cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11798496
title: Supplementary Material for: Prediction of Perinatal Mortality in Ebstein’s Anomaly Diagnosed in the Second Trimester of Pregnancy

description: <b><i>Objectives:</i></b> Firstly, to describe the outcome of a series of fetuses with Ebstein’s anomaly (EA) and, secondly, to study the utility of different second-trimester echocardiographic parameters to predict fetal and neonatal mortality. <b><i>Methods:</i></b> 39 fetuses with EA diagnosed between 18 and 28 weeks of gestation were included. Fetal echocardiography included the cardiothoracic ratio (CTR); right atrial (RA) area index; displacement of the tricuspid valve (TV); tricuspid regurgitation; pulmonary artery; and ductus arteriosus flow characteristics. Additionally, 2 novel parameters were obtained: the relative RA area ratio (RA area/cardiac area) and the TV displacement index (TVDI, TV displacement distance/longi­tudinal diameter of the left ventricle). Correlation between the echocardiographic variables and the primary outcome of perinatal mortality or survival at 1 year of life was evaluated. <b><i>Results:</i></b> From the initial cohort, 8 cases were excluded due to complex congenital heart defects. Termination of pregnancy (TOP) was performed in 15 cases, and fetal death was diagnosed in 3 cases. In the live-born cohort of 13 patients, 4 died in the neonatal period, yielding a perinatal survival rate of 29 and 56%, respectively, after excluding TOP cases. Compared with survivors, nonsurvivors showed a significantly higher CTR (56.7 ± 16.2 vs. 42.6 ± 8.6; <i>p</i> = 0.04), relative RA area ratio (0.39 ± 0.13 vs. 0.25 ± 0.05; <i>p</i> = 0.01), and TVDI (0.62 ± 0.17 vs. 0.44 ± 0.12; <i>p</i> = 0.03) at diagnosis. The best model to predict perinatal mortality was obtained by using a scoring system which included the relative RA area ratio and TVDI (AUC 0.905 [95% CI 0.732–1.000]). <b><i>Conclusions:</i></b> Fetuses with a relative RA area ratio ≥0.29 and TVDI ≥0.65 at the second trimester have the highest risk of dying in the perinatal stage.
DOI: 10.6084/m9.figshare.11798466
title: Supplementary Material for: Low Predialysis Plasma Calculated Osmolality Is Associated with Higher All-Cause Mortality: The Japanese Dialysis Outcomes and Practice Patterns Study (J-DOPPS)

description: <b><i>Introduction:</i></b> Patients undergoing hemodialysis (HD) have higher predialysis plasma osmolality. Several studies have suggested lower osmolality to be associated with worse outcomes in patients not undergoing HD. However, no studies have examined the association between osmolality and mortality among patients undergoing HD. <b><i>Objective:</i></b> We aimed to examine the association between predialysis plasma calculated osmolality and all-cause mortality. <b><i>Methods:</i></b> This was a prospective cohort study of 1,240 patients undergoing HD participating in the Japanese Dialysis Outcomes and Practice Patterns Study phase 5 (2012–2015). The exposure was predialysis plasma osmolality, calculated as 2 × (serum sodium concentration [mmol/L]) + (serum urea nitrogen [mg/dL])/2.8 + (serum glucose [mg/dL])/18. The primary outcome was all-cause mortality. The secondary outcome was the change in systolic blood pressure (SBP) during HD. We used a marginal structural model with stabilized weights to estimate the association between calculated osmolality and all-cause mortality in the presence of time-varying confounders affected by prior exposure. <b><i>Results:</i></b> Mean baseline plasma calculated osmolality was 306.8 ± 8.6 mOsm/kg. Low predialysis calculated osmolality was associated with higher mortality (adjusted hazard ratio 1.52, 95% confidence interval [CI]: 1.30–1.78 by each 10 mOsm/L lower osmolality). The association was consistent across clinically relevant subgroups. Predialysis osmolality was significantly associated with intradialytic SBP change (mean difference 0.96 [95% CI: 0.05–1.88] mm Hg per each 10 mOsm/L lower osmolality). <b><i>Conclusions:</i></b> Low predialysis calculated osmolality was an independent risk factor of all-cause mortality.
DOI: 10.6084/m9.figshare.11798418
title: Induction of 2-cyanoethyl-isoxazolin-5-one as an antifeedant against the tobacco cutworm (<i>Spodoptera litura</i>) by jasmonic acid in sweet pea leaf

