cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12806948.v1
title: Risky sexual behaviours among young adults attending Higher Learning Institutions in Mbeya-Tanzania

description: Data was collected from a cross-sectional designed study, conducted from March 2019 to January 2020 in Mbeya region, Tanzania among students attending Higher Learning Institutions (HLIs) in the region registered by the Tanzania Commission for Universities (TCU). Eligible participants included Tanzanian students enrolled in HLIs within Mbeya region in any year of study, aged 18-24 years of age and agreed to provide written informed consent prior to all study-related procedures. Participants were ineligible to take part if they were students attending short-term courses (&lt; 6 months) or elective students. Each HLI in Mbeya region was invited to participate. A complete electronic list of all students registered, aged 18-24 years of age was obtained from the Academic Registrars' offices. Based on the varying number of students from each HLI, and the study desired sample size, probability proportional to size was used to determine the total number of students by gender from each HLI. Students were not selected based on the department or course they are registered for. Each selected student was notified via phone that he/she has been randomly selected to take part in the study and if he/she was willing to take part, he/she was then requested to report to the data collection point within their respective campuses. An individual self-administered questionnaire was used in the study. The questionnaire was administered using a tablet or smart phone through a web-based software (ODK) or hard copy, which ever method the participant preferred. Data was entered in a database, and thereafter cleaned and analysed using statistical software Stata version 14 for Windows. Ethical approval was granted by the Mbeya Medical Research and Ethics Review Committee (MMREC) Reference Number SZEC-2439/R.A/V.1/07, Kilimanjaro Christian Medical College Research Ethics and Review Committee (CRERC) Reference Number 2405, and National Health Research Ethics Committee (NatHREC) Reference Number NIMR/HQ/R.8a/Vol.IX/3092. All eligible participants who agree to participate in this study received study information and were consented prior to any study related activity. Only participant’s study identification number was used in the questionnaire.
DOI: 10.6084/m9.figshare.12806792
title: Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO

description: Monthly precipitation δD at Zhangye during 2008 to 2012. The data is used in the paper titled "Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO".
DOI: 10.6084/m9.figshare.12806741
title: Integrated pharmacology of vitamin k2-treated salt-sensitive hypertension

description: Supplementary Tables
DOI: 10.6084/m9.figshare.12806741.v1
title: Integrated pharmacology of vitamin k2-treated salt-sensitive hypertension

description: Supplementary Tables
DOI: 10.6084/m9.figshare.12806711
title: The influence of individual gains and losses on the evaluation of friends' game results: Evidence from an ERPs study

description: The pre-experiment, Experiment 1, Experiment 2
DOI: 10.6084/m9.figshare.12806792.v1
title: Monthly precipitation δD at Zhangye.xlsx

description: Monthly precipitation δD at Zhangye during 2008 to 2012, which is used in the paper titled "Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO".
DOI: 10.6084/m9.figshare.12806711.v1
title: The influence of individual gains and losses on the evaluation of friends' game results: Evidence from an ERPs study

description: The pre-experiment, Experiment 1, Experiment 2
DOI: 10.6084/m9.figshare.12806675
title: Supplementary Material for: Inter-α-Trypsin Inhibitor Heavy Chain 5 (ITIH5) Is a Natural Stabilizer of Hyaluronan That Modulates Biological Processes in the Skin

description: <b><i>Introduction:</i></b> Hyaluronan (HA) is a major component of the skin that exerts a variety of biological functions. Inter-α-trypsin inhibitor heavy chain (ITIH) proteins comprise a family of hyaladherins of which ITIH5 has recently been described in skin, where it plays a functional role in skin morphology and inflammatory skin diseases including allergic contact dermatitis (ACD). <b><i>Objective:</i></b> The current study focused on the ITIH5-HA interaction and its potential clinical and functional impact in extracellular matrix (ECM) stabilization. <b><i>Methods:</i></b> Studying the molecular effects of ITIH5 in skin, we established skin models comprising murine skin cells of <i>Itih5</i> knockout mice and corresponding wild-type controls. In addition, human dermal fibroblasts with an ITIH5 knockdown as well as a murine recombinant <i>Itih5</i> protein were established to examine the interaction between ITIH5 and HA using in vitro adhesion and HA degradation assays. To understand more precisely the role of ITIH5 in inflammatory skin diseases such as ACD, we generated ITIH5 knockout cells of the KeratinoSens® cell line. <b><i>Results:</i></b> Using murine skin models, ITIH5 knockdown fibroblasts, and a reactive oxygen species (ROS)-mediated HA degradation assay, we proved that ITIH5 binds to HA, thereby acting as a stabilizer of HA. Moreover, microarray profiling revealed the impact of ITIH5 on biological processes such as skin development and ECM homeostasis. Performing the in vitro KeratinoSens skin sensitization assay, we detected that ITIH5 decreases the sensitizing potential of moderate and strong contact sensitizers. <b><i>Conclusion:</i></b> Taken together, our experiments revealed that ITIH5 forms complexes with HA, thereby on the one hand stabilizing HA and facilitating the formation of ECM structures and on the other hand modulating inflammatory responses.
DOI: 10.6084/m9.figshare.12806792.v2
title: Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO

