cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12619040
title: Kuwait spss.sav

description: <b>Background</b> Despite the public health importance of documenting the burden of physical inactivity and weight gain, there is a paucity of such data in Kuwait during the lockdown for Covid-19 pandemic. Therefore, this survey was designed to estimate: the burden of poor eating habits particularly binge eating habits, fluctuations in weight and its predictors among the Kuwaiti public. <b>Methods</b> This cross-sectional survey was conducted from 2<sup>nd</sup> to 12<sup>th</sup> April 2020 among the general public in Kuwait. All data were collected through social media platform (WhatsApp groups), through convenience and snowball sampling methods. comprised of three sections: a) demographic characteristics of respondents, b) eating habits particularly binge eating, consumption of snacks and beverages c) subjective feelings of anxiety and d) weight before and during the pandemic. The dataset is in SPSS format.<b></b>
DOI: 10.6084/m9.figshare.12619061
title: Supplementary Material for: Risk Factors of In-Hospital Mortality in Patients Treated for Pneumonia at a Tertiary Care Centre in Switzerland

description: <b><i>Background:</i></b> Little is known about risk factors upon hospital admission that are associated with in-hospital death of patients hospitalized for bacterial pneumonia. Identifying such factors may help to optimize the treatment and lower the mortality of these patients. <b><i>Objectives:</i></b> The aim of the study was to characterize baseline characteristics of patients hospitalized for bacterial pneumonia in Switzerland and to identify risk factors associated with all-cause in-hospital mortality. <b><i>Methods:</i></b> Routinely collected electronic health record data of patients discharged from a large Swiss tertiary care hospital between August 2009 and 2017 were analysed. Potential risk factors such as patient demographics, physical examination findings, vital signs, laboratory results, and comorbidities were considered within ±24 h of admission. Univariable and multivariable logistic regression models identified risk factors for in-hospital death. The area under the receiver operating characteristic (ROC) curve was used to compare the identified factors to existing pneumonia scoring systems. <b><i>Results:</i></b> Out of 1,781 hospital stays with initial and main diagnosis of bacterial pneumonia, 85 patients (4.85%) died (33.9% female, median age 62.3 years [interquartile range, 52–75]). Age, low systolic blood pressure, underweight, a missing value for body mass index, decreased haemoglobin level, raised C-reactive protein, high urea, high lactate dehydrogenase, concomitant pleural effusion, and cancer were independently associated with in-hospital death. The area under the ROC curve was 0.89 for the multivariable model containing the identified predictors. <b><i>Conclusions:</i></b> Our data are consistent with previous trials characterizing patients hospitalized for pneumonia. Additionally, we identified new and independent risk factors associated with in-hospital death among patients treated for bacterial pneumonia. Findings need to be further validated in larger multicentre cohorts.
DOI: 10.6084/m9.figshare.12619037
title: Chapter_6_Supplementary_Tables

description: Supplementary tables E1 to E10<br>
DOI: 10.6084/m9.figshare.12619040.v1
title: Kuwait spss.sav

description: <b>Background</b> Despite the public health importance of documenting the burden of physical inactivity and weight gain, there is a paucity of such data in Kuwait during the lockdown for Covid-19 pandemic. Therefore, this survey was designed to estimate: the burden of poor eating habits particularly binge eating habits, fluctuations in weight and its predictors among the Kuwaiti public. <b>Methods</b> This cross-sectional survey was conducted from 2<sup>nd</sup> to 12<sup>th</sup> April 2020 among the general public in Kuwait. All data were collected through social media platform (WhatsApp groups), through convenience and snowball sampling methods. comprised of three sections: a) demographic characteristics of respondents, b) eating habits particularly binge eating, consumption of snacks and beverages c) subjective feelings of anxiety and d) weight before and during the pandemic. The dataset is in SPSS format.<b></b>
DOI: 10.6084/m9.figshare.12619061.v1
title: Supplementary Material for: Risk Factors of In-Hospital Mortality in Patients Treated for Pneumonia at a Tertiary Care Centre in Switzerland

