cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.963386
title: The ERPs data in the decision phase

description: Mean amplitude were quantified on different electrodes in a 310-340 ms time window for the DRN. Five frontal electrodes sites (FP1/2, F3/4, Fz) were analyzed. The ANOVA were thus conducted on values of amplitude with Trait Empathy being the between-subjects factor (High empathy group, Low empathy group) and Provocation factor (High provocation, Low provocation), and Electrodes (FP1/2, F3/4, Fz) being the within-subjects factors.
DOI: 10.6084/m9.figshare.963387
title: The correlation between trait empathy and trait aggression

description: The correlations between total scores of IRI and total scores of AQ were computed in 413 male undergraduates. The correlations between subscales of IRI and subscales of AQ were respectively computed.
DOI: 10.6084/m9.figshare.963402
title: ERPs in the outcome phase

description: For EEG data in the outcome phase, the FRN, a fronto-central wave of negativity emerging for lost compared to won trials, was analyzed. The amplitude for a time window from 270 ms to 300 ms at electrode Fz were subjected to an ANOVA, in which the between-subjects factor was Trait Empathy (High Empathy Group, Low Empathy Group) and the within-subjects factors were Feedback (Won, Lost) and Provocation (High Provocation, Low Provocation).
DOI: 10.6084/m9.figshare.963474
title: Generalized Dynamic Factor Models for Mixed-Measurement Time Series

description: In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis–Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody’s rated firms from 1982 to 2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplementary materials available online.
DOI: 10.6084/m9.figshare.963474.v1
title: Generalized Dynamic Factor Models for Mixed-Measurement Time Series

description: In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis–Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody’s rated firms from 1982 to 2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplementary materials available online.
DOI: 10.6084/m9.figshare.963474.v3
title: Generalized Dynamic Factor Models for Mixed-Measurement Time Series

description: In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis–Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody’s rated firms from 1982 to 2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplementary materials available online.
DOI: 10.6084/m9.figshare.963474.v2
title: Generalized Dynamic Factor Models for Mixed-Measurement Time Series

description: In this article, we propose generalized Bayesian dynamic factor models for jointly modeling mixed-measurement time series. The framework allows mixed-scale measurements associated with each time series, with different measurements having different distributions in the exponential family conditionally on time-varying latent factor(s). Efficient Bayesian computational algorithms are developed for posterior inference on both the latent factors and model parameters, based on a Metropolis–Hastings algorithm with adaptive proposals. The algorithm relies on a Greedy Density Kernel Approximation and parameter expansion with latent factor normalization. We tested the framework and algorithms in simulated studies and applied them to the analysis of intertwined credit and recovery risk for Moody’s rated firms from 1982 to 2008, illustrating the importance of jointly modeling mixed-measurement time series. The article has supplementary materials available online.
DOI: 10.6084/m9.figshare.963477
title: Massively Parallel Nonparametric Regression, With an Application to Developmental Brain Mapping

description: A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.
DOI: 10.6084/m9.figshare.963477.v1
title: Massively Parallel Nonparametric Regression, With an Application to Developmental Brain Mapping

description: A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.
DOI: 10.6084/m9.figshare.963477.v3
title: Massively Parallel Nonparametric Regression, With an Application to Developmental Brain Mapping

description: A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.
DOI: 10.6084/m9.figshare.963477.v2
title: Massively Parallel Nonparametric Regression, With an Application to Developmental Brain Mapping

description: A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.
DOI: 10.6084/m9.figshare.963487
title: Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

description: We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes’ inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the “Supplementary Materials” section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
DOI: 10.6084/m9.figshare.963487.v1
title: Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

description: We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes’ inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the “Supplementary Materials” section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
DOI: 10.6084/m9.figshare.963487.v2
title: Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

description: We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes’ inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the “Supplementary Materials” section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
DOI: 10.6084/m9.figshare.963487.v3
title: Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection

