cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14670221
title: Additional file 4 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 4: Supplementary Table 3. Chromosome assignment of “Bottom Drawer” contigs for Phaeodactylum tricornutum and Thalassiosira pseudonana.
DOI: 10.6084/m9.figshare.14670230
title: Additional file 7 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 7: Supplementary Table 6. Bionano hybrid assembly statistics report for P. tricornutum including haplotype assignment details.
DOI: 10.6084/m9.figshare.14670224.v1
title: Additional file 5 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 5: Supplementary Table 4. Long-read contigs help close gaps in the Phaeodactylum tricornutum and Thalassiosira pseudonana reference genomes.
DOI: 10.6084/m9.figshare.14670230.v1
title: Additional file 7 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 7: Supplementary Table 6. Bionano hybrid assembly statistics report for P. tricornutum including haplotype assignment details.
DOI: 10.6084/m9.figshare.14670233
title: Additional file 8 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 8: Supplementary Table 7. SNP frequencies for each contig in the P. tricornutum Canu genome.
DOI: 10.6084/m9.figshare.14670233.v1
title: Additional file 8 of Re-examination of two diatom reference genomes using long-read sequencing

description: Additional file 8: Supplementary Table 7. SNP frequencies for each contig in the P. tricornutum Canu genome.
DOI: 10.6084/m9.figshare.14670250
title: Additional file 1 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 1. Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value &lt;0.05 and Absolute FC &gt;1.0, b) ACE2 and PRR genes
DOI: 10.6084/m9.figshare.14670250.v1
title: Additional file 1 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 1. Supplementary Table 1. Gene expression analysis between the oldest (≥80 years) and youngest (≤10) age groups: a) Differentially expressed genes with an Adjusted P Value &lt;0.05 and Absolute FC &gt;1.0, b) ACE2 and PRR genes
DOI: 10.6084/m9.figshare.14670256
title: Additional file 2 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 2. Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
DOI: 10.6084/m9.figshare.14670256.v1
title: Additional file 2 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 2. Supplementary Table 2. ToppGene enrichment results for differentially expressed genes between the oldest and youngest age groups (filtered to show KEGG pathway results)
DOI: 10.6084/m9.figshare.14670259
title: Additional file 3 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 3. Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
DOI: 10.6084/m9.figshare.14670265
title: Additional file 5 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 5. Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
DOI: 10.6084/m9.figshare.14670259.v1
title: Additional file 3 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 3. Supplementary Table 3. a) Pearson r, b) P values, and c) Confidence intervals of r for the correlation matrix shown in Fig. 2a
DOI: 10.6084/m9.figshare.14670268.v1
title: Additional file 6 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 6. Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
DOI: 10.6084/m9.figshare.14670265.v1
title: Additional file 5 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 5. Supplementary Table 4. Differentially expressed genes between TLR4 high vs low expressors
DOI: 10.6084/m9.figshare.14670268
title: Additional file 6 of Extremes of age are associated with differences in the expression of selected pattern recognition receptor genes and ACE2, the receptor for SARS-CoV-2: implications for the epidemiology of COVID-19 disease

description: Additional file 6. Supplementary Table 5. ToppGene enrichment results for differentially expressed genes between TLR4 high and low expressors (filtered to show KEGG pathway results)
DOI: 10.6084/m9.figshare.14234676
title: Additional file 3 of A diet-specific microbiota drives Salmonella Typhimurium to adapt its in vivo response to plant-derived substrates

description: Additional file 3 Table S2. OTU table.
DOI: 10.6084/m9.figshare.14234676.v1
title: Additional file 3 of A diet-specific microbiota drives Salmonella Typhimurium to adapt its in vivo response to plant-derived substrates

description: Additional file 3 Table S2. OTU table.
DOI: 10.6084/m9.figshare.14234679
title: Additional file 4 of A diet-specific microbiota drives Salmonella Typhimurium to adapt its in vivo response to plant-derived substrates

description: Additional file 4 Table S3. OTU sequences.
DOI: 10.6084/m9.figshare.14234679.v1
title: Additional file 4 of A diet-specific microbiota drives Salmonella Typhimurium to adapt its in vivo response to plant-derived substrates

description: Additional file 4 Table S3. OTU sequences.
DOI: 10.6084/m9.figshare.14670386
title: Additional file 8 of A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