description: <b><i>Background:</i></b> Little is known about risk factors upon hospital admission that are associated with in-hospital death of patients hospitalized for bacterial pneumonia. Identifying such factors may help to optimize the treatment and lower the mortality of these patients. <b><i>Objectives:</i></b> The aim of the study was to characterize baseline characteristics of patients hospitalized for bacterial pneumonia in Switzerland and to identify risk factors associated with all-cause in-hospital mortality. <b><i>Methods:</i></b> Routinely collected electronic health record data of patients discharged from a large Swiss tertiary care hospital between August 2009 and 2017 were analysed. Potential risk factors such as patient demographics, physical examination findings, vital signs, laboratory results, and comorbidities were considered within ±24 h of admission. Univariable and multivariable logistic regression models identified risk factors for in-hospital death. The area under the receiver operating characteristic (ROC) curve was used to compare the identified factors to existing pneumonia scoring systems. <b><i>Results:</i></b> Out of 1,781 hospital stays with initial and main diagnosis of bacterial pneumonia, 85 patients (4.85%) died (33.9% female, median age 62.3 years [interquartile range, 52–75]). Age, low systolic blood pressure, underweight, a missing value for body mass index, decreased haemoglobin level, raised C-reactive protein, high urea, high lactate dehydrogenase, concomitant pleural effusion, and cancer were independently associated with in-hospital death. The area under the ROC curve was 0.89 for the multivariable model containing the identified predictors. <b><i>Conclusions:</i></b> Our data are consistent with previous trials characterizing patients hospitalized for pneumonia. Additionally, we identified new and independent risk factors associated with in-hospital death among patients treated for bacterial pneumonia. Findings need to be further validated in larger multicentre cohorts.
DOI: 10.6084/m9.figshare.12619037.v1
title: Chapter_6_Supplementary_Tables

description: Supplementary tables E1 to E10<br>
DOI: 10.6084/m9.figshare.12619016.v1
title: Dataset of internal migration among researchers between states in Mexico over 1996-2018

description: This dataset contains one of the main outputs of a study of internal migration among researchers in Mexico inferred from the affiliation addresses of Scopus publications from 1996-2018. Scopus data is owned and maintained by Elsevier.<br>This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (10.6084/m9.figshare.12619016) and the reference articles listed below, and license the new creations under the identical terms. <br>For more details about the study, please refer toMiranda-González, Andrea, et al. "Scholarly migration within Mexico: Analyzing internal migration among researchers using Scopus longitudinal bibliometric data." <i>arXiv preprint arXiv:2004.06539</i> (2020). https://arxiv.org/abs/2004.06539<br><br>The dataset is provided in a comma-separated values file (.csv file) and each row represents one movement of one researcher-active scholar from a state (source) to another state (target) in Mexico in a specific year (move_year). <br>The data can be used to produce internal migration flows for the states or possibly other migration estimates. It can also be used as an edgelist for creating a network model of migration events between states (states being the nodes of the network and each movement being represented as a directed edge from source to target).<br><br>
DOI: 10.6084/m9.figshare.12618902
title: Supplementary Material for: A Comparison of Serial Position Effects in Patients with Mild Cognitive Impairment due to Parkinson’s Disease or to Alzheimer’s Disease

