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Full-Text Articles in Other Computer Sciences
Integrated Development And Parallelization Of Automated Dicentric Chromosome Identification Software To Expedite Biodosimetry Analysis, Yanxin Li
Electronic Thesis and Dissertation Repository
Manual cytogenetic biodosimetry lacks the ability to handle mass casualty events. We present an automated dicentric chromosome identification (ADCI) software utilizing parallel computing technology. A parallelization strategy combining data and task parallelism, as well as optimization of I/O operations, has been designed, implemented, and incorporated in ADCI. Experiments on an eight-core desktop show that our algorithm can expedite the process of ADCI by at least four folds. Experiments on Symmetric Computing, SHARCNET, Blue Gene/Q multi-processor computers demonstrate the capability of parallelized ADCI to process thousands of samples for cytogenetic biodosimetry in a few hours. This increase in speed underscores the …
Broad Vision: The Art & Science Of Looking, Heather Barnett, John R. A. Smith
Broad Vision: The Art & Science Of Looking, Heather Barnett, John R. A. Smith
The STEAM Journal
Undergraduate students and academic staff from diverse disciplines in the arts and sciences investigated questions of mediated vision through a year-long interdisciplinary research project at the University of Westminster, London, United Kingdom. The Broad Vision project explored the perception and interpretation of microscopic worlds, and investigated the benefits and challenges of working across disciplinary divides in a university setting. This article describes the three-phase model for interdisciplinary learning and research developed through the project, providing a valuable case study for inquiry based art/science education.
A Novel Computational Framework For Transcriptome Analysis With Rna-Seq Data, Yin Hu
A Novel Computational Framework For Transcriptome Analysis With Rna-Seq Data, Yin Hu
Theses and Dissertations--Computer Science
The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected …