The Experts below are selected from a list of 40350 Experts worldwide ranked by ideXlab platform
Yutaka Osada - One of the best experts on this subject based on the ideXlab platform.
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estimating range expansion of wildlife in heterogeneous landscapes a spatially explicit State Space Matrix model coupled with an improved numerical integration technique
Ecology and Evolution, 2018Co-Authors: Yutaka Osada, Takeo Kuriyama, Masahiko Asada, Hiroyuki Yokomizo, Tadashi MiyashitaAbstract:Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial-temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit State-Space Matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion, characteristic of animals with short dispersal. Our model framework successfully reproduced the complex population dynamics of sika deer, including rapid changes in densely populated areas and distribution fronts within a decade. Furthermore, our results revealed that the inhomogeneous range expansion of sika deer seemed to be primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth. Our State-Space Matrix model enables the inference of population dynamics for a broad range of organisms, even those with low dispersal ability, in heterogeneous landscapes, and could address many pressing issues in conservation biology and ecosystem management.
Tadashi Miyashita - One of the best experts on this subject based on the ideXlab platform.
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estimating range expansion of wildlife in heterogeneous landscapes a spatially explicit State Space Matrix model coupled with an improved numerical integration technique
Ecology and Evolution, 2018Co-Authors: Yutaka Osada, Takeo Kuriyama, Masahiko Asada, Hiroyuki Yokomizo, Tadashi MiyashitaAbstract:Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial-temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit State-Space Matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion, characteristic of animals with short dispersal. Our model framework successfully reproduced the complex population dynamics of sika deer, including rapid changes in densely populated areas and distribution fronts within a decade. Furthermore, our results revealed that the inhomogeneous range expansion of sika deer seemed to be primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth. Our State-Space Matrix model enables the inference of population dynamics for a broad range of organisms, even those with low dispersal ability, in heterogeneous landscapes, and could address many pressing issues in conservation biology and ecosystem management.
Takeo Kuriyama - One of the best experts on this subject based on the ideXlab platform.
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estimating range expansion of wildlife in heterogeneous landscapes a spatially explicit State Space Matrix model coupled with an improved numerical integration technique
Ecology and Evolution, 2018Co-Authors: Yutaka Osada, Takeo Kuriyama, Masahiko Asada, Hiroyuki Yokomizo, Tadashi MiyashitaAbstract:Dispersal as well as population growth is a key demographic process that determines population dynamics. However, determining the effects of environmental covariates on dispersal from spatial-temporal abundance proxy data is challenging owing to the complexity of model specification for directional dispersal permeability and the extremely high computational loads for numerical integration. In this paper, we present a case study estimating how environmental covariates affect the dispersal of Japanese sika deer by developing a spatially explicit State-Space Matrix model coupled with an improved numerical integration technique (Markov chain Monte Carlo with particle filters). In particular, we explored the environmental drivers of inhomogeneous range expansion, characteristic of animals with short dispersal. Our model framework successfully reproduced the complex population dynamics of sika deer, including rapid changes in densely populated areas and distribution fronts within a decade. Furthermore, our results revealed that the inhomogeneous range expansion of sika deer seemed to be primarily caused by the dispersal process (i.e., movement barriers in fragmented forests) rather than population growth. Our State-Space Matrix model enables the inference of population dynamics for a broad range of organisms, even those with low dispersal ability, in heterogeneous landscapes, and could address many pressing issues in conservation biology and ecosystem management.
Sunduz Keles - One of the best experts on this subject based on the ideXlab platform.
