cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22599137
title: Additional file 4 of Genome-wide associated variants of subclinical atherosclerosis among young people with HIV and gene-environment interactions

description: Additional file 4: Summary statistics of GWAS in relation to cIMT among HIV negative control above 45 years old.
DOI: 10.6084/m9.figshare.16829798.v1
title: Additional file 10 of The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019

description: Additional file 10: Table S8. The MMR of MSMI in different age groups, 5 SDI quintiles, 21 region, and in 204 countries.
DOI: 10.6084/m9.figshare.16829810
title: Additional file 3 of The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019

description: Additional file 3: Table S1. The number of MSMI incidence, death, DALY globally from 1990 and 2019
DOI: 10.6084/m9.figshare.22599149
title: Additional file 7 of Genome-wide associated variants of subclinical atherosclerosis among young people with HIV and gene-environment interactions

description: Additional file 7: Summary statistics of GWAS in relation to cIMT among PWH under 45 years old.
DOI: 10.6084/m9.figshare.16829816.v1
title: Additional file 5 of The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019

description: Additional file 5: Table S3. The number and rate of MSMI incidence, death, DALY in different age groups worldwide from 1990 to 2019
DOI: 10.6084/m9.figshare.16829807.v1
title: Additional file 2 of The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019

description: Additional file 2. The SDI of different countries and territories in 2019.
DOI: 10.6084/m9.figshare.16829819
title: Additional file 6 of The global burden and trends of maternal sepsis and other maternal infections in 204 countries and territories from 1990 to 2019

description: Additional file 6: Table S4. The number and age-standardized rate of MSMI incidence, death, DALY in different SDI from 1990 to 2019
DOI: 10.6084/m9.figshare.16305525.v1
title: Additional file 2 of Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development

description: Additional file 2: Table S2. Matrix of filtered transcript expression.
DOI: 10.6084/m9.figshare.16305540
title: Additional file 7 of Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development

description: Additional file 7: Table S5. Average expression of differentially expressed TFs.
DOI: 10.6084/m9.figshare.16305525
title: Additional file 2 of Transcriptome profiling provides insights into the molecular mechanisms of maize kernel and silk development

description: Additional file 2: Table S2. Matrix of filtered transcript expression.
DOI: 10.6084/m9.figshare.3201814
title: Development of marker genes for jasmonic acid signaling in shoots and roots of wheat

description: The jasmonic acid (JA) signaling pathway plays key roles in a diverse array of plant development, reproduction, and responses to biotic and abiotic stresses. Most of our understanding of the JA signaling pathway derives from the dicot model plant <i>Arabidopsis thaliana</i>, while corresponding knowledge in wheat is somewhat limited. In this study, the expression of 41 genes implicated in the JA signaling pathway has been assessed on 10 day-old bread wheat seedlings, 24 h, 48 h, and 72 h after methyl-jasmonate (MeJA) treatment using quantitative real-time PCR. The examined genes have been previously reported to be involved in JA biosynthesis and catabolism, JA perception and signaling, and pathogen defense in wheat shoots and roots. This study provides evidence to suggest that the effect of MeJA treatment is more prominent in shoots than roots of wheat seedlings, and substantial regulation of the JA pathway-dependent defense genes occurs at 72 h after MeJA treatment. Results show that the expression of 22 genes was significantly affected by MeJA treatment in wheat shoots. However, only <i>PR1.1</i> and <i>PR3</i> were significantly differentially expressed in wheat roots, both at 24 h post-MeJA treatment, with other genes showing large variation in their gene expression in roots. While providing marker genes on JA signaling in wheat, future work may focus on elucidating the regulatory function of JA-modulated transcription factors, some of which have well-studied potential orthologs in <i>Arabidopsis</i>.
DOI: 10.6084/m9.figshare.22599170.v1
title: Additional file 6 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 6. The specific information of DEcircRNAs identified in this study.
DOI: 10.6084/m9.figshare.22599167
title: Additional file 5 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 5. The P-values and log2 FC of DEmiRNAs identified in this study.
DOI: 10.6084/m9.figshare.22599167.v1
title: Additional file 5 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 5. The P-values and log2 FC of DEmiRNAs identified in this study.
DOI: 10.6084/m9.figshare.22599173
title: Additional file 7 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 7. The statistical characteristics of DEcircRNAs in this study.
DOI: 10.6084/m9.figshare.22599170
title: Additional file 6 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 6. The specific information of DEcircRNAs identified in this study.
DOI: 10.6084/m9.figshare.22599164.v1
title: Additional file 4 of FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