description: <b><i>Objective:</i></b> The first (primacy region) and last (recency region) items of a word list are generally better memorized than items from the middle region. The recency effect depends on short-term memory (STM) and the primacy effect on long-term memory (LTM), where verbal information is transferred from STM into LTM by maintenance rehearsal. We compared the serial position effects (SPE) between patients with mild cognitive impairment (MCI) due to Parkinson’s disease (PD), i.e., PD-MCI, and patients with MCI due to Alzheimer’s disease (AD-MCI), and evaluated the influence of SPE and frontostriatal deficits on verbal memory recall. <b><i>Methods:</i></b> Four similar groups of subjects participated in the study: 26 PD-MCI patients, 26 cognitively normal patients with PD (PD-CN), 26 AD-MCI patients, and 26 normal controls (NC). Verbal episodic memory, verbal span, attentional capacity, executive functions, and verbal working memory performance were assessed. Measures for primacy and recency regions were defined at the first trial of a 16-items word list. Hierarchical regression models were used to investigate the contribution of frontostriatal deficits beyond SPE on verbal memory recall performance (“long-delay free recall”) in PD and AD patients. <b><i>Results:</i></b> Primacy effects were significantly diminished in both PD-MCI and AD-MCI patients relative to NC and PD-CN (all <i>p</i> &lt; 0.01). Compared to PD-MCI patients, AD-MCI patients exhibited significantly worse “delayed-recall ‘savings’.” Reduced primacy effect was predictive for decreased recall performance in PD and AD. The conducted hierarchical regression model revealed that in PD, but not in AD patients, performance of attention and executive function significantly increased the prediction of free recalled words. <b><i>Conclusions:</i></b> Reduced recall performance is likely due to impaired transition of newly learned material from STM into LTM in AD and in PD. Whereas AD-MCI patients suffer from a storage deficit, the similarly reduced recall performance found in patients with PD-MCI may additionally be related to deficient attentional and executive capacity.
DOI: 10.6084/m9.figshare.12618884
title: Whole genome sequencing of Hypophthalmichthys molitrix (Cypriniformes: Cyprinidae) provides insights into its ecological adaptation

description: Genome sequencing of <i>Hypophthalmichthys molitrix</i>
DOI: 10.6084/m9.figshare.12618854
title: WATET code

description: WATET is a fully distributed coupled hydrological and solute transport model. In this setup it simulates the water and conservative solute flow in the Plynlimon catchment (Wales).
DOI: 10.6084/m9.figshare.12618791
title: Supplementary Material for: Nursing Students’ Perceptions of the Dedicated Education Unit Model in 2 UAE Hospitals

description: <b><i>Background:</i></b> Initially developed in Australia, Dedicated Education Unit (DEU) is an innovative approach to support the growth and development of nursing students while on clinical placement in the hospital setting. With the increasing number of nursing students requiring clinical placement, nursing needed to explore further ways to support students and preceptors in the clinical area. <b><i>Methods:</i></b> The study followed a sequential explanatory mixed-methods design to investigate participants’ perceptions of the effectiveness of the DEU model in improving nursing students’ learning experiences. The DEU model was implemented in 2 Abu Dhabi Health Services Company (SEHA) hospitals in Al Ain City, and feedback was obtained from both students and their preceptors via survey and focus group sessions. <b><i>Results:</i></b> The study showed positive results with students feeling supported to learn new knowledge and skills by their preceptors. Preceptors reported that they were better able to facilitate student placements in an environment that supported learning. <b><i>Conclusion:</i></b> The introduction of the DEU model has supported increasing numbers of students in the clinical area and provided an improved environment for learning.
DOI: 10.6084/m9.figshare.12618689
title: Supplementary Material for: Delta-9-Tetrahydrocannabinol and Cannabidiol Drug-Drug Interactions

description: Although prescribing information (PI) is often the initial source of information when identifying potential drug-drug interactions, it may only provide a limited number of exemplars or only reference a class of medications without providing any specific medication examples. In the case of medical cannabis and medicinal cannabinoids, this is further complicated by the fact that the increased therapeutic use of marijuana extracts and cannabidiol oil will not have regulatory agency approved PI. The objective of this study was to provide a detailed and comprehensive drug-drug interaction list that is aligned with cannabinoid manufacturer PI. The cannabinoid drug-drug interaction information is listed in this article and online supplementary material as a PRECIPITANT (cannabinoid) medication that either INHIBITS/INDUCES the metabolism or competes for the same SUBSTRATE target (metabolic enzyme) of an OBJECT (OTHER) medication. In addition to a comprehensive list of drug-drug interactions, we also provide a list of 57 prescription medications displaying a narrow therapeutic index that are potentially impacted by concomitant cannabinoid use (whether through prescription use of cannabinoid medications or therapeutic/recreational use of cannabis and its extracts).
DOI: 10.6084/m9.figshare.12618689.v1
title: Supplementary Material for: Delta-9-Tetrahydrocannabinol and Cannabidiol Drug-Drug Interactions

