Motivation: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at h...
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Motivation: Systems biology demands the use of several point of views to get a more comprehensive understanding of biological problems. This usually leads to take into account different data regarding the problem at hand, but it also has to do with using different perspectives of the same data. This multifaceted aspect of systems biology often requires the use of several tools, and it is often hard to get a seamless integration of all of them, which would help the analyst to have an interactive discourse with the data. Results: Focusing on expression profiling, BicOverlapper 2.0 visualizes the most relevant aspects of the analysis, including expression data, profiling analysis results and functional annotation. It also integrates several state-of-the-art numerical methods, such as differential expression analysis, gene set enrichment or biclustering.
By researching the synchronization motion of double hydraulic cylinders controlled by high-speed on-off valve, the paper aims to solve the shortage of current hydraulic synchronization system with low synchronization ...
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By researching the synchronization motion of double hydraulic cylinders controlled by high-speed on-off valve, the paper aims to solve the shortage of current hydraulic synchronization system with low synchronization precision. The flow characteristic of high-speed on-off valve with pulse width-frequency modulation is researched compared with pulse width modulation. The mathematical equations of the double hydraulic cylinders are formulated with bulk-cavity-node method in MATLAB/Simulink. The collaborative synchronization control is analyzed and the compound algorithm of collaborative synchronization control and pulse width-frequency modulation is simulated compared with collaborative synchronization control. The hydraulic loop is set up to verify the simulation result with the proposed control algorithm on the FESTO platform. The research finds that (1) the pulse width-frequency modulation control can be linear to the flow of high-speed on-off valve on the duty bound of 0 similar to 100%;(2) the collaborative synchronization control is effective to eliminate the displacement error between the double cylinders which results from the different load environment and other disturbance.
Large datasets can be screened for sequences from a specific organism, quickly and with low memory requirements, by a data structure that supports time-and memory-efficient set membership queries. Bloom filters offer ...
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Large datasets can be screened for sequences from a specific organism, quickly and with low memory requirements, by a data structure that supports time-and memory-efficient set membership queries. Bloom filters offer such queries but require that false positives be controlled. We present BioBloom Tools, a Bloom filter-based sequence-screening tool that is faster than BWA, Bowtie 2 (popular alignment algorithms) and FACS (a membership query algorithm). It delivers accuracies comparable with these tools, controls false positives and has low memory requirements.
A Composite Finite Element method approximates linear elliptic boundary value problems with discontinuous diffusion coefficient at possibly high contrast. The discontinuity appears at some interface that is not necess...
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A Composite Finite Element method approximates linear elliptic boundary value problems with discontinuous diffusion coefficient at possibly high contrast. The discontinuity appears at some interface that is not necessarily resolved by the underlying finite element mesh. The method is non-conforming in the sense that shape functions preserve continuity across the interface in only an approximate way. However, the method allows balancing this non-conformity error and the error of the best approximation in such a way that the total discretization error (in energy norm) decreases linear with regard to the mesh size and independent of contrast.
Motivation: Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed...
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Motivation: Although R packages exist for the pre-processing of metabolomic data, they currently do not incorporate additional analysis steps of summarization, filtering and normalization of aligned data. We developed the MSPrep R package to complement other packages by providing these additional steps, implementing a selection of popular normalization algorithms and generating diagnostics to help guide investigators in their analyses.
Motivation: Studies of genomic DNA copy number alteration can deal with datasets with several million probes and thousands of subjects. Analyzing these data with currently available software (e.g. as available from Bi...
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Motivation: Studies of genomic DNA copy number alteration can deal with datasets with several million probes and thousands of subjects. Analyzing these data with currently available software (e.g. as available from BioConductor) can be extremely slow and may not be feasible because of memory requirements. Results: We have developed a BioConductor package, ADaCGH2, that parallelizes the main segmentation algorithms (using forking on multicore computers or parallelization via message passing interface, etc., in clusters of computers) and uses ff objects for reading and data storage. We show examples of data with 6 million probes per array;we can analyze data that would otherwise not fit in memory, and compared with the non-parallelized versions we can achieve speed-ups of 25-40 times on a 64-cores machine.
We study the standard one-component phi(4)(4)-theory in four dimensions. A renormalized coupling is defined in a finite size renormalization scheme which becomes the standard scheme of the broken phase for large volum...
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We study the standard one-component phi(4)(4)-theory in four dimensions. A renormalized coupling is defined in a finite size renormalization scheme which becomes the standard scheme of the broken phase for large volumes. Numerical simulations are reported using the worm algorithm in the limit of infinite bare coupling. The cutoff dependence of the renormalized coupling closely follows the perturbative Callan Symanzik equation and the triviality scenario is hence further supported. (C) 2014 The Authors. Published by Elsevier B.V.
We study the complexity of deciding the equality of streams specified by systems of equations. There are several notions of stream models in the literature, each generating a different semantics of stream equality. We...
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We study the complexity of deciding the equality of streams specified by systems of equations. There are several notions of stream models in the literature, each generating a different semantics of stream equality. We pinpoint the complexity of each of these notions in the arithmetical or analytical hierarchy. Their complexity ranges from low levels of the arithmetical hierarchy such Pi(0)(2) for the most relaxed stream models, to levels of the analytical hierarchy such as Pi(1)(1) and up to subsuming the entire analytical hierarchy for more restrictive but natural stream models. Since all these classes properly include both the semi-decidable and co-semi-decidable classes, it follows that regardless of the stream semantics employed, there is no complete proof system or algorithm for determining equality or inequality of streams. We also discuss several related problems, such as the existence and uniqueness of stream solutions for systems of equations, as well as the equality of such solutions.
We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some. lag time) patterns over time. After discretizing time so there is at ...
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We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some. lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of Is (spike) and Os (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows. The nonparametric component (i.e., the gaussian process model) provides a flexible framework for modeling the underlying firing rates, and the parametric component (i.e., the copula model) allows us to make inferences regarding both contemporaneous and lagged relationships among neurons. Using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of a dependence structure among variables. Our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high-dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas.
The software defined environment (SDE) provides a powerful programmable interface to a cloud infrastructure through an abstraction of compute, network, and storage resources. A workload refers to the application to be...
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The software defined environment (SDE) provides a powerful programmable interface to a cloud infrastructure through an abstraction of compute, network, and storage resources. A workload refers to the application to be deployed in such an infrastructure. To take advantage of the SDE interface, the workload is described using a declarative workload definition language and is then deployed in the infrastructure through an automated workload orchestration and optimization layer. This paper describes the architecture and algorithms that make up this layer. Given a definition of the workload, including the virtual components of the application and their resource needs, as well as other meta-information relating to factors such as performance, availability, and privacy, the function of the workload orchestration and optimization layer is to map virtual resources to physical resources and realize such a mapping in the infrastructure. This mapping, known as placement, is optimized so that the infrastructure is efficiently utilized, and the workload requirements are satisfied. We present the overall architecture of the workload orchestration and optimization runtime. We focus on the workload placement problem and describe our optimization framework. Then, we consider a real application, IBM Connections, as a use-case to demonstrate the orchestration and optimization functionalities.
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