cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1390624.v1
title: Automated Multi-Lesion Detection for Referable Diabetic Retinopathy in Indigenous Health Care

description: Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.
DOI: 10.6084/m9.figshare.1390608
title: A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial

description: We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data distribution as the marginalization over the missing data in the working model. We then separately specify the conditional distribution of the missing data given the observed data and dropout. This approach allows us to identify the distribution of the missing data using identifying restrictions as a starting point. We propose a framework for introducing sensitivity parameters, allowing us to vary the untestable assumptions about the missing data mechanism smoothly. Informative priors on the space of missing data assumptions can be specified to combine inferences under many different assumptions into a final inference and accurately characterize uncertainty. These methods are motivated by, and applied to, data from a clinical trial assessing the efficacy of a new treatment for acute schizophrenia. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390609.v1
title: Homogeneity Pursuit

description: This article explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates are expected to be approximately the same. Sparsity corresponds to a special case of homogeneity with a large cluster of known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional structures in high-dimensional regression: homogeneity and sparsity. Our results also shed lights on the properties of the fused Lasso. The newly developed method is further illustrated by simulation studies and applications to real data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390606
title: Tuning Parameter Selection for the Adaptive Lasso Using ERIC

description: The adaptive Lasso is a commonly applied penalty for variable selection in regression modeling. Like all penalties though, its performance depends critically on the choice of the tuning parameter. One method for choosing the tuning parameter is via information criteria, such as those based on AIC and BIC. However, these criteria were developed for use with unpenalized maximum likelihood estimators, and it is not clear that they take into account the effects of penalization. In this article, we propose the extended regularized information criterion (ERIC) for choosing the tuning parameter in adaptive Lasso regression. ERIC extends the BIC to account for the effect of applying the adaptive Lasso on the bias-variance tradeoff. This leads to a criterion whose penalty for model complexity is itself a function of the tuning parameter. We show the tuning parameter chosen by ERIC is selection consistent when the number of variables grows with sample size, and that this consistency holds in a wider range of contexts compared to using BIC to choose the tuning parameter. Simulation show that ERIC can significantly outperform BIC and other information criteria proposed (for choosing the tuning parameter) in selecting the true model. For ultra high-dimensional data (<i>p</i> &gt; <i>n</i>), we consider a two-stage approach combining sure independence screening with adaptive Lasso regression using ERIC, which is selection consistent and performs strongly in simulation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390608.v1
title: A Flexible Bayesian Approach to Monotone Missing Data in Longitudinal Studies With Nonignorable Missingness With Application to an Acute Schizophrenia Clinical Trial

description: We develop a Bayesian nonparametric model for a longitudinal response in the presence of nonignorable missing data. Our general approach is to first specify a working model that flexibly models the missingness and full outcome processes jointly. We specify a Dirichlet process mixture of missing at random (MAR) models as a prior on the joint distribution of the working model. This aspect of the model governs the fit of the observed data by modeling the observed data distribution as the marginalization over the missing data in the working model. We then separately specify the conditional distribution of the missing data given the observed data and dropout. This approach allows us to identify the distribution of the missing data using identifying restrictions as a starting point. We propose a framework for introducing sensitivity parameters, allowing us to vary the untestable assumptions about the missing data mechanism smoothly. Informative priors on the space of missing data assumptions can be specified to combine inferences under many different assumptions into a final inference and accurately characterize uncertainty. These methods are motivated by, and applied to, data from a clinical trial assessing the efficacy of a new treatment for acute schizophrenia. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390606.v1
title: Tuning Parameter Selection for the Adaptive Lasso Using ERIC

description: The adaptive Lasso is a commonly applied penalty for variable selection in regression modeling. Like all penalties though, its performance depends critically on the choice of the tuning parameter. One method for choosing the tuning parameter is via information criteria, such as those based on AIC and BIC. However, these criteria were developed for use with unpenalized maximum likelihood estimators, and it is not clear that they take into account the effects of penalization. In this article, we propose the extended regularized information criterion (ERIC) for choosing the tuning parameter in adaptive Lasso regression. ERIC extends the BIC to account for the effect of applying the adaptive Lasso on the bias-variance tradeoff. This leads to a criterion whose penalty for model complexity is itself a function of the tuning parameter. We show the tuning parameter chosen by ERIC is selection consistent when the number of variables grows with sample size, and that this consistency holds in a wider range of contexts compared to using BIC to choose the tuning parameter. Simulation show that ERIC can significantly outperform BIC and other information criteria proposed (for choosing the tuning parameter) in selecting the true model. For ultra high-dimensional data (<i>p</i> &gt; <i>n</i>), we consider a two-stage approach combining sure independence screening with adaptive Lasso regression using ERIC, which is selection consistent and performs strongly in simulation. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390602
title: Semiparametric Relative-Risk Regression for Infectious Disease Transmission Data

