cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14684641.v1
title: Additional file 2 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 2: Table S1. Clinical characteristics of endometriosis and control patients in this study.
DOI: 10.6084/m9.figshare.14684644
title: Additional file 3 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 3: Table S2. Genes, UMIs counts, percentage of hemoglobin gene and percentage of mitochondrion for each individual cell.
DOI: 10.6084/m9.figshare.14684644.v1
title: Additional file 3 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 3: Table S2. Genes, UMIs counts, percentage of hemoglobin gene and percentage of mitochondrion for each individual cell.
DOI: 10.6084/m9.figshare.14684647
title: Additional file 4 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 4: Table S3. The mean gene expression in each cluster of endometriosis (EMs) and control samples.
DOI: 10.6084/m9.figshare.14684647.v1
title: Additional file 4 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 4: Table S3. The mean gene expression in each cluster of endometriosis (EMs) and control samples.
DOI: 10.6084/m9.figshare.14684650
title: Additional file 5 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 5: Table S4. List of markers information for each cluster in ascites cells.
DOI: 10.6084/m9.figshare.14684650.v1
title: Additional file 5 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 5: Table S4. List of markers information for each cluster in ascites cells.
DOI: 10.6084/m9.figshare.14684653
title: Additional file 6 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 6: Table S5. The numbers and percentages of each cell type in peritoneal fluid of endometriosis (EMs) and control samples.
DOI: 10.6084/m9.figshare.14684653.v1
title: Additional file 6 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 6: Table S5. The numbers and percentages of each cell type in peritoneal fluid of endometriosis (EMs) and control samples.
DOI: 10.6084/m9.figshare.14684656
title: Additional file 7 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 7: Table S6. List of differential genes in subtypes of macrophages.
DOI: 10.6084/m9.figshare.14684656.v1
title: Additional file 7 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 7: Table S6. List of differential genes in subtypes of macrophages.
DOI: 10.6084/m9.figshare.14684659
title: Additional file 8 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 8: Table S7. List of differential genes in subtypes of dendritic cells.
DOI: 10.6084/m9.figshare.14684659.v1
title: Additional file 8 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 8: Table S7. List of differential genes in subtypes of dendritic cells.
DOI: 10.6084/m9.figshare.14684662
title: Additional file 9 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 9: Table S8. List of differential genes in subtypes of T cells.
DOI: 10.6084/m9.figshare.14684662.v1
title: Additional file 9 of Cell subtypes and immune dysfunction in peritoneal fluid of endometriosis revealed by single-cell RNA-sequencing

description: Additional file 9: Table S8. List of differential genes in subtypes of T cells.
DOI: 10.6084/m9.figshare.19403342
title: Two QTL for kernel number per spike identified from durum wheat

description: As one of the three yield components, kernel number per spike (KNPS) plays a key role in determining wheat yield. In this study, in combination with the previously constructed genetic map for a tetraploid wheat recombinant inbred line (RIL) population developed from the cross between a durum wheat and a wild emmer wheat and phenotype data from four environments, two major and stably expressed quantitative trait loci (QTL) for KNPS were identified. The phenotypic variation explained by these two QTL, <i>Qknps-DW-3A.1</i> and <i>Qknps-DW-3A.2</i>, was up to 12.52% and 29.52%, respectively. The positive alleles were both from the durum wheat. Pyramiding analysis suggested that the combination of these two positive alleles has the largest effect on increasing KNPS. Comparison of physical interval for these two QTL with those reported previously showed that they may be novel loci. Genetic correlations between KNPS and other agronomic traits were also evaluated. Taken together, the two major and stably expressed QTL for KNPS from tetraploid wheat reported here they should be useful in wheat breeding.
DOI: 10.6084/m9.figshare.19403342.v1
title: Two QTL for kernel number per spike identified from durum wheat

description: As one of the three yield components, kernel number per spike (KNPS) plays a key role in determining wheat yield. In this study, in combination with the previously constructed genetic map for a tetraploid wheat recombinant inbred line (RIL) population developed from the cross between a durum wheat and a wild emmer wheat and phenotype data from four environments, two major and stably expressed quantitative trait loci (QTL) for KNPS were identified. The phenotypic variation explained by these two QTL, <i>Qknps-DW-3A.1</i> and <i>Qknps-DW-3A.2</i>, was up to 12.52% and 29.52%, respectively. The positive alleles were both from the durum wheat. Pyramiding analysis suggested that the combination of these two positive alleles has the largest effect on increasing KNPS. Comparison of physical interval for these two QTL with those reported previously showed that they may be novel loci. Genetic correlations between KNPS and other agronomic traits were also evaluated. Taken together, the two major and stably expressed QTL for KNPS from tetraploid wheat reported here they should be useful in wheat breeding.
DOI: 10.6084/m9.figshare.21500521
title: Additional file 1 of The effectiveness of postpartum interventions aimed at improving women’s mental health after medical complications of pregnancy: a systematic review and meta-analysis