description: Additional file 8. Raw dataset to perform the analysis.
DOI: 10.6084/m9.figshare.14670386.v1
title: Additional file 8 of A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

description: Additional file 8. Raw dataset to perform the analysis.
DOI: 10.6084/m9.figshare.14670395
title: Additional file 1 of Development of a bedside score to predict dengue severity

description: Additional file 1: S1 Fig. R code enabling the implementation of the k-fold cross-validation procedure.
DOI: 10.6084/m9.figshare.14670395.v1
title: Additional file 1 of Development of a bedside score to predict dengue severity

description: Additional file 1: S1 Fig. R code enabling the implementation of the k-fold cross-validation procedure.
DOI: 10.6084/m9.figshare.14670398
title: Additional file 2 of Development of a bedside score to predict dengue severity

description: Additional file 2: S2 Fig. Excel spreadsheet enabling the calculation of a bedside score predictive of severe dengue. In the operating tool, scores derived from the logistic regression models can be calculated using an Excel spreadsheet by inserting 1 if the characteristic is present. A score ≥ 0.36 for females and 0.34 for males indicates a high probability to develop severe dengue. The hospitalization decision is made according to the medical opinion. During hospitalization, patients are submitted to close surveillance, hyperhydration, symptomatic treatments and sometimes blood support and resuscitation measures.
DOI: 10.6084/m9.figshare.14670398.v1
title: Additional file 2 of Development of a bedside score to predict dengue severity

description: Additional file 2: S2 Fig. Excel spreadsheet enabling the calculation of a bedside score predictive of severe dengue. In the operating tool, scores derived from the logistic regression models can be calculated using an Excel spreadsheet by inserting 1 if the characteristic is present. A score ≥ 0.36 for females and 0.34 for males indicates a high probability to develop severe dengue. The hospitalization decision is made according to the medical opinion. During hospitalization, patients are submitted to close surveillance, hyperhydration, symptomatic treatments and sometimes blood support and resuscitation measures.
DOI: 10.6084/m9.figshare.14670413.v1
title: Additional file 1 of A new mouse SNP genotyping assay for speed congenics: combining flexibility, affordability, and power

description: Additional file 1: Table S1. Standard-format “bed” file showing the genomic positions of the SNPs in the mouse genotyping assay. First column = chromosome number, second column = start of the SNP position, third column = end of the SNP position, fourth column = SNP name. Table S2. Standard-format “bed” file showing the genomic positions of the probes in the mouse genotyping assay. First column = chromosome number, second column = start of the SNP position, third column = end of the SNP position, fourth column = SNP name, fifth column = score, sixth column = positive or negative DNA strand. Table S3. Metadata and genotype summaries for all samples included in the study, including samples used in the experiment comparing three batches of 48 samples (“Experiment1_3Batches”), the experiment identifying diagnostic SNPs for different strains (“Experiment2_Strains”), and the backcross experiment (“Experiment3_Backcross”). Samples included in each of these experiments are indicated by “yes” in the corresponding column; some samples were used in more than one experiment, and some samples were sequenced more than one time as technical replicates, starting from the same genomic DNA extraction (indicated in “ReplicateNumber” column). “LibraryID” = name of the sequencing library; “BatchID” = batch ID for the sequencing run; “Strain” = the strain ID for samples that were not backcrossed individuals; “DonorStrain” = the donor strain for backcrossed individuals; “BackcrossNumber” = backcross number for backcrossed individuals; “BB” = number of SNPs that were homozygous for the alternate allele; “AB” = number of SNPs that were heterozygous for the alternate allele; “AA” = number of SNPs that were homozygous for the reference allele; “failed” = number of SNPs that failed to genotype; “TotalGenotyped” = total number of SNPs successfully genotyped; “ProportionAA” = proportion of genotyped SNPs that were homozygous for the reference allele; “ProportionA” = proportion of reference allele for genotyped SNPs. Table S4. Predicted numbers of diagnostic SNPs in the mouse genotyping assay for crosses between each pair of 102 inbred and wild-derived inbred mouse strains. Numbers were calculated based on genotypes reported in [13] for the SNPs that were shared between that study and the assay described here (i.e., n = 1499 SNPs).
DOI: 10.6084/m9.figshare.14670413
title: Additional file 1 of A new mouse SNP genotyping assay for speed congenics: combining flexibility, affordability, and power

