cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25132526
title: Assessing the risk to develop a growing teratoma syndrome based on molecular and epigenetic subtyping as well as novel secreted biomarkers

description: In germ cell tumors (GCT), a growing teratoma during chemotherapy with decreasing tumor markers was defined as ´growing teratoma syndrome` (GTS) by Logothetis et al. in 1982. So far, its pathogenesis and specific treatment options remain elusive. We aimed at updating the GTS definition based on molecular and epigenetic features as well as identifying circulating biomarkers. We selected 50 GTS patients for clinical characterization and subsequently 12 samples were molecularly analyzed. We further included 7 longitudinal samples of 2 GTS patients. Teratomas (TER) showing no features of GTS served as controls. GTS were stratified based on growth rates into a slow (&lt; 0.5 cm / month), medium (0.5 - 1.5) and rapid (&gt; 1.5) group. By analyzing DNA methylation, microRNA expression and the secretome, we identified putative epigenetic and secreted biomarkers for the GTS subgroups. We found that proteins enriched in the GTS groups compared to TER were involved in proliferation, DNA replication and the cell cycle, while proteins interacting with the immune system were depleted. Additionally, GTSrapid seem to interact more strongly with the surrounding microenvironment than GTSslow. Detection of expression of pluripotency and yolk-sac tumor associated genes in GTS and formation of a yolk-sac tumor or somatic-type malignancy in the longitudinal GTS samples, pointed at an additional occult non-seminomatous component after chemotherapy. Thus, updating the Logothetis GTS definition is necessary, which we propose as follows: The GTS describes a continuously growing teratoma that might harbor occult non-seminomatous components considerably reduced during therapy but outgrowing over time again.
DOI: 10.6084/m9.figshare.25132526.v1
title: Assessing the risk to develop a growing teratoma syndrome based on molecular and epigenetic subtyping as well as novel secreted biomarkers

description: In germ cell tumors (GCT), a growing teratoma during chemotherapy with decreasing tumor markers was defined as ´growing teratoma syndrome` (GTS) by Logothetis et al. in 1982. So far, its pathogenesis and specific treatment options remain elusive. We aimed at updating the GTS definition based on molecular and epigenetic features as well as identifying circulating biomarkers. We selected 50 GTS patients for clinical characterization and subsequently 12 samples were molecularly analyzed. We further included 7 longitudinal samples of 2 GTS patients. Teratomas (TER) showing no features of GTS served as controls. GTS were stratified based on growth rates into a slow (&lt; 0.5 cm / month), medium (0.5 - 1.5) and rapid (&gt; 1.5) group. By analyzing DNA methylation, microRNA expression and the secretome, we identified putative epigenetic and secreted biomarkers for the GTS subgroups. We found that proteins enriched in the GTS groups compared to TER were involved in proliferation, DNA replication and the cell cycle, while proteins interacting with the immune system were depleted. Additionally, GTSrapid seem to interact more strongly with the surrounding microenvironment than GTSslow. Detection of expression of pluripotency and yolk-sac tumor associated genes in GTS and formation of a yolk-sac tumor or somatic-type malignancy in the longitudinal GTS samples, pointed at an additional occult non-seminomatous component after chemotherapy. Thus, updating the Logothetis GTS definition is necessary, which we propose as follows: The GTS describes a continuously growing teratoma that might harbor occult non-seminomatous components considerably reduced during therapy but outgrowing over time again.
DOI: 10.6084/m9.figshare.24172171
title: Additional file 2 of Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

description: Additional file 2: Supplementary Table 1. Candidate CpG sites for cocaine use after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811). Supplementary Table 2. Selected SNP-CpG pairs (FDR &lt; 0.05) after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811) and clumping. Supplementary Table 3. Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine-related DNA methylations. Supplementary Table 4. Significant traits associated with the top index meQTLs. A total of 60 studies reached the significance level after bonferroni correction (p &lt; 1.7e-7), which consisted of 36 phenotypes (phenotype number). Supplementary Table 5. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine-related DNA methylations were used. Supplementary Table 6. Candidate CpG sites for cocaine use frequency (high frequency users VS low frequency users). Supplementary Table 7. MeQTLs (FDR &lt; 0.05) for cocaine use frequency-related DNA methylations after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 293) and clumping. Supplementary Table 8.  Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine use frequency-related DNA methylations. Supplementary Table 9. Significant traits associated with the top index meQTLs for cocaine use frequency-related DNA methylations. A total of 4 studies reached the significance level after bonferroni correction (p &lt; 7.0e-7), which consisted of 4 phenotypes (phenotype number). Supplementary Table 10. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine use frequency-related DNA methylations were used.
DOI: 10.6084/m9.figshare.24172171.v1
title: Additional file 2 of Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits

description: Additional file 2: Supplementary Table 1. Candidate CpG sites for cocaine use after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811). Supplementary Table 2. Selected SNP-CpG pairs (FDR &lt; 0.05) after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 811) and clumping. Supplementary Table 3. Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine-related DNA methylations. Supplementary Table 4. Significant traits associated with the top index meQTLs. A total of 60 studies reached the significance level after bonferroni correction (p &lt; 1.7e-7), which consisted of 36 phenotypes (phenotype number). Supplementary Table 5. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine-related DNA methylations were used. Supplementary Table 6. Candidate CpG sites for cocaine use frequency (high frequency users VS low frequency users). Supplementary Table 7. MeQTLs (FDR &lt; 0.05) for cocaine use frequency-related DNA methylations after meta-analysis to combine results for the 450K and EPIC cohorts (Ntotal = 293) and clumping. Supplementary Table 8.  Enriched Ingenuity Canonical Pathways identified using genes mapped by the meQTLs for cocaine use frequency-related DNA methylations. Supplementary Table 9. Significant traits associated with the top index meQTLs for cocaine use frequency-related DNA methylations. A total of 4 studies reached the significance level after bonferroni correction (p &lt; 7.0e-7), which consisted of 4 phenotypes (phenotype number). Supplementary Table 10. meQTLs trait enrichment using associates downloaded from GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ). MeQTLs for cocaine use frequency-related DNA methylations were used.
DOI: 10.6084/m9.figshare.5822157
title: iTRAQ-based proteomics analysis of autophagy-mediated immune responses against the vascular fungal pathogen <i>Verticillium dahliae</i> in <i>Arabidopsis</i>

description: The mechanisms underlying the functional link between autophagy and plant innate immunity remain largely unknown. In this study, we investigated the autophagy-mediated plant defense responses against <i>Verticillium dahliae</i> (<i>V. dahliae</i>) infection by comparative proteomics and cellular analyses. An assessment of the autophagy activity and disease development showed that autophagic processes were tightly related to the tolerance of <i>Arabidopsis</i> plant to Verticillium wilt. An isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics analysis was performed, and we identified a total of 780 differentially accumulated proteins (DAPs) between wild-type and mutant <i>atg10-1 Arabidopsis</i> plants upon <i>V. dahliae</i> infection, of which, 193 ATG8-family-interacting proteins were identified <i>in silico</i> and their associations with autophagy were verified for several selected proteins. Three important aspects of autophagy-mediated defense against <i>V. dahliae</i> infection were revealed: 1) autophagy is required for the activation of upstream defense responses; 2) autophagy-mediated mitochondrial degradation (mitophagy) occurs and is an important player in the defense process; and 3) autophagy promotes the transdifferentiation of perivascular cells and the formation of xylem hyperplasia, which are crucial for protection against this vascular disease. Together, our results provide several novel insights for understanding the functional association between autophagy and plant immune responses.
DOI: 10.6084/m9.figshare.20499900.v1
title: Additional file 6 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 6. Survival analysis results for top50 ranking driver genes of PDGPCS and IMCDriver on four cancers by using an online tool GEPIA2.
DOI: 10.6084/m9.figshare.20499888
title: Additional file 2 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 2. Results of ranking genes for PDGPCS, PRODIGY and three centrality measures methods on BLCA, BRCA, COAD and HNSC cancers, respectively. In addition, we provided the full list of the gold standard driver genes from the CGC dataset.
DOI: 10.6084/m9.figshare.20499888.v1
title: Additional file 2 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 2. Results of ranking genes for PDGPCS, PRODIGY and three centrality measures methods on BLCA, BRCA, COAD and HNSC cancers, respectively. In addition, we provided the full list of the gold standard driver genes from the CGC dataset.
DOI: 10.6084/m9.figshare.20499891.v1
title: Additional file 3 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 3. The exact values of the precision, recall and F1-score of our PDGPCS, PRODIGY and three centrality measures methods on BLCA, BRCA, COAD and HNSC cancers, respectively.
DOI: 10.6084/m9.figshare.20499897
title: Additional file 5 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 5. The exact values of the precision, recall and F1-score of Paired-SSN and SSN on our PDGPCS on BLCA, BRCA, COAD and HNSC cancers, respectively.
DOI: 10.6084/m9.figshare.20499897.v1
title: Additional file 5 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 5. The exact values of the precision, recall and F1-score of Paired-SSN and SSN on our PDGPCS on BLCA, BRCA, COAD and HNSC cancers, respectively.
DOI: 10.6084/m9.figshare.1627968
title: MALAT1 functions as a competing endogenous RNA to mediate Rac1 expression by sequestering miR-101b in liver fibrosis

