cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5144299.v2
title: Functional Feature Construction for Individualized Treatment Regimes

description: Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data preprocessing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than <i>Q</i>-learning with an ad hoc scalar summary and is consistent for the optimal treatment regime. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5144308.v1
title: BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data

description: We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed differential expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5144299.v1
title: Functional feature construction for individualized treatment regimes

description: Evidence-based personalized medicine formalizes treatment selection as an individualized treatment regime that maps up-to-date patient information into the space of possible treatments. Available patient information may include static features such race, gender, family history, genetic and genomic information, as well as longitudinal information including the emergence of comorbidities, waxing and waning of symptoms, side-effect burden, and adherence. Dynamic information measured at multiple time points before treatment assignment should be included as input to the treatment regime. However, subject longitudinal measurements are typically sparse, irregularly spaced, noisy, and vary in number across subjects. Existing estimators for treatment regimes require equal information be measured on each subject and thus standard practice is to summarize longitudinal subject information into a scalar, ad hoc summary during data pre-processing. This reduction of the longitudinal information to a scalar feature precedes estimation of a treatment regime and is therefore not informed by subject outcomes, treatments, or covariates. Furthermore, we show that this reduction requires more stringent causal assumptions for consistent estimation than are necessary. We propose a data-driven method for constructing maximally prescriptive yet interpretable features that can be used with standard methods for estimating optimal treatment regimes. In our proposed framework, we treat the subject longitudinal information as a realization of a stochastic process observed with error at discrete time points. Functionals of this latent process are then combined with outcome models to estimate an optimal treatment regime. The proposed methodology requires weaker causal assumptions than <i>Q</i>-learning with an ad hoc scalar summary and is consistent for the optimal treatment regime.
DOI: 10.6084/m9.figshare.5144269
title: On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification

description: The Hájek–Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon–Nikodym density for each measure with respect to the other one) or mutually singular. Unlike the case of finite-dimensional Gaussian measures, there are nontrivial examples of both situations when dealing with Gaussian stochastic processes. This article provides: (a) Explicit expressions for the optimal (Bayes) rule and the minimal classification error probability in several relevant problems of supervised binary classification of mutually absolutely continuous Gaussian processes. The approach relies on some classical results in the theory of reproducing kernel Hilbert spaces (RKHS). (b) An interpretation, in terms of mutual singularity, for the so-called “near perfect classification” phenomenon. We show that the asymptotically optimal rule proposed by these authors can be identified with the sequence of optimal rules for an approximating sequence of classification problems in the absolutely continuous case. (c) As an application, we discuss a natural variable selection method, which essentially consists of taking the original functional data <i>X</i>(<i>t</i>), <i>t</i> ∈ [0, 1] to a <i>d</i>-dimensional marginal (<i>X</i>(<i>t</i><sub>1</sub>), …, <i>X</i>(<i>t<sub>d</sub></i>)), which is chosen to minimize the classification error of the corresponding Fisher’s linear rule. We give precise conditions under which this discrimination method achieves the minimal classification error of the original functional problem. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5144275.v2
title: False Discovery Rate Smoothing

description: We present false discovery rate (FDR) smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a nonstandard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is presented. In simulation studies, FDR smoothing exhibits state-of-the-art performance at modest computational cost. In particular, it is shown to be far more robust than existing methods for spatially dependent multiple testing. We also apply the method to a dataset from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods. All code for FDR smoothing is publicly available in Python and R (<i>https://github.com/tansey/smoothfdr</i>). Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5144269.v2
title: On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification

description: The Hájek–Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon–Nikodym density for each measure with respect to the other one) or mutually singular. Unlike the case of finite-dimensional Gaussian measures, there are nontrivial examples of both situations when dealing with Gaussian stochastic processes. This article provides: (a) Explicit expressions for the optimal (Bayes) rule and the minimal classification error probability in several relevant problems of supervised binary classification of mutually absolutely continuous Gaussian processes. The approach relies on some classical results in the theory of reproducing kernel Hilbert spaces (RKHS). (b) An interpretation, in terms of mutual singularity, for the so-called “near perfect classification” phenomenon. We show that the asymptotically optimal rule proposed by these authors can be identified with the sequence of optimal rules for an approximating sequence of classification problems in the absolutely continuous case. (c) As an application, we discuss a natural variable selection method, which essentially consists of taking the original functional data <i>X</i>(<i>t</i>), <i>t</i> ∈ [0, 1] to a <i>d</i>-dimensional marginal (<i>X</i>(<i>t</i><sub>1</sub>), …, <i>X</i>(<i>t<sub>d</sub></i>)), which is chosen to minimize the classification error of the corresponding Fisher’s linear rule. We give precise conditions under which this discrimination method achieves the minimal classification error of the original functional problem. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5144275.v1
title: False discovery rate smoothing

description: We present false discovery rate smoothing, an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. The approach requires solving a non-standard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is presented. In simulation studies, FDR smoothing exhibits state-of-the-art performance at modest computational cost. In particular, it is shown to be far more robust than existing methods for spatially dependent multiple testing. We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods. All code for FDR smoothing is publicly available in Python and R.
DOI: 10.6084/m9.figshare.5144269.v1
title: On the use of reproducing kernel Hilbert spaces in functional classification

