cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12286700
title: Supplementary Material for: Complete Superior and Inferior Vena Cava Obstruction Associated with Systemic-to-Pulmonary Venous Shunts in a Young Female with Heterozygous Prothrombin G20210A Gene Mutation

description: Complete superior vena cava (SVC) and inferior vena cava (IVC) obstruction is not uncommon and most commonly associated with malignancy. The risk increases in patients with central lines and hypercoagulable states such as with malignancy, thrombophilia, or use of oral contraceptive pills. According to our knowledge, complete SVC and IVC obstruction associated with systemic-to-pulmonary venous shunts in patients with prothrombin G20210A gene mutation has not been reported in the literature. Here we report the case of a 34-year-old female with complete SVC and IVC obstruction presenting with oxygen desaturation and shortness of breath due to systemic-to-pulmonary venous shunts. The unusual collateral pathway was secondary to SVC obstruction. The patient was managed conservatively, and she remained stable.
DOI: 10.6084/m9.figshare.12286700.v1
title: Supplementary Material for: Complete Superior and Inferior Vena Cava Obstruction Associated with Systemic-to-Pulmonary Venous Shunts in a Young Female with Heterozygous Prothrombin G20210A Gene Mutation

description: Complete superior vena cava (SVC) and inferior vena cava (IVC) obstruction is not uncommon and most commonly associated with malignancy. The risk increases in patients with central lines and hypercoagulable states such as with malignancy, thrombophilia, or use of oral contraceptive pills. According to our knowledge, complete SVC and IVC obstruction associated with systemic-to-pulmonary venous shunts in patients with prothrombin G20210A gene mutation has not been reported in the literature. Here we report the case of a 34-year-old female with complete SVC and IVC obstruction presenting with oxygen desaturation and shortness of breath due to systemic-to-pulmonary venous shunts. The unusual collateral pathway was secondary to SVC obstruction. The patient was managed conservatively, and she remained stable.
DOI: 10.6084/m9.figshare.12286661
title: Supplementary Material for: Shaping of the Female Human Brain by Sex Hormones: A Review

description: Traditionally sex hormones have been associated with reproductive and developmental processes only. Since the 1950s we know that hormones can have organizational effects on the developing brain and initiate hormonal transition periods such as puberty. However, recent evidence shows that sex hormones additionally structure the brain during important hormonal transition periods across a woman’s life including short-term fluctuations during the menstrual cycle. However, a comprehensive review focusing on structural changes during all hormonal transition phases of women is still missing. Therefore, in this review structural changes across hormonal transition periods (i.e., puberty, menstrual cycle, oral contraceptive intake, pregnancy and menopause) were investigated in a structured way and correlations with sex hormones evaluated. Results show an overall reduction in grey matter and region-specific decreases in prefrontal, parietal and middle temporal areas during puberty. Across the menstrual cycle grey matter plasticity in the hippocampus, the amygdala as well as temporal and parietal regions were most consistently reported. Studies reporting on pre- and post-pregnancy measurements revealed volume reductions in midline structures as well as prefrontal and temporal cortices. During perimenopause, the decline in sex hormones was paralleled with a reduction in hippocampal and parietal cortex volume. Brain volume changes were significantly correlated with estradiol, testosterone and progesterone levels in some studies, but directionality remains inconclusive between studies. These results indicate that sex hormones play an important role in shaping women’s brain structure during different transition periods and are not restricted to specific developmental periods.
DOI: 10.6084/m9.figshare.12286658
title: Additional File 1. Sequence alignments of selected candidate HGTs in S. rosetta used in phylogenetic analysis

