cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5410966
title: Dataset for: Vitamin D metabolic loci and preeclampsia risk in multi-ethnic pregnant women

description: Allelic variants in vitamin D metabolism genes may increase risk of preeclampsia, but few studies have systematically tested this hypothesis. Our objective was to evaluate the relationship between maternal allelic variants in three vitamin D metabolism genes and preeclampsia risk. Samples were from two case-control studies of pregnant women who delivered in Pittsburgh, PA from 1999-2010 and twelve recruiting sites across the United States from 1959-65. Single nucleotide polymorphisms (SNPs) were genotyped 50 kilobases up- and down-stream in three genes (VDR, GC, and CYP27B1) in the samples from both studies, for a total of 744 preeclampsia cases and 2411 controls. Using multivariable logistic regression, we estimated the associations between allelic variation of each locus and preeclampsia risk by maternal race and study. Meta-analysis was used to estimate the association across race-study groups for each SNP. Minor allele of a non-coding region of the VDR gene was significantly associated with preeclampsia risk, which was verified in the meta-analysis [odds ratio (OR), 95 % confidence intervals (CI)] after adjusting for multiple comparisons [rs12831006:1.5 (1.2,2.0), p&lt;0.0001]. The meta-analysis identified associations for one intron GC variant [rs843010:1.4 (1.1, 1.9) p&lt;0.05] and two variants of the flanking region of GC [rs842991:1.5 (1.1, 2.0) p&lt;0.05; rs16846876:0.75 (0.58, 0.98) p&lt;0.05]. There were no statistically significant associations for CYP27B1 SNPs. Our results provide additional support for a biological role of vitamin D in preeclampsia.
DOI: 10.6084/m9.figshare.5410969.v1
title: The decentralized enforcement of European law: national court decisions on EU directives with and without preliminary reference submissions

description: This article contributes to the study of the preliminary reference procedure and the literature on the decentralized enforcement of European Union (EU) law through national courts. Drawing on the ‘compliance pull’ explanation for why national courts submit preliminary references to the Court of Justice of the European Union (ECJ), it suggests that a greater need to clarify ‘the validity and interpretation’ of one act compared to another is associated with variation in Article 267 submissions. Drawing on a sample of 1,300 national court decisions on EU directives with variation in Article 267 Treaty on the Functioning of the European Union (TFEU) submissions, this article finds that disputes relating to directives leaving more room for manoeuvre in implementation (‘delegation’) and relating to more complex regulatory issues (‘information intensity’) are more likely to be referred to the ECJ for interpretation. It argues that neglecting the ‘compliance pull’ explanations has consequences for how we conceptualize ‘decentralized enforcement’.
DOI: 10.6084/m9.figshare.5410966.v1
title: Dataset for: Vitamin D metabolic loci and preeclampsia risk in multi-ethnic pregnant women

description: Allelic variants in vitamin D metabolism genes may increase risk of preeclampsia, but few studies have systematically tested this hypothesis. Our objective was to evaluate the relationship between maternal allelic variants in three vitamin D metabolism genes and preeclampsia risk. Samples were from two case-control studies of pregnant women who delivered in Pittsburgh, PA from 1999-2010 and twelve recruiting sites across the United States from 1959-65. Single nucleotide polymorphisms (SNPs) were genotyped 50 kilobases up- and down-stream in three genes (VDR, GC, and CYP27B1) in the samples from both studies, for a total of 744 preeclampsia cases and 2411 controls. Using multivariable logistic regression, we estimated the associations between allelic variation of each locus and preeclampsia risk by maternal race and study. Meta-analysis was used to estimate the association across race-study groups for each SNP. Minor allele of a non-coding region of the VDR gene was significantly associated with preeclampsia risk, which was verified in the meta-analysis [odds ratio (OR), 95 % confidence intervals (CI)] after adjusting for multiple comparisons [rs12831006:1.5 (1.2,2.0), p&lt;0.0001]. The meta-analysis identified associations for one intron GC variant [rs843010:1.4 (1.1, 1.9) p&lt;0.05] and two variants of the flanking region of GC [rs842991:1.5 (1.1, 2.0) p&lt;0.05; rs16846876:0.75 (0.58, 0.98) p&lt;0.05]. There were no statistically significant associations for CYP27B1 SNPs. Our results provide additional support for a biological role of vitamin D in preeclampsia.
DOI: 10.6084/m9.figshare.5410981.v1
title: The Wikipedia Network of Mathematicians

