cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21069468.v1
title: Additional file 10 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 10: Table S7. List of GO terms represented between early and late PD cases in the frontal cortex. Biological processes, molecular functions and cellular components are split into different columns. Top 30 GO terms that overlap between both sets of brains compared to controls are given as well as those only found in early PD and only found in late PD.
DOI: 10.6084/m9.figshare.21069471
title: Additional file 11 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 11: Table S8. List of GO terms represented between early and late PD cases in putamen. Biological processes, molecular functions and cellular components are split into different columns. Top 30 GO terms that overlap between both sets of brains compared to controls are given as well as those only found in early PD and only found in late PD.
DOI: 10.6084/m9.figshare.21069471.v1
title: Additional file 11 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 11: Table S8. List of GO terms represented between early and late PD cases in putamen. Biological processes, molecular functions and cellular components are split into different columns. Top 30 GO terms that overlap between both sets of brains compared to controls are given as well as those only found in early PD and only found in late PD.
DOI: 10.6084/m9.figshare.21069483
title: Additional file 4 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 4:Table S1. Peptide list for candidates. Peptide chosen for each protein that was validated with multiple reaction monitoring.
DOI: 10.6084/m9.figshare.21069483.v1
title: Additional file 4 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 4:Table S1. Peptide list for candidates. Peptide chosen for each protein that was validated with multiple reaction monitoring.
DOI: 10.6084/m9.figshare.21069486
title: Additional file 5 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 5:Table S2. Biological processes represented across brain regions for early PD cases compared to controls. Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069486.v1
title: Additional file 5 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 5:Table S2. Biological processes represented across brain regions for early PD cases compared to controls. Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069489
title: Additional file 6 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 6:Table S3. Molecular functions represented across brain regions for early PD cases compared to controls. Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069489.v1
title: Additional file 6 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 6:Table S3. Molecular functions represented across brain regions for early PD cases compared to controls. Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069492
title: Additional file 7 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 7:Table S4. Cellular components represented across brain regions for early PD cases compared to controls Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069492.v1
title: Additional file 7 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 7:Table S4. Cellular components represented across brain regions for early PD cases compared to controls Shaded box reflects GO term represented in that brain region.
DOI: 10.6084/m9.figshare.21069495
title: Additional file 8 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 8: Table S5. Comparison of candidate proteins to cell type transcriptomic and proteomic data. Each protein that was compared to downloaded datasets from the articles listed to determine which cell type they associated with. * indicates &lt; 5% FDR significant association in enrichment analysis.
DOI: 10.6084/m9.figshare.21069495.v1
title: Additional file 8 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional file 8: Table S5. Comparison of candidate proteins to cell type transcriptomic and proteomic data. Each protein that was compared to downloaded datasets from the articles listed to determine which cell type they associated with. * indicates &lt; 5% FDR significant association in enrichment analysis.
DOI: 10.6084/m9.figshare.21069498
title: Additional file 9 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional File 9: Table S6. Proteins with &gt; 1.5 fold expression change within each region for early PD compared to controls. Protein ID is shown for each protein in the list. Red shading indicates upregulation and green shading indicates downregulation. Each region is highlighted to show the severity of pathology present in that region at Braak stage 3/4 with deepest severely affected &gt; moderately affected &gt; mildly affected &gt; not affected.
DOI: 10.6084/m9.figshare.21069498.v1
title: Additional file 9 of Mitochondrial dysfunction is a key pathological driver of early stage Parkinson’s

description: Additional File 9: Table S6. Proteins with &gt; 1.5 fold expression change within each region for early PD compared to controls. Protein ID is shown for each protein in the list. Red shading indicates upregulation and green shading indicates downregulation. Each region is highlighted to show the severity of pathology present in that region at Braak stage 3/4 with deepest severely affected &gt; moderately affected &gt; mildly affected &gt; not affected.
DOI: 10.6084/m9.figshare.21069501
title: Additional file 1 of Plastome structure, phylogenomics, and divergence times of tribe Cinnamomeae (Lauraceae)

description: Additional file 1: Table S1. The plastomes used in different analyses of this study. Table S2. Collection information and accession numbers of the 15 samples of tribe Cinnamomeae. Table S3. The GenBank accession numbers of ITS, RPB2, and LEAFY. Table S4. Gene content of the 15 newly generated plastomes. Table S5. The sequences of primers. Table S6. Number of dispersed repeats, SSRs, and tandem repeats of the 39 species of tribe Cinnamomeae. Table S7.p value of the likelihood ratio tests and positively selected codon sites.
DOI: 10.6084/m9.figshare.21069501.v1
title: Additional file 1 of Plastome structure, phylogenomics, and divergence times of tribe Cinnamomeae (Lauraceae)