description: Emerging evidence shows that Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays a pivotal role in cell proliferation, migration, and invasion in tumors. However, the biological role and underlying mechanism of MALAT1 in liver fibrosis remains undefined. In this study, up-regulation of MALAT1 was observed in fibrotic liver tissues and in activated hepatic stellate cells (HSCs). In addition, depletion of MALAT1 inhibited the activation of HSCs <i>in vitro</i> and attenuated collagen deposits <i>in vivo</i>. Our results demonstrated that MALAT1 expression is negatively correlated with microRNA-101b (miR-101b) expression. Furthermore, there was a negative feedback loop between the levels of MALAT1 and miR-101b. Luciferase reporter assay indicated that MALAT1 and RAS-related C3 botulinum substrate 1 (Rac1) are targets of miR-101b. We uncovered that MALAT1 regulates Rac1 expression through miR-101b as a competing endogenous RNA (ceRNA), thereby influencing the proliferation, cell cycle and activation of primary HSCs. Collectively, The ceRNA regulatory network may prompt a better understanding of liver fibrogenesis and contribute to a novel therapeutic strategy for liver fibrosis.
DOI: 10.6084/m9.figshare.20499900
title: Additional file 6 of Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network

description: Additional file 6. Survival analysis results for top50 ranking driver genes of PDGPCS and IMCDriver on four cancers by using an online tool GEPIA2.
DOI: 10.6084/m9.figshare.1627968.v1
title: MALAT1 functions as a competing endogenous RNA to mediate Rac1 expression by sequestering miR-101b in liver fibrosis

description: Emerging evidence shows that Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays a pivotal role in cell proliferation, migration, and invasion in tumors. However, the biological role and underlying mechanism of MALAT1 in liver fibrosis remains undefined. In this study, up-regulation of MALAT1 was observed in fibrotic liver tissues and in activated hepatic stellate cells (HSCs). In addition, depletion of MALAT1 inhibited the activation of HSCs <i>in vitro</i> and attenuated collagen deposits <i>in vivo</i>. Our results demonstrated that MALAT1 expression is negatively correlated with microRNA-101b (miR-101b) expression. Furthermore, there was a negative feedback loop between the levels of MALAT1 and miR-101b. Luciferase reporter assay indicated that MALAT1 and RAS-related C3 botulinum substrate 1 (Rac1) are targets of miR-101b. We uncovered that MALAT1 regulates Rac1 expression through miR-101b as a competing endogenous RNA (ceRNA), thereby influencing the proliferation, cell cycle and activation of primary HSCs. Collectively, The ceRNA regulatory network may prompt a better understanding of liver fibrogenesis and contribute to a novel therapeutic strategy for liver fibrosis.
DOI: 10.6084/m9.figshare.16305568
title: Additional file 13 of Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

description: Additional file 13: Table 5. Comparison of models produced with Bayesian networks between the three groups of samples (Normal, Adjacent and Tumor). For each group, the number of meQTLs, CpGs, SNPs and genes in each model.
DOI: 10.6084/m9.figshare.3557550
title: Appendix B. Details of individual studies used in the meta-analysis, including data source, experimental treatments, and maximum species richness.