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a mad bayes algorithm for State Space inference and clustering with application to querying large collections of chip seq data sets
Journal of Computational Biology, 2017Co-Authors: Chandler Zuo, Kailei Chen, Sunduz KelesAbstract:Abstract Current analytic approaches for querying large collections of chromatin immunoprecipitation followed by sequencing (ChIP-seq) data from multiple cell types rely on individual analysis of each data set (i.e., peak calling) independently. This approach discards the fact that functional elements are frequently shared among related cell types and leads to overestimation of the extent of divergence between different ChIP-seq samples. Methods geared toward multisample investigations have limited applicability in settings that aim to integrate 100s to 1000s of ChIP-seq data sets for query loci (e.g., thousands of genomic loci with a specific binding site). Recently, Zuo et al. developed a hierarchical framework for State-Space Matrix inference and clustering, named MBASIC, to enable joint analysis of user-specified loci across multiple ChIP-seq data sets. Although this versatile framework estimates both the underlying State-Space (e.g., bound vs. unbound) and also groups loci with similar patterns toget...
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a hierarchical framework for State Space Matrix inference and clustering
The Annals of Applied Statistics, 2016Co-Authors: Chandler Zuo, Kailei Chen, Kyle J Hewitt, Emery H Bresnick, Sunduz KelesAbstract:In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden State of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred States. Although there are a number of methods tailored for specific datasets, there is currently no State-of-the-art modeling framework for this general class of problems. In this paper, we develop the MBASIC (Matrix Based Analysis for State-Space Inference and Clustering) framework. MBASIC consists of two parts: State-Space mapping and State-Space clustering. In State-Space mapping, it maps observations onto a finite State-Space, representing the activation States of units across conditions. In State-Space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred State-Space profiles across all conditions. Both the State-Space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. It allows for imposing structural assumptions on each cluster, and enables model selection using information criterion. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying State-Space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. The first application grouped genes based on transcription factor occupancy profiles of their promoter regions in two different cell types. The second application focused on identifying groups of loci that are similar to a GATA2 binding site that is functional at its endogenous locus by utilizing transcription factor occupancy data and illustrated applicability of MBASIC in a wide variety of problems. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.
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a mad bayes algorithm for State Space inference and clustering with application to querying large collections of chip seq data sets
Research in Computational Molecular Biology, 2016Co-Authors: Chandler Zuo, Kailei Chen, Sunduz KelesAbstract:Current analytic approaches for querying large collections of chromatin immunoprecipitation followed by sequencing (ChIP-seq) data from multiple cell types rely on individual analysis of each dataset (i.e., peak calling) independently. This approach discards the fact that functional elements are frequently shared among related cell types and leads to overestimation of the extent of divergence between different ChIP-seq samples. Methods geared towards multi-sample investigations have limited applicability in settings that aim to integrate 100s to 1000s of ChIP-seq datasets for query loci (e.g., thousands of genomic loci with a specific binding site). Recently, [1] developed a hierarchical framework for State-Space Matrix inference and clustering, named MBASIC, to enable joint analysis of user-specified loci across multiple ChIP-seq datasets. Although this versatile framework both estimates the underlying State-Space (e.g., bound vs. unbound) and also groups loci with similar patterns together, its Expectation-Maximization based estimation structure hinders its applicability with large numbers of loci and samples. We address this limitation by developing a MAP-based Asymptotic Derivations from Bayes (MAD-Bayes) framework for MBASIC. This results in a K-means-like optimization algorithm which converges rapidly and hence enables exploring multiple initialization schemes and flexibility in tuning. Comparisons with MBASIC indicates that this speed comes at a relatively insignificant loss in estimation accuracy. Although MAD-Bayes MBASIC is specifically designed for the analysis of user-specified loci, it is able to capture overall patterns of histone marks from multiple ChIP-seq datasets similar to those identified by genome-wide segmentation methods such as ChromHMM and Spectacle.