description: Additional file 1: Table S1. The plastomes used in different analyses of this study. Table S2. Collection information and accession numbers of the 15 samples of tribe Cinnamomeae. Table S3. The GenBank accession numbers of ITS, RPB2, and LEAFY. Table S4. Gene content of the 15 newly generated plastomes. Table S5. The sequences of primers. Table S6. Number of dispersed repeats, SSRs, and tandem repeats of the 39 species of tribe Cinnamomeae. Table S7.p value of the likelihood ratio tests and positively selected codon sites.
DOI: 10.6084/m9.figshare.21069504
title: Additional file 2 of Plastome structure, phylogenomics, and divergence times of tribe Cinnamomeae (Lauraceae)

description: Additional file 2: Fig. S1. Comparison of the SC/IR junctions among the 15 newly generated plastomes of tribe Cinnamomeae. JLA, LSC/IRa boundary; JSA, SSC/IRa boundary; JSB, SSC/IRb boundary; JLB, LSC/IRb boundary. Fig. S2. Map of the gel electrophoresis experiments. XTBG, Cinnamomum chartophyllum; D053, C. cassia; wh020, C. longepaniculatum. Fig. S3. Structural alignment of the 15 newly generated plastomes of tribe Cinnamomeae inferred from Mauve. Fig. S4. Visualized alignments of 39 plastomes of tribe Cinnamomeae using mVISTA. The vertical scale indicates percentage of identity ranging from 50 to 100%. Exons are colored in dark blue, non-coding sequences (CNS) are colored in red, tRNA and rRNA genes (UTR) are colored in green. Fig. S5. Phylogenetic tree inferred from maximum likelihood analysis using concatenated complete plastomes with one IR removed (CP-c). Bootstrap values are indicated above branches. Fig. S6. Phylogenetic tree inferred from maximum likelihood analysis using concatenated non-protein-coding genes (NPCG-c). Bootstrap values are indicated above branches.
DOI: 10.6084/m9.figshare.21069504.v1
title: Additional file 2 of Plastome structure, phylogenomics, and divergence times of tribe Cinnamomeae (Lauraceae)

description: Additional file 2: Fig. S1. Comparison of the SC/IR junctions among the 15 newly generated plastomes of tribe Cinnamomeae. JLA, LSC/IRa boundary; JSA, SSC/IRa boundary; JSB, SSC/IRb boundary; JLB, LSC/IRb boundary. Fig. S2. Map of the gel electrophoresis experiments. XTBG, Cinnamomum chartophyllum; D053, C. cassia; wh020, C. longepaniculatum. Fig. S3. Structural alignment of the 15 newly generated plastomes of tribe Cinnamomeae inferred from Mauve. Fig. S4. Visualized alignments of 39 plastomes of tribe Cinnamomeae using mVISTA. The vertical scale indicates percentage of identity ranging from 50 to 100%. Exons are colored in dark blue, non-coding sequences (CNS) are colored in red, tRNA and rRNA genes (UTR) are colored in green. Fig. S5. Phylogenetic tree inferred from maximum likelihood analysis using concatenated complete plastomes with one IR removed (CP-c). Bootstrap values are indicated above branches. Fig. S6. Phylogenetic tree inferred from maximum likelihood analysis using concatenated non-protein-coding genes (NPCG-c). Bootstrap values are indicated above branches.
DOI: 10.6084/m9.figshare.21069519
title: Additional file 1 of People with serious mental illness are at higher risk for acute care hospitalization in Israel, 2000–2019

description: Additional file 1: Table S1. Rate ratios of discharges of SMI group compared to total population, 2016–2019, with 95% CI, by age and diagnoses, and by sex for total discharges. Table S2. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, sex and diagnoses. Table S3. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, type of admission and hospital ownership.
DOI: 10.6084/m9.figshare.21069519.v1
title: Additional file 1 of People with serious mental illness are at higher risk for acute care hospitalization in Israel, 2000–2019

description: Additional file 1: Table S1. Rate ratios of discharges of SMI group compared to total population, 2016–2019, with 95% CI, by age and diagnoses, and by sex for total discharges. Table S2. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, sex and diagnoses. Table S3. Standardized discharge ratios (SDR) for SMI group compared to total population, aged 18–74, with 95% CI, by period of discharge, type of admission and hospital ownership.
DOI: 10.6084/m9.figshare.17798777
title: Additional file 2 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 2. Pre-examination Introductory Video.
DOI: 10.6084/m9.figshare.17798777.v2
title: Additional file 2 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 2. Pre-examination Introductory Video.
DOI: 10.6084/m9.figshare.17798783
title: Additional file 3 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 3. Complete video recordings of examinations.
DOI: 10.6084/m9.figshare.17798783.v2
title: Additional file 3 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 3. Complete video recordings of examinations.
DOI: 10.6084/m9.figshare.17798777.v1
title: Additional file 2 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 2. Pre-examination Introductory Video.
DOI: 10.6084/m9.figshare.17798783.v1
title: Additional file 3 of Empowering the willing: the feasibility of tele-mentored self-performed pleural ultrasound assessment for the surveillance of lung health

