cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8942225
title: Echolocation parameters of Lonchorhina aurita from Unusual echolocation behaviour of the Common Sword-nosed bat <i>Lonchorhina aurita</i>: an adaptation to aerial insectivory in a phyllostomid bat?

description: Temporal and spectral echolocation parameters of Lonchorhina aurita obtained from free-flying individuals and individuals in a flight cage.
DOI: 10.6084/m9.figshare.8942225.v1
title: Echolocation parameters of Lonchorhina aurita from Unusual echolocation behaviour of the Common Sword-nosed bat <i>Lonchorhina aurita</i>: an adaptation to aerial insectivory in a phyllostomid bat?

description: Temporal and spectral echolocation parameters of Lonchorhina aurita obtained from free-flying individuals and individuals in a flight cage.
DOI: 10.6084/m9.figshare.8942138
title: Supplementary Material for: Altruistic Behavior Depending on Opponents’ Body Weight: An Experimental Approach

description: <b><i>Background:</i></b> Obesity is a worldwide public health issue, not only because it is associated with numerous comorbid diseases but also because of its impact on the social level. This study aims to investigate (a) whether altruistic behavior differs between people with obesity and people who are not obese, (b) altruistic behavior toward people who are obese, in particular, women who are obese, and (c) implicit and explicit attitudes toward people with obesity as predictors for divergent altruistic behavior. <b><i>Methods:</i></b> An experimental approach called the dictator game was applied. In total, 168 participants of normal weight were asked to divide a monetary reward between themselves and two opponents, one person who was obese and one with normal weight. In addition, participants’ implicit and explicit attitudes toward people with obesity as well as other sociodemographic data were assessed. <b><i>Results:</i></b> We found that altruistic behavior did not differ between obese and nonobese opponents (<i>t</i>(166) = –0.57, <i>p</i> = 0.71), nor did we find less altruistic attitudes toward women with obesity (<i>t</i>(85) = –0.03, <i>p</i> = 0.39). Linear regression models show less altruistic behavior among people with stronger explicit antifat attitudes (<i>p</i> = 0.015). However, although we found that participants held implicit attitudes toward people with obesity, these attitudes did not predict altruistic behavior toward people with obesity. <b><i>Conclusion:</i></b> Although numerous studies reported discrimination toward people with obesity in several areas of life, we did not find discrimination in form of less altruistic behavior toward people with obesity. Moreover, although we found implicit antifat attitudes among the participants, these attitudes did not predict altruistic behavior toward people of divergent weight groups. Therefore, future research should investigate more intensively the diverse facets of discrimination toward people with obesity, if and to what extent implicit antifat attitudes have a quantifiable impact on the stigmatization and or discrimination process, and the hypotheses in a more hidden way, since manipulation check revealed that the majority of participants anticipated the underlying idea of the dictator game.
DOI: 10.6084/m9.figshare.8942129
title: Supplementary Material for: Responsiveness to Change of the Montreal Cognitive Assessment, Mini-Mental State Examination, and SCOPA-Cog in Non-Demented Patients with Parkinson’s Disease

description: <b><i>Background:</i></b> Clinical monitoring of patients with Parkinson’s disease (PD) for cognitive decline is an important element of care. The Montreal Cognitive Assessment (MoCA) has been proposed to be a sensitive tool for assessing cognitive impairment in PD. The aim of our study was to compare the responsiveness of the MoCA to decline in cognition to the responsiveness of the Mini Mental State Examination (MMSE) and the Scales for Outcomes of Parkinson’s disease-cognition (SCOPA-Cog). <b><i>Methods:</i></b> PD patients without dementia were enrolled at 6 North American movement disorders centers between 2008 and 2011. Participants received annual evaluations including the MoCA, MMSE, and SCOPA-Cog followed by formal neuropsychological testing. The gold standard for change in cognition was defined as the change on the neuropsychological test scores over the annual assessments. The Reliable Change Method was used to provide an estimate of the probability that a given difference score would be obtained by chance. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change was quantified using receiver operating characteristics (ROC) curves. <b><i>Results:</i></b> One hundred seventeen patients were included in the analysis. Participants were followed at mean intervals of 11 ± 2 months for a median of 2 (maximum 5) visits. According to the reliable change index, 56 intervals of cognitive testing showed a decline in global cognition. ROC analysis of change in MoCA, MMSE, and SCOPA-Cog global scores compared to gold standard testing found an area under the curve (AUC) of 0.55 (95% CI 0.48–0.62), 0.56 (0.48–0.63), and 0.63 (0.55–0.70) respectively. There were no significant differences in the AUCs across the tests. The sensitivity of the MoCA, MMSE, and SCOPA-Cog to change at various thresholds for decline in scores reached a maximum of 71% for a cut-off of 1 point change on the SCOPA-Cog. <b><i>Conclusion:</i></b> Using neuropsychological testing as a gold standard comparator, the performance of the MoCA, MMSE, and SCOPA-Cog for detecting decline in non-demented PD patients over a 1-year interval is poor. This has implications for clinical practice; stable scores may not be taken as reassurance of the absence of cognitive decline.
DOI: 10.6084/m9.figshare.8941949
title: Static Feature Evaluation - CoevFeat Tiebreaker Against Baseline