description: Although sweet pea (<i>Lathyrus odoratus</i>) beans contain toxic β-aminopropionitrile, the plant itself is readily attacked by insects and is, therefore, protected through the use of pesticides. Consequently, the induction of <i>L. odoratus</i> resistance to insect attack via exogenous treatment is promising for pest control development. Screening of inducible elicitor effects showed that treatment of sweet pea foliage with jasmonic acid (JA) can induce antifeeding-based resistance to tobacco cutworm (<i>Spodoptera litura</i>) larvae. Spectroscopic analysis identified 2-cyanoethyl-isoxazolin-5-one (2-CEIX) as the antifeedant with a half-maximal effective concentration of 33.6 µmol/g fr. wt., i.e., exogenous JA treatment induced antifeeding activity due to the accumulation of 2-CEIX. Moreover, 2-CEIX-induced mortality of <i>S. litura</i> larvae was evaluated by a dipping test and the half-maximal lethal dose was determined to be 5.9 mg/mL. Therefore, 2-CEIX was concluded to be a suitable induced resistance target for elicitors or a lead compound for insecticide development. Induction of 2-Cyanoethyl-isoxazolin-5-one as an antifeedant against the tobacco cutworm, <i>Spodoptera litura</i>, by jasmonic acid in sweet pea leaf.
DOI: 10.6084/m9.figshare.11798418.v1
title: Induction of 2-cyanoethyl-isoxazolin-5-one as an antifeedant against the tobacco cutworm (<i>Spodoptera litura</i>) by jasmonic acid in sweet pea leaf

description: Although sweet pea (<i>Lathyrus odoratus</i>) beans contain toxic β-aminopropionitrile, the plant itself is readily attacked by insects and is, therefore, protected through the use of pesticides. Consequently, the induction of <i>L. odoratus</i> resistance to insect attack via exogenous treatment is promising for pest control development. Screening of inducible elicitor effects showed that treatment of sweet pea foliage with jasmonic acid (JA) can induce antifeeding-based resistance to tobacco cutworm (<i>Spodoptera litura</i>) larvae. Spectroscopic analysis identified 2-cyanoethyl-isoxazolin-5-one (2-CEIX) as the antifeedant with a half-maximal effective concentration of 33.6 µmol/g fr. wt., i.e., exogenous JA treatment induced antifeeding activity due to the accumulation of 2-CEIX. Moreover, 2-CEIX-induced mortality of <i>S. litura</i> larvae was evaluated by a dipping test and the half-maximal lethal dose was determined to be 5.9 mg/mL. Therefore, 2-CEIX was concluded to be a suitable induced resistance target for elicitors or a lead compound for insecticide development. Induction of 2-Cyanoethyl-isoxazolin-5-one as an antifeedant against the tobacco cutworm, <i>Spodoptera litura</i>, by jasmonic acid in sweet pea leaf.
DOI: 10.6084/m9.figshare.11798376
title: Additional file 8 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 8. Deblur sOTU table (biom file) for the external microbiomes (1193 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798379
title: Additional file 9 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 9. Deblur sOTU table (biom file) for the marine external microbiomes (266 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798379.v1
title: Additional file 9 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 9. Deblur sOTU table (biom file) for the marine external microbiomes (266 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798376.v1
title: Additional file 8 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 8. Deblur sOTU table (biom file) for the external microbiomes (1193 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798370
title: Additional file 6 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 6. Deblur sOTU table (biom file) for the full dataset, not rarefied.
DOI: 10.6084/m9.figshare.11798373
title: Additional file 7 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 7. Deblur sOTU table (biom file) for the internal microbiome (741 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798370.v1
title: Additional file 6 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 6. Deblur sOTU table (biom file) for the full dataset, not rarefied.
DOI: 10.6084/m9.figshare.11798373.v1
title: Additional file 7 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 7. Deblur sOTU table (biom file) for the internal microbiome (741 samples), rarefied to 1000 reads/sample.
DOI: 10.6084/m9.figshare.11798364
title: Additional file 4 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 4. Filtered metadata file for external microbiomes (1193 samples).
DOI: 10.6084/m9.figshare.11798367
title: Additional file 5 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 5. Filtered metadata file for marine external microbiomes (266 samples).
DOI: 10.6084/m9.figshare.11798367.v1
title: Additional file 5 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 5. Filtered metadata file for marine external microbiomes (266 samples).
DOI: 10.6084/m9.figshare.11798358
title: Additional file 2 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 2. Full metadata file (15,790 samples), unfiltered.
DOI: 10.6084/m9.figshare.11798361
title: Additional file 3 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 3. Filtered metadata file for internal microbiomes (741 samples).
DOI: 10.6084/m9.figshare.11798364.v1
title: Additional file 4 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 4. Filtered metadata file for external microbiomes (1193 samples).
DOI: 10.6084/m9.figshare.11798358.v1
title: Additional file 2 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 2. Full metadata file (15,790 samples), unfiltered.
DOI: 10.6084/m9.figshare.11798361.v1
title: Additional file 3 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 3. Filtered metadata file for internal microbiomes (741 samples).
DOI: 10.6084/m9.figshare.11798343
title: Additional file 11 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 11. Fasta file to match OTU number with deblur sequence ID.
DOI: 10.6084/m9.figshare.11798346
title: Additional file 12 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 12. Phylogenetic tree file used for analyses on data subsets.
DOI: 10.6084/m9.figshare.11798343.v1
title: Additional file 11 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 11. Fasta file to match OTU number with deblur sequence ID.
DOI: 10.6084/m9.figshare.11798346.v1
title: Additional file 12 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 12. Phylogenetic tree file used for analyses on data subsets.
DOI: 10.6084/m9.figshare.11798334
title: Additional file 10 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 10. RepSet fasta file for the full dataset.
DOI: 10.6084/m9.figshare.11798334.v1
title: Additional file 10 of Host-associated microbiomes are predicted by immune system complexity and climate

