cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7764503
title: Example 1 observations from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764599.v1
title: dataset.dta

description: This is the dataset of a manuscript titled: "Does source credibility matter for point-of-decision prompts? A quasi-experimental field study to increase stair use"<br><br>
DOI: 10.7284/108360
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AE1109 using a Hawaii UHDAS instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7764494
title: Example 1 observation simulation times from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764500
title: Example 2 observations from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764503.v1
title: Example 1 observations from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764491
title: Example 2 observation simulation times from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764491.v1
title: Example 2 observation simulation times from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764494.v1
title: Example 1 observation simulation times from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.6084/m9.figshare.7764500.v1
title: Example 2 observations from Exploiting network topology for large-scale inference of nonlinear reaction models

description: The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved ‘between-model’ proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.
DOI: 10.7284/108361
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise AE1109 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/108359
title: Fluorometer data collected during research cruise AE1109 using a Turner 10-AU instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7764422
title: Supplementary Material for: Type of Re-Hospitalization and Association with Neonatal Morbidities in Infants of Very Low Birth Weight

description: <b><i>Background:</i></b> Preterm infants are at high risk for long-term morbidities and an increased rate of re-hospitalization. <b><i>Objective:</i></b> The aim of this study was to evaluate the type of re-hospitalization of very low birth weight (VLBW) infants, from infancy through adolescence, and to assess the association of neonatal morbidities with specific types of re-hospitalization. <b><i>Study Design:</i></b> The study cohort comprised 6,385 VLBW infants who were registered with the Maccabi Healthcare Services (MHS) from their birth hospitalization. Data were collected for up to 18 years (median 10.7 years) following neonatal intensive care unit discharge. Hospitalization types were determined from the MHS coding. Neonatal morbidities included necrotizing enterocolitis (NEC), grades 3–4 intraventricular hemorrhage (IVH), periventricular leukomalacia (PVL), bronchopulmonary dysplasia (BPD), and retinopathy of prematurity (ROP). Adjusted hazard ratios (aHR) and 95% confidence intervals (CI) were calculated using the Cox proportional hazards model. <b><i>Results:</i></b> Overall, 3,956 infants were re-hospitalized at least once and a total of 11,595 hospitalization types were identified. NEC, IVH, PVL, and BPD were associated with significantly higher aHRs for general pediatric (aHR 1.28–1.55), general surgical (aHR 1.18–1.46), and pediatric intensive care unit (aHR 1.57–2.04) hospitalizations. IVH and PVL were associated with significantly higher aHRs for orthopedic (aHR 2.12 and 4.88, respectively) and ophthalmology (1.76 and 4.02, respectively) hospitalizations. IVH was associated with a 14.2-fold higher aHR for neurosurgical admissions, and ROP with a 1.62-fold higher aHR for ophthalmology hospitalizations. <b><i>Conclusion:</i></b> Among VLBW infants, specific patterns of re-hospitalization types associated with major neonatal morbidities were identified as particularly high risks for orthopedic and ophthalmology hospitalizations in infants with IVH and PVL, and for intensive care admissions in infants with NEC and BPD.
DOI: 10.7284/106529
title: GNSS (Global Navigation Satellite System) data collected during research cruise AE1109 using a Furuno GP-90 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7764371
title: Effects of root and leaf litter identity and diversity on oribatid mite abundance, diversity and community composition.csv

description: The table includes data of remaining litter masss, microbial biomass and oribatid mite abundance, diversity and the proportion of litter living and parthenogentic individuals in the presence of different leaf litter and root species combinations.<br>
DOI: 10.6084/m9.figshare.7764422.v1
title: Supplementary Material for: Type of Re-Hospitalization and Association with Neonatal Morbidities in Infants of Very Low Birth Weight

description: <b><i>Background:</i></b> Preterm infants are at high risk for long-term morbidities and an increased rate of re-hospitalization. <b><i>Objective:</i></b> The aim of this study was to evaluate the type of re-hospitalization of very low birth weight (VLBW) infants, from infancy through adolescence, and to assess the association of neonatal morbidities with specific types of re-hospitalization. <b><i>Study Design:</i></b> The study cohort comprised 6,385 VLBW infants who were registered with the Maccabi Healthcare Services (MHS) from their birth hospitalization. Data were collected for up to 18 years (median 10.7 years) following neonatal intensive care unit discharge. Hospitalization types were determined from the MHS coding. Neonatal morbidities included necrotizing enterocolitis (NEC), grades 3–4 intraventricular hemorrhage (IVH), periventricular leukomalacia (PVL), bronchopulmonary dysplasia (BPD), and retinopathy of prematurity (ROP). Adjusted hazard ratios (aHR) and 95% confidence intervals (CI) were calculated using the Cox proportional hazards model. <b><i>Results:</i></b> Overall, 3,956 infants were re-hospitalized at least once and a total of 11,595 hospitalization types were identified. NEC, IVH, PVL, and BPD were associated with significantly higher aHRs for general pediatric (aHR 1.28–1.55), general surgical (aHR 1.18–1.46), and pediatric intensive care unit (aHR 1.57–2.04) hospitalizations. IVH and PVL were associated with significantly higher aHRs for orthopedic (aHR 2.12 and 4.88, respectively) and ophthalmology (1.76 and 4.02, respectively) hospitalizations. IVH was associated with a 14.2-fold higher aHR for neurosurgical admissions, and ROP with a 1.62-fold higher aHR for ophthalmology hospitalizations. <b><i>Conclusion:</i></b> Among VLBW infants, specific patterns of re-hospitalization types associated with major neonatal morbidities were identified as particularly high risks for orthopedic and ophthalmology hospitalizations in infants with IVH and PVL, and for intensive care admissions in infants with NEC and BPD.
DOI: 10.6084/m9.figshare.7764365
title: Supplementary material for Thomas et al., 2019.pdf