description: Supplementary Data supporting CoevFeat for Frazier Baker's MS Thesis
DOI: 10.6084/m9.figshare.8941949.v1
title: Static Feature Evaluation - CoevFeat Tiebreaker Against Baseline

description: Supplementary Data supporting CoevFeat for Frazier Baker's MS Thesis
DOI: 10.6084/m9.figshare.8941382
title: MOESM5 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 5: Text S1. Script to automatically analyse photos in ImageJ.
DOI: 10.6084/m9.figshare.8941382.v1
title: MOESM5 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 5: Text S1. Script to automatically analyse photos in ImageJ.
DOI: 10.6084/m9.figshare.8941349
title: MOESM2 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 2: Table S2. Number of mosquito specimens per mosquito taxa for each sample analysed.
DOI: 10.6084/m9.figshare.8941349.v1
title: MOESM2 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 2: Table S2. Number of mosquito specimens per mosquito taxa for each sample analysed.
DOI: 10.6084/m9.figshare.8941337
title: MOESM1 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 1: Table S1. Number of mosquito specimens and taxa for each sample analysed.
DOI: 10.6084/m9.figshare.8941337.v1
title: MOESM1 of Comparative analysis of subsampling methods for large mosquito samples

description: Additional file 1: Table S1. Number of mosquito specimens and taxa for each sample analysed.
DOI: 10.6084/m9.figshare.8941313
title: MOESM2 of What do young doctors know of palliative care; how do they expect the concept to work?

description: Additional file 2: Appendix 2. Data Sheet. All data retrieved from the online survey platform.
DOI: 10.6084/m9.figshare.8941313.v1
title: MOESM2 of What do young doctors know of palliative care; how do they expect the concept to work?

description: Additional file 2: Appendix 2. Data Sheet. All data retrieved from the online survey platform.
DOI: 10.6084/m9.figshare.8941307
title: Additional file 9: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S5. Integrator complex subunit homology. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.8941301
title: Additional file 1: of GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network

description: One excel file with 6 master tables divided into 18 table-sheets. (XLSX 227 kb)
DOI: 10.6084/m9.figshare.8941307.v1
title: Additional file 9: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S5. Integrator complex subunit homology. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.8941298
title: Additional file 8: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S4. Gene dsRNAs that induce numr-1p::GFP. (XLSX 20 kb)
DOI: 10.6084/m9.figshare.8941301.v1
title: Additional file 1: of GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network