description: Additional file 10. RepSet fasta file for the full dataset.
DOI: 10.6084/m9.figshare.11798220
title: Additional file 6 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 6: Table S2. Environmental fit results for the entire community, bacteria, archaea and fungi.
DOI: 10.6084/m9.figshare.11798220.v1
title: Additional file 6 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 6: Table S2. Environmental fit results for the entire community, bacteria, archaea and fungi.
DOI: 10.6084/m9.figshare.11798205
title: Additional file 1 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 1: Table S1. Sequence counts of all samples before and after quality-filtering with fastp.
DOI: 10.6084/m9.figshare.11798202
title: Additional file 17 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 17: Table S5. Count matrix containing functional assignments for all samples. The first column shows the ID the last columns the function according to the KEGG database including the KO number and the full pathway description.
DOI: 10.6084/m9.figshare.11798202.v1
title: Additional file 17 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 17: Table S5. Count matrix containing functional assignments for all samples. The first column shows the ID the last columns the function according to the KEGG database including the KO number and the full pathway description.
DOI: 10.6084/m9.figshare.11798205.v1
title: Additional file 1 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 1: Table S1. Sequence counts of all samples before and after quality-filtering with fastp.
DOI: 10.6084/m9.figshare.11798196
title: Additional file 16 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 16: Table S4. Taxonomic counts of all analysed sequences. The first column shows ID and the last column the detected taxonomy.
DOI: 10.6084/m9.figshare.11798190
title: Additional file 15 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 15: Table S3. Used abiotic measurements for all samples.
DOI: 10.6084/m9.figshare.11798196.v1
title: Additional file 16 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 16: Table S4. Taxonomic counts of all analysed sequences. The first column shows ID and the last column the detected taxonomy.
DOI: 10.6084/m9.figshare.11798190.v1
title: Additional file 15 of Unravelling the effects of tropical land use conversion on the soil microbiome

description: Additional file 15: Table S3. Used abiotic measurements for all samples.
DOI: 10.6084/m9.figshare.11798163
title: Additional file 1 of Reduced nonverbal interpersonal synchrony in autism spectrum disorder independent of partner diagnosis: a motion energy study

description: Additional file 1. Supplementary Materials – Synchrony (IPS). Supplementary Materials – Motion Energy. Supplementary Materials – Evaluation measures
DOI: 10.6084/m9.figshare.11798163.v1
title: Additional file 1 of Reduced nonverbal interpersonal synchrony in autism spectrum disorder independent of partner diagnosis: a motion energy study

description: Additional file 1. Supplementary Materials – Synchrony (IPS). Supplementary Materials – Motion Energy. Supplementary Materials – Evaluation measures
DOI: 10.6084/m9.figshare.11797965
title: Additional file 1 of Comprehensive analysis of PM20D1 QTL in Alzheimer’s disease

description: Additional file 1: Table S1. List of primers used for the different techniques.
DOI: 10.6084/m9.figshare.11797965.v1
title: Additional file 1 of Comprehensive analysis of PM20D1 QTL in Alzheimer’s disease

description: Additional file 1: Table S1. List of primers used for the different techniques.
DOI: 10.6084/m9.figshare.11797953
title: Additional file 1 of Sex differences in the evolution of left ventricle remodeling in rats with severe volume overload

description: Additional file 1. Raw data of the study.
DOI: 10.6084/m9.figshare.11797953.v1
title: Additional file 1 of Sex differences in the evolution of left ventricle remodeling in rats with severe volume overload

description: Additional file 1. Raw data of the study.
DOI: 10.6084/m9.figshare.11797932
title: Additional file 1 of Validity of the dispersion relations in magnetotellurics: Part I—theory