description: Although prescribing information (PI) is often the initial source of information when identifying potential drug-drug interactions, it may only provide a limited number of exemplars or only reference a class of medications without providing any specific medication examples. In the case of medical cannabis and medicinal cannabinoids, this is further complicated by the fact that the increased therapeutic use of marijuana extracts and cannabidiol oil will not have regulatory agency approved PI. The objective of this study was to provide a detailed and comprehensive drug-drug interaction list that is aligned with cannabinoid manufacturer PI. The cannabinoid drug-drug interaction information is listed in this article and online supplementary material as a PRECIPITANT (cannabinoid) medication that either INHIBITS/INDUCES the metabolism or competes for the same SUBSTRATE target (metabolic enzyme) of an OBJECT (OTHER) medication. In addition to a comprehensive list of drug-drug interactions, we also provide a list of 57 prescription medications displaying a narrow therapeutic index that are potentially impacted by concomitant cannabinoid use (whether through prescription use of cannabinoid medications or therapeutic/recreational use of cannabis and its extracts).
DOI: 10.6084/m9.figshare.12618791.v1
title: Supplementary Material for: Nursing Students’ Perceptions of the Dedicated Education Unit Model in 2 UAE Hospitals

description: <b><i>Background:</i></b> Initially developed in Australia, Dedicated Education Unit (DEU) is an innovative approach to support the growth and development of nursing students while on clinical placement in the hospital setting. With the increasing number of nursing students requiring clinical placement, nursing needed to explore further ways to support students and preceptors in the clinical area. <b><i>Methods:</i></b> The study followed a sequential explanatory mixed-methods design to investigate participants’ perceptions of the effectiveness of the DEU model in improving nursing students’ learning experiences. The DEU model was implemented in 2 Abu Dhabi Health Services Company (SEHA) hospitals in Al Ain City, and feedback was obtained from both students and their preceptors via survey and focus group sessions. <b><i>Results:</i></b> The study showed positive results with students feeling supported to learn new knowledge and skills by their preceptors. Preceptors reported that they were better able to facilitate student placements in an environment that supported learning. <b><i>Conclusion:</i></b> The introduction of the DEU model has supported increasing numbers of students in the clinical area and provided an improved environment for learning.
DOI: 10.6084/m9.figshare.12618479
title: Additional file 7 of The regulatory landscape of early maize inflorescence development

description: Additional file 7 Dataset S6. Normalized expression levels (TPM) of PCGs, binned by expression group.
DOI: 10.6084/m9.figshare.12618482
title: Additional file 8 of The regulatory landscape of early maize inflorescence development

description: Additional file 8 Dataset S7. Functional annotations, differential accessibility and expression data for differentially regulated PCGs in tassel vs. ear.
DOI: 10.6084/m9.figshare.12618485
title: Additional file 9 of The regulatory landscape of early maize inflorescence development

description: Additional file 9 Dataset S8. Genes bound by FEA4 and/or KN1 TFs in MNase HS regions of their proximal promoters, and functional enrichment using GO.
DOI: 10.6084/m9.figshare.12618485.v1
title: Additional file 9 of The regulatory landscape of early maize inflorescence development

description: Additional file 9 Dataset S8. Genes bound by FEA4 and/or KN1 TFs in MNase HS regions of their proximal promoters, and functional enrichment using GO.
DOI: 10.6084/m9.figshare.12618467
title: Additional file 15 of The regulatory landscape of early maize inflorescence development

description: Additional file 15: Dataset S14. SNP-trait associations and closest gene annotations from GWAS analyses of TBN and ERN using all HapMap V3 SNPs and a subset within MNase HS regions only.
DOI: 10.6084/m9.figshare.12618479.v1
title: Additional file 7 of The regulatory landscape of early maize inflorescence development

description: Additional file 7 Dataset S6. Normalized expression levels (TPM) of PCGs, binned by expression group.
DOI: 10.6084/m9.figshare.12618482.v1
title: Additional file 8 of The regulatory landscape of early maize inflorescence development

description: Additional file 8 Dataset S7. Functional annotations, differential accessibility and expression data for differentially regulated PCGs in tassel vs. ear.
DOI: 10.6084/m9.figshare.12618461
title: Additional file 13 of The regulatory landscape of early maize inflorescence development