description: We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes’ inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in the online Appendix (see the “Supplementary Materials” section). We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white-matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
DOI: 10.6084/m9.figshare.963494
title: Diversity and Community Structure of Primary Wood-Inhabiting Bacteria in Boreal Forest

description: DNA-based pyrosequencing analysis of the V1- V3 16S rRNA gene region was used to identify bacteria community and shift during early stages of wood colonization in boreal forest soils. The dataset comprised 142,447 sequences and was affiliated to 11 bacteria phyla, 25 classes and 233 genera. The dominant groups across all samples were <i>Proteobacteria</i>, followed by <i>Bacteroidetes</i>, <i>Acidobacteria</i>, <i>Actinobacteria</i>, <i>Amatimonadetes</i>, <i>Planctomycetes</i> and TM7 group. The community structure of the primary wood-inhabiting bacteria differed between types of forest soils and the composition of bacteria remained stable over prolonged incubation time. The results suggest that variations in soil bacterial community composition have an influence on the wood-inhabiting bacterial structure.
DOI: 10.6084/m9.figshare.963494.v1
title: Diversity and Community Structure of Primary Wood-Inhabiting Bacteria in Boreal Forest

description: DNA-based pyrosequencing analysis of the V1- V3 16S rRNA gene region was used to identify bacteria community and shift during early stages of wood colonization in boreal forest soils. The dataset comprised 142,447 sequences and was affiliated to 11 bacteria phyla, 25 classes and 233 genera. The dominant groups across all samples were <i>Proteobacteria</i>, followed by <i>Bacteroidetes</i>, <i>Acidobacteria</i>, <i>Actinobacteria</i>, <i>Amatimonadetes</i>, <i>Planctomycetes</i> and TM7 group. The community structure of the primary wood-inhabiting bacteria differed between types of forest soils and the composition of bacteria remained stable over prolonged incubation time. The results suggest that variations in soil bacterial community composition have an influence on the wood-inhabiting bacterial structure.
DOI: 10.6084/m9.figshare.963527
title: The first enantiornithine bird from the Upper Cretaceous of China

description: ABSTRACTA new Late Cretaceous avian taxon, <i>Parvavis chuxiongensis</i>, gen. et sp. nov., is reported here based on an incomplete skeleton from Upper Cretaceous lake deposits in Yunnan Province, southern China. A phylogenetic analysis of 32 taxa and 242 morphological characters resulted in three most parsimonious trees, the strict consensus tree of which places <i>Parvavis chuxiongensis</i> within Enantiornithes. Histological study shows that the bones of <i>Parvavis</i> were composed of parallel-fibered bone tissue without lines of arrested growth, and indicated that growth rate had slowed but had not stopped at any stage prior to death. The bones also lack the rough surface texture seen in juvenile birds. Therefore, the new bird was probably close to adult body size at the time of death. However, the specimen is surprisingly small, highlighting the wide range of body sizes in Upper Cretaceous enantiornithines. The new specimen also represents both the first known bird from the Upper Cretaceous of China and the first Mesozoic bird from the south of China, and thus extends the temporal and geographic range of Mesozoic birds in China.
 SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline.com/UJVP
DOI: 10.6084/m9.figshare.963527.v1
title: The first enantiornithine bird from the Upper Cretaceous of China

description: ABSTRACTA new Late Cretaceous avian taxon, <i>Parvavis chuxiongensis</i>, gen. et sp. nov., is reported here based on an incomplete skeleton from Upper Cretaceous lake deposits in Yunnan Province, southern China. A phylogenetic analysis of 32 taxa and 242 morphological characters resulted in three most parsimonious trees, the strict consensus tree of which places <i>Parvavis chuxiongensis</i> within Enantiornithes. Histological study shows that the bones of <i>Parvavis</i> were composed of parallel-fibered bone tissue without lines of arrested growth, and indicated that growth rate had slowed but had not stopped at any stage prior to death. The bones also lack the rough surface texture seen in juvenile birds. Therefore, the new bird was probably close to adult body size at the time of death. However, the specimen is surprisingly small, highlighting the wide range of body sizes in Upper Cretaceous enantiornithines. The new specimen also represents both the first known bird from the Upper Cretaceous of China and the first Mesozoic bird from the south of China, and thus extends the temporal and geographic range of Mesozoic birds in China.
 SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline.com/UJVP
DOI: 10.6084/m9.figshare.978001
title: Downward-Shifting Temperature Range for the Growth of Snow-Bacteria on Glaciers of the Tibetan Plateau