description: Supplementary Figures and Tables for Candidate Genes for Atrial Fibrillation Identified using Differential Gene Expression from Paired Human Left and Right Atrial Appendages.Supplementary Figures:S1: HeatmapS2: MD plot of A) SR_LAA vs AF_LAA and B) SR_RAA vs AF_RAA.S3: Volcano plot of A) SR_LAA vs AF_LAA and B) SR_RAA vs AF_RAA.S4: Differentially expressed genes between SR_LAA vs AF_LAA heatmap.S5: Differentially expressed genes between SR_RAA vs AF_RAA heatmap.<br>Supplementary Tables:<br>ST1: SR-LAA vs AF-LAA genes identified by EdgeR using 5% FDR cutoff.<br>ST2: SR-RAA vs AF-RAA genes identified by EdgeR using 5% FDR cutoff.ST3: Enrichment results.ST4: KEGG enrichment.ST5: Enrichment by Appendage.ST6: SNPs identified by GWAS to be associated with Atrial Fibrillation.
DOI: 10.6084/m9.figshare.7764368
title: BenSassi et al.TransectSoundness-250219.xlsx

description: These data are of a marked sub-population of broiler chickens that was tracked to investigate the soundness of a welfare assessment method called the transect method. Transects are paths delimited by feeder and drinker lines walked by the observer when assessing welfare. In this study, eighty birds were individually marked within the commercial house, and their movement was tracked during two days in morning and afternoon samplings. Data describe the farm and house ids, the transect where the bird was detected, the bird id, the time in which the assessment was conducted, the number of transects per house and the flock density. From these data, we calculated the detection rate per house sampling, the repetition rates per house and within transect. We also estimated the number of transects that repeated birds traveled as well as the sub-population distribution index. This study complied with the Spanish legislation regarding the use of animals for experimental and other scientific purposes (Real Decreto 1201/2005).<br>
DOI: 10.6084/m9.figshare.7764371.v1
title: Effects of root and leaf litter identity and diversity on oribatid mite abundance, diversity and community composition.csv

description: The table includes data of remaining litter masss, microbial biomass and oribatid mite abundance, diversity and the proportion of litter living and parthenogentic individuals in the presence of different leaf litter and root species combinations.<br>
DOI: 10.6084/m9.figshare.7764359
title: Dataset for: mRNA 5’ ends targeted by cytoplasmic recapping cluster at CAGE tags and select transcripts are alternatively spliced

description: Until cytoplasmic recapping was discovered, decapping was thought to irreversibly destine an mRNA to degradation. Contradicting this idea, we readily observe mRNAs targeted by cytoplasmic capping in uncapped, yet stable forms. 5’ RACE shows that nearly all uncapped ends correspond to CAGE tags and that the recapping of <i>ZNF207 </i>mRNA may be restricted to a single splice isoform. A modified RACE approach detected uncapped 5’ RNA ends mapping to 46 mRNAs in dominant negative cytoplasmic capping enzyme expressing and normal cells. 11 of 46 cloned mRNAs also contained splice isoform-limiting sequences. Collectively, these data reinforce earlier work and suggest that alternative splicing may play a role in targeting transcripts for– and/or determining the position of– cytoplasmic capping.
DOI: 10.6084/m9.figshare.7764365.v1
title: Supplementary material for Thomas et al., 2019.pdf

description: Supplementary Figures and Tables for Candidate Genes for Atrial Fibrillation Identified using Differential Gene Expression from Paired Human Left and Right Atrial Appendages.Supplementary Figures:S1: HeatmapS2: MD plot of A) SR_LAA vs AF_LAA and B) SR_RAA vs AF_RAA.S3: Volcano plot of A) SR_LAA vs AF_LAA and B) SR_RAA vs AF_RAA.S4: Differentially expressed genes between SR_LAA vs AF_LAA heatmap.S5: Differentially expressed genes between SR_RAA vs AF_RAA heatmap.<br>Supplementary Tables:<br>ST1: SR-LAA vs AF-LAA genes identified by EdgeR using 5% FDR cutoff.<br>ST2: SR-RAA vs AF-RAA genes identified by EdgeR using 5% FDR cutoff.ST3: Enrichment results.ST4: KEGG enrichment.ST5: Enrichment by Appendage.ST6: SNPs identified by GWAS to be associated with Atrial Fibrillation.
DOI: 10.7284/108357
title: TSG (Thermosalinograph) data collected during research cruise AE1109 using a Sea-Bird SBE-21 instrument system onboard the platform RV Atlantic Explorer

description: Original (unprocessed) data collected during a Field_expedition