description: Peptide sequences of candidate HGTs were aligned to representative sequences from selected metazoa, eukaryotic, and prokaryotic taxa using MAFFT version 7.
DOI: 10.6084/m9.figshare.12286625
title: Table S1: Bulk rock major and trace elements composition of the Paleogene rocks from oceanic tract of NW Iran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S1: Bulk rock major and trace elements composition of the Paleogene rocks from oceanic tract of NW Iran.
DOI: 10.6084/m9.figshare.12286622
title: Table S5: Zircon SIMS U–Pb ages for the magmatic rocks from NW parts of Kamyaran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S5: Zircon SIMS U–Pb ages for the magmatic rocks from NW parts of Kamyaran.
DOI: 10.6084/m9.figshare.12286616
title: Table S6: Zircon Lu–Hf and O isotope composition of the Paleogene rocks from oceanic tract of NW Iran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S6: Zircon Lu–Hf and O isotope composition of the Paleogene rocks from oceanic tract of NW Iran.
DOI: 10.6084/m9.figshare.12286622.v1
title: Table S5: Zircon SIMS U–Pb ages for the magmatic rocks from NW parts of Kamyaran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S5: Zircon SIMS U–Pb ages for the magmatic rocks from NW parts of Kamyaran.
DOI: 10.6084/m9.figshare.12286625.v1
title: Table S1: Bulk rock major and trace elements composition of the Paleogene rocks from oceanic tract of NW Iran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S1: Bulk rock major and trace elements composition of the Paleogene rocks from oceanic tract of NW Iran.
DOI: 10.6084/m9.figshare.12286607
title: Table S4: Zircon LA–ICPMS U–Pb ages for the magmatic rocks from SE parts of Kamyaran.The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S4: Zircon LA–ICPMS U–Pb ages for the magmatic rocks from SE parts of Kamyaran.
DOI: 10.6084/m9.figshare.12286610
title: Table S2: Geological standard values for whole rock major and trace elements analyses. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S2: Geological standard values for whole rock major and trace elements analyses.
DOI: 10.6084/m9.figshare.12286613
title: Table S3: Bulk rock Sr–Nd–Pb isotopic composition of the Paleogene rocks from oceanic tract of NW Iran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S3: Bulk rock Sr–Nd–Pb isotopic composition of the Paleogene rocks from oceanic tract of NW Iran.
DOI: 10.6084/m9.figshare.12286613.v1
title: Table S3: Bulk rock Sr–Nd–Pb isotopic composition of the Paleogene rocks from oceanic tract of NW Iran. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S3: Bulk rock Sr–Nd–Pb isotopic composition of the Paleogene rocks from oceanic tract of NW Iran.
DOI: 10.6084/m9.figshare.12286607.v1
title: Table S4: Zircon LA–ICPMS U–Pb ages for the magmatic rocks from SE parts of Kamyaran.The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S4: Zircon LA–ICPMS U–Pb ages for the magmatic rocks from SE parts of Kamyaran.
DOI: 10.6084/m9.figshare.12286610.v1
title: Table S2: Geological standard values for whole rock major and trace elements analyses. The Paleogene Ophiolite Conundrum of the Iran–Iraq Border Region

description: Table S2: Geological standard values for whole rock major and trace elements analyses.
DOI: 10.6084/m9.figshare.12286517
title: Capture-history.xlsx

description: Capture history of two small cats from the paper "Population density and habitat use of two sympatric small cats in a central Indian reserve"
DOI: 10.6084/m9.figshare.12286517.v1
title: Capture-history.xlsx

description: Capture history of two small cats from the paper "Population density and habitat use of two sympatric small cats in a central Indian reserve"
DOI: 10.6084/m9.figshare.12493229.v1
title: The Combined Outcomes of the COVID-19 Pandemic and a Collapsing Economy on Quality of Life: Perspectives from a Developing Country

description: <b>Background</b>This study aimed at examining the combined outcomes of the COVID-19 pandemic and a collapsing economy on the quality of life (QOL) of the general Lebanese population.<b></b><b>Methods</b>A cross-sectional study was conducted from 10-18 May 2020, via an online-based questionnaire using the snowball sampling technique. It enrolled 502 adult participants.<b></b>
DOI: 10.6084/m9.figshare.12286340.v2
title: Dataset-Alternative Working Solutions During A Pandemic-The Case of Professionals Working from Home in Bangladesh

description: dataset
DOI: 10.6084/m9.figshare.12285533
title: Additional file 1 of Altered behavior in mice overexpressing soluble ST2

description: Additional file 1.
DOI: 10.6084/m9.figshare.12285533.v1
title: Additional file 1 of Altered behavior in mice overexpressing soluble ST2

