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opencc-zeroFeb 2022View details →

Rates of premature fruit drop for 201 plant species on Barro Colorado Island, Panama

Pre-dispersal seed mortality caused by premature fruit drop is a potentially important source of plant mortality, but one which has rarely been studied in the context of tropical forest plants. Of particular interest is premature fruit drop triggered by enemies, which – if density-dependent – could contribute to species co-existence in tropical forest plant communities. We used a long-term (31 year) dataset on seed and fruit fall obtained through weekly collections from a network of seed traps in a lowland tropical forest (Barro Colorado Island, Panama) to estimate the proportion of seeds prematurely abscised for 201 woody plant species. To determine whether enemy attack might contribute to premature fruit drop we tested whether plant species abscise more of their fruit prematurely if they: (1) have attributes hypothesised to be associated with high levels of enemy attack, and (2) are known to be attacked by one enemy-group (insect seed predators). We also tested (3) whether mean rates of premature fruit drop for plant species are phylogenetically conserved. Overall rates of premature fruit drop were high in the plant community. Across all species, 39% of seeds were abscised before completing their development. Rates of premature seed abscission varied considerably among species and could not be explained by phylogeny. Premature seed abscission rates were higher in species which are known to host pre-dispersal insect seed predators and species with attributes that were hypothesised to make them more susceptible to attack by pre-dispersal enemies, namely species which (1) have larger seeds, (2) have a greater average height, (3) have temporally predictable fruiting patterns, and (4) are more abundant at the study site. Synthesis. Premature fruit drop is likely to be a major source of seed mortality for many plant species on Barro Colorado Island. It is plausible that pre-dispersal seed enemies, such as insect seed predators, contribute to community-level patterns of premature fruit drop and have the potential to mediate species co-existence through stabilising negative density dependence. Our study suggests that the role of pre-dispersal enemies in structuring tropical plant communities should be considered alongside the more commonly studied post-dispersal seed and seedling enemies.

zenodo40/100

Supplementary Materials: The molecular landscape of premature aging diseases defined by multilayer network exploration

This dataset contains supplementary materials related to study "The molecular landscape of premature aging diseases defined by multilayer network exploration" (Beust C., Valdeolivas A., Baptista A., Brière G., Lévy N., Ozisik O., Baudot A.) Supplementary Figures:   Supplementary Figure S1: Clustering of the premature aging disease communities obtained with 50 iterations of  itRWR   Supplementary Figure S2: Clustering of the premature aging disease communities obtained with 150 iterations of  itRWR   Supplementary Figure S3: Clustering of the premature aging disease communities obtained with 100 iteration of  itRWR, with a cutoff value at 0.5 on the dendrogram   Supplementary Tables:   Supplementary Table S1: Premature Aging HPO phenotypes   Supplementary Table S2:Tthe 67 PA diseases and their 132 associated genes from ORPHANET   Supplementary Table S3: Number of nodes, edges, and densities of the 4 network layers composing the multilayer  biological network   Supplementary Table S4: Parameters used for MultiXrank   Supplementary Files:   Supplementary File S1: Csv file containing the enrichment analysis results computed using the genes associated with  physiological aging   Supplementary File S2: Excel file containing the gene nodes belonging to each of the 67 communities   Supplementary File S3: Excel file containing the gene nodes belonging to each cluster (i.e., the union of the genes belonging to the set of communities composing the cluster)   Supplementary File S4: Csv file containing the diseases belonging to each cluster   Supplementary File S5: Excel file containing the enrichment of clusters using lists of physiological aging genes   Supplementary File S6: Csv file containing the lists of genes differentially expressed in human blood, skin, brain, muscle and breast during aging, from the study of Irizar et al.   Supplementary File S7: Excel file containing the enrichment analysis results of the 67 communities, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations   Supplementary File S8: Excel file containing the enrichment analysis results of the 6 clusters, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations. The last 2 columns contain  the seed nodes of the cluster annotated for the corresponding function and their corresponding diseases. Supplementary File S8bis: Excel file containing the enrichment analysis results of the 8 clusters obtained with an alternative cutoff of 0.5 in the dendrogram, using GO Biological Processes and Cellular Components, and Reactome pathways functional annotations. Supplementary File S9: Excel file containing enrichment analysis results of the 6 clusters, using HPO phenotypes   Supplementary Text:   Supplementary Text S1: Comparison with alternative network exploration strategies, including classical (non- iterative) Random Walk with Restart, Multilayer network partitioning, and computation of network distances with shortest paths.

opencc-by-4.0Dec 2023View details →

zenodo40/100

Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity

Running title: series2signal gestational age "clock" for pregnancy monitoring Summary:  Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep. Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.   Data description (brief): the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.

opencc-by-4.0Feb 2023View details →

ClinicalTrials.gov40/100

RAINBOW Study: RAnibizumab Compared With Laser Therapy for the Treatment of INfants BOrn Prematurely With Retinopathy of Prematurity

ClinicalTrials.gov study NCT02375971. IPD Sharing: UNDECIDED. Countries: 26. Publications: 2.