description: Additional file 3. Complete video recordings of examinations.
DOI: 10.6084/m9.figshare.21069537
title: Additional file 1 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 1. Survey questionnaire.
DOI: 10.6084/m9.figshare.21069537.v1
title: Additional file 1 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 1. Survey questionnaire.
DOI: 10.6084/m9.figshare.21069543
title: Additional file 3 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 3. Summary statistics for known D2T GWS signals in our dataset.
DOI: 10.6084/m9.figshare.21069543.v1
title: Additional file 3 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 3. Summary statistics for known D2T GWS signals in our dataset.
DOI: 10.6084/m9.figshare.21069549
title: Additional file 5 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 5. PGS Catalog models for T2D and Hypertension.
DOI: 10.6084/m9.figshare.21069549.v1
title: Additional file 5 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

description: Additional file 5. PGS Catalog models for T2D and Hypertension.
DOI: 10.6084/m9.figshare.21069582
title: Additional file 1 of Pathling: analytics on FHIR

description: Additional file 1. Parameters used with the Aggregate Operation as part of the comparison of solutions.
DOI: 10.6084/m9.figshare.21069582.v1
title: Additional file 1 of Pathling: analytics on FHIR

description: Additional file 1. Parameters used with the Aggregate Operation as part of the comparison of solutions.
DOI: 10.6084/m9.figshare.21069585
title: Additional file 2 of Pathling: analytics on FHIR

description: Additional file 2. Parameters used with the Extract Operation as part of the comparison of solutions.
DOI: 10.6084/m9.figshare.21069585.v1
title: Additional file 2 of Pathling: analytics on FHIR

description: Additional file 2. Parameters used with the Extract Operation as part of the comparison of solutions.
DOI: 10.6084/m9.figshare.21069588
title: Additional file 3 of Pathling: analytics on FHIR

description: Additional file 3. Jupyter Notebook showing a comparable analysis process using a Pathling server.
DOI: 10.6084/m9.figshare.21069588.v1
title: Additional file 3 of Pathling: analytics on FHIR

description: Additional file 3. Jupyter Notebook showing a comparable analysis process using a Pathling server.
DOI: 10.6084/m9.figshare.21069591
title: Additional file 4 of Pathling: analytics on FHIR

description: Additional file 4. Jupyter Notebook showing a comparable analysis process using the Pathling Python API.
DOI: 10.6084/m9.figshare.21069591.v1
title: Additional file 4 of Pathling: analytics on FHIR

description: Additional file 4. Jupyter Notebook showing a comparable analysis process using the Pathling Python API.
DOI: 10.6084/m9.figshare.21069594
title: Additional file 5 of Pathling: analytics on FHIR

description: Additional file 5. Jupyter Notebook showing a comparable analysis process using Python, the Python standard library and the Pandas data analysis package.
DOI: 10.6084/m9.figshare.21069594.v1
title: Additional file 5 of Pathling: analytics on FHIR

description: Additional file 5. Jupyter Notebook showing a comparable analysis process using Python, the Python standard library and the Pandas data analysis package.
DOI: 10.6084/m9.figshare.21069597
title: Additional file 6 of Pathling: analytics on FHIR

description: Additional file 6 FHIR value set representing COVID-19 vaccine codes.
DOI: 10.6084/m9.figshare.21069597.v1
title: Additional file 6 of Pathling: analytics on FHIR

description: Additional file 6 FHIR value set representing COVID-19 vaccine codes.
DOI: 10.6084/m9.figshare.21069046.v1
title: Supplementary Material for: Capsular Warning Syndrome: Features, Risk Profile, and Prognosis in a Large Prospective TIA Cohort

