The Experts below are selected from a list of 181329 Experts worldwide ranked by ideXlab platform
Soma Trenggana - One of the best experts on this subject based on the ideXlab platform.
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Spatial Modeling of cold lava flood evacuation in kali putih magelang regency using network analyst
International Journal of Environment Agriculture and Biotechnology, 2018Co-Authors: Soma TrengganaAbstract:The event of Mount Merapi's phreatic eruption, at least reminds us that the riverbank of Kali Putih which is located in Magelang Regency, Central Java Province has not been completely free from the threat of cold lava flood, especially during the rainy season. Therefore, a preventive action in relation to cold lava flood mitigation matters needs to be done. As part of the preventive action, a Spatial Modeling was carried out in Kali Putih to get an overview of the number and distribution of affected residential areas, the location and distribution of temporary evacuation sites (TES), the most effective number of final evacuation sites (FES), and various evacuation routes formed. Modeling began by calculating the level of vulnerability of cold lava in Sub Watershed of Kali Putih to get the most vulnerable areas for cold lava flood. 3D analyst and Spatial analyst were used at this stage. The analysis was continued to calculate the number of affected settlements, using vector-based analysis. Furthermore, the determination of the number and distribution of TES, the number and distribution of FES, and determination of evacuation routes were carried out using Network Analyst. From this Spatial Modeling, the following results were obtained: 66 out of 179 residential areas were most likely affected by cold lava flood, 23 temporary evacuation sites (TES), and 7 final evacuation sites (FES), 57 evacuation routes from affected settlements to TES, and 22 evacuation routes from TES to FES
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Spatial Modeling of cold lava flood evacuation in kali putih magelang regency using network analyst
International Journal of Environment Agriculture and Biotechnology, 2018Co-Authors: Soma TrengganaAbstract:The event of Mount Merapi's phreatic eruption, at least reminds us that the riverbank of Kali Putih which is located in Magelang Regency, Central Java Province has not been completely free from the threat of cold lava flood, especially during the rainy season. Therefore, a preventive action in relation to cold lava flood mitigation matters needs to be done. As part of the preventive action, a Spatial Modeling was carried out in Kali Putih to get an overview of the number and distribution of affected residential areas, the location and distribution of temporary evacuation sites (TES), the most effective number of final evacuation sites (FES), and various evacuation routes formed. Modeling began by calculating the level of vulnerability of cold lava in Sub Watershed of Kali Putih to get the most vulnerable areas for cold lava flood. 3D analyst and Spatial analyst were used at this stage. The analysis was continued to calculate the number of affected settlements, using vector-based analysis. Furthermore, the determination of the number and distribution of TES, the number and distribution of FES, and determination of evacuation routes were carried out using Network Analyst. From this Spatial Modeling, the following results were obtained: 66 out of 179 residential areas were most likely affected by cold lava flood, 23 temporary evacuation sites (TES), and 7 final evacuation sites (FES), 57 evacuation routes from affected settlements to TES, and 22 evacuation routes from TES to FES
Elja Arjas - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Spatial Modeling of genetic population structure
Computational Statistics, 2008Co-Authors: Jukka Corander, Jukka Sir??n, Elja ArjasAbstract:Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g., due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to Modeling the genetic structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing Spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently. While the standard Bayesian hierarchical Modeling techniques through Markov chain Monte Carlo simulation provide flexible means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical data analysis. Here we develop a method for Modeling the Spatial genetic structure using a combination of analytical and stochastic methods. We achieve this by extending a novel theory of Bayesian predictive classification with the Spatial information available, described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data sets illustrate well the benefits of incorporating Spatial information to such an analysis.
Clinton D Kilts - One of the best experts on this subject based on the ideXlab platform.
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a bayesian hierarchical framework for Spatial Modeling of fmri data
NeuroImage, 2008Co-Authors: Dubois F Bowman, Brian S Caffo, Susan Spear Bassett, Clinton D KiltsAbstract:Abstract Applications of functional magnetic resonance imaging (fMRI) have provided novel insights into the neuropathophysiology of major psychiatric, neurological, and substance abuse disorders and their treatments. Modern activation studies often compare localized task-induced changes in brain activity between experimental groups. Complementary approaches consider the ensemble of voxels constituting an anatomically defined region of interest (ROI) or summary statistics, such as means or quantiles, of the ROI. In this work, we present a Bayesian extension of voxel-level analyses that offers several notable benefits. Among these, it combines whole-brain voxel-by-voxel Modeling and ROI analyses within a unified framework. Secondly, an unstructured variance/covariance matrix for regional mean parameters allows for the study of inter-regional (long-range) correlations, and the model employs an exchangeable correlation structure to capture intra-regional (short-range) correlations. Estimation is performed using Markov Chain Monte Carlo (MCMC) techniques implemented via Gibbs sampling. We apply our Bayesian hierarchical model to two novel fMRI data sets: one considering inhibitory control in cocaine-dependent men and the second considering verbal memory in subjects at high risk for Alzheimer’s disease.