description: The Hájek-Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon-Nikodym density for each measure with respect to the other one) or mutually singular. Unlike the case of finite dimensional Gaussian measures, there are non-trivial examples of both situations when dealing with Gaussian stochastic processes. This paper provides: (a) Explicit expressions for the optimal (Bayes) rule and the minimal classification error probability in several relevant problems of supervised binary classification of mutually absolutely continuous Gaussian processes. The approach relies on some classical results in the theory of Reproducing Kernel Hilbert Spaces (RKHS). (b) An interpretation, in terms of mutual singularity, for the “near perfect classification” phenomenon described by Delaigle and Hall (2012a). We show that the asymptotically optimal rule proposed by these authors can be identified with the sequence of optimal rules for an approximating sequence of classification problems in the absolutely continuous case. (c) As an application, we discuss a natural variable selection method, which essentially consists of taking the original functional data <i>X</i>(<i>t</i>), <i>t</i> ∈ [0, 1] to a <i>d</i>-dimensional marginal (<i>X</i>(<i>t</i><sub>1</sub>), …, <i>X</i>(<i>t</i><sub><i>d</i></sub>)) which is chosen in order to minimize the classification error of the corresponding Fisher's linear rule. We give precise conditions under which this discrimination method achieves the minimal classification error of the original functional problem.
DOI: 10.6084/m9.figshare.5144173
title: Supplementary Table 3 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Basal Gammaproteobacteria 16S rRNA reference sequences
DOI: 10.6084/m9.figshare.5144173.v1
title: Supplementary Table 3 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Basal Gammaproteobacteria 16S rRNA reference sequences
DOI: 10.6084/m9.figshare.5144176
title: Supplementary Table 2 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Ciliate 18S rRNA reference sequences
DOI: 10.6084/m9.figshare.5144164
title: Root Shoot global data individual trees

description: Dataset for the research article entitled: "Tree size and climatic water deficit control root to shoot ratio in individual trees globally" by Ledo et al, 2017 - New Phytologist<br><b><br>A global dataset of individual tree Root:Shoot values, compiled from whole-tree harvesting studies. The dataset encompasses 409 sites and a total of 3416 trees of 212 species of both above- and below-ground biomass.</b>
DOI: 10.6084/m9.figshare.5144170
title: Supplementary Table 1 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Summary of morphospecies and available sequences
DOI: 10.6084/m9.figshare.5144176.v1
title: Supplementary Table 2 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Ciliate 18S rRNA reference sequences
DOI: 10.6084/m9.figshare.5144170.v1
title: Supplementary Table 1 from Specificity in diversity: single origin of a widespread ciliate-bacteria symbiosis

description: Summary of morphospecies and available sequences
DOI: 10.6084/m9.figshare.5144032
title: Workspace-file containing the values calculated for the variables used in MatlabScript.m

description: The workspace file allows to save the results of previous runs of the script, preventing one has to run the script again as a whole before conducting further computations. It can be used to comprehend the calculations we describe in the manuscript.
DOI: 10.6084/m9.figshare.5143993
title: Gene expression microarray datasets in NETCDF format from MEM database

description: The raw .CEL files and annotations were downloaded from ArrayExpress https://www.ebi.ac.uk/arrayexpress/ and stored as a part of the collection in Multi Experiment Matrix http://biit.cs.ut.ee/mem/ .As a part of MEM data preprocessing pipeline the samples were quantile-normalised and background corrected using just.rma() function from affy R package with default parameters. Preprocessed datasets were converted to ncdf format using ncdf R package. <br>List of the datasets in this collection:E-MEXP-2280 <br>E-GEOD-5281E-GEOD-4757 E-GEOD-29652 E-GEOD-28146 E-GEOD-18309<br>
DOI: 10.6084/m9.figshare.5143969
title: Platelet lysate as a serum replacement for skin cell culture on biomimetic PCL nanofibers

description: Platelets are a popular source of native growth factors for tissue engineering applications. The aim of the study was to verify the use of platelet lysate as a fetal bovine serum (FBS) replacement for skin cell culture. The cytokine content of the platelet lysate was characterized using the Bio-Plex system. The cells (fibroblasts, melanocytes, and keratinocytes) were cultured on PCL nanofibrous scaffolds to mimic their natural microenvironment. The cytokine content of the platelet lysate was determined, and to the cells, a medium containing platelet lysate or platelet lysate in combination with FBS was added. The results showed that 7% (v/v) platelet lysate was sufficient to supplement 10% (v/v) FBS in the culture of fibroblasts and keratinocytes. The combination of platelet lysate and FBS had a rather inhibitory effect on fibroblasts, in contrary to keratinocytes, where the effect was synergic. Platelet lysate did not sufficiently promote proliferation in melanocytes; however, the combination of FBS and platelet lysate yielded a better outcome and resulted in bipolar morphology of the cultured melanocytes. The data indicated that platelet lysate improved cell proliferation and metabolic activity and may be used as an additive to the cell culture media.
DOI: 10.6084/m9.figshare.5144032.v3
title: Workspace-file containing the values calculated for the variables used in MatlabScript.m