description: Monthly precipitation δD at Zhangye during 2008 to 2012. The data is used in the paper titled "Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO".
DOI: 10.6084/m9.figshare.12806642
title: Supplementary Material for: Allostatic Load and Its Impact on Health: A Systematic Review

description: <b><i>Introduction:</i></b> Allostatic load refers to the cumulative burden of chronic stress and life events. It involves the interaction of different physiological systems at varying degrees of activity. When environmental challenges exceed the individual ability to cope, then allostatic overload ensues. Allostatic load is identified by the use of biomarkers and clinical criteria. <b><i>Objective:</i></b> To summarize the current knowledge on allostatic load and overload and its clinical implications based on a systematic review of the literature. <b><i>Methods:</i></b> PubMed, PsycINFO, Web of Science, and the Cochrane Library were searched from inception to December 2019. A manual search of the literature was also performed, and reference lists of the retrieved articles were examined.<b><i></i></b>We considered only studies in which allostatic load or overload were adequately described and assessed in either clinical or non-clinical adult populations. <b><i>Results:</i></b> A total of 267 original investigations were included. They encompassed general population studies, as well as clinical studies on consequences of allostatic load/overload on both physical and mental health across a variety of settings. <b><i>Conclusions:</i></b> The findings indicate that allostatic load and overload are associated with poorer health outcomes. Assessment of allostatic load provides support to the understanding of psychosocial determinants of health and lifestyle medicine. An integrated approach that includes both biological markers and clinimetric criteria is recommended.
DOI: 10.6084/m9.figshare.12806636
title: JVASP-18850 to JVASP-19888

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12806639
title: JVASP-19889 to JVASP-20573

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12806792.v3
title: Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO

description: Monthly precipitation δD at Zhangye during 2008 to 2012. The data is used in the paper titled "Interannual Variation in Stable Isotopes in Water Vapor over the Northern Tibetan Plateau Linked to ENSO".
DOI: 10.6084/m9.figshare.12806633
title: JVASP-18227 to JVASP-18849

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12806636.v1
title: JVASP-18850 to JVASP-19888

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12806639.v1
title: JVASP-19889 to JVASP-20573