Jon Wakefield - One of the best experts on this subject based on the ideXlab platform.
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pointless Spatial Modeling
Biostatistics, 2020Co-Authors: Katie Wilson, Jon WakefieldAbstract:The analysis of area-level aggregated summary data is common in many disciplines including epidemiology and the social sciences. Typically, Markov random field Spatial models have been employed to acknowledge Spatial dependence and allow data-driven smoothing. In the context of an irregular set of areas, these models always have an ad hoc element with respect to the definition of a neighborhood scheme. In this article, we exploit recent theoretical and computational advances to carry out Modeling at the continuous Spatial level, which induces a Spatial model for the discrete areas. This approach also allows reconstruction of the continuous underlying surface, but the interpretation of such surfaces is delicate since it depends on the quality, extent and configuration of the observed data. We focus on models based on stochastic partial differential equations. We also consider the interesting case in which the aggregate data are supplemented with point data. We carry out Bayesian inference and, in the language of generalized linear mixed models, if the link is linear, an efficient implementation of the model is available via integrated nested Laplace approximations. For nonlinear links, we present two approaches: a fully Bayesian implementation using a Hamiltonian Monte Carlo algorithm and an empirical Bayes implementation, that is much faster and is based on Laplace approximations. We examine the properties of the approach using simulation, and then apply the model to the classic Scottish lip cancer data.
Walter L Ruzzo - One of the best experts on this subject based on the ideXlab platform.
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Spatial Modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities
bioRxiv, 2019Co-Authors: Yuliang Wang, Walter L RuzzoAbstract:Abstract Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling Spatial heterogeneity in neoplastic metabolic aberrations is critical for tumor treatment. Genome-scale metabolic network models have been used successfully to simulate cancer metabolic networks. However, most models use bulk gene expression data of entire tumor biopsies, ignoring Spatial heterogeneity in the TME. To account for Spatial heterogeneity, we performed Spatially-resolved metabolic network Modeling of the prostate cancer microenvironment. We discovered novel malignant-cell-specific metabolic vulnerabilities targetable by small molecule compounds. We predicted that inhibiting the fatty acid desaturase SCD1 may selectively kill cancer cells based on our discovery of Spatial separation of fatty acid synthesis and desaturation. We also uncovered higher prostaglandin metabolic gene expression in the tumor, relative to the surrounding tissue. Therefore, we predicted that inhibiting the prostaglandin transporter SLCO2A1 may selectively kill cancer cells. Importantly, SCD1 and SLCO2A1 have been previously shown to be potently and selectively inhibited by compounds such as CAY10566 and suramin, respectively. We also uncovered cancer-selective metabolic liabilities in central carbon, amino acid, and lipid metabolism. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer and other cancers where Spatial transcriptomics datasets are available.
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Spatial Modeling of prostate cancer metabolism reveals extensive heterogeneity and selective vulnerabilities
bioRxiv, 2019Co-Authors: Yuliang Wang, Walter L RuzzoAbstract:Abstract Metabolic reprogramming is a hallmark of cancer, and there is an urgent need to exploit metabolic aberrations in cancer to identify perturbations that may selectively kill cancer cells. Spatial heterogeneity is a fundamental feature of the tumor microenvironment (TME), and tackling Spatial heterogeneity is critical for understanding tumor progression and drug resistance. Genome-scale metabolic network models have been used successfully to model multiple cancer types. However, most models are based on bulk gene expression data of entire tumor biopsies, ignoring Spatial heterogeneity in the TME. Prostate cancer is a common malignancy with unique metabolic characteristics. We performed Spatially-resolved metabolic network Modeling of the prostate cancer microenvironment, which revealed extensive Spatial heterogeneity of metabolic gene expression. Specifically, we discovered novel malignant-cell-specific metabolic vulnerabilities in central carbon, lipid and amino acid metabolism that are missed by bulk models of the entire tumor biopsy. In particular, we identified the Spatial separation of fatty acid uptake and synthesis vs. fatty acid oxidation and desaturation, and predicted that prostate cancer cells are more dependent on exogeneous unsaturated fatty acids. Our novel cancer-specific predictions provide new opportunities to develop selective drug targets for prostate cancer treatment.