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.21500521.v1
title: Additional file 1 of The effectiveness of postpartum interventions aimed at improving women’s mental health after medical complications of pregnancy: a systematic review and meta-analysis

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.21500527
title: Additional file 3 of The effectiveness of postpartum interventions aimed at improving women’s mental health after medical complications of pregnancy: a systematic review and meta-analysis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.21500527.v1
title: Additional file 3 of The effectiveness of postpartum interventions aimed at improving women’s mental health after medical complications of pregnancy: a systematic review and meta-analysis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.12063372
title: Supplementary data files related to the article 'Molecular Basis for Treating COVID-19 with Official Chinese Herbal Formula LCTE'

description: This data record contains the supplementary files associated with article 'Network pharmacology suggests biochemical rationale for treating COVID-19 symptoms with a Traditional Chinese Medicine'. The supplementary files consist of eight spreadsheets in <b>.xlsx</b> format and one document in <b>.pdf</b> format.<br>The data are as follows.<b>- supplementary_Doc and Fig S1-S15.pdf</b>: The protocol for preparing LCTE medicine, and fhe network of plants, chemical ingredients and target proteins for relieving each symptom of COVID-19 and treating viral respiratory infection by LCTE.<b>- Supplementary Table 1.xlsx</b>: The chemical ingredients contained in each plant of LCTE.<b>- Supplementary Table 2.xlsx</b>: The protein targets of each chemical ingredient in LCTE.<b>- Supplementary Table 3.xlsx</b>: The occurrences of the chemical ingredients in LCTE and the anti-inflammatory reports for the main ingredients.<b>- Supplementary Table 4.xlsx</b>: The proteins related to main symptoms of COVID-19.<b>- Supplementary Table 5.xlsx</b>: The enrichment and proteins overlapped between ingredients-targeted and symptoms or disease-related.<b>- Supplementary Table 6.xlsx</b>: The summary of important plants, chemical ingredients, protein targets for relieving each symptom of COVID-19 and treating viral respiratory infection by LCTE.<b>- Supplementary Table 7.xlsx</b>: The full list of KEGG pathways enriched for the proteins targeted by chemical ingredients of LCTE.<b>- Supplementary Table 8.xlsx</b>: The full list of Disease Ontology pathways enriched for the proteins targeted by chemical ingredients of LCTE.<br>The related article aimed to illuminate the chemical and biological mechanisms behind the Chinese herbal formula <i>lung-cleaning and toxicity-excluding</i> (LCTE) soup’s effects by exploring the chemical compounds contained in LCTE ingredients, the proteins targeted by these compounds as well as undertaking the network pharmacology analysis. This is because LCTE soup may play an important role in treating the ongoing and life-threating worldwide pandemic COVID-19.
DOI: 10.6084/m9.figshare.12063372.v1
title: Supplementary data files related to the article 'Molecular Basis for Treating COVID-19 with Official Chinese Herbal Formula LCTE'