description: This article introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from <i>i</i> to <i>j</i> consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for <i>i</i>, susceptibliity covariates for <i>j</i>, and pairwise covariates. When who-infects-whom is observed, we derive a profile likelihood maximized over all possible baseline hazard functions that is similar to the Cox partial likelihood. When who-infects-whom is not observed, we derive an EM algorithm to maximize the profile likelihood integrated over all possible combinations of who-infected-whom. This extends the most important class of regression models in survival analysis to infectious disease epidemiology. These methods can be implemented in standard statistical software, and they will be able to address important scientific questions about emerging infectious diseases with greater clarity, flexibility, and rigor than current statistical methods allow. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390602.v1
title: Semiparametric Relative-Risk Regression for Infectious Disease Transmission Data

description: This article introduces semiparametric relative-risk regression models for infectious disease data. The units of analysis in these models are pairs of individuals at risk of transmission. The hazard of infectious contact from <i>i</i> to <i>j</i> consists of a baseline hazard multiplied by a relative risk function that can be a function of infectiousness covariates for <i>i</i>, susceptibliity covariates for <i>j</i>, and pairwise covariates. When who-infects-whom is observed, we derive a profile likelihood maximized over all possible baseline hazard functions that is similar to the Cox partial likelihood. When who-infects-whom is not observed, we derive an EM algorithm to maximize the profile likelihood integrated over all possible combinations of who-infected-whom. This extends the most important class of regression models in survival analysis to infectious disease epidemiology. These methods can be implemented in standard statistical software, and they will be able to address important scientific questions about emerging infectious diseases with greater clarity, flexibility, and rigor than current statistical methods allow. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1390515
title: IAT_DATA

description: This excel file contains the data of the IAT (Experiment 1)
DOI: 10.6084/m9.figshare.1390515.v1
title: IAT_DATA

description: This excel file contains the data of the IAT (Experiment 1)
DOI: 10.6084/m9.figshare.1387775
title: Vitamin D levels decline with rising number of cardiometabolic risk factors in healthy adults: association with adipokines, inflammation, oxidative stress and advanced glycation markers

description: Data from a cross sectional study in apparently healthy medication-free non-diabetic adults
DOI: 10.6084/m9.figshare.1390474
title: Mötesplats OA 2015 - OA och prestige

description: Artiklar 2009-2014. Uttag från DiVA. Matchat mot norska listan och DOAJ.
DOI: 10.6084/m9.figshare.1390474.v2
title: Mötesplats OA 2015 - OA och prestige

description: Artiklar 2009-2014. Uttag från DiVA. Matchat mot norska listan och DOAJ.
DOI: 10.6084/m9.figshare.1390474.v1
title: Mötespats OA 2015 - OA och prestige

description: Artiklar 2009-2014. Uttag från DiVA. Matchat mot norska listan och DOAJ.
DOI: 10.6084/m9.figshare.1387733
title: S1 Dataset Botanical Composition

description: Supporting information for Table 1: Botanical composition estimated before first cut in 2007 on fields, which were cut, cut and grazed in combination or only grazed on dairy farms with organic production system (ORG) and short-term (ORG-SG) or long-term grassland management (ORG-LG) and dairy farms with conventional production system (CON) and short-term (CON-SG) or long-term grassland management (CON-LG)
DOI: 10.6084/m9.figshare.1387732
title: S2 Dataset Plant Species

description: Supporting information for Table 1: Botanical composition estimated before first cut in 2007 on fields, which were cut, cut and grazed in combination or only grazed on dairy farms with organic production system (ORG) and short-term (ORG-SG) or long-term grassland management (ORG-LG) and dairy farms with conventional production system (CON) and short-term (CON-SG) or long-term grassland management (CON-LG)
DOI: 10.6084/m9.figshare.1387733.v1
title: S1 Dataset Botanical Composition