description: One excel file with 6 master tables divided into 18 table-sheets. (XLSX 227 kb)
DOI: 10.6084/m9.figshare.8941298.v1
title: Additional file 8: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S4. Gene dsRNAs that induce numr-1p::GFP. (XLSX 20 kb)
DOI: 10.6084/m9.figshare.8941280
title: Additional file 3: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S2. DAVID gene enrichments. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8941283
title: Additional file 4: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S3. numr-1 RNAi alternative splicing. (XLSX 796 kb)
DOI: 10.6084/m9.figshare.8941283.v1
title: Additional file 4: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S3. numr-1 RNAi alternative splicing. (XLSX 796 kb)
DOI: 10.6084/m9.figshare.8941280.v1
title: Additional file 3: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S2. DAVID gene enrichments. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8941265
title: Additional file 15: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S9. hsf-1 longevity and cadmium survival. (XLSX 8 kb)
DOI: 10.6084/m9.figshare.8941274
title: Additional file 2: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S1. Differential gene expression. (XLSX 3482 kb)
DOI: 10.6084/m9.figshare.8941274.v1
title: Additional file 2: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S1. Differential gene expression. (XLSX 3482 kb)
DOI: 10.6084/m9.figshare.8941265.v1
title: Additional file 15: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S9. hsf-1 longevity and cadmium survival. (XLSX 8 kb)
DOI: 10.6084/m9.figshare.8941253
title: Additional file 13: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S8. ints-4 RNAi alternative splicing. (XLSX 893 kb)
DOI: 10.6084/m9.figshare.8941253.v1
title: Additional file 13: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S8. ints-4 RNAi alternative splicing. (XLSX 893 kb)
DOI: 10.6084/m9.figshare.8941244
title: Additional file 12: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S7. Cadmium exposure alternative splicing. (XLSX 757 kb)
DOI: 10.6084/m9.figshare.8941244.v1
title: Additional file 12: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S7. Cadmium exposure alternative splicing. (XLSX 757 kb)
DOI: 10.6084/m9.figshare.8941229
title: Additional file 10: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S6. Integrator longevity and cadmium survival. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8941229.v1
title: Additional file 10: of RNA processing errors triggered by cadmium and integrator complex disruption are signals for environmental stress

description: Table S6. Integrator longevity and cadmium survival. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.8941205
title: Additional file 20: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S6. Number of SNPs called, after QC filtering (MAF&gt;1%) and useful for GWAS (MAF≥5%). Note that GBS1 have SNPs with 100% missing genotypes which were removed while GBS2 used external haplotype library which allow to impute loci with 100% missing data. It conducted to a smaller number of SNPs for GBS1 than GBS2. (XLS 33 kb)
DOI: 10.6084/m9.figshare.8941205.v1
title: Additional file 20: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S6. Number of SNPs called, after QC filtering (MAF&gt;1%) and useful for GWAS (MAF≥5%). Note that GBS1 have SNPs with 100% missing genotypes which were removed while GBS2 used external haplotype library which allow to impute loci with 100% missing data. It conducted to a smaller number of SNPs for GBS1 than GBS2. (XLS 33 kb)
DOI: 10.6084/m9.figshare.8941187
title: Additional file 17: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S4. Description of inbred lines. Variety and accession along with the breeders, seeds providers and genetic groups obtained using ADMIXTURE for K=4 (Stiff Stalk, Iodent, Lancaster, Other). (CSV 260 kb)
DOI: 10.6084/m9.figshare.8941187.v1
title: Additional file 17: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S4. Description of inbred lines. Variety and accession along with the breeders, seeds providers and genetic groups obtained using ADMIXTURE for K=4 (Stiff Stalk, Iodent, Lancaster, Other). (CSV 260 kb)
DOI: 10.6084/m9.figshare.8941136
title: Additional file 10: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S1. Summary of all the QTLs identified for the male flowering time (DTA), plant height (plantHT) and grain yield (GY). “LowerLimit” and “UpperLimit” columns are the lower and upper physical limits for each QTL. The “Rec” column indicates if the QTL is located in a high or low region of recombination. “NbSNP50”, “LogPvaMax50”, “NbSNP600”, “LogPvaMax600”, “NbSNPGBS”, “LogPvaMaxGBS” are the number of significant SNPs and the most significant –log10(Pval) within the QTL for each technology across all environments. The physical position (“PosMax”), the proportion of the variance explained (“R2_LDMax”) and the effect (“EffectMax”) of the most significant SNP within the QTL is shown. “NbDiffEnv” gives the number of different situations that detected the QTL. (CSV 50 kb)
DOI: 10.6084/m9.figshare.8941136.v1
title: Additional file 10: of Genotyping-by-sequencing and SNP-arrays are complementary for detecting quantitative trait loci by tagging different haplotypes in association studies

