on this information, the computer delivers training stimuli from a universal serial bus module board that connects to the testing tanks through a small electrical circuit board. The training protocol is pre-programmed and the entire process is automatic, so no experimenter needs to be present and no offline video analysis is required. less

关键词： Optical Imaging Image Analysis Data analysis programming language Danio rerio Fear conditioning Behavior Webcam Light-emitting diodes Instrumental response Active avoidance Universal serial bus Relay

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Detecting Rare Variants

Detecting Rare Variants

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作者： Tao Feng Xiaofeng Zhu

The limitations of genome-wide association (GWA) studies that are based on the common disease common variants (CDCV) hypothesis have motivated geneticists to test the hypothesis that rare variants contribute to the variation of common diseases, i.e., common disease/rare variants (CDRV). The newly developed high-throughput sequencing technologies have made the studies of rare variants practicable. Statistical approaches to test associations between a phenotype and rare variants are quickly developing. The central idea of these methods is to test a set of rare variants in a defined region or regions by collapsing or aggregating rare variants, thereby improving the statistical power. In this chapter, we introduce these methods as well as their applications in *** limitations of genome-wide association (GWA) studies that are based on the common disease common variants (CDCV) hypothesis have motivated geneticists to test the hypothesis that rare variants contribute to the variation of common diseases, i.e., common disease/rare variants (CDRV). The newly developed high-throughput sequencing technologies have made the studies of rare variants practicable. Statistical approaches to test associations between a phenotype and rare variants are quickly developing. The central idea of these methods is to test a set of rare variants in a defined region or regions by collapsing or aggregating rare variants, thereby improving the statistical power. In this chapter, we introduce these methods as well as their applications in practice.

关键词： High-Throughput Sequencing programming language Aggregation SNPs Haplotype Collapsing GWA Common disease common variants Common disease rare variants

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Analysis of High-Throughput Ancient DNA Sequencing Data

Analysis of High-Throughput Ancient DNA Sequencing Data

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作者： Martin Kircher

Advances in sequencing technologies have dramatically changed the field of ancient DNA (aDNA). It is now possible to generate an enormous quantity of aDNA sequence data both rapidly and inexpensively. As aDNA sequences are generally short in length, damaged, and at low copy number relative to coextracted environmental DNA, high-throughput approaches offer a tremendous advantage over traditional sequencing approaches in that they enable a complete characterization of an aDNA extract. However, the particular qualities of aDNA also present specific limitations that require careful consideration in data analysis. For example, results of high-throughout analyses of aDNA libraries may include chimeric sequences, sequencing error and artifacts, damage, and alignment ambiguities due to the short read lengths. Here, I describe typical primary data analysis workflows for high-throughput aDNA sequencing experiments, including (1) separation of individual samples in multiplex experiments; (2) removal of protocol-specific library artifacts; (3) trimming adapter sequences and merging paired-end sequencing data; (4) base quality score filtering or quality score propagation during data analysis; (5) identification of endogenous molecules from an environmental background; (6) quantification of contamination from other DNA sources; and (7) removal of clonal amplification products or the compilation of a consensus from clonal amplification products, and their exploitation for estimation of library complexity. less

关键词： High-Throughput Sequencing DNA Sequencing PCR Sample Pooling Library Preparation Sequence Annotation Statistical Calculation Data analysis Python programming language Genome Analysis Next-generation sequencing Adapters Contamination Ancient DNA Quality scores 454 Illumina/Solexa SOLiD-barcode Sample index Chimeric sequences Endogenous DNA

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Discovery of RNA Motifs Using a Computational Pipeline that Allows Insertions in Paired Regions and Filtering of Candidate Sequences

Discovery of RNA Motifs Using a Computational Pipeline that ...