description: JARVIS-DFT
DOI: 10.6084/m9.figshare.12806155
title: Additional file 8 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 8: Table S8. binding prediction of ViPR HLA-I epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the Coronaviridae family and have a human host that had been scored using our HLA-I binding predictor. (CSV 522 kb)
DOI: 10.6084/m9.figshare.12806158
title: Additional file 9 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 9: Table S9. binding prediction of ViPR HLA-II epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the Coronaviridae family and have a human host that had been scored using our HLA-II binding predictor. (CSV 39 kb)
DOI: 10.6084/m9.figshare.12806155.v1
title: Additional file 8 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 8: Table S8. binding prediction of ViPR HLA-I epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the Coronaviridae family and have a human host that had been scored using our HLA-I binding predictor. (CSV 522 kb)
DOI: 10.6084/m9.figshare.12806158.v1
title: Additional file 9 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 9: Table S9. binding prediction of ViPR HLA-II epitopes. The peptide-HLA alleles pairs from the ViPR database which belong to the Coronaviridae family and have a human host that had been scored using our HLA-II binding predictor. (CSV 39 kb)
DOI: 10.6084/m9.figshare.12806149
title: Additional file 6 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 6: Table S6. SARS-CoV-2 25mers Ranked by HLA-II Population Coverage. Table of all SARS-CoV-2-derived 25mers containing at least 3 predicted HLA-II binders as subsequences. (CSV 1268 kb)
DOI: 10.6084/m9.figshare.12806152
title: Additional file 7 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 7: Table S7. Broadly Binding HLA-II 25mers. The top HLA-II predicted binders from each of the three SARS-CoV-2 proteins: spike, nucleocapsid. (CSV 16 kb)
DOI: 10.6084/m9.figshare.12806146
title: Additional file 5 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 5: Table S5. Broadly Binding HLA-I Peptides. The top HLA-I predicted binders from each of the three SARS-CoV-2 proteins: spike, nucleocapsid and membrane with the broadest cumulative allele coverage. (CSV 19 kb)
DOI: 10.6084/m9.figshare.12806152.v1
title: Additional file 7 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 7: Table S7. Broadly Binding HLA-II 25mers. The top HLA-II predicted binders from each of the three SARS-CoV-2 proteins: spike, nucleocapsid. (CSV 16 kb)
DOI: 10.6084/m9.figshare.12806143
title: Additional file 4 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 4: Table S4. Predicted HLA-I Binders Ranked by Population Coverage. Table of all predicted HLA-I binders and their associated allele coverage, including additional indicators for overlap with the human proteome or overlap with the ViPR dataset used. (CSV 1448 kb)
DOI: 10.6084/m9.figshare.12806146.v1
title: Additional file 5 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 5: Table S5. Broadly Binding HLA-I Peptides. The top HLA-I predicted binders from each of the three SARS-CoV-2 proteins: spike, nucleocapsid and membrane with the broadest cumulative allele coverage. (CSV 19 kb)
DOI: 10.6084/m9.figshare.12806149.v1
title: Additional file 6 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 6: Table S6. SARS-CoV-2 25mers Ranked by HLA-II Population Coverage. Table of all SARS-CoV-2-derived 25mers containing at least 3 predicted HLA-II binders as subsequences. (CSV 1268 kb)
DOI: 10.6084/m9.figshare.12806137
title: Additional file 2 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 2: Table S2. HLA-II Alleles Covered by Binding Predictor. List of the 83 alleles covered by the HLA-II binding predictor. (CSV 1 kb)
DOI: 10.6084/m9.figshare.12806140
title: Additional file 3 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 3: Table S3. HLA-I and HLA-II Allele Population Frequencies. HLA-I and HLA-II allele frequencies for USA, European (EUR), Asian Pacific Islander (API), African American (AFA), and Hispanic (HIS) populations.
DOI: 10.6084/m9.figshare.12806143.v1
title: Additional file 4 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 4: Table S4. Predicted HLA-I Binders Ranked by Population Coverage. Table of all predicted HLA-I binders and their associated allele coverage, including additional indicators for overlap with the human proteome or overlap with the ViPR dataset used. (CSV 1448 kb)
DOI: 10.6084/m9.figshare.12806131
title: Additional file 13 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 13. Custom R script for figure plotting. This R script can be used to plot the precision-recall analyses from Fig. 1, reproduce Fig. 5, and to produce Table 1. (R 9 kb)
DOI: 10.6084/m9.figshare.12806134
title: Additional file 1 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 1: Table S1. HLA-I Alleles Covered by Binding Predictors. List of the 74 alleles covered by the HLA-I binding predictor. (CSV 896 bytes)
DOI: 10.6084/m9.figshare.12806137.v1
title: Additional file 2 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 2: Table S2. HLA-II Alleles Covered by Binding Predictor. List of the 83 alleles covered by the HLA-II binding predictor. (CSV 1 kb)
DOI: 10.6084/m9.figshare.12806140.v1
title: Additional file 3 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 3: Table S3. HLA-I and HLA-II Allele Population Frequencies. HLA-I and HLA-II allele frequencies for USA, European (EUR), Asian Pacific Islander (API), African American (AFA), and Hispanic (HIS) populations.
DOI: 10.6084/m9.figshare.12806131.v1
title: Additional file 13 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 13. Custom R script for figure plotting. This R script can be used to plot the precision-recall analyses from Fig. 1, reproduce Fig. 5, and to produce Table 1. (R 9 kb)
DOI: 10.6084/m9.figshare.12806122
title: Additional file 10 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 10: Table S10. spectral counts from published SARS-CoV-2 proteomic datasets. MS/MS spectra assigned to peptides from SARS-CoV-2 proteins were tallied across datasets, divided by protein length, and normalized within each dataset to generate Fig. 5.
DOI: 10.6084/m9.figshare.12806134.v1
title: Additional file 1 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 1: Table S1. HLA-I Alleles Covered by Binding Predictors. List of the 74 alleles covered by the HLA-I binding predictor. (CSV 896 bytes)
DOI: 10.6084/m9.figshare.12806122.v1
title: Additional file 10 of Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes

