cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31287007
title: Supplementary Material for: Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study

description: Introduction 
Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, clinical information required to inform prediction tools is often missing. We evaluated the performance of machine learning (ML) prediction models of adverse stroke outcome at 90 days post-admission that exploit non-clinical data, and missingness, alongside traditional clinical and demographic predictors.
Methods 
We used routine hospital data from UK clinical sites (NHS SafeHaven) to train three Gradient Boosted (GBM) models. We compared baseline clinical features with non-clinical features and missingness to predict a composite 90-day adverse stroke outcome: mortality, stroke recurrence or new care-home discharge. Model validation used 10% of the data. Model performance was evaluated by accuracy (correct predictions/total predictions) and Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) while DeLong’s test was used to compare performance of the three models. We used Brier score to evaluate model calibration. SHapley Additive exPlanations (SHAP) analyses determined the contribution of each model feature in predicting adverse stroke outcome.
Results 
The final sample included 3530 stroke patients with 51% males (mean age=72 years; SD=14). Clinical data were incomplete with five clinical features having &gt;63% missing values. The performance of the three models was not significantly different (p=0.5 to 0.9). The model with non-clinical and missingness features demonstrated 71% accuracy and AUC of 0.76 with Brier score of 0.19. Non-clinical factors, such as time to clinical assessment and time to admission, were amongst the five most important predictors of adverse stroke outcome (mean |SHAP|=0.03 and 0.05), alongside Glasgow Coma Scale (0.08), age (0.03) and temperature (0.02). Missing clinical values (pulse and LDL) predicted adverse stroke outcome (mean |SHAP|=0.02 and 0.02) and were correlated with age (ρ=0.2), arrival by ambulance (ρ=0.3), length of stay (ρ=-0.3) and Transient Ischemic Attack (ρ=0.3).
Conclusion
We demonstrate that non-clinical factors and missingness of data can assist in early predictions of 90-day adverse stroke outcomes. As these factors are often well documented in electronic health systems they could complement or supplement traditional clinical predictive factors.
DOI: 10.6084/m9.figshare.31287007.v1
title: Supplementary Material for: Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study

description: Introduction 
Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, clinical information required to inform prediction tools is often missing. We evaluated the performance of machine learning (ML) prediction models of adverse stroke outcome at 90 days post-admission that exploit non-clinical data, and missingness, alongside traditional clinical and demographic predictors.
Methods 
We used routine hospital data from UK clinical sites (NHS SafeHaven) to train three Gradient Boosted (GBM) models. We compared baseline clinical features with non-clinical features and missingness to predict a composite 90-day adverse stroke outcome: mortality, stroke recurrence or new care-home discharge. Model validation used 10% of the data. Model performance was evaluated by accuracy (correct predictions/total predictions) and Area Under the Receiver Operating Characteristics (ROC) Curve (AUC) while DeLong’s test was used to compare performance of the three models. We used Brier score to evaluate model calibration. SHapley Additive exPlanations (SHAP) analyses determined the contribution of each model feature in predicting adverse stroke outcome.
Results 
The final sample included 3530 stroke patients with 51% males (mean age=72 years; SD=14). Clinical data were incomplete with five clinical features having &gt;63% missing values. The performance of the three models was not significantly different (p=0.5 to 0.9). The model with non-clinical and missingness features demonstrated 71% accuracy and AUC of 0.76 with Brier score of 0.19. Non-clinical factors, such as time to clinical assessment and time to admission, were amongst the five most important predictors of adverse stroke outcome (mean |SHAP|=0.03 and 0.05), alongside Glasgow Coma Scale (0.08), age (0.03) and temperature (0.02). Missing clinical values (pulse and LDL) predicted adverse stroke outcome (mean |SHAP|=0.02 and 0.02) and were correlated with age (ρ=0.2), arrival by ambulance (ρ=0.3), length of stay (ρ=-0.3) and Transient Ischemic Attack (ρ=0.3).
Conclusion
We demonstrate that non-clinical factors and missingness of data can assist in early predictions of 90-day adverse stroke outcomes. As these factors are often well documented in electronic health systems they could complement or supplement traditional clinical predictive factors.
DOI: 10.6084/m9.figshare.31287012
title: Additional file 7 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.31287012.v1
title: Additional file 7 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.31287015
title: Additional file 8 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.31287015.v1
title: Additional file 8 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.31287018
title: Additional file 9 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 9
DOI: 10.6084/m9.figshare.31287018.v1
title: Additional file 9 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 9
DOI: 10.6084/m9.figshare.31287021
title: Additional file 10 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 10
DOI: 10.6084/m9.figshare.31287021.v1
title: Additional file 10 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 10
DOI: 10.6084/m9.figshare.31287024
title: Additional file 11 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 11
DOI: 10.6084/m9.figshare.31287024.v1
title: Additional file 11 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 11
DOI: 10.6084/m9.figshare.31287027
title: Additional file 12 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 12
DOI: 10.6084/m9.figshare.31287027.v1
title: Additional file 12 of MTHFD2 is required for DNA repair and implicated in LUAD radiotherapy resistance