description: This is data used in the paper "Analysis of the Wikipedia Network of Mathematicians". The data is taken from three snapshots of Wikipedia, taken in 2013, 2017 and 2018. It contains the raw Wikipedia data used as our source as well the results of our processing. Includes are edgelists, networks of the largest comopnent in graphml format, and tables of data on the nodes (the mathematicians). <br>
DOI: 10.6084/m9.figshare.5410810
title: Data and code for: "How does a widespread reef coral maintain a population in an isolated environment?"

description: Data files and R code supporting "How does a widespread reef coral maintain a population in an isolated environment?" at https://doi.org/10.3354/meps12537<br>
DOI: 10.6084/m9.figshare.5410780
title: Dataset for: Interaction of Spatial and Non-Spatial Cues in Auditory Stream Segregation in the European Starling

description: Integrating sounds from the same source and segregating sounds from different sources in an acoustic scene is an essential function of the auditory system. Naturally, the auditory system simultaneously makes use of multiple cues. Here, we investigate the interaction between spatial cues and frequency cues in stream segregation of European starlings (Sturnus vulgaris) using an objective measure of perception. Neural responses to streaming sounds were recorded while the bird was performing a behavioral task that results in a higher sensitivity during a one-stream than a two-stream percept. Birds were trained to detect an onset time shift of a B tone in an ABA- triplet sequence in which A and B could differ in frequency and/or spatial location. If the frequency difference or spatial separation between the signal sources or both were increased, the behavioral time shift detection performance deteriorated. Spatial separation had a smaller effect on the performance compared to the frequency difference and both cues additively affected the performance. Neural responses in the primary auditory forebrain were affected by the frequency and spatial cues. However, frequency and spatial cue differences being sufficiently large to elicit behavioral effects did not reveal correlated neural response differences. The difference between the neuronal response pattern and behavioral response is discussed with relation to the task given to the bird. Perceptual effects of combining different cues in auditory scene analysis indicate that these cues are analyzed independently and given different weights suggesting that the streaming percept arises consecutively to initial cue analysis.
DOI: 10.6084/m9.figshare.5410810.v1
title: Data and code for: "How does a widespread reef coral maintain a population in an isolated environment?"

description: Data files and R code supporting "How does a widespread reef coral maintain a population in an isolated environment?" at https://doi.org/10.3354/meps12537<br>
DOI: 10.6084/m9.figshare.5410780.v1
title: Dataset for: Interaction of Spatial and Non-Spatial Cues in Auditory Stream Segregation in the European Starling

description: Integrating sounds from the same source and segregating sounds from different sources in an acoustic scene is an essential function of the auditory system. Naturally, the auditory system simultaneously makes use of multiple cues. Here, we investigate the interaction between spatial cues and frequency cues in stream segregation of European starlings (Sturnus vulgaris) using an objective measure of perception. Neural responses to streaming sounds were recorded while the bird was performing a behavioral task that results in a higher sensitivity during a one-stream than a two-stream percept. Birds were trained to detect an onset time shift of a B tone in an ABA- triplet sequence in which A and B could differ in frequency and/or spatial location. If the frequency difference or spatial separation between the signal sources or both were increased, the behavioral time shift detection performance deteriorated. Spatial separation had a smaller effect on the performance compared to the frequency difference and both cues additively affected the performance. Neural responses in the primary auditory forebrain were affected by the frequency and spatial cues. However, frequency and spatial cue differences being sufficiently large to elicit behavioral effects did not reveal correlated neural response differences. The difference between the neuronal response pattern and behavioral response is discussed with relation to the task given to the bird. Perceptual effects of combining different cues in auditory scene analysis indicate that these cues are analyzed independently and given different weights suggesting that the streaming percept arises consecutively to initial cue analysis.
DOI: 10.6084/m9.figshare.5410750
title: A Powerful Bayesian Test for Equality of Means in High Dimensions