publicJun 2025View details →

Data from: USP37 prevents premature disassembly of stressed replisomes by TRAIP

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publicApr 2022View details →

Rates of premature fruit drop for 201 plant species on Barro Colorado Island, Panama

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publicFeb 2025View details →

A single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants

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zenodo36/100

Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States

This repo includes supporting material for the publication: Qiu, M., Zigler, C. M., & Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. Science Advances, 8(48), eabn8762. Please download and unzip the file "<a href="https://zenodo.org/api/files/bef77248-ca65-4ae6-9a92-ad00fc068665/mhqiu/wind_pollution_EJ-v1.0.zip?versionId=a9b5b8e9-273c-4431-a101-4f9a4c331675">mhqiu/wind_pollution_EJ-v1.0.zip</a>". Please see README for a full description of the sample data included in this repo.  README 1. Regression results:  EGU_regression_scenario_results.xlsx  It includes regression results for each EGU in our sample (1264 EGUs in total).  2. Air quality simulation results: 2.1 GEOS-Chem simulation GC_daily_pm25_o3_scenarios.nc  It contains surface level annual mean PM2.5 and MDA8 O3 concentration under different emission scenarios. We include four scenarios in total: - baseline scenario: air quality **without** the amount of wind power associated with 2014 RPS targets  - expost scenario:  air quality with the wind power associated with 2014 RPS targets under the current dispatch decisions - health damage minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the health damage - CO2 minimizing scenario: air quality with the wind power associated with 2014 RPS targets under a hypothetical dispatch scenario that minimizes the CO2 emissions Therefore, to calculate the air quality impacts of wind power under different dispatch decisions:   current (ex post) = ex post - baseline.   health damage minimizing =  health damage minimizing - baseline.   CO2 minimizing scenario = CO2 minimizing - baseline.   2.2 InMAP simulations InMAP/xx.shp   The shapefiles contain annual mean PM2.5 concentration simulated with InMAP under different emission scenarios.   baseline.shp: baseline scenario   ex post.shp: ex post scenario   health_damage_minimizing.shp: health damage minimizing scenario   co2_minimizing.shp: CO2 minimizing scenario Descriptions of the four scenarios are the same as above for GEOS-Chem. 3. County-level air quality change for different demographic groups (GEOS-Chem) county_pm_o3_changes_by_groups_GEOS_CHEM.xlsx   This file contains changes in county-level simulated PM2.5 and O3 concentrations due to wind power under different scenarios. It also includes the total population at the county level and the population for each subgroup. This data can be used to calculate the distributional effects of air quality benefits across different population groups.

openother-openNov 2022View details →

zenodo36/100

Data for Bernabeu-Herrero et al, Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT

This dataset is for the 2024 manuscript: Bernabéu-Herrero ME, Patel D, Bielowka A, Zhu J, Jain K, Mackay IS, Chaves Guerrero P, Emanuelli G, Jovine L, Noseda M, Marciniak SJ, Aldred MA, Shovlin CL. Mutations causing premature termination codons discriminate and generate cellular and clinical variability in HHT. Blood. 2024 May 30;143(22):2314-2331. doi: 10.1182/blood.2023021777. PMID: 38457357; PMCID: PMC11181359. It was originally uploaded in 2021 ahead of an earlier manuscript submission - see https://www.biorxiv.org/content/10.1101/2021.12.05.471269v1

opencc-by-4.0Aug 2021View details →

Premature Newborns Treated With Less Invasive Surfactant Administration Under Heated Humidified High-flow

ClinicalTrials.gov study NCT06398691. IPD Sharing: YES. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →

Safety of Sildenafil in Premature Infants With Severe Bronchopulmonary Dysplasia

ClinicalTrials.gov study NCT04447989. IPD Sharing: NO. Countries: 1. Publications: 11.

Inhaled Nitric Oxide and Neuroprotection in Premature Infants

ClinicalTrials.gov study NCT00515281. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.

Bevacizumab Eliminates the Angiogenic Threat for Retinopathy of Prematurity

ClinicalTrials.gov study NCT00622726. IPD Sharing: Not stated. Countries: 1. Publications: 9.

Transfusion of Prematures Trial

ClinicalTrials.gov study NCT01702805. IPD Sharing: YES. Countries: 1. Publications: 4.

closedIPD-NOFeb 2026View details →

NEO Rehab Program for Premature Infants at Risk for Cerebral Palsy

ClinicalTrials.gov study NCT04330859. IPD Sharing: NO. Countries: 1. Publications: 1.

Non-invasive Intervention for Apnea of Prematurity

ClinicalTrials.gov study NCT02641249. IPD Sharing: YES. Countries: 1. Publications: 1.

Prophylactic Probiotics to Extremely Low Birth Weight Prematures

ClinicalTrials.gov study NCT01603368. IPD Sharing: Not stated. Countries: 1. Publications: 2.

Efficacy and Safety of Mydriatic Microdrops for Retinopathy Of Prematurity Screening

ClinicalTrials.gov study NCT05043077. IPD Sharing: YES. Countries: 1. Publications: 5.