cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25375/uct.11854752
title: Data: Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registry

description: Quantitative data from the Obstetric Airway Management Registry. <br>The first 403 records were used for the manuscript:<br>Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registry<br>Trial registry number: NHRD WC_201810_002<br>
DOI: 10.25375/uct.11854752.v2
title: Data: Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registry

description: Quantitative data from the Obstetric Airway Management Registry. <br>The first 403 records were used for the manuscript:<br>Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registry<br>Trial registry number: NHRD WC_201810_002<br>
DOI: 10.25375/uct.11854752.v1
title: Data: Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registryy

description: Quantitative data from the Obstetric Airway Management Registry. <br>The first 403 records were used for the manuscript:<br>Desaturation during tracheal intubation in patients with hypertensive disorders of pregnancy: an analysis of a prospective obstetric airway management registry<br>Trial registry number: NHRD WC_201810_002<br>
DOI: 10.25392/leicester.data.11854713
title: SuperDARN Heppner-Maynard Boundaries

description: SuperDARN Heppner-Maynard Boundary latitudes for the Northern Hemisphere for 2011 and 2015. Determined using the traditional latitude determination condition and all available high and polar latitude radar data. Data files provide date, time and the midnight meridian value of the Heppner-Maynard Boundary.
DOI: 10.6084/m9.figshare.11854611
title: Composite measure

description: The code used to determine the protective index of MSRO-Br and MSRO-Ug infected Drosophila against the Lb17, NSRef, Lh14 and Lh-Mad wasp strains.
DOI: 10.6084/m9.figshare.11854611.v1
title: Composite measure

description: The code used to determine the protective index of MSRO-Br and MSRO-Ug infected Drosophila against the Lb17, NSRef, Lh14 and Lh-Mad wasp strains.
DOI: 10.6084/m9.figshare.11854587
title: Fertility data

description: Fertility data
DOI: 10.6084/m9.figshare.11854608
title: Survival data

description: Fly and wasp survival data
DOI: 10.6084/m9.figshare.11854587.v1
title: Fertility data

description: Fertility data
DOI: 10.6084/m9.figshare.11854608.v1
title: Survival data

description: Fly and wasp survival data
DOI: 10.6084/m9.figshare.11854563
title: miRNAlipidomics.zip

description: We present here lipidomic data from HepG2 cell lysates treated with miRNA mimics (NT, miR-16, miR-30b, miR-30c). NT stands for non-targeting control siRNA, and miRNA mimics are ready-to-transfect mimics that behave like endogenous miRNAs. Lipid extraction was performed according to the method Bligh and Dyer. The following lipid species were added as internal standards: PC 14:0/14:0, PC 22:0/22:0, PE 14:0/14:0, PE 20:0/20:0 (di-phytanoyl), PS 14:0/14:0, PS 20:0/20:0 (di-phytanoyl), PI 17:0/17:0, LPC 13:0, LPC 19:0, LPE 13:0, Cer d18:1/14:0, Cer 17:0, D7-FC, CE 17:0, and CE 22:0. The residues were dissolved either in 10 mM ammonium acetate in methanol/chloroform (3:1, v/v) (for low mass resolution tandem mass spectrometry), or chloroform/methanol/2-propanol (1:2:4 v/v/v) with 7.5 mM ammonium formate (for high resolution mass spectrometry). The analysis of lipids was performed by direct flow injection analysis (FIA) using either a triple quadrupole mass spectrometer (FIA-MS/MS; QQQ triple quadrupole) or a hybrid quadrupole-Orbitrap mass spectrometer. Data processing was performed using the ALEX software which includes peak assignment and intensity picking. The extracted data were exported to Microsoft Excel 2010 and further processed by self-programmed Macros.
DOI: 10.6084/m9.figshare.11854542
title: Supplementary Material for: Abdominal Wall Dyskinesia: Case Report

description: The clinical presentation of repetitive choreiform involuntary movements of the anterior abdominal wall was first introduced as “belly dancer’s dyskinesia.” Etiologies of this rare condition include idiopathic causes, medication inducement, or post-abdominal surgery. We report a case of orobuccal stereotypic movements and abdominal wall dyskinesia secondary to prochlorperazine intake. The movements began 2 weeks after cessation of prochlorperazine. The patient took this dopamine receptor-blocking medication for 6 months to treat nausea due to chemotherapy. To our knowledge, abdominal wall dyskinesia as a tardive syndrome of prochlorperazine has not been previously reported.
DOI: 10.6084/m9.figshare.11854563.v1
title: miRNAlipidomics.zip