description: Additional file 1: Table S1. Standard-format “bed” file showing the genomic positions of the SNPs in the mouse genotyping assay. First column = chromosome number, second column = start of the SNP position, third column = end of the SNP position, fourth column = SNP name. Table S2. Standard-format “bed” file showing the genomic positions of the probes in the mouse genotyping assay. First column = chromosome number, second column = start of the SNP position, third column = end of the SNP position, fourth column = SNP name, fifth column = score, sixth column = positive or negative DNA strand. Table S3. Metadata and genotype summaries for all samples included in the study, including samples used in the experiment comparing three batches of 48 samples (“Experiment1_3Batches”), the experiment identifying diagnostic SNPs for different strains (“Experiment2_Strains”), and the backcross experiment (“Experiment3_Backcross”). Samples included in each of these experiments are indicated by “yes” in the corresponding column; some samples were used in more than one experiment, and some samples were sequenced more than one time as technical replicates, starting from the same genomic DNA extraction (indicated in “ReplicateNumber” column). “LibraryID” = name of the sequencing library; “BatchID” = batch ID for the sequencing run; “Strain” = the strain ID for samples that were not backcrossed individuals; “DonorStrain” = the donor strain for backcrossed individuals; “BackcrossNumber” = backcross number for backcrossed individuals; “BB” = number of SNPs that were homozygous for the alternate allele; “AB” = number of SNPs that were heterozygous for the alternate allele; “AA” = number of SNPs that were homozygous for the reference allele; “failed” = number of SNPs that failed to genotype; “TotalGenotyped” = total number of SNPs successfully genotyped; “ProportionAA” = proportion of genotyped SNPs that were homozygous for the reference allele; “ProportionA” = proportion of reference allele for genotyped SNPs. Table S4. Predicted numbers of diagnostic SNPs in the mouse genotyping assay for crosses between each pair of 102 inbred and wild-derived inbred mouse strains. Numbers were calculated based on genotypes reported in [13] for the SNPs that were shared between that study and the assay described here (i.e., n = 1499 SNPs).
DOI: 10.25345/c5n237
title: MassIVE MSV000087498 - Data Independent Acquisition Mass Spectrometry of the Human Lens Enhances Spatiotemporal Measurement of Fiber Cell Aging
title: Comparison of DIA and DDA of 18 year old human lens treated with solubility fraction. OC, Cortex; ON, Outer Nucleus; IN, Inner Nucleus. 3 technical replicates included. Full details available at biorxiv preprint https://doi.org/10.1101/2021.05.13.444062
DOI: 10.6084/m9.figshare.14670456.v1
title: Additional file 2 of Single-cell transcriptomics reveals the effect of PD-L1/TGF-β blockade on the tumor microenvironment

description: Additional file 2. Supplementary tables. Table S1. Differentially expressed (DE) genes determined from the EMT6 single cell RNA-seq experiment. Table S2. Differentially expressed (DE) genes in fibroblasts. Table S3. Differentially expressed (DE) genes in macrophages.
DOI: 10.6084/m9.figshare.14670456
title: Additional file 2 of Single-cell transcriptomics reveals the effect of PD-L1/TGF-β blockade on the tumor microenvironment

description: Additional file 2. Supplementary tables. Table S1. Differentially expressed (DE) genes determined from the EMT6 single cell RNA-seq experiment. Table S2. Differentially expressed (DE) genes in fibroblasts. Table S3. Differentially expressed (DE) genes in macrophages.
DOI: 10.6084/m9.figshare.14670495
title: Additional file 1 of Correction to: A full sequence of the Matuyama–Brunhes geomagnetic reversal in the Chiba composite section, Central Japan

description: Additional file 2. Paleomagnetic and rock magnetic results for the Yoro River and Yoro-Tabuchi sections by Okada et al. (2017), Simon et al. (2019), and this study
DOI: 10.6084/m9.figshare.14670495.v1
title: Additional file 1 of Correction to: A full sequence of the Matuyama–Brunhes geomagnetic reversal in the Chiba composite section, Central Japan