description: Additional file 10: Table S10. spectral counts from published SARS-CoV-2 proteomic datasets. MS/MS spectra assigned to peptides from SARS-CoV-2 proteins were tallied across datasets, divided by protein length, and normalized within each dataset to generate Fig. 5.
DOI: 10.6084/m9.figshare.12806094
title: Additional file 2 of Lipid-specific IgMs induce antiviral responses in the CNS: implications for progressive multifocal leukoencephalopathy in multiple sclerosis

description: Additional file 2: Table S1. Fifty most upregulated genes upregulated in DIV24 rat myelinating cultures treated 24 hrs with O4 compared to IgM control. Table S2. Significantly altered human disease pathways. Table S3. Significantly altered organismal system pathways. Table S4. Primer sequences.
DOI: 10.6084/m9.figshare.12806089
title: Additional file 1 of Assessing inequalities and regional disparities in child nutrition outcomes in India using MANUSH – a more sensitive yardstick

description: Additional file 1. Consists of 9 worksheets. NFHS-3 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-3 data for States in India, 2005–06. NFHS-4 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for States in India, 2015–16. CNNS scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using CNNS data for States in India, 2016–18. NFHS-4 scores & ranks (Districts). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for Districts in India, 2016–18. Monotonicity Cases. Examples explaining Monotonicity axiom. Uniformity Cases. Examples explaining Uniformity axiom. Shortfall Sensitivity Cases. Examples explaining Shortfall Sensitivity axiom. Hiatus Sensitivity Cases. Examples explaining Hiatus Sensitivity to Level. Districts under NNM and MANUSH. List and ranking of districts phased under National Nutrition Mission (NNM) and its priority categorisation based on MANUSH scores.
DOI: 10.6084/m9.figshare.12806094.v1
title: Additional file 2 of Lipid-specific IgMs induce antiviral responses in the CNS: implications for progressive multifocal leukoencephalopathy in multiple sclerosis

description: Additional file 2: Table S1. Fifty most upregulated genes upregulated in DIV24 rat myelinating cultures treated 24 hrs with O4 compared to IgM control. Table S2. Significantly altered human disease pathways. Table S3. Significantly altered organismal system pathways. Table S4. Primer sequences.
DOI: 10.6084/m9.figshare.12806089.v1
title: Additional file 1 of Assessing inequalities and regional disparities in child nutrition outcomes in India using MANUSH – a more sensitive yardstick

description: Additional file 1. Consists of 9 worksheets. NFHS-3 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-3 data for States in India, 2005–06. NFHS-4 scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for States in India, 2015–16. CNNS scores & ranks (State). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using CNNS data for States in India, 2016–18. NFHS-4 scores & ranks (Districts). Calculation and Tabulation for computing Linear Aggregation (LA), Geometric Mean (GM) and MANUSH scores and Ranks using NFHS-4 data for Districts in India, 2016–18. Monotonicity Cases. Examples explaining Monotonicity axiom. Uniformity Cases. Examples explaining Uniformity axiom. Shortfall Sensitivity Cases. Examples explaining Shortfall Sensitivity axiom. Hiatus Sensitivity Cases. Examples explaining Hiatus Sensitivity to Level. Districts under NNM and MANUSH. List and ranking of districts phased under National Nutrition Mission (NNM) and its priority categorisation based on MANUSH scores.
DOI: 10.6084/m9.figshare.12805990
title: Additional file 4 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 4. R code.
DOI: 10.6084/m9.figshare.12805990.v1
title: Additional file 4 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 4. R code.
DOI: 10.6084/m9.figshare.12805981
title: Additional file 1 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 1. Start dates of travel/internal restrictions in each African country.
DOI: 10.6084/m9.figshare.12805987
title: Additional file 3 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 3. Data file for Table 1.
DOI: 10.6084/m9.figshare.12805981.v1
title: Additional file 1 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 1. Start dates of travel/internal restrictions in each African country.
DOI: 10.6084/m9.figshare.12805987.v1
title: Additional file 3 of Importations of COVID-19 into African countries and risk of onward spread

description: Additional file 3. Data file for Table 1.
DOI: 10.6084/m9.figshare.12805950
title: Additional file 2 of Y chromosome in health and diseases

description: Additional file 2: Table S1. Diseases and pathways associated with Y chromosome genes revealed by gene ontology analysis.
DOI: 10.6084/m9.figshare.12805950.v1
title: Additional file 2 of Y chromosome in health and diseases

description: Additional file 2: Table S1. Diseases and pathways associated with Y chromosome genes revealed by gene ontology analysis.