cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5066/p91sonlm
title: Invertebrate data (2012-2021) from the Colorado River in Grand Canyon and flow data (1921-2021) from the Lees Ferry gage (09380000) on the Colorado River near Page, AZ

description: These data were compiled for an outreach article published in the Boatman's Quarterly Review, which is a magazine published by Grand Canyon River Guides Association. The objectives of our study was to describe the outcomes of the 2018-2020 Bug Flows experiment to a general audience. These data represent 10 years (2012-2021) of invertebrate data and 100 years (1921-2021) of flow data. These data were collected from the Colorado River in Glen Canyon National Recreation Area and Grand Canyon National Park. These data were collected by recreational river runners through a community science effort where we provided river runners with light trapping equipment to sample emergent aquatic insects each night of their expeditions. Invertebrate counts represent total numbers of adult insects sampled by light traps, which can be divided by hours elapsed (sampling duration) to calculate a catch rate. Flow data were attained from the USGS gage 09380000 at Lees Ferry on the Colorado River. These data can be used to interpret changes in aquatic insect emergence rates over time and to look at changes in stage variation at Lees Ferry over time.
DOI: 10.6084/m9.figshare.11950539
title: Genome-Wide Association Study of Ocular Sarcoidosis Confirms HLA Associations and Implicates Barrier Function and Autoimmunity in African Americans

description: <b>Purpose</b>: Identify genes associated with ocular sarcoidosis (OS). <b>Methods</b>: We genotyped 1.1 million genetic variants to identify significant OS associations, defined as those that achieved <i>p</i> &lt; 5 × 10<sup>−8</sup> in a genome-wide comparison of OS cases to healthy controls in our European- or African-American cohorts (EA, AA). Potential functional roles of all associated variants were assessed. <b>Results</b>: Eight significant non-HLA variants were found in AA OS cases compared to healthy controls and confirmed as at least suggestive when comparing OS to non-OS cases. Seven of these were within <i>MAGI1</i> and include transcription factor binding sites and expression quantitative trait loci. Our EA cohort, while showing similar effect sizes at variants within <i>MAGI1</i>, had no significant variants. Association analysis of <i>HLA-DRB1</i> alleles confirmed association to OS in EA to *04:01. <b>Conclusion</b>: Our results support organ-specific genetic risk in OS in a compelling candidate, <i>MAGI1</i>, known to be associated with barrier function and autoimmunity.
DOI: 10.6084/m9.figshare.11950539.v1
title: Genome-Wide Association Study of Ocular Sarcoidosis Confirms HLA Associations and Implicates Barrier Function and Autoimmunity in African Americans

description: <b>Purpose</b>: Identify genes associated with ocular sarcoidosis (OS). <b>Methods</b>: We genotyped 1.1 million genetic variants to identify significant OS associations, defined as those that achieved <i>p</i> &lt; 5 × 10<sup>−8</sup> in a genome-wide comparison of OS cases to healthy controls in our European- or African-American cohorts (EA, AA). Potential functional roles of all associated variants were assessed. <b>Results</b>: Eight significant non-HLA variants were found in AA OS cases compared to healthy controls and confirmed as at least suggestive when comparing OS to non-OS cases. Seven of these were within <i>MAGI1</i> and include transcription factor binding sites and expression quantitative trait loci. Our EA cohort, while showing similar effect sizes at variants within <i>MAGI1</i>, had no significant variants. Association analysis of <i>HLA-DRB1</i> alleles confirmed association to OS in EA to *04:01. <b>Conclusion</b>: Our results support organ-specific genetic risk in OS in a compelling candidate, <i>MAGI1</i>, known to be associated with barrier function and autoimmunity.
DOI: 10.6084/m9.figshare.7231958
title: Additional file 10: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S10. The potential networks in RS218-infected group. (XLSX 71Â kb)
DOI: 10.6084/m9.figshare.7231958.v1
title: Additional file 10: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S10. The potential networks in RS218-infected group. (XLSX 71Â kb)
DOI: 10.6084/m9.figshare.7231973
title: Additional file 1: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S1. Protein profile of HB101-infected hBMECs. (XLSX 287Â kb)
DOI: 10.6084/m9.figshare.7231973.v1
title: Additional file 1: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S1. Protein profile of HB101-infected hBMECs. (XLSX 287Â kb)
DOI: 10.6084/m9.figshare.9853427
title: The Paf1 complex transcriptionally regulates the mitochondrial-anchored protein Atg32 leading to activation of mitophagy

