cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4479467
title: Real-space phase field investigation of evolving magnetic domains and twin structures in a ferromagnetic shape memory alloy

description: A real-space phase field model combining Landau–Lifshitz–Gilbert equationand time-dependent Ginzburg–Landau equation is developed to investigate the evolution of ferromagnetic domains and martensitic twin structures in aferromagnetic shape memory alloy at different lengthscales. Both domain and twin structures are obtained by simultaneously solving for minimization of magnetic, elastic, and magnetoelastic coupling energy terms via a nonlinear finite element method. The model is applied to simulate magneto-structural evolution within a nanoparticle and a bulk single-crystal of the alloy Ni2MnGa, which are subjected to mechanical strains. It is shown that a nanoparticlecontains magnetic vortex structures within a single twin variant, whereas for a bulk crystal both 90° and 180° domain structures are present within multiple twin variants.
DOI: 10.6084/m9.figshare.4479443
title: miRNA-seq - Differential Expression Analysis

description: miRNA-seq differential expression analysis results.
DOI: 10.6084/m9.figshare.4479440
title: RNA-seq - Differential Expression Analysis Results

description: RNA-seq differential expression analysis results of a portion of HCC patients, available from TCGA. This results is a part of my unpublished doctoral thesis.
DOI: 10.6084/m9.figshare.4479470.v2
title: Reanalyzed Cardisoma carnifex results

description: This analyzes the data on <i>C. carnifex</i>. It assesses the population density in each habitat using a Wilcoxon Rank Sum test. A general linear model was used to analyze the crabs's food preferences. The variables analyzed consisted of habitat, population density, type of food, and mangrove vs. non-mangrove food. The GLM also looked for interactions between habitat and population density, as well as type of food and treatment.
DOI: 10.6084/m9.figshare.4479440.v1
title: RNA-seq - Differential Expression Analysis Results

description: RNA-seq differential expression analysis results of a portion of HCC patients, available from TCGA. This results is a part of my unpublished doctoral thesis.
DOI: 10.6084/m9.figshare.4479467.v2
title: Real-space phase field investigation of evolving magnetic domains and twin structures in a ferromagnetic shape memory alloy

description: A real-space phase field model combining Landau–Lifshitz–Gilbert equationand time-dependent Ginzburg–Landau equation is developed to investigate the evolution of ferromagnetic domains and martensitic twin structures in aferromagnetic shape memory alloy at different lengthscales. Both domain and twin structures are obtained by simultaneously solving for minimization of magnetic, elastic, and magnetoelastic coupling energy terms via a nonlinear finite element method. The model is applied to simulate magneto-structural evolution within a nanoparticle and a bulk single-crystal of the alloy Ni2MnGa, which are subjected to mechanical strains. It is shown that a nanoparticlecontains magnetic vortex structures within a single twin variant, whereas for a bulk crystal both 90° and 180° domain structures are present within multiple twin variants.
DOI: 10.6084/m9.figshare.4479443.v1
title: miRNA-seq - Differential Expression Analysis

description: miRNA-seq differential expression analysis results.
DOI: 10.6084/m9.figshare.4479431
title: COMPARATIVE STUDY OF HERMETIC AND NON-HERMETIC STORAGE ON QUALITY OF COWPEA IN GHANA

description: The data presented is a comparative study of hermetic and Non-hermetic storage on quality of cowpea in Ghana. Measured parameters include: Moisture content, Insect infestation, 1000 grain mass and Usable proportion of grains after 12 weeks of storage
DOI: 10.6084/m9.figshare.4479434
title: Problematic social media use: Results from a large-scale nationally representative adolescent sample

description: This is the data base for the paper called 'Problematic social media use: Results from a large-scale nationally representative adolescent sample' submitted in PlosONE.<br>
DOI: 10.6084/m9.figshare.4479431.v1
title: COMPARATIVE STUDY OF HERMETIC AND NON-HERMETIC STORAGE ON QUALITY OF COWPEA IN GHANA