description: Supplementary Material 12
DOI: 10.6084/m9.figshare.31287036
title: Additional file 2 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file2 (XLSX 13 kb)
DOI: 10.6084/m9.figshare.31287036.v1
title: Additional file 2 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file2 (XLSX 13 kb)
DOI: 10.6084/m9.figshare.31287039
title: Additional file 3 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file3 (XLSX 2975 kb)
DOI: 10.6084/m9.figshare.31287039.v1
title: Additional file 3 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file3 (XLSX 2975 kb)
DOI: 10.6084/m9.figshare.31287042
title: Additional file 4 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file4 (XLSX 8187 kb)
DOI: 10.6084/m9.figshare.31287042.v1
title: Additional file 4 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file4 (XLSX 8187 kb)
DOI: 10.6084/m9.figshare.31287045
title: Additional file 5 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file5 (XLSX 2070 kb)
DOI: 10.6084/m9.figshare.31287045.v1
title: Additional file 5 of Mapping the secondary response to traumatic brain injury using spatial transcriptomics shows acute 4-aminopyridine treatment mitigates axonal and molecular pathology

description: Additional file5 (XLSX 2070 kb)
DOI: 10.6084/m9.figshare.31287054
title: Additional file 2 of Trends in mortality and major adverse cardiovascular events following incident acute myocardial infarction

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31287054.v1
title: Additional file 2 of Trends in mortality and major adverse cardiovascular events following incident acute myocardial infarction

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.31287060
title: Additional file 1 of Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm

description: Supplementary material 1. Table 1. Wavelengths of bands. The exact wavelength of each band is reported for the VNIR and SWIR cameras.
DOI: 10.6084/m9.figshare.31287060.v1
title: Additional file 1 of Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm

description: Supplementary material 1. Table 1. Wavelengths of bands. The exact wavelength of each band is reported for the VNIR and SWIR cameras.
DOI: 10.6084/m9.figshare.31287063
title: Additional file 2 of Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm

description: Supplementary material 2. Table 2. Feature importances of models. The feature importances (from most to least important) are reported for the binary and multiclass models for both Random Forest and Neural Network. The feature importances were calculated using LIME.
DOI: 10.6084/m9.figshare.31287063.v1
title: Additional file 2 of Assisting species differentiation and taxonomic classification by hyperspectral imaging: an example from the parasitic plant realm

description: Supplementary material 2. Table 2. Feature importances of models. The feature importances (from most to least important) are reported for the binary and multiclass models for both Random Forest and Neural Network. The feature importances were calculated using LIME.
DOI: 10.6084/m9.figshare.31287090
title: Additional file 1 of Mycophenolate mofetil reduces the branching of microglial processes

description: Additional file1.
DOI: 10.6084/m9.figshare.31287090.v1
title: Additional file 1 of Mycophenolate mofetil reduces the branching of microglial processes

description: Additional file1.
DOI: 10.6084/m9.figshare.31287095
title: Additional file 20 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 20
DOI: 10.6084/m9.figshare.31287095.v1
title: Additional file 20 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 20
DOI: 10.6084/m9.figshare.31287098
title: Additional file 21 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 21
DOI: 10.6084/m9.figshare.31287098.v1
title: Additional file 21 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 21
DOI: 10.6084/m9.figshare.31287101
title: Additional file 22 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 22
DOI: 10.6084/m9.figshare.31287101.v1
title: Additional file 22 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 22
DOI: 10.6084/m9.figshare.31287104
title: Additional file 23 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 23
DOI: 10.6084/m9.figshare.31287104.v1
title: Additional file 23 of KNTC1 initiates a KNTC1/E2F8/MYC positive feedback loop to facilitate tumorigenesis and enhance chemoresistance in bladder cancer