description: We develop a Bayes factor-based testing procedure for comparing two population means in high-dimensional settings. In ‘large-p-small-n” settings, Bayes factors based on proper priors require eliciting a large and complex <i>p</i> × <i>p</i> covariance matrix, whereas Bayes factors based on Jeffrey’s prior suffer the same impediment as the classical Hotelling <i>T</i><sup>2</sup> test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be based on lower dimensional random projections of the high-dimensional data vectors. We choose the prior under the alternative to maximize the power of the test for a fixed threshold level, yielding a restricted most powerful Bayesian test (RMPBT). The final test statistic is based on the ensemble of Bayes factors corresponding to multiple replications of randomly projected data. We show that the test is unbiased and, under mild conditions, is also locally consistent. We demonstrate the efficacy of the approach through simulated and real data examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410747
title: Tractable Bayesian Variable Selection: Beyond Normality

description: Bayesian variable selection often assumes normality, but the effects of model misspecification are not sufficiently understood. There are sound reasons behind this assumption, particularly for large <i>p</i>: ease of interpretation, analytical, and computational convenience. More flexible frameworks exist, including semi- or nonparametric models, often at the cost of some tractability. We propose a simple extension that allows for skewness and thicker-than-normal tails but preserves tractability. It leads to easy interpretation and a log-concave likelihood that facilitates optimization and integration. We characterize asymptotically parameter estimation and Bayes factor rates, under certain model misspecification. Under suitable conditions, misspecified Bayes factors induce sparsity at the same rates than under the correct model. However, the rates to detect signal change by an exponential factor, often reducing sensitivity. These deficiencies can be ameliorated by inferring the error distribution, a simple strategy that can improve inference substantially. Our work focuses on the likelihood and can be combined with any likelihood penalty or prior, but here we focus on nonlocal priors to induce extra sparsity and ameliorate finite-sample effects caused by misspecification. We show the importance of considering the likelihood rather than solely the prior, for Bayesian variable selection. The methodology is in R package ‘mombf.’ Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410744
title: Sparse Pairwise Likelihood Estimation for Multivariate Longitudinal Mixed Models

description: It is becoming increasingly common in longitudinal studies to collect and analyze data on multiple responses. For example, in the social sciences we may be interested in uncovering the factors driving mental health of individuals over time, where mental health is measured using a set of questionnaire items. One approach to analyzing such multi-dimensional data is multivariate mixed models, an extension of the standard univariate mixed model to handle multiple responses. Estimating multivariate mixed models presents a considerable challenge however, let alone performing variable selection to uncover which covariates are important in driving each response. Motivated by composite likelihood ideas, we propose a new approach for estimation and fixed effects selection in multivariate mixed models, called approximate pairwise likelihood estimation and shrinkage (APLES). The method works by constructing a quadratic approximation to each term in the pairwise likelihood function, and then augmenting this approximate pairwise likelihood with a penalty that encourages both individual and group coefficient sparsity. This leads to a relatively fast method of selection, as we can use coordinate ascent type methods to then construct the full regularization path for the model. Our method is the first to extend penalized likelihood estimation to multivariate generalized linear mixed models. We show that the APLES estimator attains a composite likelihood version of the oracle property. We propose a new information criterion for selecting the tuning parameter, which employs a dynamic model complexity penalty to facilitate aggressive shrinkage, and demonstrate that it asymptotically leads to selection consistency, that is, leads to the true model being selected. A simulation study demonstrates that the APLES estimator outperforms several univariate selection methods based on analyzing each outcome separately. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410744.v1
title: Sparse Pairwise Likelihood Estimation for Multivariate Longitudinal Mixed Models