description: The data presented is a comparative study of hermetic and Non-hermetic storage on quality of cowpea in Ghana. Measured parameters include: Moisture content, Insect infestation, 1000 grain mass and Usable proportion of grains after 12 weeks of storage
DOI: 10.6084/m9.figshare.4479425
title: Kalahari vulture declines, through the eyes of meerkats<sup>§</sup>

description: Vulture populations are experiencing rapid declines across the globe. While the declines have been most precipitous in Asia, recent reports suggest African populations are likewise imminently threatened. As the factors underlying these general population trends are multifaceted and will vary in their relative intensity spatially, it is imperative that monitoring data across different vulture populations is assimilated if targeted conservation action is to prove most effective. In this study, we highlight a medium-term decline in the African White-backed Vulture <i>Gyps africanus</i> population inhabiting the southern Kalahari, South Africa, using a long-term behavioural data set collected from a habituated population of meerkats <i>Suricata suricatta</i>. Meerkats emit an alarm call on sighting airborne vultures, which elicits a group-level response, such that the rates at which this behaviour is recorded in meerkats provides a high-resolution proxy for local vulture abundance. Although unconventional, this sampling method uncovered a steady decline over 17 years in White-backed Vulture numbers that mirrors the temporal decline recently documented in other southern African populations.
DOI: 10.6084/m9.figshare.4479434.v1
title: SNSdatasetENG_161217.sav

description: This is the data base for the paper called 'Problematic social media use: Results from a large-scale nationally representative adolescent sample' submitted in PlosONE.<br>
DOI: 10.6084/m9.figshare.4479425.v2
title: Kalahari vulture declines, through the eyes of meerkats<sup>§</sup>

description: Vulture populations are experiencing rapid declines across the globe. While the declines have been most precipitous in Asia, recent reports suggest African populations are likewise imminently threatened. As the factors underlying these general population trends are multifaceted and will vary in their relative intensity spatially, it is imperative that monitoring data across different vulture populations is assimilated if targeted conservation action is to prove most effective. In this study, we highlight a medium-term decline in the African White-backed Vulture <i>Gyps africanus</i> population inhabiting the southern Kalahari, South Africa, using a long-term behavioural data set collected from a habituated population of meerkats <i>Suricata suricatta</i>. Meerkats emit an alarm call on sighting airborne vultures, which elicits a group-level response, such that the rates at which this behaviour is recorded in meerkats provides a high-resolution proxy for local vulture abundance. Although unconventional, this sampling method uncovered a steady decline over 17 years in White-backed Vulture numbers that mirrors the temporal decline recently documented in other southern African populations.
DOI: 10.6084/m9.figshare.4479413
title: Motoneuron activity and locomotor modules in human locomotion

description: The file contains EMG and force data for each subject.
DOI: 10.6084/m9.figshare.4479413.v1
title: Motoneuron activity and locomotor modules in human locomotion

description: The file contains EMG and force data for each subject.
DOI: 10.6084/m9.figshare.4479269
title: Raw data.xls

description: dsDNA autoantbody reference intervals
DOI: 10.6084/m9.figshare.4479269.v1
title: Raw data.xls

description: dsDNA autoantbody reference intervals
DOI: 10.6084/m9.figshare.4478408
title: Conditional Modeling of Longitudinal Data With Terminal Event

description: We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straightforward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478408.v2
title: Conditional Modeling of Longitudinal Data With Terminal Event

description: We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straightforward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478399
title: Finding Common Modules in a Time-Varying Network with Application to the <i>Drosophila Melanogaster</i> Gene Regulation Network

description: Finding functional modules in gene regulation networks is an important task in systems biology. Many methods have been proposed for finding communities in static networks; however, the application of such methods is limited due to the dynamic nature of gene regulation networks. In this article, we first propose a statistical framework for detecting common modules in the <i>Drosophila melanogaster</i> time-varying gene regulation network. We then develop both a significance test and a robustness test for the identified modular structure. We apply an enrichment analysis to our community findings, which reveals interesting results. Moreover, we investigate the consistency property of our proposed method under a time-varying stochastic block model framework with a temporal correlation structure. Although we focus on gene regulation networks in our work, our method is general and can be applied to other time-varying networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478405.v1
title: Error Variance Estimation in Ultrahigh Dimensional Additive Models

