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We performed a phylogenetic analysis of 2359 hemagglutinin sequences of influenza A. We find multiple host shifts of all polarities among swine, humans, and birds. We also describe novel methods to assess the quality of surveillance and apply these methods to the public sequence record.
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Mendel's Accountant (hereafter referred to as Mendel
) is a user-friendly
biologically realistic simulation program for investigating the processes of
mutation and selection in sexually reproducing diploid populations. Mendel
represents an advance over previous forward-time programs in that it
incorporates several new features that enhance biological realism including:
(a) variable mutation effect and (b) environmental variance that affects
phenotype. In Mendel, as in nature, mutations have a continuous range of
effect from lethal to beneficial, and may vary in expression from fully
dominant to fully recessive. Mendel allows mutational effects to be combined
in either a multiplicative or additive manner to determine overall genotypic
fitness and provides the option of either truncation or probability selection.
Environmental variance is specified via a heritability parameter and a
non-scaling noise standard deviation. Mendel is computationally efficient,
so many problems of interest can be run on ordinary personal computers.
Parallelized using MPI, Mendel readily handles large population size and
population substructure on cluster computers. We report a series of validation
experiments which show consistently that Mendel results conform to theoretical
predictions. Its graphical user interface is designed to make problem
specification intuitive and simple, and it provides a variety of visual
representations in the program output. The program is a versatile research
tool and is useful also as an interactive teaching resource.
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Theα-helical coiled-coil is a protein structure motif well suited for computational prediction. To study the occurrence of coiled-coil proteins throughout different kingdoms, we have computationally identified and clustered long coiled-coil proteins from 23 fully-sequenced genomes. Our results indicate that long coiled-coil proteins occur with higher frequency in eukaryotes than prokaryotes, with kingdom-specific families observed in plants and animals. We have established searchable protein databases containing prediction data for Arabidopsis, rice and Chlamydomonas coiled-coil proteins to facilitate further studies.
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Modern molecular biology experiments generate large amounts of data, which the biologists then have to analyze these manually through slow and error-prone processes. Bioinformatics and scalable computing provide essential tools for a speedup of the proteomics analysis. An example for such a proteomic analysis and the influences of bioinformatics is presented in this paper.
The stomach is a versatile organ with a prominent role in digestion and with endocrine function. While stomach has been the target of several proteomic analyses interested in cancer or ulcer biology, no studies have analyzed the proteome of stomach with the onset of diabetes, despite the fact that diabetes has a significant impact on gastric function. In this study, proteomic analyses were performed on stomach samples collected from C57BL/6J mice with obesity and diabetes and compared with samples from non-diabetic, lean controls. Obesity and diabetes were induced in mice by placing 3 week old mice on a high fat diet for 16 weeks, while control mice remained on a low fat standard chow diet. Once diabetes was established in the high fat fed mice, 4 diabetic and 4 control mice were sacrificed and stomachs removed for proteomic analysis using 2 dimension gel electrophoresis (2-DE). The protein spots
that made up the stomach proteomic profiles were quantified using PDQuest 7.0.0 software. Protein spots found to be increased or decreased in diabetic stomach as compared to control stomach were removed from the gel for identification by database searches from peak lists generated by both MALDI-TOF and MS/MS analyses. In conclusion, a total of 23 proteins are reported herein with 14 being increased and 9 being decreased in diabetic stomach as compared to control.
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We present an agent-based, three-dimensional model of phage lambda, a virus infecting E. coli which exhibits two phases, lysogenic and lytic. This process is useful for bio-based sensors or switches and is a widely studied gene regulatory system. We model system constituents as software agents. Complex system behavior emerges from local agent interactions. Agent-based modeling lets us study how individual parameters affect overall system behavior. This bottom-up approach is an alternative to top-down approaches using differential equations and stochastic simulation. It can model any system of biomolecular reactions, with applications in physiology, pharmacology, medicine, environmental monitoring, and homeland security.
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In a distributed computing environment, it is vital to maintain the states of the processes involved in order to cater to failures that are arbitrary in nature. To reach a consistent state among all the processes, checkpoints are taken locally by each process and are combined together based on uniformity criteria such as consistency, transitlessness, and strong consistency. In this article, first, the necessary and sufficient conditions of consistency criteria are stated and then an expert system, implemented based on these criteria, is presented. The expert system discovers and illustrates consistent, transitless, strongly consistent and globally consistent checkpoints in a given distributed system. Moreover, it offers facilities for evaluating checkpointing algorithms by measuring different quality assessment parameters.
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Distributed computing offers increased performance over single machine systems by spreading computations among several networked machines. Converting a problem to run on a distributed system is not trivial and often involves many trade-offs. Many higher level communication packages exist but for a variety of reasons(portability, performance, ease of development etc.), developers may choose to implement a distributed algorithm using one of the four Java API communication mechanisms (RMI with Serializable or Externalizable Interface, socket and datagram socket). This paper provides a performance programming complexity analysis of these communication mechanisms based upon experimental results using well known algorithms to provide data points. Numerical results and insights offer guidance towards understanding the communication and computational trade-offs as well as the programming complexities encountered when adapting an algorithm to a distributed system.
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