description: Supporting information for Table 1: Botanical composition estimated before first cut in 2007 on fields, which were cut, cut and grazed in combination or only grazed on dairy farms with organic production system (ORG) and short-term (ORG-SG) or long-term grassland management (ORG-LG) and dairy farms with conventional production system (CON) and short-term (CON-SG) or long-term grassland management (CON-LG)
DOI: 10.6084/m9.figshare.1387731
title: S3 Dataset Production and Fertility

description: Supporting information for Table 2: Feed intake, milk production, milk gross composition and fertility indicators on dairy farms with organic production system (ORG) and short-term (ORG-SG) or long-term grassland management (ORG-LG) and dairy farms with conventional production system (CON) and short-term (CON-SG) or long-term grassland management (CON-LG)
DOI: 10.6084/m9.figshare.1387716
title: Seattle Street Inventories: 1924-1928

description: Three inventories of every clock in downtown Seattle. They were made in 1924, 1927, and 1928.
DOI: 10.6084/m9.figshare.1386888
title: WHAPP fieldwork presentation, 2008

description: This is a presentation about the fieldwork conducted by the WHAPP in 2008
DOI: 10.6084/m9.figshare.1387716.v1
title: Seattle Street Inventories: 1924-1928

description: Three inventories of every clock in downtown Seattle. They were made in 1924, 1927, and 1928.
DOI: 10.6084/m9.figshare.1386887
title: Description of the WHAPP project

description: This is short description of the WHAPP project.
DOI: 10.6084/m9.figshare.1386888.v1
title: WHAPP fieldwork presentation, 2008

description: This is a presentation about the fieldwork conducted by the WHAPP in 2008
DOI: 10.6084/m9.figshare.1386868
title: A VAMP-associated protein, PVA31 is involved in leaf senescence in <i>Arabidopsis</i>

description: VAMP-associated proteins (VAPs) are highly conserved among eukaryotes. Here, we report a functional analysis of one of the VAPs, PVA31, and demonstrate its novel function on leaf senescence in <i>Arabidopsis</i>. The expression of <i>PVA31</i> is highly induced in senescence leaves, and localizes to the plasma membrane as well as the ARA7-positive endosomes. Yeast two-hybrid analysis demonstrates that PVA31 is interacted with the plasma membrane localized-VAMP proteins, VAMP721/722/724 but not with the endosome-localized VAMPs, VAMP711 and VAMP727, indicating that PVA31 is associated with VAMP721/722/724 on the plasma membrane. Strong constitutive expression of <i>PVA31</i> under the control of the Cauliflower mosaic virus 35S promoter induces the typical symptom of leaf senescence earlier than WT in normal growth and an artificially induced senescence conditions. In addition, the marker genes for the SA-mediated signaling pathways, <i>PR-1</i>, is promptly expressed with elicitor application. These data indicate that <i>PVA31</i>-overexpressing plants exhibit the early senescence phenotype in their leaves, and suggest that PVA31 is involved in the SA-mediated programmed cell death process during leaf senescence and PR-protein secretion during pathogen infection in <i>Arabidopsis</i>.
DOI: 10.6084/m9.figshare.1386887.v1
title: Description of the WHAPP project

description: This is short description of the WHAPP project.
DOI: 10.6084/m9.figshare.1387714.v1
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386867
title: Gaussian Job Archive for C6H6Br4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1386867.v1
title: Gaussian Job Archive for C6H6Br4