description: Additional file 2. Paleomagnetic and rock magnetic results for the Yoro River and Yoro-Tabuchi sections by Okada et al. (2017), Simon et al. (2019), and this study
DOI: 10.6084/m9.figshare.14670502
title: Additional file 1 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 1: Table S1. Differentially expressed genes in the five investigated G9a stressor datasets.
DOI: 10.6084/m9.figshare.14670502.v1
title: Additional file 1 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 1: Table S1. Differentially expressed genes in the five investigated G9a stressor datasets.
DOI: 10.6084/m9.figshare.14670505
title: Additional file 2 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 2: Table S2. Overinduced and overrepressed genes in G9a versus control datasets that are shared by three or more studies, two studies, or are uniquely dysregulated.
DOI: 10.6084/m9.figshare.14670505.v1
title: Additional file 2 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 2: Table S2. Overinduced and overrepressed genes in G9a versus control datasets that are shared by three or more studies, two studies, or are uniquely dysregulated.
DOI: 10.6084/m9.figshare.14670508.v1
title: Additional file 3 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 3: Table S3. Functional annotation of unique overinduces genes annotated with stress response GO terms.
DOI: 10.6084/m9.figshare.14670508
title: Additional file 3 of The epigenetic regulator G9a attenuates stress-induced resistance and metabolic transcriptional programs across different stressors and species

description: Additional file 3: Table S3. Functional annotation of unique overinduces genes annotated with stress response GO terms.
DOI: 10.6084/m9.figshare.14670519
title: Additional file 1 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 1.
DOI: 10.6084/m9.figshare.14670519.v1
title: Additional file 1 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 1.
DOI: 10.6084/m9.figshare.14670522
title: Additional file 2 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 2.
DOI: 10.6084/m9.figshare.14670525.v1
title: Additional file 3 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 3.
DOI: 10.6084/m9.figshare.14670528
title: Additional file 4 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 4.
DOI: 10.6084/m9.figshare.14670528.v1
title: Additional file 4 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 4.
DOI: 10.6084/m9.figshare.14670522.v1
title: Additional file 2 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 2.
DOI: 10.6084/m9.figshare.14670525
title: Additional file 3 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 3.
DOI: 10.6084/m9.figshare.14670531
title: Additional file 5 of Transcriptomic and metabolomic characterization of post-hatch metabolic reprogramming during hepatic development in the chicken

description: Additional file 5.
DOI: 10.6084/m9.figshare.14670556
title: Additional file 1 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 1: Provides a comprehensive list of all the extracted features.
DOI: 10.6084/m9.figshare.14670556.v1
title: Additional file 1 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 1: Provides a comprehensive list of all the extracted features.
DOI: 10.6084/m9.figshare.14670559
title: Additional file 2 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 2: The different types of features obtained after two-step, mRMR and SU, feature selection approach for pS/pT/pY sites prediction.
DOI: 10.6084/m9.figshare.14670559.v1
title: Additional file 2 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 2: The different types of features obtained after two-step, mRMR and SU, feature selection approach for pS/pT/pY sites prediction.
DOI: 10.6084/m9.figshare.14670565.v1
title: Additional file 4 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 4–39: All the RF and SVM models generated in present study for prediction of Ser, Thr and Tyr phosphorylation sites.
DOI: 10.6084/m9.figshare.14670565
title: Additional file 4 of Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

description: Additional file 4–39: All the RF and SVM models generated in present study for prediction of Ser, Thr and Tyr phosphorylation sites.
DOI: 10.25919/p4j2-8h84
title: Parkes observations for project P885 semester 2021APRS_06

description: We propose to continue observing the magnetars 1E 1547.0-5408 and PSR J1622-4950 weekly, to track their rotation, flux density, and polarisation. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810-197, again a radio magnetar after 10 years dormant, and of the hot-off-the-press magnetar Swift J1818.0-1607, for which our exciting recent results with UWL motivate an increased monitoring cadence to once weekly.
DOI: 10.5281/zenodo.4719128
title: Dataset in "Sedimentary structure from multi-mode ambient noise tomography with dense OBS network at the Japan Trench"

description: The cross-correlation functions are used in "Sedimentary structure from multi-mode ambient noise tomography with dense OBS network at the Japan Trench", by Lina Yamaya, Kimihiro Mochizuki, Takeshi Akuhara, and Kiwamu Nishida. The structure file is the final 3-D S-wave velocity structure model shown in the paper. For more information, please contact the first author.