description: Mitophagy is a critical process that safeguards mitochondrial quality control in order to maintain proper cellular homeostasis. Although the mitochondrial-anchored receptor Atg32-mediated cargo-recognition system has been well characterized to be essential for this process, the signaling pathway modulating its expression as a contribution of governing the mitophagy process remains largely unknown. Here, bioinformatics analyses of epigenetic or transcriptional regulators modulating gene expression allow us to identify the Paf1 complex (the polymerase-associated factor 1 complex, Paf1C) as a transcriptional repressor of <i>ATG</i> genes. We show that Paf1C suppresses glucose starvation-induced autophagy, but does not affect nitrogen starvation- or rapamycin-induced autophagy. Moreover, we show that Paf1C specifically regulates mitophagy through modulating <i>ATG32</i> expression. Deletion of the genes encoding two core subunits of Paf1C, Paf1 and Ctr9, increases <i>ATG32</i> and <i>ATG11</i> expression and facilitates mitophagy activity. Although Paf1C is required for many histone modifications and gene activation, we show that Paf1C regulates mitophagy independent of its positive regulatory role in other processes. More importantly, we also demonstrate the mitophagic role of PAF1C in mammals. Overall, we conclude that Paf1C maintains mitophagy at a low level through binding the promoter of the <i>ATG32</i> gene in glucose-rich conditions. Dissociation of Paf1C from <i>ATG32</i> leads to the increased expression of this gene, and mitophagy induction upon glucose starvation. Thus, we uncover a new role of Paf1C in modulating the mitophagy process at the transcriptional level. AMPK: AMP-activated protein kinase; ATP5F1A: ATP synthase F1 subunit alpha; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CCCP: chlorophenylhydrazone; DFP: chelator deferiprone; GFP: green fluorescent protein; H2B-Ub1: H2B monoubiquitination; HSPD1/HSP60: heat shock protein family D (Hsp60) member 1; KD: kinase dead; OPTN, optineurin; Paf1: polymerase-associated factor 1; PINK1: PTEN induced kinase 1; PRKN/Parkin: parkin RBR E3 ubiquitin protein ligase; RT-qPCR: real-time quantitative PCR; SD-N: synthetic dropout without nitrogen base; TIMM23: translocase of inner mitochondrial membrane 23; TOMM20: translocase of outer mitochondrial membrane 20; WT: wild-type; YPD: yeast extract peptone dextrose; YPL: yeast extract peptone lactate
DOI: 10.6084/m9.figshare.9853427.v1
title: The Paf1 complex transcriptionally regulates the mitochondrial-anchored protein Atg32 leading to activation of mitophagy

description: Mitophagy is a critical process that safeguards mitochondrial quality control in order to maintain proper cellular homeostasis. Although the mitochondrial-anchored receptor Atg32-mediated cargo-recognition system has been well characterized to be essential for this process, the signaling pathway modulating its expression as a contribution of governing the mitophagy process remains largely unknown. Here, bioinformatics analyses of epigenetic or transcriptional regulators modulating gene expression allow us to identify the Paf1 complex (the polymerase-associated factor 1 complex, Paf1C) as a transcriptional repressor of <i>ATG</i> genes. We show that Paf1C suppresses glucose starvation-induced autophagy, but does not affect nitrogen starvation- or rapamycin-induced autophagy. Moreover, we show that Paf1C specifically regulates mitophagy through modulating <i>ATG32</i> expression. Deletion of the genes encoding two core subunits of Paf1C, Paf1 and Ctr9, increases <i>ATG32</i> and <i>ATG11</i> expression and facilitates mitophagy activity. Although Paf1C is required for many histone modifications and gene activation, we show that Paf1C regulates mitophagy independent of its positive regulatory role in other processes. More importantly, we also demonstrate the mitophagic role of PAF1C in mammals. Overall, we conclude that Paf1C maintains mitophagy at a low level through binding the promoter of the <i>ATG32</i> gene in glucose-rich conditions. Dissociation of Paf1C from <i>ATG32</i> leads to the increased expression of this gene, and mitophagy induction upon glucose starvation. Thus, we uncover a new role of Paf1C in modulating the mitophagy process at the transcriptional level. AMPK: AMP-activated protein kinase; ATP5F1A: ATP synthase F1 subunit alpha; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CCCP: chlorophenylhydrazone; DFP: chelator deferiprone; GFP: green fluorescent protein; H2B-Ub1: H2B monoubiquitination; HSPD1/HSP60: heat shock protein family D (Hsp60) member 1; KD: kinase dead; OPTN, optineurin; Paf1: polymerase-associated factor 1; PINK1: PTEN induced kinase 1; PRKN/Parkin: parkin RBR E3 ubiquitin protein ligase; RT-qPCR: real-time quantitative PCR; SD-N: synthetic dropout without nitrogen base; TIMM23: translocase of inner mitochondrial membrane 23; TOMM20: translocase of outer mitochondrial membrane 20; WT: wild-type; YPD: yeast extract peptone dextrose; YPL: yeast extract peptone lactate
DOI: 10.6084/m9.figshare.7231988
title: Additional file 2: of Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial

description: Supplementary Tables S1-S6. (XLSX 6624 kb)
DOI: 10.6084/m9.figshare.7231988.v1
title: Additional file 2: of Discovering in vivo cytokine-eQTL interactions from a lupus clinical trial

description: Supplementary Tables S1-S6. (XLSX 6624 kb)
DOI: 10.6084/m9.figshare.22960629
title: Fast Robust Location and Scatter Estimation: A Depth-based Method

description: The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis, the critical step of which is to select a subset of a given size with the lowest sample covariance determinant. The concentration step (C-step) is a common tool for subset-seeking; however, it becomes computationally demanding for high-dimensional data. To alleviate the challenge, we propose a depth-based algorithm, termed as FDB, which replaces the optimal subset with the trimmed region induced by statistical depth. We show that the depth-based region is consistent with the MCD-based subset under a specific class of depth notions, for instance, the projection depth. With the two suggested depths, the FDB estimator is not only computationally more efficient but also reaches the same level of robustness as the MCD estimator. Extensive simulation studies are conducted to assess the empirical performance of our estimators. We also validate the computational efficiency and robustness of our estimators under several typical tasks such as principal component analysis, linear discriminant analysis, image denoise and outlier detection on real-life datasets. An R package FDB and additional results are available in the supplementary materials.
DOI: 10.6084/m9.figshare.22960629.v2
title: Fast Robust Location and Scatter Estimation: A Depth-based Method

description: The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis, the critical step of which is to select a subset of a given size with the lowest sample covariance determinant. The concentration step (C-step) is a common tool for subset-seeking; however, it becomes computationally demanding for high-dimensional data. To alleviate the challenge, we propose a depth-based algorithm, termed as FDB, which replaces the optimal subset with the trimmed region induced by statistical depth. We show that the depth-based region is consistent with the MCD-based subset under a specific class of depth notions, for instance, the projection depth. With the two suggested depths, the FDB estimator is not only computationally more efficient but also reaches the same level of robustness as the MCD estimator. Extensive simulation studies are conducted to assess the empirical performance of our estimators. We also validate the computational efficiency and robustness of our estimators under several typical tasks such as principal component analysis, linear discriminant analysis, image denoise and outlier detection on real-life datasets. An R package FDB and additional results are available in the supplementary materials.
DOI: 10.6084/m9.figshare.22960629.v1
title: Fast robust location and scatter estimation: a depth-based method

description: The minimum covariance determinant (MCD) estimator is ubiquitous in multivariate analysis, the critical step of which is to select a subset of a given size with the lowest sample covariance determinant. The concentration step (C-step) is a common tool for subset-seeking; however, it becomes computationally demanding for high-dimensional data. To alleviate the challenge, we propose a depth-based algorithm, termed as FDB, which replaces the optimal subset with the trimmed region induced by statistical depth. We show that the depth-based region is consistent with the MCD-based subset under a specific class of depth notions, for instance, the projection depth. With the two suggested depths, the FDB estimator is not only computationally more efficient but also reaches the same level of robustness as the MCD estimator. Extensive simulation studies are conducted to assess the empirical performance of our estimators. We also validate the computational efficiency and robustness of our estimators under several typical tasks such as principal component analysis, linear discriminant analysis, image denoise and outlier detection on real-life datasets. An R package FDB and additional results are available in the Supplementary Materials.
DOI: 10.6084/m9.figshare.7231991
title: Additional file 2: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S2. Protein profile of PCN033-infected hBMECs. (XLSX 290Â kb)
DOI: 10.6084/m9.figshare.7231991.v1
title: Additional file 2: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S2. Protein profile of PCN033-infected hBMECs. (XLSX 290Â kb)
DOI: 10.6084/m9.figshare.7231997
title: Additional file 4: of Transcriptome analysis reveals crucial genes involved in the biosynthesis of nervonic acid in woody Malania oleifera oilseeds

description: Table S3. The KEGG classification of unigenes identified in Malania oleifera. (XLS 41 kb)
DOI: 10.6084/m9.figshare.7231997.v1
title: Additional file 4: of Transcriptome analysis reveals crucial genes involved in the biosynthesis of nervonic acid in woody Malania oleifera oilseeds