description: We present here lipidomic data from HepG2 cell lysates treated with miRNA mimics (NT, miR-16, miR-30b, miR-30c). NT stands for non-targeting control siRNA, and miRNA mimics are ready-to-transfect mimics that behave like endogenous miRNAs. Lipid extraction was performed according to the method Bligh and Dyer. The following lipid species were added as internal standards: PC 14:0/14:0, PC 22:0/22:0, PE 14:0/14:0, PE 20:0/20:0 (di-phytanoyl), PS 14:0/14:0, PS 20:0/20:0 (di-phytanoyl), PI 17:0/17:0, LPC 13:0, LPC 19:0, LPE 13:0, Cer d18:1/14:0, Cer 17:0, D7-FC, CE 17:0, and CE 22:0. The residues were dissolved either in 10 mM ammonium acetate in methanol/chloroform (3:1, v/v) (for low mass resolution tandem mass spectrometry), or chloroform/methanol/2-propanol (1:2:4 v/v/v) with 7.5 mM ammonium formate (for high resolution mass spectrometry). The analysis of lipids was performed by direct flow injection analysis (FIA) using either a triple quadrupole mass spectrometer (FIA-MS/MS; QQQ triple quadrupole) or a hybrid quadrupole-Orbitrap mass spectrometer. Data processing was performed using the ALEX software which includes peak assignment and intensity picking. The extracted data were exported to Microsoft Excel 2010 and further processed by self-programmed Macros.
DOI: 10.6084/m9.figshare.11854470
title: Supplementary Material for: Unspecified Strokes: Time Trends, Determinants, and Long-Term Prognosis in the General Population

description: <b><i>Introduction:</i></b> In the absence of neuroimaging, a stroke is typically labelled as unspecified. While the majority of clinic-based stroke research focuses on hemorrhagic or ischemic stroke, in the general population, a substantial proportion of strokes remains unspecified. <b><i>Objective:</i></b> To investigate time trends in the occurrence and determinants of unspecified strokes and differences in patient characteristics and survival compared to ischemic or hemorrhagic stroke. <b><i>Methods:</i></b> We included 1,546 participants from the population-based Rotterdam Study who suffered a first-ever stroke during follow-up (1990–2016). We calculated the proportion of unspecified strokes per year and compared their characteristics between 3 time periods (1990–1999, 2000–2009, and 2010–2016) using a chi-square test, and furthermore investigated differences between unspecified, ischemic, and hemorrhagic stroke in patient characteristics and survival using age- and sex-adjusted survival curves. <b><i>Results:</i></b> The occurrence of unspecified stroke among all strokes decreased from 75% in 1990 to 16% in 2016. Compared to the first time period (1991–1999), diagnosis of unspecified strokes was more often done by nursing home physicians (13 vs. 40%) and unspecified stroke patients had more often dementia (30 vs. 43%) in the last time period (2010–2016). Compared to patients with ischemic or hemorrhagic stroke, patients with unspecified stroke were on average older (84.3 vs. 78.5 years) and had more often physical impairments and dementia. Furthermore, patients with unspecified stroke had a lower survival probability up to 10 years after stroke than those with ischemic stroke. <b><i>Conclusions:</i></b> The proportion of unspecified strokes decreased drastically from 75 to 16% in the last decades. Patients who do not undergo neuroimaging and therefore are classified as unspecified stroke represent an older, more frail patient group that suffers more often from multimorbidities and poor long-term prognosis than those who do undergo neuroimaging and are thus classified as ischemic or hemorrhagic stroke.
DOI: 10.6084/m9.figshare.11854470.v1
title: Supplementary Material for: Unspecified Strokes: Time Trends, Determinants, and Long-Term Prognosis in the General Population