description: Supplementary Material 23
DOI: 10.6084/m9.figshare.31287138
title: Additional file 2 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 2.
DOI: 10.6084/m9.figshare.31287138.v1
title: Additional file 2 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 2.
DOI: 10.6084/m9.figshare.31287141
title: Additional file 3 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 3.
DOI: 10.6084/m9.figshare.31287141.v1
title: Additional file 3 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 3.
DOI: 10.6084/m9.figshare.31287144
title: Additional file 4 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 4.
DOI: 10.6084/m9.figshare.31287144.v1
title: Additional file 4 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 4.
DOI: 10.6084/m9.figshare.31287147
title: Additional file 5 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 5.
DOI: 10.6084/m9.figshare.31287147.v1
title: Additional file 5 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 5.
DOI: 10.6084/m9.figshare.31287162
title: Additional file 9 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 9.
DOI: 10.6084/m9.figshare.31287162.v1
title: Additional file 9 of Proteomics analysis of human mesenchymal stromal/stem cell sarcomagenesis model identifies ALDH1A3 and CD99 as potential targets in the transformation process

description: Additional file 9.
DOI: 10.15468/dl.tw8s9q
title: Occurrence Download

description: A dataset containing 5179 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schoenoplectus pungens",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 5179 records from 233 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017875-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.31287170
title: Additional file 2 of Learning from the past: a scoping review of hospital disaster preparedness assessment

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31287170.v1
title: Additional file 2 of Learning from the past: a scoping review of hospital disaster preparedness assessment

description: Supplementary Material 2
DOI: 10.15468/uzxpbn
title: NZ National Vegetation Survey occurrence data

description: The National Vegetation Survey Databank (NVS) is a physical archive and electronic databank containing records of over 121,000 vegetation survey plots - including data from over 25,000 permanent plots. These data can be explored online as well as requested for download. NVS provides a unique record, spanning more than 70 years, of indigenous and exotic plants in New Zealand's terrestrial ecosystems, from Northland to Stewart Island and the Kermadec and Chatham islands. A broad range of habitats are covered, with special emphasis on indigenous forests and grasslands. The physical archive includes plot sheets, maps, and photographs from many years of vegetation surveys.
DOI: 10.6084/m9.figshare.31287176
title: Additional file 2 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31287176.v1
title: Additional file 2 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.31287179
title: Additional file 3 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.31287179.v1
title: Additional file 3 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.31287182
title: Additional file 4 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31287182.v1
title: Additional file 4 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 4
DOI: 10.6084/m9.figshare.31287185
title: Additional file 5 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31287185.v1
title: Additional file 5 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 5
DOI: 10.6084/m9.figshare.31287188
title: Additional file 6 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.31287188.v1
title: Additional file 6 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 6
DOI: 10.6084/m9.figshare.31287192
title: Additional file 2 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 2. ToxRTool template.
DOI: 10.6084/m9.figshare.31287192.v1
title: Additional file 2 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 2. ToxRTool template.
DOI: 10.6084/m9.figshare.31287194
title: Additional file 7 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.31287194.v1
title: Additional file 7 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 7
DOI: 10.6084/m9.figshare.31287197
title: Additional file 8 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.31287197.v1
title: Additional file 8 of Immuno-metabolic dysregulation in type 2 diabetes is associated with altered neutrophil functional plasticity, mitochondrial dysfunction, and compromised responses in sepsis

description: Supplementary Material 8
DOI: 10.6084/m9.figshare.31287195
title: Additional file 3 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 3. Data extraction template.
DOI: 10.6084/m9.figshare.31287195.v1
title: Additional file 3 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 3. Data extraction template.
DOI: 10.6084/m9.figshare.31287198
title: Additional file 4 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 4. Meta-analysis script.
DOI: 10.6084/m9.figshare.31287198.v1
title: Additional file 4 of Circadian rhythm disruption in cardiovascular disease: a systematic review and meta-analysis of mechanistic evidence from animal models

description: Additional file 4. Meta-analysis script.
DOI: 10.15468/dl.3ksr43
title: Occurrence Download

description: A dataset containing 5508 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chenopodiastrum hybridum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2026"
 ]
}

 The dataset includes 5508 records from 130 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017876-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.