description: Error variance estimation plays an important role in statistical inference for high dimensional regression models. This paper concerns with error variance estimation in high dimensional sparse additive model. We study the asymptotic behavior of the traditional mean squared errors, the naive estimate of error variance, and show that it may significantly underestimate the error variance due to spurious correlations which are even higher in nonparametric models than linear models. We further propose an accurate estimate for error variance in ultrahigh dimensional sparse additive model by effectively integrating sure independence screening and refitted cross-validation techniques (Fan, Guo and Hao, 2012). The root <i>n</i> consistency and the asymptotic normality of the resulting estimate are established. We conduct Monte Carlo simulation study to examine the finite sample performance of the newly proposed estimate. A real data example is used to illustrate the proposed methodology.
DOI: 10.6084/m9.figshare.4478408.v1
title: Conditional modeling of longitudinal data with terminal event

description: We consider a random effects model for longitudinal data with the occurrence of an informative terminal event that is subject to right censoring. Existing methods for analyzing such data include the joint modeling approach using latent frailty and the marginal estimating equation approach using inverse probability weighting; in both cases the effect of the terminal event on the response variable is not explicit and thus not easily interpreted. In contrast, we treat the terminal event time as a covariate in a conditional model for the longitudinal data, which provides a straightforward interpretation while keeping the usual relationship of interest between the longitudinally measured response variable and covariates for times that are far from the terminal event. A two-stage semiparametric likelihood-based approach is proposed for estimating the regression parameters; first, the conditional distribution of the right-censored terminal event time given other covariates is estimated and then the likelihood function for the longitudinal event given the terminal event and other regression parameters is maximized. The method is illustrated by numerical simulations and by analyzing medical cost data for patients with end-stage renal disease. Desirable asymptotic properties are provided.
DOI: 10.6084/m9.figshare.4478399.v2
title: Finding Common Modules in a Time-Varying Network with Application to the <i>Drosophila Melanogaster</i> Gene Regulation Network

description: Finding functional modules in gene regulation networks is an important task in systems biology. Many methods have been proposed for finding communities in static networks; however, the application of such methods is limited due to the dynamic nature of gene regulation networks. In this article, we first propose a statistical framework for detecting common modules in the <i>Drosophila melanogaster</i> time-varying gene regulation network. We then develop both a significance test and a robustness test for the identified modular structure. We apply an enrichment analysis to our community findings, which reveals interesting results. Moreover, we investigate the consistency property of our proposed method under a time-varying stochastic block model framework with a temporal correlation structure. Although we focus on gene regulation networks in our work, our method is general and can be applied to other time-varying networks. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478399.v1
title: Finding Common Modules in a Time-Varying Network with Application to the Drosophila Melanogaster Gene Regulation Network

description: Finding functional modules in gene regulation networks is an important task in systems biology. Many methods have been proposed for finding communities in static networks; however, the application of such methods is limited due to the dynamic nature of gene regulation networks. In this paper, we first propose a statistical framework for detecting common modules in the Drosophila melanogaster time-varying gene regulation network. We then develop both a significance test and a robustness test for the identified modular structure. We apply an enrichment analysis to our community findings, which reveals interesting results. Moreover, we investigate the consistency property of our proposed method under a time-varying stochastic block model framework with a temporal correlation structure. Although we focus on gene regulation networks in our work, our method is general and can be applied to other time-varying networks.
DOI: 10.6084/m9.figshare.4478396.v1
title: The Spike-and-Slab LASSO