description: Additional file 13: Dataset S12. High-confidence lncRNAs supported by Sanger sequencing.
DOI: 10.6084/m9.figshare.12618461.v1
title: Additional file 13 of The regulatory landscape of early maize inflorescence development

description: Additional file 13: Dataset S12. High-confidence lncRNAs supported by Sanger sequencing.
DOI: 10.6084/m9.figshare.12618464
title: Additional file 14 of The regulatory landscape of early maize inflorescence development

description: Additional file 14: Dataset S13. Classification of inflorescence-expressed high-confidence lncRNAs.
DOI: 10.6084/m9.figshare.12618467.v1
title: Additional file 15 of The regulatory landscape of early maize inflorescence development

description: Additional file 15: Dataset S14. SNP-trait associations and closest gene annotations from GWAS analyses of TBN and ERN using all HapMap V3 SNPs and a subset within MNase HS regions only.
DOI: 10.6084/m9.figshare.12618455
title: Additional file 10 of The regulatory landscape of early maize inflorescence development

description: Additional file 10 Dataset S9. Distance matrix between ABI3 motifs and FEA4 binding sites, closest gene models and annotations associated with these combinatorial cis-element modules, and functional enrichment analysis using maize-GAMER GO for genes within 1 kb of FEA4-ABI3 cis-element modules.
DOI: 10.6084/m9.figshare.12618464.v1
title: Additional file 14 of The regulatory landscape of early maize inflorescence development

description: Additional file 14: Dataset S13. Classification of inflorescence-expressed high-confidence lncRNAs.
DOI: 10.6084/m9.figshare.12618458
title: Additional file 12 of The regulatory landscape of early maize inflorescence development

description: Additional file 12: Dataset S11. Annotated high-confidence lncRNAs from inflorescence primordia (genomic coordinates, sequences and expression levels).
DOI: 10.6084/m9.figshare.12618455.v1
title: Additional file 10 of The regulatory landscape of early maize inflorescence development

description: Additional file 10 Dataset S9. Distance matrix between ABI3 motifs and FEA4 binding sites, closest gene models and annotations associated with these combinatorial cis-element modules, and functional enrichment analysis using maize-GAMER GO for genes within 1 kb of FEA4-ABI3 cis-element modules.
DOI: 10.6084/m9.figshare.12618458.v1
title: Additional file 12 of The regulatory landscape of early maize inflorescence development

description: Additional file 12: Dataset S11. Annotated high-confidence lncRNAs from inflorescence primordia (genomic coordinates, sequences and expression levels).
DOI: 10.6084/m9.figshare.12618444
title: Additional file 2 of PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

description: Additional file 2: Supplementary Table 1. File type: Excel. Gene ontology and annotation analysis result of PDXGEM biomarkers.
DOI: 10.6084/m9.figshare.12618444.v1
title: Additional file 2 of PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

description: Additional file 2: Supplementary Table 1. File type: Excel. Gene ontology and annotation analysis result of PDXGEM biomarkers.
DOI: 10.6084/m9.figshare.12618422
title: Additional file 8 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 8. Antibody panels used for immunophenotyping.
DOI: 10.6084/m9.figshare.12618419
title: Additional file 7 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 7. SNPs in the chromosome 14 peak from the combined TZL + MCT GWAS.
DOI: 10.6084/m9.figshare.12618422.v1
title: Additional file 8 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 8. Antibody panels used for immunophenotyping.
DOI: 10.6084/m9.figshare.12618419.v1
title: Additional file 7 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 7. SNPs in the chromosome 14 peak from the combined TZL + MCT GWAS.
DOI: 10.6084/m9.figshare.12618404
title: Additional file 2 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 2. Summary of snpEff annotations for single nucleotide variants.
DOI: 10.6084/m9.figshare.12618407
title: Additional file 3 of Genome-wide association analysis of canine T zone lymphoma identifies link to hypothyroidism and a shared association with mast-cell tumors

description: Additional file 3. Summary of snpEff annotations for indels.