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作者： Randi M. Jimenez Ladislav Rampášek Broňa Brejová Tomáš Vinař Andrej Lupták

The enormous impact of noncoding RNAs on biology and biotechnology has motivated the development of systematic approaches to their discovery and characterization. Here we present a methodology for reliable detection of genomic ribozymes that centers on pipelined structure-based searches, utilizing two versatile algorithms for structure prediction. RNArobo is a prototype structure-based search package that enables a single search to return all sequences matching a designated motif descriptor, taking into account the possibility of single nucleotide insertions within base-paired regions. These outputs are then filtered through a structure prediction algorithm based on free energy minimization in order to maximize the proportion of catalytically active RNA motifs. This pipeline provides a fast approach to uncovering new catalytic RNAs with known secondary structures and verifying their activity in *** enormous impact of noncoding RNAs on biology and biotechnology has motivated the development of systematic approaches to their discovery and characterization. Here we present a methodology for reliable detection of genomic ribozymes that centers on pipelined structure-based searches, utilizing two versatile algorithms for structure prediction. RNArobo is a prototype structure-based search package that enables a single search to return all sequences matching a designated motif descriptor, taking into account the possibility of single nucleotide insertions within base-paired regions. These outputs are then filtered through a structure prediction algorithm based on free energy minimization in order to maximize the proportion of catalytically active RNA motifs. This pipeline provides a fast approach to uncovering new catalytic RNAs with known secondary structures and verifying their activity in vitro.

关键词： Structure Prediction NCBI BLAST Multiple Sequence Alignment programming language Python Ribozymes Pseudoknots RNABOB RNArobo RNAfold

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TagFinder: Preprocessing Software for the Fingerprinting and the Profiling of Gas Chromatography–Mass Spectrometry Based Metabolome Analyses

TagFinder: Preprocessing Software for the Fingerprinting and...

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作者： Alexander Luedemann Luise Malotky Alexander Erban Joachim Kopka

GC-MS based metabolome studies aim for the complete identification and relative or absolute quantification of metabolites in complex extracts from a large diversity of biological materials. The resulting high-throughput chromatography data files are typically processed following two complementary workflows, namely, fingerprinting and profiling. For fingerprinting studies all observed mass features, here called mass spectral tags (MSTs), are quantified in a nontargeted and (within the limits of the GC-MS technology) comprehensive approach. Fingerprinting allows for the discovery of MSTs, which, in the sense of a biomarker, indicate significant changes of metabolite pool sizes. The significance and relevance of such MSTs are typically tested in comparison to standardized reference samples. Only after this confirmation step are the relevant MSTs identified and the underlying metabolic biomarkers elucidated. Both the metabolite fingerprinting and profiling approaches are essential to modern biotechnological investigations. Studies which are aimed at establishing the substantial equivalence at metabolic level or aim to breed for optimum quality of human food or animal feed especially benefit from the potential to discover novel unforeseen metabolic factors in fingerprinting approaches and from the option to demonstrate unchanged pool sizes of known metabolites in the metabolic profiling mode. As GC-MS technology represents one essential element which contributes to investigations of substantial equivalence, we have developed a dedicated software tool, the TagFinder chromatography data preprocessing suite, which has all essential functions to support both fundamental workflows of modern metabolomic studies. In this chapter, we describe the TagFinder software and its application to the assessment of metabolic phenotypes in fingerprinting and profiling analyses. less

关键词： Gas Chromatography-mass Spectrometry Chromatography Spectroscopy Metabolomics Chromatographic Alignment Gas Chromatography-mass Spectrometry Metabolite Profiling Data Mining Pull-down Assay programming language Peak extraction Spectral reconstruction Mass spectral tags Nontargeted fingerprint analysis Targeted profiling analysis

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Rapid Strain Evaluation Using Dynamic DO-Stat Fed-Batch Fermentation Under Scale-Down Conditions

Rapid Strain Evaluation Using Dynamic DO-Stat Fed-Batch Ferm...