description: <b><i>Introduction:</i></b> In the absence of neuroimaging, a stroke is typically labelled as unspecified. While the majority of clinic-based stroke research focuses on hemorrhagic or ischemic stroke, in the general population, a substantial proportion of strokes remains unspecified. <b><i>Objective:</i></b> To investigate time trends in the occurrence and determinants of unspecified strokes and differences in patient characteristics and survival compared to ischemic or hemorrhagic stroke. <b><i>Methods:</i></b> We included 1,546 participants from the population-based Rotterdam Study who suffered a first-ever stroke during follow-up (1990–2016). We calculated the proportion of unspecified strokes per year and compared their characteristics between 3 time periods (1990–1999, 2000–2009, and 2010–2016) using a chi-square test, and furthermore investigated differences between unspecified, ischemic, and hemorrhagic stroke in patient characteristics and survival using age- and sex-adjusted survival curves. <b><i>Results:</i></b> The occurrence of unspecified stroke among all strokes decreased from 75% in 1990 to 16% in 2016. Compared to the first time period (1991–1999), diagnosis of unspecified strokes was more often done by nursing home physicians (13 vs. 40%) and unspecified stroke patients had more often dementia (30 vs. 43%) in the last time period (2010–2016). Compared to patients with ischemic or hemorrhagic stroke, patients with unspecified stroke were on average older (84.3 vs. 78.5 years) and had more often physical impairments and dementia. Furthermore, patients with unspecified stroke had a lower survival probability up to 10 years after stroke than those with ischemic stroke. <b><i>Conclusions:</i></b> The proportion of unspecified strokes decreased drastically from 75 to 16% in the last decades. Patients who do not undergo neuroimaging and therefore are classified as unspecified stroke represent an older, more frail patient group that suffers more often from multimorbidities and poor long-term prognosis than those who do undergo neuroimaging and are thus classified as ischemic or hemorrhagic stroke.
DOI: 10.6084/m9.figshare.11854461
title: 1995-2014_balanced.csv

description: Data on democratic qualities, CO2 emissions per capita, Oil production per capita, BNP per capita, Trade Openness, Urbanization. Balanced Panel.
DOI: 10.6084/m9.figshare.11854461.v1
title: 1995-2014_balanced.csv

description: Data on democratic qualities, CO2 emissions per capita, Oil production per capita, BNP per capita, Trade Openness, Urbanization. Balanced Panel.
DOI: 10.6084/m9.figshare.11854371
title: ChromSCape_scChIP_scATAC_compiled_datasets

description: This folder contains three analysis of one scChIP-seq and one scATAC-seq datasets for the purpose of being visualised by ChromSCape (https://github.com/vallotlab/ChromSCape). <b><br></b><b>Datasets</b><br><b><br></b><b>H3K27me3 scChIP-seq datasets:</b>The samples correspond to mouse cells from patient-derived xenograft (PDX) originating from two different human donors (Grosselin et al., 2019). Raw FASTQ reads were processed using the latest version of our scChIP-seq data engineering pipeline that allowed a more precise removal of PCR and RT duplicates (code available at https://github.com/vallotlab/scChIPseq_DataEngineering) to produce 50kbp binned count matrices given as input to ChromSCape (matrices available at https://figshare.com/projects/Single-Cell_ChIP-seq_of_Mouse_Stromal_Cells_in_PDX_tumour_models_of_resistance/66419).<br><b>scATAC-seq datasets:</b> The single-cell ATAC-seq dataset is composed of two cell types derived from two acute myeloid leukaemia (AML) patient (blastocytes (blast) and leukemic stem cells (LSC) produced in (Corces et al., 2016)) as well as multiple cell lines : GM12878, TF1, BJ, H1, HL60, K562 (3 replicates) produced in (Buenrostro et al., 2015), K562 (3 replicates) produced in (Schep et al., 2017); monocytes (Mono) and lymphoid primed multipotent progenitor (LMPP) produced in (Corces et al., 2016)). The count matrix of reads in peaks was downloaded from GEO accession number GSE99172, split into distinct matrices for each sample and formatted to be accepted as input by ChromSCape. Code availability Source code, guidelines for installation and use of the application are provided at https://github.com/vallotlab/ChromSCape. A Docker environment containing all libraries required to launch the application is also available on DockerHub at https://hub.docker.com/repository/docker/pacomito/chromscape, with all set-up steps clearly described on the GitHub repository.
DOI: 10.6084/m9.figshare.11854371.v1
title: ChromSCape_scChIP_scATAC_compiled_datasets