description: The workspace file allows to save the results of previous runs of the script, preventing one has to run the script again as a whole before conducting further computations. It can be used to comprehend the calculations we describe in the manuscript.
DOI: 10.6084/m9.figshare.5144032.v2
title: Workspace-file containing the values calculated for the variables used in MatlabScript.m

description: The workspace file allows to save the results of previous runs of the script, preventing one has to run the script again as a whole before conducting further computations. It can be used to comprehend the calculations we describe in the manuscript.
DOI: 10.6084/m9.figshare.5143951
title: Supplementary Material for: A Role for Neuronal Alpha-Synuclein in Gastrointestinal Immunity

description: <i>Background:</i> Alpha-synuclein (αS) is a nerve cell protein associated with Parkinson disease (PD). Accumulation of αS within the enteric nervous system (ENS) and its traffic from the gut to the brain are implicated in the pathogenesis and progression of PD. αS has no known function in humans and the reason for its accumulation within the ENS is unknown. Several recent studies conducted in rodents have linked αS to immune cell activation in the central nervous system. We hypothesized that αS in the ENS might play a role in the innate immune defenses of the human gastrointestinal (GI) tract. <i>Methods:</i> We immunostained endoscopic biopsies for αS from children with documented gastric and duodenal inflammation and intestinal allograft recipients who contracted norovirus. To determine whether αS exhibited immune-modulatory activity, we examined whether human αS induced leukocyte migration and dendritic cell maturation. <i>Findings:</i> We showed that the expression of αS in the enteric neurites of the upper GI tract of pediatric patients positively correlated with the degree of acute and chronic inflammation in the intestinal wall. In intestinal allograft subjects who were closely monitored for infection, expression of αS was induced during norovirus infection. We also demonstrated that both monomeric and oligomeric αS have potent chemoattractant activity, causing the migration of neutrophils and monocytes dependent on the presence of the integrin subunit, CD11b, and that both forms of αS stimulate dendritic cell maturation. <i>Interpretation:</i> These findings strongly suggest that αS is expressed within the human ENS to direct intestinal inflammation and implicates common GI infections in the pathogenesis of PD.
DOI: 10.6084/m9.figshare.5143948
title: Supplementary Material to BEH 3443

description: Video clips made during Canteloup et al.’s (2016) visual perspective-taking study. The study took place in three experimental areas (Canteloup et al., 2016) and subjects’ participation was voluntary. Subjects were assigned as dominant or subordinate depending on which other monkey came to participate in a given session. Each monkey started on opposite sides of the middle test area, and both were given access to the test area after an experimenter had placed two pieces of food there. One piece was placed on top of one of two breezeblocks so as to be visible to both individuals; the second piece was placed inside the other breezeblock so as to be hidden from the dominant. No training was used in this experiment.We ran two conditions, as follows: In condition 1 the subordinate was released into the test area simultaneously with the dominant; in condition 2 the subordinate had a short head start on the dominant (i.e., the dominant was released as soon as the subordinate’s entire body was in the test area). nalyses were done on a total of 360 trials involving 20 different dominant-subordinate dyads.<br><br>
DOI: 10.6084/m9.figshare.5143951.v1
title: Supplementary Material for: A Role for Neuronal Alpha-Synuclein in Gastrointestinal Immunity

description: <i>Background:</i> Alpha-synuclein (αS) is a nerve cell protein associated with Parkinson disease (PD). Accumulation of αS within the enteric nervous system (ENS) and its traffic from the gut to the brain are implicated in the pathogenesis and progression of PD. αS has no known function in humans and the reason for its accumulation within the ENS is unknown. Several recent studies conducted in rodents have linked αS to immune cell activation in the central nervous system. We hypothesized that αS in the ENS might play a role in the innate immune defenses of the human gastrointestinal (GI) tract. <i>Methods:</i> We immunostained endoscopic biopsies for αS from children with documented gastric and duodenal inflammation and intestinal allograft recipients who contracted norovirus. To determine whether αS exhibited immune-modulatory activity, we examined whether human αS induced leukocyte migration and dendritic cell maturation. <i>Findings:</i> We showed that the expression of αS in the enteric neurites of the upper GI tract of pediatric patients positively correlated with the degree of acute and chronic inflammation in the intestinal wall. In intestinal allograft subjects who were closely monitored for infection, expression of αS was induced during norovirus infection. We also demonstrated that both monomeric and oligomeric αS have potent chemoattractant activity, causing the migration of neutrophils and monocytes dependent on the presence of the integrin subunit, CD11b, and that both forms of αS stimulate dendritic cell maturation. <i>Interpretation:</i> These findings strongly suggest that αS is expressed within the human ENS to direct intestinal inflammation and implicates common GI infections in the pathogenesis of PD.