description: It is becoming increasingly common in longitudinal studies to collect and analyze data on multiple responses. For example, in the social sciences we may be interested in uncovering the factors driving mental health of individuals over time, where mental health is measured using a set of questionnaire items. One approach to analyzing such multi-dimensional data is multivariate mixed models, an extension of the standard univariate mixed model to handle multiple responses. Estimating multivariate mixed models presents a considerable challenge however, let alone performing variable selection to uncover which covariates are important in driving each response. Motivated by composite likelihood ideas, we propose a new approach for estimation and fixed effects selection in multivariate mixed models, called approximate pairwise likelihood estimation and shrinkage (APLES). The method works by constructing a quadratic approximation to each term in the pairwise likelihood function, and then augmenting this approximate pairwise likelihood with a penalty that encourages both individual and group coefficient sparsity. This leads to a relatively fast method of selection, as we can use coordinate ascent type methods to then construct the full regularization path for the model. Our method is the first to extend penalized likelihood estimation to multivariate generalized linear mixed models. We show that the APLES estimator attains a composite likelihood version of the oracle property. We propose a new information criterion for selecting the tuning parameter, which employs a dynamic model complexity penalty to facilitate aggressive shrinkage, and demonstrate that it asymptotically leads to selection consistency, that is, leads to the true model being selected. A simulation study demonstrates that the APLES estimator outperforms several univariate selection methods based on analyzing each outcome separately. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410750.v2
title: A Powerful Bayesian Test for Equality of Means in High Dimensions

description: We develop a Bayes factor-based testing procedure for comparing two population means in high-dimensional settings. In ‘large-p-small-n” settings, Bayes factors based on proper priors require eliciting a large and complex <i>p</i> × <i>p</i> covariance matrix, whereas Bayes factors based on Jeffrey’s prior suffer the same impediment as the classical Hotelling <i>T</i><sup>2</sup> test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be based on lower dimensional random projections of the high-dimensional data vectors. We choose the prior under the alternative to maximize the power of the test for a fixed threshold level, yielding a restricted most powerful Bayesian test (RMPBT). The final test statistic is based on the ensemble of Bayes factors corresponding to multiple replications of randomly projected data. We show that the test is unbiased and, under mild conditions, is also locally consistent. We demonstrate the efficacy of the approach through simulated and real data examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410747.v2
title: Tractable Bayesian Variable Selection: Beyond Normality

description: Bayesian variable selection often assumes normality, but the effects of model misspecification are not sufficiently understood. There are sound reasons behind this assumption, particularly for large <i>p</i>: ease of interpretation, analytical, and computational convenience. More flexible frameworks exist, including semi- or nonparametric models, often at the cost of some tractability. We propose a simple extension that allows for skewness and thicker-than-normal tails but preserves tractability. It leads to easy interpretation and a log-concave likelihood that facilitates optimization and integration. We characterize asymptotically parameter estimation and Bayes factor rates, under certain model misspecification. Under suitable conditions, misspecified Bayes factors induce sparsity at the same rates than under the correct model. However, the rates to detect signal change by an exponential factor, often reducing sensitivity. These deficiencies can be ameliorated by inferring the error distribution, a simple strategy that can improve inference substantially. Our work focuses on the likelihood and can be combined with any likelihood penalty or prior, but here we focus on nonlocal priors to induce extra sparsity and ameliorate finite-sample effects caused by misspecification. We show the importance of considering the likelihood rather than solely the prior, for Bayesian variable selection. The methodology is in R package ‘mombf.’ Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5410747.v1
title: Tractable Bayesian variable selection: beyond normality