description: Table S3. The KEGG classification of unigenes identified in Malania oleifera. (XLS 41 kb)
DOI: 10.6084/m9.figshare.7232009
title: Additional file 3: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S3. Protein profile of RS218-infected hBMECs. (XLSX 289Â kb)
DOI: 10.6084/m9.figshare.7232009.v1
title: Additional file 3: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S3. Protein profile of RS218-infected hBMECs. (XLSX 289Â kb)
DOI: 10.6084/m9.figshare.20339215
title: Conformal Sensitivity Analysis for Individual Treatment Effects

description: Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this article proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unmeasured confounding through a marginal sensitivity model, and adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis as a conformal inference problem under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The resulting predictive interval has guaranteed nominal coverage of the ITE and provides this coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20339215.v1
title: Conformal Sensitivity Analysis for Individual Treatment Effects

description: Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this article proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unmeasured confounding through a marginal sensitivity model, and adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis as a conformal inference problem under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The resulting predictive interval has guaranteed nominal coverage of the ITE and provides this coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20339218
title: A New and Unified Family of Covariate Adaptive Randomization Procedures and Their Properties

description: In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first moment. In this article, we propose a family of CAR procedures that can balance general covariate features, such as quadratic and interaction terms. Our framework not only unifies many existing methods, but also introduces a much broader class of new and useful CAR procedures. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors is OP(nϵ) for any ϵ&gt;0 if all of the moments are finite for the covariate features, relative to OP(n) under complete randomization, where <i>n</i> is the sample size. Both the resulting convergence rate and its proof are novel. These favorable balancing properties lead to increased precision of treatment effect estimation in the presence of nonlinear covariate effects. The framework is applied to balance covariate means and covariance matrices simultaneously. Simulation and empirical studies demonstrate the excellent and robust performance of the proposed procedures. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20339218.v1
title: A New and Unified Family of Covariate Adaptive Randomization Procedures and Their Properties

description: In clinical trials and other comparative studies, covariate balance is crucial for credible and efficient assessment of treatment effects. Covariate adaptive randomization (CAR) procedures are extensively used to reduce the likelihood of covariate imbalances occurring. In the literature, most studies have focused on balancing of discrete covariates. Applications of CAR with continuous covariates remain rare, especially when the interest goes beyond balancing only the first moment. In this article, we propose a family of CAR procedures that can balance general covariate features, such as quadratic and interaction terms. Our framework not only unifies many existing methods, but also introduces a much broader class of new and useful CAR procedures. We show that the proposed procedures have superior balancing properties; in particular, the convergence rate of imbalance vectors is OP(nϵ) for any ϵ&gt;0 if all of the moments are finite for the covariate features, relative to OP(n) under complete randomization, where <i>n</i> is the sample size. Both the resulting convergence rate and its proof are novel. These favorable balancing properties lead to increased precision of treatment effect estimation in the presence of nonlinear covariate effects. The framework is applied to balance covariate means and covariance matrices simultaneously. Simulation and empirical studies demonstrate the excellent and robust performance of the proposed procedures. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7232030
title: Additional file 6: of Transcriptome analysis reveals crucial genes involved in the biosynthesis of nervonic acid in woody Malania oleifera oilseeds

description: Table S4. Differentially expressed transcriptor factors during seed development of Malania oleifera. (XLS 37 kb)
DOI: 10.6084/m9.figshare.7232030.v1
title: Additional file 6: of Transcriptome analysis reveals crucial genes involved in the biosynthesis of nervonic acid in woody Malania oleifera oilseeds

description: Table S4. Differentially expressed transcriptor factors during seed development of Malania oleifera. (XLS 37 kb)
DOI: 10.6084/m9.figshare.7232039
title: Additional file 5: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S5. Ingenuity Canonical Pathways of HB101-infected group. (XLSX 71Â kb)
DOI: 10.6084/m9.figshare.7232039.v1
title: Additional file 5: of New insights into meningitic Escherichia coli infection of brain microvascular endothelial cells from quantitative proteomics analysis

description: Table S5. Ingenuity Canonical Pathways of HB101-infected group. (XLSX 71Â kb)
DOI: 10.6084/m9.figshare.7232045
title: Additional file 7: of Transcriptome analysis reveals crucial genes involved in the biosynthesis of nervonic acid in woody Malania oleifera oilseeds

description: Table S5. Unigenes involved into the pathway of fatty acids synthesis, modification, elongation and triacylglycerol assembly in Malania oleifera seeds. (XLS 71 kb)