description: Additional file 1.
DOI: 10.6084/m9.figshare.12285518
title: Additional file 6 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Hetnetpy metaedge summary. Network summary of edge and node counts for each gene set collection.
DOI: 10.6084/m9.figshare.12285509
title: Additional file 9 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 9: Table S9. Differential pathways in the Control+Baseline group of CD (Adjusted P-value
DOI: 10.6084/m9.figshare.12285512
title: Additional file 4 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Model coefficients for predicting TP53 loss of function. Using all compressed features in the model implicates compressed features with cancer hallmark signatures. Associated with Fig. 7.
DOI: 10.6084/m9.figshare.12285515
title: Additional file 5 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Tissue types and counts for TARGET, TCGA, and GTEx.
DOI: 10.6084/m9.figshare.12285506
title: Additional file 3 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Neutrophil and Monocyte Gene Sets. Entrez gene IDs and gene symbols for two xCell gene signatures (Neutrophil_HPCA_2 and Monocyte_FANTOM_2). Associated with Fig. 6.
DOI: 10.6084/m9.figshare.12285515.v1
title: Additional file 5 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Tissue types and counts for TARGET, TCGA, and GTEx.
DOI: 10.6084/m9.figshare.12285518.v1
title: Additional file 6 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Hetnetpy metaedge summary. Network summary of edge and node counts for each gene set collection.
DOI: 10.6084/m9.figshare.12285503
title: Additional file 8 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 8: Table S8. Pathways that correlated with HDCs in untreated patients and controls from CD project (P-value
DOI: 10.6084/m9.figshare.12285509.v1
title: Additional file 9 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 9: Table S9. Differential pathways in the Control+Baseline group of CD (Adjusted P-value
DOI: 10.6084/m9.figshare.12285512.v1
title: Additional file 4 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Model coefficients for predicting TP53 loss of function. Using all compressed features in the model implicates compressed features with cancer hallmark signatures. Associated with Fig. 7.
DOI: 10.6084/m9.figshare.12285497
title: Additional file 7 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 7: Table S7. Differential species in the Control+Baseline group of CD (Adjusted P-value
DOI: 10.6084/m9.figshare.12285506.v1
title: Additional file 3 of Compressing gene expression data using multiple latent space dimensionalities learns complementary biological representations

description: Neutrophil and Monocyte Gene Sets. Entrez gene IDs and gene symbols for two xCell gene signatures (Neutrophil_HPCA_2 and Monocyte_FANTOM_2). Associated with Fig. 6.
DOI: 10.6084/m9.figshare.12285491
title: Additional file 6 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 6: Table S6. Species that correlated with HDCs in untreated patients and controls from CD project (P-value
DOI: 10.6084/m9.figshare.12285503.v1
title: Additional file 8 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 8: Table S8. Pathways that correlated with HDCs in untreated patients and controls from CD project (P-value
DOI: 10.6084/m9.figshare.12285497.v1
title: Additional file 7 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 7: Table S7. Differential species in the Control+Baseline group of CD (Adjusted P-value
DOI: 10.6084/m9.figshare.12285488
title: Additional file 5 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 5: Table S5. A list of samples of the CD project (SRP057027) and their HDC% produced by two ways. Our HDC% were generated using Bowtie2, while Lewis’s HDC% were generated using BMtagger (pmid:26468751).
DOI: 10.6084/m9.figshare.12285491.v1
title: Additional file 6 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 6: Table S6. Species that correlated with HDCs in untreated patients and controls from CD project (P-value
DOI: 10.6084/m9.figshare.12285476
title: Additional file 2 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 2: Table S2. Metadata of participants in CRC projects and their HDC%. HDC means the human DNA content.
DOI: 10.6084/m9.figshare.12285482
title: Additional file 3 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 3: Table S3. Species that correlated with HDC in more than two CRC datasets statistically. Species whose Spearman p-value
DOI: 10.6084/m9.figshare.12285485
title: Additional file 4 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 4: Table S4. Pathways that correlated with HDC in more than two CRC datasets statistically. Pathways whose Spearman p-value
DOI: 10.6084/m9.figshare.12285488.v1
title: Additional file 5 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 5: Table S5. A list of samples of the CD project (SRP057027) and their HDC% produced by two ways. Our HDC% were generated using Bowtie2, while Lewis’s HDC% were generated using BMtagger (pmid:26468751).
DOI: 10.6084/m9.figshare.12285482.v1
title: Additional file 3 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 3: Table S3. Species that correlated with HDC in more than two CRC datasets statistically. Species whose Spearman p-value
DOI: 10.6084/m9.figshare.12285476.v1
title: Additional file 2 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 2: Table S2. Metadata of participants in CRC projects and their HDC%. HDC means the human DNA content.
DOI: 10.6084/m9.figshare.12285485.v1
title: Additional file 4 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 4: Table S4. Pathways that correlated with HDC in more than two CRC datasets statistically. Pathways whose Spearman p-value
DOI: 10.6084/m9.figshare.12285467
title: Additional file 1 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 1: Table S1. A list of CRC and CD projects used in this study and the numbers of controls and cases.
DOI: 10.6084/m9.figshare.12285461
title: Additional file 19 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 19: Table S14. The contribution of each dataset on identification of HDC-related species and HDC-related pathways. The column “count_HDCrelated_species” and “count_HDCrelated_pathways” mean the count of identified HDC-related species and HDC-related pathways respectively in the corresponding dataset.
DOI: 10.6084/m9.figshare.12285467.v1
title: Additional file 1 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 1: Table S1. A list of CRC and CD projects used in this study and the numbers of controls and cases.
DOI: 10.6084/m9.figshare.12285455
title: Additional file 18 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 18: Table S13. Generalized linear model based on the samples from PRJEB27928 to identify the significant predictors associated with CRC.
DOI: 10.6084/m9.figshare.12285461.v1
title: Additional file 19 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 19: Table S14. The contribution of each dataset on identification of HDC-related species and HDC-related pathways. The column “count_HDCrelated_species” and “count_HDCrelated_pathways” mean the count of identified HDC-related species and HDC-related pathways respectively in the corresponding dataset.
DOI: 10.6084/m9.figshare.12285449
title: Additional file 17 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 17: Table S12. Characteristics of controls and CRC patients in seven CRC projects.
DOI: 10.6084/m9.figshare.12285455.v1
title: Additional file 18 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 18: Table S13. Generalized linear model based on the samples from PRJEB27928 to identify the significant predictors associated with CRC.
DOI: 10.6084/m9.figshare.12285449.v1
title: Additional file 17 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 17: Table S12. Characteristics of controls and CRC patients in seven CRC projects.
DOI: 10.6084/m9.figshare.12285437
title: Additional file 15 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 15: Table S11. AUC of classifiers based on species/pathways profiles (SRP057027) when predicting treatment response of external dataset (PRJNA384246). Features column means which type of features used for constructing random forest model.
DOI: 10.6084/m9.figshare.12285437.v1
title: Additional file 15 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 15: Table S11. AUC of classifiers based on species/pathways profiles (SRP057027) when predicting treatment response of external dataset (PRJNA384246). Features column means which type of features used for constructing random forest model.
DOI: 10.6084/m9.figshare.12285407
title: Additional file 11 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 11: Table S10. Overlapped features of the top 30 important features between CD and CRC. The columns “Importance_in_CRC” and “Importance_in_CD” mean the median importance scores of the top 30 features in the CRC models and CD models, respectively. The columns “ranking_in_CRC” and “ranking_in_CD” mean the importance degrees of the shared features in the CRC models and CD models separately.
DOI: 10.6084/m9.figshare.12285407.v1
title: Additional file 11 of Host DNA contents in fecal metagenomics as a biomarker for intestinal diseases and effective treatment