description: Details of individual studies used in the meta-analysis, including data source, experimental treatments, and maximum species richness.
DOI: 10.6084/m9.figshare.3557547
title: Supplement 1. Meta-data used in the analyses.

description: File List Sheet1_AllTrophicLevels_RawSpeciesRichness.csv (MD5: 1c6a74635b9d434c17d57b5e01956e37) Sheet2_AllTrophicLevels_EqualSpeciesRichness.csv (MD5: bcbb140e77975d7b4c05bcea81d80dc4) Sheet3_Predators_Scale_TD.csv (MD5: f8991b46252038707c93524494b0a614) Description The supplement contains three sheets corresponding to different analyses reported in the main text. The data all show the standing stock of resources for the final time points of substitutive biodiversity experiments. Sheets 1 and 2 is data for diversity effects of experiments manipulating predators, herbivores, detritivores and plants. The highest level of richness used in experiments (which varied across studies - see main text <i>Methods</i>) was considered to calculate richness effect sizes shown in Sheet 1. Meanwhile, only data for species richness levels 3–4 was used (3 was only used in absence of a richness level of 4) to calculate richness effect sizes shown in Sheet 2. Sheet 3 focuses on effects of predator manipulations and factors hypothesized to influence these effects. Note that experiment #136 was excluded from the data-set in Sheet 3 as TD could not be calculated since multiple compositions were used in the high diversity treatment. In all sheets, the column ‘Study’ indicates the particular study (from a single publication noted in the ‘Ref’ column) within which a single or multiple individual experiments (‘Expt’) were performed. ‘LR_mean’ and ‘LR-max’ are the log-response ratios used to describe effects of predator diversity as detailed in the main text, and have corresponding variances and 95% confidence intervals (CIs). In Sheet 3, individual measures of spatial (ln_spscale) and temporal (ln_temporal) scales are shown, in addition to the synthetic measure of ‘scale’ derived from principal component analysis (Scale (PC1)); finally, taxonomic distinctness (TD) of predator species in the high diversity mixture of each study is shown after it was mean-centered and scaled (to SD units) (i.e., TD(scaled)).
DOI: 10.6084/m9.figshare.3557544
title: Appendix A. Computation of proportions of the mixture.

description: Computation of proportions of the mixture.
DOI: 10.6084/m9.figshare.3557544.v1
title: Appendix A. Computation of proportions of the mixture.

description: Computation of proportions of the mixture.
DOI: 10.6084/m9.figshare.3557541
title: Supplement 1. Source code and example data for fitting the kittiwake data set by maximum likelihood.

description: File List likelihood_and_data.m (MD5: 34a30beedca4926806b03ae1e1527fe2) Description The likelihood_and_data.m is matlab code developed to fit the kittiwake data set by maximum likelihood (f1_individuelle) as implemented in Matlab 8 R2012b. The kittiwake capture–recapture data set is provided and imputed by the function kittiwake. The function likelihood_and_data reads the data, computes the maximum likelihood estimator for survival (s), transitions (tr_NN, tr_PP) and means (m_N, m_P) and variances (var_N, var_P) of the two normal distributions.
DOI: 10.6084/m9.figshare.3557541.v1
title: Supplement 1. Source code and example data for fitting the kittiwake data set by maximum likelihood.

description: File List likelihood_and_data.m (MD5: 34a30beedca4926806b03ae1e1527fe2) Description The likelihood_and_data.m is matlab code developed to fit the kittiwake data set by maximum likelihood (f1_individuelle) as implemented in Matlab 8 R2012b. The kittiwake capture–recapture data set is provided and imputed by the function kittiwake. The function likelihood_and_data reads the data, computes the maximum likelihood estimator for survival (s), transitions (tr_NN, tr_PP) and means (m_N, m_P) and variances (var_N, var_P) of the two normal distributions.
DOI: 10.6084/m9.figshare.3557538
title: Appendix A. Map of the Big Sur study region with monitoring plot locations and fire perimeters.