description: Table S1. Summary of all the QTLs identified for the male flowering time (DTA), plant height (plantHT) and grain yield (GY). “LowerLimit” and “UpperLimit” columns are the lower and upper physical limits for each QTL. The “Rec” column indicates if the QTL is located in a high or low region of recombination. “NbSNP50”, “LogPvaMax50”, “NbSNP600”, “LogPvaMax600”, “NbSNPGBS”, “LogPvaMaxGBS” are the number of significant SNPs and the most significant –log10(Pval) within the QTL for each technology across all environments. The physical position (“PosMax”), the proportion of the variance explained (“R2_LDMax”) and the effect (“EffectMax”) of the most significant SNP within the QTL is shown. “NbDiffEnv” gives the number of different situations that detected the QTL. (CSV 50 kb)
DOI: 10.6084/m9.figshare.8941124
title: Additional file 5: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Significant correlations on mRNA expression level. This table contains all significant correlations on mRNA expression level for all 15 miRNAs. ‘Gene’ is the predicted miRNA target, ‘PCC’ = Pearson’s correlation coefficient, ‘adj_P_value’ = adjusted p-value. (CSV 880 kb)
DOI: 10.6084/m9.figshare.8941118
title: Additional file 4: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-885-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-885-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941124.v1
title: Additional file 5: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Significant correlations on mRNA expression level. This table contains all significant correlations on mRNA expression level for all 15 miRNAs. ‘Gene’ is the predicted miRNA target, ‘PCC’ = Pearson’s correlation coefficient, ‘adj_P_value’ = adjusted p-value. (CSV 880 kb)
DOI: 10.6084/m9.figshare.8941118.v1
title: Additional file 4: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-885-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-885-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941109
title: Additional file 3: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-106b-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-106b-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941109.v1
title: Additional file 3: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-106b-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-106b-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941100
title: Additional file 2: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-24-3p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-24-3p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941097
title: Additional file 1: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-22-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-22-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 5 kb)
DOI: 10.6084/m9.figshare.8941097.v1
title: Additional file 1: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-22-5p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-22-5p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 5 kb)
DOI: 10.6084/m9.figshare.8941100.v1
title: Additional file 2: of miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer

description: Hsa-miR-24-3p KEGG pathways. Table of enriched KEGG pathways for hsa-miR-24-3p. ‘N’ describes number of genes described for a specific KEGG pathway, ‘DE’ describes number of genes enriched for the pathway and ‘P.DE’ is the p-value. (CSV 3 kb)
DOI: 10.6084/m9.figshare.8941088
title: Additional file 1: of Hypertension management in rural western Kenya: a needs-based health workforce estimation model

description: Draft Excel model 20160508 BACKUP. (XLSX 677 kb)
DOI: 10.6084/m9.figshare.8941088.v1
title: Additional file 1: of Hypertension management in rural western Kenya: a needs-based health workforce estimation model

description: Draft Excel model 20160508 BACKUP. (XLSX 677 kb)
DOI: 10.6084/m9.figshare.8941070
title: Additional file 1: of The accuracy and reliability of digital measurements of gingival recession versus conventional methods

description: Gingival recession measurments for all methods. (XLSX 19 kb)
DOI: 10.6084/m9.figshare.8941070.v1
title: Additional file 1: of The accuracy and reliability of digital measurements of gingival recession versus conventional methods

description: Gingival recession measurments for all methods. (XLSX 19 kb)
DOI: 10.6084/m9.figshare.8940917
title: Additional file 3: of Functional DNA methylation signatures for autism spectrum disorder genomic risk loci: 16p11.2 deletions and CHD8 variants

description: Table S1. Demographic information for heterogeneous ASD cases and age- and sex-matched neurotypical controls. Table S2. Number of probes removed and remaining for analysis following quality control. Table S3. List of overlapping DMRs for 16p11.2del. Table S4. List of overlapping DMRs for CHD8+/−. Table S5. List of samples extracted from GEO database and respective 16p11.2del and CHD8+/− signature scores for specificity analysis. All sample data are from blood and from individuals with known ages &lt; 50 years old. Table S6. List of significant (q &lt; 0.05, |Δβ| ≥ 5%) sites in 16p11.2del DNAm signature. Table S7. List of significant (q &lt; 0.05, |Δβ| ≥ 5%) sites in CHD8+/− DNAm signature. Table S8. GO Biological Process enrichment terms following GREAT enrichment analysis of differentially methylated genes in 16p11.2del DNAm signature. Table S9. GO Biological Process enrichment terms following GREAT enrichment analysis of differentially methylated genes in CHD8+/− DNAm signature. (XLSX 156 kb)