description: Additional file 11: Table S10. Overlapped features of the top 30 important features between CD and CRC. The columns “Importance_in_CRC” and “Importance_in_CD” mean the median importance scores of the top 30 features in the CRC models and CD models, respectively. The columns “ranking_in_CRC” and “ranking_in_CD” mean the importance degrees of the shared features in the CRC models and CD models separately.
DOI: 10.6084/m9.figshare.12285353
title: Additional file 1 of Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy

description: Additional file 1 Configuration files for the network architectures. As part of the supplementary material, it is possible to download the configuration files (ConfigFiles.zip) for DeepMedic (DeepMedic_model.cfg, DeepMedic_inference.cfg, DeepMedic_train.cfg) and dV-net (NiftyNet_train.ini). The configuration files for DeepMedic are three and contains the information regarding the model (DeepMedic_model.cfg), training (DeepMedic_train.cfg) and inference (DeepMedic_inference.cfg) of the network.
DOI: 10.6084/m9.figshare.12285353.v1
title: Additional file 1 of Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy

description: Additional file 1 Configuration files for the network architectures. As part of the supplementary material, it is possible to download the configuration files (ConfigFiles.zip) for DeepMedic (DeepMedic_model.cfg, DeepMedic_inference.cfg, DeepMedic_train.cfg) and dV-net (NiftyNet_train.ini). The configuration files for DeepMedic are three and contains the information regarding the model (DeepMedic_model.cfg), training (DeepMedic_train.cfg) and inference (DeepMedic_inference.cfg) of the network.
DOI: 10.6084/m9.figshare.12285278
title: Additional file 1 of Dry needling for spine related disorders: a scoping review

description: Additional file 1: Table 4.1. Complete Data Cervical Region. Table 4.2. Complete Data TLP Region.
DOI: 10.6084/m9.figshare.12285278.v1
title: Additional file 1 of Dry needling for spine related disorders: a scoping review

description: Additional file 1: Table 4.1. Complete Data Cervical Region. Table 4.2. Complete Data TLP Region.
DOI: 10.6084/m9.figshare.12285242
title: Additional file 2 of GC × GC–HRMS nontarget fingerprinting of organic micropollutants in urban freshwater sediments

description: Additional file 2. Detailed list of identified compounds in sediment samples from Copenhagen.
DOI: 10.6084/m9.figshare.12285242.v1
title: Additional file 2 of GC × GC–HRMS nontarget fingerprinting of organic micropollutants in urban freshwater sediments

description: Additional file 2. Detailed list of identified compounds in sediment samples from Copenhagen.