description: Map of the Big Sur study region with monitoring plot locations and fire perimeters.
DOI: 10.6084/m9.figshare.3557535
title: Appendix B. Understanding fire-related redwood mortality through model comparisons of fire severity indicators and estimates of fireline intensities for stands in different stages of the disease progression.

description: Understanding fire-related redwood mortality through model comparisons of fire severity indicators and estimates of fireline intensities for stands in different stages of the disease progression.
DOI: 10.6084/m9.figshare.3557532
title: Appendix A. Comparison of environmental data for the Lienhuachih forest plot with those used by De Cáceres et al. (2012).

description: Comparison of environmental data for the Lienhuachih forest plot with those used by De Cáceres et al. (2012).
DOI: 10.6084/m9.figshare.3557532.v1
title: Appendix A. Comparison of environmental data for the Lienhuachih forest plot with those used by De Cáceres et al. (2012).

description: Comparison of environmental data for the Lienhuachih forest plot with those used by De Cáceres et al. (2012).
DOI: 10.6084/m9.figshare.3557529
title: Appendix B. Details of soil sample analyses.

description: Details of soil sample analyses.
DOI: 10.6084/m9.figshare.3557529.v1
title: Appendix B. Details of soil sample analyses.

description: Details of soil sample analyses.
DOI: 10.6084/m9.figshare.3557526
title: Appendix C. Variation partitioning analysis between environmental (only topographical, only soil, or both) and spatial variables.

description: Variation partitioning analysis between environmental (only topographical, only soil, or both) and spatial variables.
DOI: 10.6084/m9.figshare.3557526.v1
title: Appendix C. Variation partitioning analysis between environmental (only topographical, only soil, or both) and spatial variables.

description: Variation partitioning analysis between environmental (only topographical, only soil, or both) and spatial variables.
DOI: 10.6084/m9.figshare.3557523
title: Appendix A. Details of savanna model cover fractions ft, fg, and fb.

description: Details of savanna model cover fractions ft, fg, and fb.
DOI: 10.6084/m9.figshare.3557523.v1
title: Appendix A. Details of savanna model cover fractions ft, fg, and fb.

description: Details of savanna model cover fractions ft, fg, and fb.
DOI: 10.6084/m9.figshare.3557520
title: Appendix B. Processing of remote-sensing data.

description: Processing of remote-sensing data.
DOI: 10.6084/m9.figshare.3557520.v1
title: Appendix B. Processing of remote-sensing data.

description: Processing of remote-sensing data.
DOI: 10.6084/m9.figshare.3557517
title: Appendix C. Sample frequency justification of MaxEnt.

description: Sample frequency justification of MaxEnt.
DOI: 10.6084/m9.figshare.3557517.v1
title: Appendix C. Sample frequency justification of MaxEnt.

description: Sample frequency justification of MaxEnt.
DOI: 10.6084/m9.figshare.3557514
title: Appendix D. Calculating the MaxEnt probability distribution using Lagrange multipliers.

description: Calculating the MaxEnt probability distribution using Lagrange multipliers.
DOI: 10.6084/m9.figshare.3557514.v1
title: Appendix D. Calculating the MaxEnt probability distribution using Lagrange multipliers.

description: Calculating the MaxEnt probability distribution using Lagrange multipliers.
DOI: 10.6084/m9.figshare.3557511
title: Appendix E. Adding higher order moment constraints.

description: Adding higher order moment constraints.
DOI: 10.6084/m9.figshare.3557511.v1
title: Appendix E. Adding higher order moment constraints.

description: Adding higher order moment constraints.
DOI: 10.6084/m9.figshare.3557508
title: Appendix F. Choice of ET functions et(E), eg(E), and eb(E).

description: Choice of ET functions et(E), eg(E), and eb(E).
DOI: 10.6084/m9.figshare.3557508.v1
title: Appendix F. Choice of ET functions et(E), eg(E), and eb(E).

description: Choice of ET functions et(E), eg(E), and eb(E).
DOI: 10.6084/m9.figshare.3557505
title: Appendix G. Further histogram comparisons for tree, grass, and bare-ground cover.

description: Further histogram comparisons for tree, grass, and bare-ground cover.