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1387714.v2
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386868.v1
title: A VAMP-associated protein, PVA31 is involved in leaf senescence in <i>Arabidopsis</i>

description: VAMP-associated proteins (VAPs) are highly conserved among eukaryotes. Here, we report a functional analysis of one of the VAPs, PVA31, and demonstrate its novel function on leaf senescence in <i>Arabidopsis</i>. The expression of <i>PVA31</i> is highly induced in senescence leaves, and localizes to the plasma membrane as well as the ARA7-positive endosomes. Yeast two-hybrid analysis demonstrates that PVA31 is interacted with the plasma membrane localized-VAMP proteins, VAMP721/722/724 but not with the endosome-localized VAMPs, VAMP711 and VAMP727, indicating that PVA31 is associated with VAMP721/722/724 on the plasma membrane. Strong constitutive expression of <i>PVA31</i> under the control of the Cauliflower mosaic virus 35S promoter induces the typical symptom of leaf senescence earlier than WT in normal growth and an artificially induced senescence conditions. In addition, the marker genes for the SA-mediated signaling pathways, <i>PR-1</i>, is promptly expressed with elicitor application. These data indicate that <i>PVA31</i>-overexpressing plants exhibit the early senescence phenotype in their leaves, and suggest that PVA31 is involved in the SA-mediated programmed cell death process during leaf senescence and PR-protein secretion during pathogen infection in <i>Arabidopsis</i>.
DOI: 10.6084/m9.figshare.1387714.v3
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386861
title: Gaussian Job Archive for C6H6Br10

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1386868.v2
title: A VAMP-associated protein, PVA31 is involved in leaf senescence in <i>Arabidopsis</i>

description: VAMP-associated proteins (VAPs) are highly conserved among eukaryotes. Here, we report a functional analysis of one of the VAPs, PVA31, and demonstrate its novel function on leaf senescence in <i>Arabidopsis</i>. The expression of <i>PVA31</i> is highly induced in senescence leaves, and localizes to the plasma membrane as well as the ARA7-positive endosomes. Yeast two-hybrid analysis demonstrates that PVA31 is interacted with the plasma membrane localized-VAMP proteins, VAMP721/722/724 but not with the endosome-localized VAMPs, VAMP711 and VAMP727, indicating that PVA31 is associated with VAMP721/722/724 on the plasma membrane. Strong constitutive expression of <i>PVA31</i> under the control of the Cauliflower mosaic virus 35S promoter induces the typical symptom of leaf senescence earlier than WT in normal growth and an artificially induced senescence conditions. In addition, the marker genes for the SA-mediated signaling pathways, <i>PR-1</i>, is promptly expressed with elicitor application. These data indicate that <i>PVA31</i>-overexpressing plants exhibit the early senescence phenotype in their leaves, and suggest that PVA31 is involved in the SA-mediated programmed cell death process during leaf senescence and PR-protein secretion during pathogen infection in <i>Arabidopsis</i>.
DOI: 10.6084/m9.figshare.1386861.v1
title: Gaussian Job Archive for C6H6Br10

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1387714.v4
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386868.v3
title: A VAMP-associated protein, PVA31 is involved in leaf senescence in <i>Arabidopsis</i>

description: VAMP-associated proteins (VAPs) are highly conserved among eukaryotes. Here, we report a functional analysis of one of the VAPs, PVA31, and demonstrate its novel function on leaf senescence in <i>Arabidopsis</i>. The expression of <i>PVA31</i> is highly induced in senescence leaves, and localizes to the plasma membrane as well as the ARA7-positive endosomes. Yeast two-hybrid analysis demonstrates that PVA31 is interacted with the plasma membrane localized-VAMP proteins, VAMP721/722/724 but not with the endosome-localized VAMPs, VAMP711 and VAMP727, indicating that PVA31 is associated with VAMP721/722/724 on the plasma membrane. Strong constitutive expression of <i>PVA31</i> under the control of the Cauliflower mosaic virus 35S promoter induces the typical symptom of leaf senescence earlier than WT in normal growth and an artificially induced senescence conditions. In addition, the marker genes for the SA-mediated signaling pathways, <i>PR-1</i>, is promptly expressed with elicitor application. These data indicate that <i>PVA31</i>-overexpressing plants exhibit the early senescence phenotype in their leaves, and suggest that PVA31 is involved in the SA-mediated programmed cell death process during leaf senescence and PR-protein secretion during pathogen infection in <i>Arabidopsis</i>.
DOI: 10.6084/m9.figshare.1387714.v5
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386857
title: Gaussian Job Archive for C6H6Br10

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1387714.v6
title: Seattle Streets, Renamed over time

description: This group contains datasets with tables of street names before and after particular ordinances.
DOI: 10.6084/m9.figshare.1386857.v1
title: Gaussian Job Archive for C6H6Br10

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.