description: <b><i>Introduction:</i></b> Features and prognosis of capsular warning syndrome (CWS) have been poorly investigated prospectively. <b><i>Aims:</i></b> The study aimed to characterize CWS clinical features, risk profile, short- and long-term prognosis, among a large TIA cohort. <b><i>Methods:</i></b> Prospective cohort study of consecutive TIAs was conducted from August 1, 2010, to December 31, 2017. Demographic and clinical characteristics, risk profile, primary (stroke and composite outcome) and secondary (TIA recurrence, cerebral hemorrhage, new onset atrial fibrillation) outcomes were compared between CWS, lacunar (L), and nonlacunar (NL) TIAs. <b><i>Results:</i></b> 1,035 patients (33 CWS, 189 L-TIAs, 813 NL-TIAs) were enrolled. Newly diagnosed (ND) hypertension, hypercholesterolemia, cigarette smoking, and leukoaraiosis were independent risk factors of CWS (<i>p</i> &lt; 0.05). CWS showed the highest stroke (30.3% vs. 0.5% and 1.5% for L-TIAs and NL-TIAs, respectively) and composite outcome risk at follow-up (<i>p</i> &lt; 0.001), but better 3-month post-stroke prognosis (mRS 0–2 90.0% vs. 36.8%; <i>p</i> = 0.002). CWS-related stroke mostly occurred &lt;48 h (80.0%) and had a small vessel occlusion etiology (100%), affecting more often the internal capsule (60.0%). Dual antiplatelet therapy (DAPT) versus single antiplatelet therapy was associated with lower 3-month cumulative stroke incidence (12.5% vs. 57.1%; <i>p</i> = 0.010). Intravenous thrombolysis (IVT) showed similar 3-month efficacy and safety in strokes after TIAs groups (median mRS 0, IQR 0–1; <i>p</i> = 0.323). <b><i>Conclusions:</i></b> CWS is associated with higher stroke risk and better functional prognosis than L- and NL-TIAs. CWS risk profile is consistent with severe small vessel disease, and ND hypertension could represent a major risk factor. DAPT and IVT seem effective and safe in preventing and treating stroke following CWS.
DOI: 10.5281/zenodo.5861580
title: Digital Approaches to Analyzing and Translating Emotion: What Is Love?

description: This repository contains the data used for and created during the research for the article "Digital Approaches to Analyzing and Translating Emotion: What Is Love?" in Karen Sonik and Ulrike Steinert (eds.), <em>The Routledge Handbook of Emotions in the Ancient Near East</em> (2022), https://doi.org/10.4324/9780367822873-6. <strong>Lists/</strong> contains various lists used for and created while analyzing love words in Akkadian. <strong>Networks and figures/</strong> contains the networks and figures produced during our research. <strong>Oracc data/</strong> contains the raw data extracted from the Oracc texts. <strong>PMI and fastText results/</strong> contains the results produced with PMI and fastText. <strong>Texts/</strong> contains the text file used for producing the results with PMI and fastText. We gratefully acknowledge that our research has been funded by the Academy of Finland (decision numbers 298647, 312051, and 330727). Our research data originates from the Open Richly Annotated Cuneiform Corpus (Oracc). We thank Oracc for their efforts in making linguistically annotated cuneiform texts available online. We are indebted to everyone who has been involved in creating this research data, including the authors of the original publications and the researchers who have made the data Oracc-compatible and enriched it through lemmatizations and by adding other metadata (for a list of projects and their contributors, see the file OraccCredits.txt). In the context of this article and dataset, we want to acknowledge the work of the Munich Open-access Cuneiform Corpus Initiative (PIs Karen Radner and Jamie Novotny), the Royal Inscriptions of the Neo-Assyrian Period project (PI Grant Frame), and the Akkadian Love Literature project (Nathan Wasserman and Yigal Bloch) in particular.
DOI: 10.6084/m9.figshare.21069639
title: Additional file 1 of The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) study: International consensus on outcome measures for trials of interventions for adults with single-sided deafness

description: Additional file 1. Outcome domains and Round 2 e-Delphi survey consensus results
DOI: 10.6084/m9.figshare.21069639.v1
title: Additional file 1 of The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) study: International consensus on outcome measures for trials of interventions for adults with single-sided deafness

description: Additional file 1. Outcome domains and Round 2 e-Delphi survey consensus results
DOI: 10.6084/m9.figshare.21069651
title: Additional file 6 of The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) study: International consensus on outcome measures for trials of interventions for adults with single-sided deafness

description: Additional file 6. Suggested additional outcomes at Round 1 e-Delphi with final decisions
DOI: 10.6084/m9.figshare.21069651.v1
title: Additional file 6 of The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) study: International consensus on outcome measures for trials of interventions for adults with single-sided deafness

description: Additional file 6. Suggested additional outcomes at Round 1 e-Delphi with final decisions
DOI: 10.6084/m9.figshare.21069684
title: Additional file 2 of Clinical validation of a population-based input function for 20-min dynamic whole-body 18F-FDG multiparametric PET imaging

description: Additional file 2: The 120-min data PBIF based on Eq.2 + Eq.4. The PBIF was calculated using the parameters in Table 2 (last column) and convoluted with the shape of the MedRad Intego injection. The AUC 0-60 is 100, and this PBIF needs to be scaled to late arterial blood data (blood samples or IDIF) before it is used for parametric imaging. This is the PBIF that we use for our 20-min multiparametric PET protocol.