description: This data record contains the supplementary files associated with article 'Network pharmacology suggests biochemical rationale for treating COVID-19 symptoms with a Traditional Chinese Medicine'. The supplementary files consist of eight spreadsheets in <b>.xlsx</b> format and one document in <b>.pdf</b> format.<br>The data are as follows.<b>- supplementary_Doc and Fig S1-S15.pdf</b>: The protocol for preparing LCTE medicine, and fhe network of plants, chemical ingredients and target proteins for relieving each symptom of COVID-19 and treating viral respiratory infection by LCTE.<b>- Supplementary Table 1.xlsx</b>: The chemical ingredients contained in each plant of LCTE.<b>- Supplementary Table 2.xlsx</b>: The protein targets of each chemical ingredient in LCTE.<b>- Supplementary Table 3.xlsx</b>: The occurrences of the chemical ingredients in LCTE and the anti-inflammatory reports for the main ingredients.<b>- Supplementary Table 4.xlsx</b>: The proteins related to main symptoms of COVID-19.<b>- Supplementary Table 5.xlsx</b>: The enrichment and proteins overlapped between ingredients-targeted and symptoms or disease-related.<b>- Supplementary Table 6.xlsx</b>: The summary of important plants, chemical ingredients, protein targets for relieving each symptom of COVID-19 and treating viral respiratory infection by LCTE.<b>- Supplementary Table 7.xlsx</b>: The full list of KEGG pathways enriched for the proteins targeted by chemical ingredients of LCTE.<b>- Supplementary Table 8.xlsx</b>: The full list of Disease Ontology pathways enriched for the proteins targeted by chemical ingredients of LCTE.<br>The related article aimed to illuminate the chemical and biological mechanisms behind the Chinese herbal formula <i>lung-cleaning and toxicity-excluding</i> (LCTE) soup’s effects by exploring the chemical compounds contained in LCTE ingredients, the proteins targeted by these compounds as well as undertaking the network pharmacology analysis. This is because LCTE soup may play an important role in treating the ongoing and life-threating worldwide pandemic COVID-19.
DOI: 10.6084/m9.figshare.14684806.v1
title: Additional file 4 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 4: Table S4. Validation of the 189 common DEGs in E-GEOD-30219 datasets. Table S4–1. Validation of the 162 common down-regulated DEGs in E-GEOD-30219 datasets. Table S4–2. Validation of the 27 common up-regulated DEGs in E-GEOD-30219 datasets.
DOI: 10.6084/m9.figshare.14684809
title: Additional file 5 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 5: Table S5. Validation of the 189 common DEGs in E-GEOD-19188 datasets. Table S5–1. Validation of the 162 common down-regulated DEGs in E-GEOD-19188 datasets. Table S5–2. Validation of the 27 common up-regulated DEGs in E-GEOD-19188 datasets.
DOI: 10.6084/m9.figshare.14684809.v1
title: Additional file 5 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 5: Table S5. Validation of the 189 common DEGs in E-GEOD-19188 datasets. Table S5–1. Validation of the 162 common down-regulated DEGs in E-GEOD-19188 datasets. Table S5–2. Validation of the 27 common up-regulated DEGs in E-GEOD-19188 datasets.
DOI: 10.6084/m9.figshare.14684797
title: Additional file 1 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 1: Table S1. General information of the differentially expressed genes in GSE27262 datasets. Table S1–1. Down-regulated genes in GSE27262 datasets. Table S1–2. Up-regulated genes in GSE27262 datasets.
DOI: 10.6084/m9.figshare.14684806
title: Additional file 4 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 4: Table S4. Validation of the 189 common DEGs in E-GEOD-30219 datasets. Table S4–1. Validation of the 162 common down-regulated DEGs in E-GEOD-30219 datasets. Table S4–2. Validation of the 27 common up-regulated DEGs in E-GEOD-30219 datasets.
DOI: 10.6084/m9.figshare.14684797.v1
title: Additional file 1 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 1: Table S1. General information of the differentially expressed genes in GSE27262 datasets. Table S1–1. Down-regulated genes in GSE27262 datasets. Table S1–2. Up-regulated genes in GSE27262 datasets.
DOI: 10.6084/m9.figshare.14684800
title: Additional file 2 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 2: Table S2. General information of the differentially expressed genes in GSE118370 datasets. Table S2–1. Down-regulated genes in GSE118370 datasets. Table S2–2. Up-regulated genes in GSE118370 datasets.
DOI: 10.6084/m9.figshare.14684800.v1
title: Additional file 2 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 2: Table S2. General information of the differentially expressed genes in GSE118370 datasets. Table S2–1. Down-regulated genes in GSE118370 datasets. Table S2–2. Up-regulated genes in GSE118370 datasets.
DOI: 10.6084/m9.figshare.14684803
title: Additional file 3 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 3: Table S3. Key properties of PPI network (related to Fig. 2). Table S3–1. Key properties of PPI network of common DEGs (related to Fig. 2a). Table S3–2. Key properties of PPI network of 17 central DEGs (related to Fig. 2b).
DOI: 10.6084/m9.figshare.14684803.v1
title: Additional file 3 of Identification of significant genes as prognostic markers and potential tumor suppressors in lung adenocarcinoma via bioinformatical analysis

description: Additional file 3: Table S3. Key properties of PPI network (related to Fig. 2). Table S3–1. Key properties of PPI network of common DEGs (related to Fig. 2a). Table S3–2. Key properties of PPI network of 17 central DEGs (related to Fig. 2b).
DOI: 10.15156/bio/sh2684430.08fu
title: SH2684430.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2684431.08fu
title: SH2684431.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1993944.08fu
title: SH1993944.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1993945.08fu
title: SH1993945.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2684432.08fu
title: SH2684432.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2684433.08fu
title: SH2684433.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.