description: Additional file 1. Field data pack.
DOI: 10.6084/m9.figshare.11797932.v1
title: Additional file 1 of Validity of the dispersion relations in magnetotellurics: Part I—theory

description: Additional file 1. Field data pack.
DOI: 10.6084/m9.figshare.11797902
title: Additional file 3 of Host proteome linked to HPV E7-mediated specific gene hypermethylation in cancer pathways

description: Additional file 3 : Table S2. List of total identified proteins of E7-transfected HEK293 by Maxquant.
DOI: 10.6084/m9.figshare.11797902.v1
title: Additional file 3 of Host proteome linked to HPV E7-mediated specific gene hypermethylation in cancer pathways

description: Additional file 3 : Table S2. List of total identified proteins of E7-transfected HEK293 by Maxquant.
DOI: 10.6084/m9.figshare.11797893
title: Additional file 9 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 9: Table S8. Summary of alignment statistics of RNA-Seq libraries mapped to reference genome.
DOI: 10.6084/m9.figshare.11797896
title: Additional file 2 of Host proteome linked to HPV E7-mediated specific gene hypermethylation in cancer pathways

description: Additional file 2 : Table S1. List of total identified proteins of E7-transfected HEK293 by Proteome Discover.
DOI: 10.6084/m9.figshare.11797893.v1
title: Additional file 9 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 9: Table S8. Summary of alignment statistics of RNA-Seq libraries mapped to reference genome.
DOI: 10.6084/m9.figshare.11797896.v1
title: Additional file 2 of Host proteome linked to HPV E7-mediated specific gene hypermethylation in cancer pathways

description: Additional file 2 : Table S1. List of total identified proteins of E7-transfected HEK293 by Proteome Discover.
DOI: 10.6084/m9.figshare.11797884
title: Additional file 8 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 8: Table S7. The primers of 7 DEMs validated by RT-qPCR analysis.
DOI: 10.6084/m9.figshare.11797881
title: Additional file 7 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 7: Table S6. The primers of 15 DEGs validated by RT-qPCR analysis.
DOI: 10.6084/m9.figshare.11797884.v1
title: Additional file 8 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 8: Table S7. The primers of 7 DEMs validated by RT-qPCR analysis.
DOI: 10.6084/m9.figshare.11797881.v1
title: Additional file 7 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 7: Table S6. The primers of 15 DEGs validated by RT-qPCR analysis.
DOI: 10.6084/m9.figshare.11797872
title: Additional file 6 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 6: Table S5. Identification of miRNAs target genes in expansion stage.
DOI: 10.6084/m9.figshare.11797872.v1
title: Additional file 6 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 6: Table S5. Identification of miRNAs target genes in expansion stage.
DOI: 10.6084/m9.figshare.11797863
title: Additional file 5 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 5: Table S4. Analysis of clean reads for small RNA sequencing in initiation and expansion stages.
DOI: 10.6084/m9.figshare.11797860
title: Additional file 4 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 4: Table S3. The differentially expressed transcription factors involved in expansion stage.
DOI: 10.6084/m9.figshare.11797863.v1
title: Additional file 5 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 5: Table S4. Analysis of clean reads for small RNA sequencing in initiation and expansion stages.
DOI: 10.6084/m9.figshare.11797851
title: Additional file 3 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 3: Table S2. The differentially expressed genes involved in signal transduction pathways in expansion stage (FPKM&gt;â€‰5).
DOI: 10.6084/m9.figshare.11797860.v1
title: Additional file 4 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 4: Table S3. The differentially expressed transcription factors involved in expansion stage.
DOI: 10.6084/m9.figshare.11797845
title: Additional file 2 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 2: Table S1. Differentially expressed genes (DEGs) in expansion stage compared with initiation stage.
DOI: 10.6084/m9.figshare.11797851.v1
title: Additional file 3 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 3: Table S2. The differentially expressed genes involved in signal transduction pathways in expansion stage (FPKM&gt;â€‰5).
DOI: 10.6084/m9.figshare.11797845.v1
title: Additional file 2 of Integrated mRNA and miRNA transcriptome analysis reveals a regulatory network for tuber expansion in Chinese yam (Dioscorea opposita)

description: Additional file 2: Table S1. Differentially expressed genes (DEGs) in expansion stage compared with initiation stage.
DOI: 10.6084/m9.figshare.11797809
title: Additional file 3 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 3: Metastatic cancer samples were taken as a separate group and compared to non-metastatic.
DOI: 10.6084/m9.figshare.11797809.v1
title: Additional file 3 of Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

description: Additional file 3: Metastatic cancer samples were taken as a separate group and compared to non-metastatic.