description: Fifty-seven snow-bacteria strains were isolated from the snow of the Zadang and Mengdagangri Glaciers located in the central and southern part of the Tibetan Plateau, respectively. 16S rRNA gene sequence analysis showed that strains isolated from the Zadang Glacier belonged to the Alphaproteobacteria, Gammaproteobacteria, Actinobacteria, Firmicutes and Bacteroidetes, and were dominated by the Firmicutes. Strains from the Mengdagangri Glacier belonged to the Actinobacteria, Alphaproteobacteria and Gammaproteobacteria, and were dominated by the Actinobacteria. Sixty-one percent of the isolates were colored with pigment. Sixty-nine percent of isolates from the Zadang Glacier were psychrotolerants, and there were no psychrophiles. We compared the growth-temperature range of 26 snow-bacteria strains to their closest mesophilic type strains and found that 46% of them had an optimum growth-temperature at or lower than 20°C, and 65% were all able to grow at 0°C. However, only 5.3% of mesophilic strains had optimum growth-temperatures at or lower than 20°C, and 9% could grow at 0°C. Snow-bacteria shift their growth-temperature downward; and doing this, in terms of the minimum and optimum temperatures for growth, might be an important strategy for them to adapt to low temperature after they have been deposited on the glacier. Our results suggested that, in order to adapt from mesophilic environments to a cold habitat, snow-bacteria widen their temperature range for growth, convert from mesophiles to psychrotolerants, but not to psychrophiles. In addition, eight isolates formed pigmented colonies, while their mesophilic counterparts were achromogenic. This helped us to confirm through comparative analysis that pigmented microorganisms were more abundant in high-altitude glaciers than in mesophilic environments.
DOI: 10.6084/m9.figshare.978001.v1
title: Downward-shifting Temperature Range for the Growth of Snow-bacteria on Glaciers of the Tibetan Plateau

description: Fifty-seven snow-bacteria strains were isolated from the snow of the Zadang and Mengdagangri Glaciers located in the central and southern part of the Tibetan Plateau, respectively. 16S rRNA gene sequence analysis showed that strains isolated from the Zadang Glacier belonged to the Alphaproteobacteria, Gammaproteobacteria, Actinobacteria, Firmicutes and Bacteroidetes, and were dominated by the Firmicutes. Strains from the Mengdagangri Glacier belonged to the Actinobacteria, Alphaproteobacteria and Gammaproteobacteria, and were dominated by the Actinobacteria. Sixty-one percent of the isolates were colored with pigment. Sixty-nine percent of isolates from the Zadang Glacier were psychrotolerants, and there were no psychrophiles. We compared the growth-temperature range of 26 snow-bacteria strains to their closest mesophilic type strains and found that 46% of them had an optimum growth-temperature at or lower than 20°C, and 65% were all able to grow at 0°C. However, only 5.3% of mesophilic strains had optimum growth-temperatures at or lower than 20°C, and 9% could grow at 0°C. Snow-bacteria shift their growth-temperature downward; and doing this, in terms of the minimum and optimum temperatures for growth, might be an important strategy for them to adapt to low temperature after they have been deposited on the glacier. Our results suggested that, in order to adapt from mesophilic environments to a cold habitat, snow-bacteria widen their temperature range for growth, convert from mesophiles to psychrotolerants, but not to psychrophiles. In addition, eight isolates formed pigmented colonies, while their mesophilc counterparts were achromogenic. This helped us to confirm through comparative analysis that pigmented microorganisms were more abundant in high-altitude glaciers than in mesophilic environments.