description: Bayesian variable selection often assumes normality, but the effects of model misspecification are not sufficiently understood. There are sound reasons behind this assumption, particularly for large <i>p</i>: ease of interpretation, analytical and computational convenience. More flexible frameworks exist, including semi- or non-parametric models, often at the cost of some tractability. We propose a simple extension that allows for skewness and thicker-than-normal tails but preserves tractability. It leads to easy interpretation and a log-concave likelihood that facilitates optimization and integration. We characterize asymptotically parameter estimation and Bayes factor rates, under certain model misspecification. Under suitable conditions misspecified Bayes factors induce sparsity at the same rates than under the correct model. However, the rates to detect signal change by an exponential factor, often reducing sensitivity. These deficiencies can be ameliorated by inferring the error distribution, a simple strategy that can improve inference substantially. Our work focuses on the likelihood and can be combined with any likelihood penalty or prior, but here we focus on non-local priors to induce extra sparsity and ameliorate finite-sample effects caused by misspecification. We show the importance of considering the likelihood rather than solely the prior, for Bayesian variable selection. The methodology is in R package ‘mombf’.
DOI: 10.6084/m9.figshare.5410750.v1
title: A Powerful Bayesian Test for Equality of Means in High Dimensions

description: We develop a Bayes factor based testing procedure for comparing two population means in high dimensional settings. In ‘large-p-small-n’ settings, Bayes factors based on proper priors require eliciting a large and complex <i>p</i> × <i>p</i> covariance matrix, whereas Bayes factors based on Jeffrey's prior suffer the same impediment as the classical Hotelling <i>T</i><sup>2</sup> test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be based on lower dimensional random projections of the high dimensional data vectors. We choose the prior under the alternative to maximize the power of the test for a fixed threshold level, yielding a restricted most powerful Bayesian test (RMPBT). The final test statistic is based on the ensemble of Bayes factors corresponding to multiple replications of randomly projected data. We show that the test is unbiased and, under mild conditions, is also locally consistent. We demonstrate the efficacy of the approach through simulated and real data examples.
DOI: 10.6084/m9.figshare.5410726
title: Factors that influence tear meniscus area and conjunctivochalasis: The Singapore Indian eye study

description: <b><i>Purpose</i></b>: Assessment of tear film and conjunctiva is critical to define presence and severity of ocular surface disease. We aimed to characterize tear meniscus area (TMA) and conjunctivochalasis by anterior segment optical coherence tomography (ASOCT) in population-based patients and identify potential factors associated with low TMA and severe conjunctivochalasis. <b><i>Methods</i></b>: Study subjects were enrolled from The Singapore Indian Eye Study, a population-based study of Asian Indian in Singapore. Imaging with ASOCT was performed on three ocular regions (nasal, central and temporal). TMA was obtained by measuring the cross-sectional area of the inferior tear meniscus. Severity of conjunctivochalasis was quantified by measuring the conjunctivochalasis ratio (CCR), the ratio of area of redundant conjunctiva to the TMA. Ocular symptoms and demographic factors were assessed by standardized questionnaires. <b><i>Results</i></b>: A total of 403 participants (52.9% women) 40 years of age and older were included. TMA centrally was 2818 ± 5308 pixel<sup>2</sup>. Female sex and the presence of meibomian gland dysfunction (MGD), but not older age, were associated with a lower TMA (p = 0.031, p = 0.031 and p = 0.956 respectively). In this population, 9.2% had severe conjunctivochalasis (CCR&gt;0.7) whereas 39.0% had mild to no conjunctivochalasis (CCR≤0.3). Conjunctivochalasis was more severe in temporal, followed by nasal and central sections. Older age was associated with severe conjunctivochalasis (p <b><i>Conclusion</i></b>: MGD and female gender were associated with lower TMA, while older age was associated with increased severity of conjunctivochalasis. Objective measurement of TMA and CCR using ASOCT imaging may be useful in the assessment of tear volume and ocular surface tear function.