description: Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potential for penalized likelihood estimation has largely been overlooked. In this paper, we bridge this gap by cross-fertilizing these two paradigms with the <i>Spike-and-Slab LASSO</i> procedure for variable selection and parameter estimation in linear regression. We introduce a new class of self-adaptive penalty functions that arise from a fully Bayes spike-and-slab formulation, ultimately moving beyond the separable penalty framework. A virtue of these non-separable penalties is their ability to borrow strength across coordinates, adapt to ensemble sparsity information and exert multiplicity adjustment. The <i>Spike-and-Slab LASSO</i> procedure harvests efficient coordinate-wise implementations with a path-following scheme for dynamic posterior exploration. We show on simulated data that the fully Bayes penalty mimics oracle performance, providing a viable alternative to cross-validation. We develop theory for the separable and non-separable variants of the penalty, showing rate-optimality of the global mode as well as optimal posterior concentration when <i>p</i> &gt; <i>n</i>.
DOI: 10.6084/m9.figshare.4478384
title: Mixture Models With a Prior on the Number of Components

description: A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components—that is, to use a mixture of finite mixtures (MFM). The most commonly used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet process mixture (DPM) models that are relatively simple and are easily adapted to new applications. It turns out that, in fact, many of the essential properties of DPMs are also exhibited by MFMs—an exchangeable partition distribution, restaurant process, random measure representation, and stick-breaking representation—and crucially, the MFM analogues are simple enough that they can be used much like the corresponding DPM properties. Consequently, many of the powerful methods developed for inference in DPMs can be directly applied to MFMs as well; this simplifies the implementation of MFMs and can substantially improve mixing. We illustrate with real and simulated data, including high-dimensional gene expression data used to discriminate cancer subtypes. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478384.v2
title: Mixture Models With a Prior on the Number of Components

description: A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components—that is, to use a mixture of finite mixtures (MFM). The most commonly used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet process mixture (DPM) models that are relatively simple and are easily adapted to new applications. It turns out that, in fact, many of the essential properties of DPMs are also exhibited by MFMs—an exchangeable partition distribution, restaurant process, random measure representation, and stick-breaking representation—and crucially, the MFM analogues are simple enough that they can be used much like the corresponding DPM properties. Consequently, many of the powerful methods developed for inference in DPMs can be directly applied to MFMs as well; this simplifies the implementation of MFMs and can substantially improve mixing. We illustrate with real and simulated data, including high-dimensional gene expression data used to discriminate cancer subtypes. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4478384.v1
title: Mixture models with a prior on the number of components

description: A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components—that is, to use a mixture of finite mixtures (MFM). The most commonly-used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet process mixture (DPM) models that are relatively simple and are easily adapted to new applications. It turns out that, in fact, many of the essential properties of DPMs are also exhibited by MFMs—an exchangeable partition distribution, restaurant process, random measure representation, and stick-breaking representation—and crucially, the MFM analogues are simple enough that they can be used much like the corresponding DPM properties. Consequently, many of the powerful methods developed for inference in DPMs can be directly applied to MFMs as well; this simplifies the implementation of MFMs and can substantially improve mixing. We illustrate with real and simulated data, including high-dimensional gene expression data used to discriminate cancer subtypes.
DOI: 10.6084/m9.figshare.4478396.v3
title: The Spike-and-Slab LASSO

description: Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potential for penalized likelihood estimation has largely been overlooked. In this paper, we bridge this gap by cross-fertilizing these two paradigms with the <i>Spike-and-Slab LASSO</i> procedure for variable selection and parameter estimation in linear regression. We introduce a new class of self-adaptive penalty functions that arise from a fully Bayes spike-and-slab formulation, ultimately moving beyond the separable penalty framework. A virtue of these non-separable penalties is their ability to borrow strength across coordinates, adapt to ensemble sparsity information and exert multiplicity adjustment. The <i>Spike-and-Slab LASSO</i> procedure harvests efficient coordinate-wise implementations with a path-following scheme for dynamic posterior exploration. We show on simulated data that the fully Bayes penalty mimics oracle performance, providing a viable alternative to cross-validation. We develop theory for the separable and non-separable variants of the penalty, showing rate-optimality of the global mode as well as optimal posterior concentration when <i>p</i> &gt; <i>n</i>.