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作者： Jun Sun

While large amount of strains can be quickly generated via metabolic engineering, the speed/efficiency of evaluating each strain becomes the bottleneck in the process from strain development to final production. In this chapter, a method is introduced to rapidly evaluate strain performance in fed-batch fermentation mode by using dynamic dissolved oxygen stat feed back control with no additional advanced online measurement. In addition, a scale-down feature is integrated in the method to mimic the limitation of oxygen transfer in large-scale vessels, so that strains can be evaluated under the conditions close to that in large-scale bioreactors. The method has been implemented in several commercial standard benchtop scale fermentation systems with different fermentation control software. less

关键词： Fermentation Metabolic Engineering Recombinant Protein Expression HPLC programming language Saccharomyces cerevisiae Escherichia coli Process development Scale-down Strain evaluation Dynamic DO-Stat control

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Development and Application of Online Judge System

Development and Application of Online Judge System

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2012 International Symposium on Information Technologies in Medicine and Education(ITME2012)

作者： Jianhua Wu Electrical and Information College Jinan University Zhuhai China Shuangping Chen Rongrong Yang Electrical and Information College Jinan University Zhuhai China

programming language and algorithm design are core courses in computer *** are the foundation of software ***,in the experiment teaching of courses there are still many *** paper first analyzes those problems,then some advantages are discussed when ACM contest mode is brought in those courses;and last an online judge system is introduced in detail,including system design,implementation and *** results of the system prove that reform experiment teaching can effectively improve the students’ programming ability and study interest.

关键词： component programming language Algorithm design Experiment teaching ACM Online judge

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Metabolic Fingerprinting Using Comprehensive Two-Dimensional Gas Chromatography – Time-of-Flight Mass Spectrometry

Metabolic Fingerprinting Using Comprehensive Two-Dimensional...

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作者： Martin F. Almstetter Peter J. Oefner Katja Dettmer

Comprehensive two-dimensional gas chromatography – time-of-flight mass spectrometry (GC × GC–TOF-MS) is applied to the comparative metabolic fingerprinting of physiological fluids. Stable isotope-labeled internal standards plus norvaline serve as extraction standards and are added to the blanks, controls and patient samples prior to protein precipitation with methanol. The extracts are evaporated to complete dryness and derivatized in two steps using methoximation with methoxylamine hydrochloride (MeOx) and silylation with N-methyl-N-trimethylsily- trifluoroacetamide (MSTFA). Between derivatization steps a second internal standard containing odd-numbered, saturated straight chain fatty acids is added for quality control and to normalize retention time shifts. After GC × GC–TOF-MS analysis raw data are processed, aligned, and combined in one data matrix for subsequent statistical evaluation. Both a custom-made and the NIST 05 library are used to preliminarily identify significant metabolites. For verification purposes, commercial standards are run individually. Absolute quantification of selected metabolites is achieved by using a multi-point calibration curve and isotope-labeled internal standards. less

关键词： Two-dimensional Gas Chromatography Time-of-flight Mass Spectrometry Protein Precipitation Metabolomics Statistical Calculation MATLAB Principal Component Analysis Cell And Tissue Culture Data analysis programming language Alignment algorithms Metabolic fingerprinting Physiological fluids Mass spectral libraries

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Genotype Calling for the Affymetrix Platform

Genotype Calling for the Affymetrix Platform

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作者： Arne Schillert Andreas Ziegler

The analysis of high-throughput genotyping data in genome-wide association (GWA) studies has become a standard approach in genetic epidemiology. Data of high quality are crucial for the success of these studies. The first step in the statistical analysis is the generation of genotypes from signal intensities, and several approaches have been proposed for obtaining as accurate genotypes as possible. For the Affymetrix Genome-Wide Human SNP Array 6.0, the genotype calling algorithms Birdseed and CRLMM are commonly used in applications. After a brief description of the statistical methods for both algorithms, their usage is described in detail. Links are provided to the software and to sample code for the installation and execution of the algorithms. Additionally, a suggestion for processing the result files is made. less

关键词： Genotyping Statistical Calculation programming language Python Affymetrix Birdseed CEL files CRLMM Genotype calling algorithm Signal intensities

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