description: This folder contains three analysis of one scChIP-seq and one scATAC-seq datasets for the purpose of being visualised by ChromSCape (https://github.com/vallotlab/ChromSCape). <b><br></b><b>Datasets</b><br><b><br></b><b>H3K27me3 scChIP-seq datasets:</b>The samples correspond to mouse cells from patient-derived xenograft (PDX) originating from two different human donors (Grosselin et al., 2019). Raw FASTQ reads were processed using the latest version of our scChIP-seq data engineering pipeline that allowed a more precise removal of PCR and RT duplicates (code available at https://github.com/vallotlab/scChIPseq_DataEngineering) to produce 50kbp binned count matrices given as input to ChromSCape (matrices available at https://figshare.com/projects/Single-Cell_ChIP-seq_of_Mouse_Stromal_Cells_in_PDX_tumour_models_of_resistance/66419).<br><b>scATAC-seq datasets:</b> The single-cell ATAC-seq dataset is composed of two cell types derived from two acute myeloid leukaemia (AML) patient (blastocytes (blast) and leukemic stem cells (LSC) produced in (Corces et al., 2016)) as well as multiple cell lines : GM12878, TF1, BJ, H1, HL60, K562 (3 replicates) produced in (Buenrostro et al., 2015), K562 (3 replicates) produced in (Schep et al., 2017); monocytes (Mono) and lymphoid primed multipotent progenitor (LMPP) produced in (Corces et al., 2016)). The count matrix of reads in peaks was downloaded from GEO accession number GSE99172, split into distinct matrices for each sample and formatted to be accepted as input by ChromSCape. Code availability Source code, guidelines for installation and use of the application are provided at https://github.com/vallotlab/ChromSCape. A Docker environment containing all libraries required to launch the application is also available on DockerHub at https://hub.docker.com/repository/docker/pacomito/chromscape, with all set-up steps clearly described on the GitHub repository.
DOI: 10.6084/m9.figshare.11854173
title: Additional file 3 of Predictability of drug-induced liver injury by machine learning

description: Additional file 3 All classification results. The file includes performance tables for each classification experiment run in the manuscript.
DOI: 10.6084/m9.figshare.11854173.v1
title: Additional file 3 of Predictability of drug-induced liver injury by machine learning

description: Additional file 3 All classification results. The file includes performance tables for each classification experiment run in the manuscript.
DOI: 10.6084/m9.figshare.11854167
title: Additional file 2 of Predictability of drug-induced liver injury by machine learning

description: Additional file 2 CMap sample annotation file. Sample annotation file downloaded from the CMap project website (https://portals.broadinstitute.org/cmap/). The file includes the following information for all samples available in CMap build2: batch_id, cmap_name, INN, concentration, duration, cell, array, perturbation_scan_id, vehicle_scan_id, scanner, vehicle, vendor, catalog_number, catalog_name.
DOI: 10.6084/m9.figshare.11854164
title: Additional file 1 of Predictability of drug-induced liver injury by machine learning

description: Additional file 1 CAMDA filenames. The spreadsheet provided by the CAMDA2018 conference organizers. It contains the names of the files included in the CMap Drug Safety challenge, as well as the binary DILI label for each compound and the split into TR and TS sets.
DOI: 10.6084/m9.figshare.11854167.v1
title: Additional file 2 of Predictability of drug-induced liver injury by machine learning

description: Additional file 2 CMap sample annotation file. Sample annotation file downloaded from the CMap project website (https://portals.broadinstitute.org/cmap/). The file includes the following information for all samples available in CMap build2: batch_id, cmap_name, INN, concentration, duration, cell, array, perturbation_scan_id, vehicle_scan_id, scanner, vehicle, vendor, catalog_number, catalog_name.
DOI: 10.6084/m9.figshare.11854158
title: Additional file 2 of Indirect impact of violent events on emergency department utilization and disease patterns

description: Additional file 2. Assessment of the publication based on STROBE guidelines checklist.
DOI: 10.6084/m9.figshare.11854164.v1
title: Additional file 1 of Predictability of drug-induced liver injury by machine learning

description: Additional file 1 CAMDA filenames. The spreadsheet provided by the CAMDA2018 conference organizers. It contains the names of the files included in the CMap Drug Safety challenge, as well as the binary DILI label for each compound and the split into TR and TS sets.
DOI: 10.6084/m9.figshare.11854158.v1
title: Additional file 2 of Indirect impact of violent events on emergency department utilization and disease patterns

description: Additional file 2. Assessment of the publication based on STROBE guidelines checklist.
DOI: 10.6084/m9.figshare.11854152
title: Additional file 3 of Transcriptome profiling of differentially expressed genes in cytoplasmic male-sterile line and its fertility restorer line in pigeon pea (Cajanus cajan L.)