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a hierarchical framework for State Space Matrix inference and clustering
arXiv: Methodology, 2015Co-Authors: Chandler Zuo, Kailei Chen, Kyle J Hewitt, Emery H Bresnick, Sunduz KelesAbstract:In recent years, a large number of genomic and epigenomic studies have been focusing on the integrative analysis of multiple experimental datasets measured over a large number of observational units. The objectives of such studies include not only inferring a hidden State of activity for each unit over individual experiments, but also detecting highly associated clusters of units based on their inferred States. In this paper, we develop the MBASIC (Matrix Based Analysis for State-Space Inference and Clustering) framework. MBASIC consists of two parts: State-Space mapping and State-Space clustering. In State-Space mapping, it maps observations onto a finite State-Space, representing the activation States of units across conditions. In State-Space clustering, MBASIC incorporates a finite mixture model to cluster the units based on their inferred State-Space profiles across all conditions. Both the State-Space mapping and clustering can be simultaneously estimated through an Expectation-Maximization algorithm. MBASIC flexibly adapts to a large number of parametric distributions for the observed data, as well as the heterogeneity in replicate experiments. In our data-driven simulation studies, MBASIC showed significant accuracy in recovering both the underlying State-Space variables and clustering structures. We applied MBASIC to two genome research problems using large numbers of datasets from the ENCODE project. In both studies, MBASIC showed higher levels of raw data fidelity than analyzing these data with a two-step approach using ENCODE results on transcription factor occupancy data.
D G Holmes - One of the best experts on this subject based on the ideXlab platform.
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analytical modelling of voltage balance dynamics for a flying capacitor multilevel converter
IEEE Transactions on Power Electronics, 2008Co-Authors: B P Mcgrath, D G HolmesAbstract:This paper presents a strategy for the analytic determination of the natural voltage balancing dynamics of flying capacitor converters. The approach substitutes double Fourier series representations of the pulsewidth modulation (PWM) switching signals into a nonlinear dynamic circuit model of the converter. The result reduces to a linearized State-Space model that can be readily solved, with the Fourier solution coefficients defining the State-Space Matrix terms. The solution can be readily developed for converters of any level, and allows rapid analytical investigation of the dynamic (and static) balancing behavior over a wide range of conditions. Furthermore, the approach allows powerful strategies such as root locus to be used to investigate the converter's performance as a function of changes in parameters such as modulation index and load. The analysis approach has been fully verified by comparing it against experimental results on a low voltage prototype converter.
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analytical determination of the capacitor voltage balancing dynamics for three phase flying capacitor converters
IEEE Industry Applications Society Annual Meeting, 2007Co-Authors: B P Mcgrath, D G HolmesAbstract:This paper presents a new strategy for the analytical determination of the natural voltage balancing dynamics of three-phase flying capacitor converters. The approach substitutes double Fourier series representations of the pulsewidth modulation switching signals into a nonlinear transient circuit model of the three-phase converter. This results in a linearized State Space model with the Fourier coefficients of the modulation strategy defining the State Space Matrix terms. The State Space model can be readily developed for converters of any level and allows for the rapid analytical investigation of the dynamic (and static) balancing behavior over a wide range of operating conditions. Furthermore, the approach allows powerful linear analysis strategies such as root-locus methods to be used to investigate the converter performance as a function of changes in parameters, such as modulation index and load. The analysis approach has been fully verified by comparison with experimental results obtained on a low-voltage three-phase prototype converter.
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analytical modelling of voltage balance dynamics for a flying capacitor multilevel converter
Power Electronics Specialists Conference, 2007Co-Authors: B P Mcgrath, D G HolmesAbstract:This paper presents a strategy for the analytic determination of the natural voltage balancing dynamics of flying capacitor converters. The approach substitutes Double Fourier series representations of the PWM switching signals into a nonlinear dynamic circuit model of the converter. The result reduces to a linearised State Space model that can be readily solved, with the Fourier solution coefficients defining the State Space Matrix terms. The solution can be readily developed for converters of any level, and allows rapid analytical investigation of the dynamic (and static) balancing behaviour over a wide range of conditions. Furthermore, the approach allows powerful strategies such as root locus to be used to investigate the converter's performance as a function of changes in parameters such as modulation index and load. The analysis approach has been fully verified by comparing it against experimental results on a low voltage prototype converter.