description: Additional file 3: Figure S1. Gene ontology classification of the assembled unigenes. The Y-axis indicates the number of unigenes and X-axis indicates the GO categories. Figure S2 Functional classification of KEGG pathways of the assembled unigenes. The KEGG pathways were classified into six functional categories: A- Metabolism; B- Genetic Information Processing; C- Environmental Information Processing; D- Cellular Processes; E- Organismal Systems; F- Human Diseases. The Y-axis represents the KEGG metabolic pathways. The X-axis represents number of unigenes annotated in that particular pathway. Figure S3 Hierarchical tree graph of over-represented GO terms in the biological process category of down-regulated DEGS. Boxes in the graph represent GO terms labeled according to their GO ID, term definition, and statistical information. Significant terms (adjusted P ≤ 0.05) are in color (red, orange, or yellow), while non-significant terms are shown as white boxes. In the diagram, the degree of color saturation of a box is positively correlated with the enrichment level of the term. Solid, dashed, and dotted lines represent two, one, and zero enriched terms at both ends connected by the line, respectively. The rank direction of the graph is set from top to bottom. Figure S4 Hierarchical tree graph of over-represented GO terms in the biological process category of up-regulated DEGS. Boxes in the graph represent GO terms labeled according to their GO ID, term definition, and statistical information. Significant terms (adjusted P ≤ 0.05) are in color (red, orange, or yellow), while non-significant terms are shown as white boxes. In the diagram, the degree of color saturation of a box is positively correlated with the enrichment level of the term. Solid, dashed, and dotted lines represent two, one, and zero enriched terms at both ends connected by the line, respectively. The rank direction of the graph is set from top to bottom.
DOI: 10.6084/m9.figshare.11854146
title: Additional file 1 of Drought and heat stress tolerance screening in wheat using computed tomography

description: Additional file 1. List of Triticum aestivum accessions.
DOI: 10.6084/m9.figshare.11854149
title: Additional file 2 of Transcriptome profiling of differentially expressed genes in cytoplasmic male-sterile line and its fertility restorer line in pigeon pea (Cajanus cajan L.)

description: Additional File 2: Table S1. Sequences with significant BLASTX hits against nr protein database. Table S2. Sequences with significant BLASTX hits against Virdiplantae database. Table S3. Gene Ontology classification of the assembled unigenes. Table S4. KEGG classification of Cajanus cajan unigenes. Table S5. Transcription factors identified in transcriptome of Cajanus cajan. Table S6. List of differentially expressed genes between sterile AKCMS11 and fertile AKPR303 in pigeon pea. Table S7. List of significantly enriched GO terms in down-regulated DEGs. Table S8. List of significantly enriched GO terms in up-regulated DEGs. Table S9. Summary of KEGG annotations for down and up-regulated DEGs. Table S10. TAIR database BLASTX results of pigeon pea, 951 DEGs in MapMan pathway analysis. Table S11. OrthoDB homologs search of 3167 pigeon pea DEGs against TAIR database. Table S12. List of qRT-PCR primers used in this study.
DOI: 10.6084/m9.figshare.11854149.v1
title: Additional file 2 of Transcriptome profiling of differentially expressed genes in cytoplasmic male-sterile line and its fertility restorer line in pigeon pea (Cajanus cajan L.)

description: Additional File 2: Table S1. Sequences with significant BLASTX hits against nr protein database. Table S2. Sequences with significant BLASTX hits against Virdiplantae database. Table S3. Gene Ontology classification of the assembled unigenes. Table S4. KEGG classification of Cajanus cajan unigenes. Table S5. Transcription factors identified in transcriptome of Cajanus cajan. Table S6. List of differentially expressed genes between sterile AKCMS11 and fertile AKPR303 in pigeon pea. Table S7. List of significantly enriched GO terms in down-regulated DEGs. Table S8. List of significantly enriched GO terms in up-regulated DEGs. Table S9. Summary of KEGG annotations for down and up-regulated DEGs. Table S10. TAIR database BLASTX results of pigeon pea, 951 DEGs in MapMan pathway analysis. Table S11. OrthoDB homologs search of 3167 pigeon pea DEGs against TAIR database. Table S12. List of qRT-PCR primers used in this study.