description: Additional file 4. The statistical characteristics of DEmRNAs identified in this study.
DOI: 10.6084/m9.figshare.11590536
title: Association between adverse perinatal outcomes and sleep disturbances during pregnancy: a systematic review and meta-analysis

description: Due to physiological and hormonal factors, sleep disturbances are prevalent in the obstetric population. Multiple studies have evaluated the relationship between sleep disturbances and perinatal outcomes, with inconsistent results. The purpose of this systematic review and meta-analysis was to further assess the overall relationship between four types of sleep disturbances (snoring, obstructive sleep apnea, extreme sleep duration, and poor sleep quality) and adverse perinatal outcomes. We performed electronic searches in PubMed, Embase, the Cochrane Library, CNKI, VIP, and Wanfang Data in February 2019 to identify studies that assessed adverse perinatal outcomes among expectant mothers with sleep disturbances. In total, 5044 studies were identified in the initial search and 65 met the defined criteria. Pooled analysis showed that sleep disturbances were associated with an increased risk for preeclampsia (aOR: 2.77; 95% CI: 1.81–4.24), gestational diabetes mellitus (aOR: 1.96; 95% CI: 1.62–2.38), cesarean delivery (aOR: 1.99; 95% CI: 1.70–2.33), depression (aOR: 3.98; 95% CI: 2.74–5.77) and preterm birth (aOR: 1.95; 95% CI: 1.55–2.45). The four studied sleep disturbances (OSA, snoring, extreme sleep duration and poor sleep quality) during pregnancy were associated with adverse perinatal outcomes. Expectant mothers should be advised to practice healthy sleep hygiene measures.
DOI: 10.6084/m9.figshare.11590536.v1
title: Association between adverse perinatal outcomes and sleep disturbances during pregnancy: a systematic review and meta-analysis

description: Due to physiological and hormonal factors, sleep disturbances are prevalent in the obstetric population. Multiple studies have evaluated the relationship between sleep disturbances and perinatal outcomes, with inconsistent results. The purpose of this systematic review and meta-analysis was to further assess the overall relationship between four types of sleep disturbances (snoring, obstructive sleep apnea, extreme sleep duration, and poor sleep quality) and adverse perinatal outcomes. We performed electronic searches in PubMed, Embase, the Cochrane Library, CNKI, VIP, and Wanfang Data in February 2019 to identify studies that assessed adverse perinatal outcomes among expectant mothers with sleep disturbances. In total, 5044 studies were identified in the initial search and 65 met the defined criteria. Pooled analysis showed that sleep disturbances were associated with an increased risk for preeclampsia (aOR: 2.77; 95% CI: 1.81–4.24), gestational diabetes mellitus (aOR: 1.96; 95% CI: 1.62–2.38), cesarean delivery (aOR: 1.99; 95% CI: 1.70–2.33), depression (aOR: 3.98; 95% CI: 2.74–5.77) and preterm birth (aOR: 1.95; 95% CI: 1.55–2.45). The four studied sleep disturbances (OSA, snoring, extreme sleep duration and poor sleep quality) during pregnancy were associated with adverse perinatal outcomes. Expectant mothers should be advised to practice healthy sleep hygiene measures.
DOI: 10.6084/m9.figshare.11590539
title: Two new Bi(III) and Ce(III) chelates incorporating 1,10-phenanthroline-2,9-dicarboxylic acid: structure elucidation and anti-lung cancer activity study

description: Reactions of 1,10-phenanthroline-2,9-dicarboxylic acid (H<sub>2</sub>phenda) with Bi(NO<sub>3</sub>)<sub>3</sub>·5H<sub>2</sub>O or Ce(NO<sub>3</sub>)<sub>3</sub>·6H<sub>2</sub>O under solvothermal conditions yielded two new coordination complexes Bi(NO<sub>3</sub>)<sub>2</sub>(phenda)<sub>2</sub>(H<sub>2</sub>O)<sub>3</sub> (<b>1</b>) and [Ce(SO<sub>4</sub>)(phenda)<sub>2</sub>(H<sub>2</sub>O)<sub>6</sub>](H<sub>2</sub>O)<sub>2</sub> (<b>2</b>). The structures of the two complexes have been elucidated via elemental analyses and single crystal X-ray diffraction analysis. The results show that complex <b>1</b> is a discrete binuclear structure based on two seven-coordinated Bi(III) ions while complex <b>2</b> is a 1D v-shape chain structure based on two nine-coordinated Ce(III) ions. Furthermore, the anti-lung cancer activity of the complexes has been evaluated via the Cell Counting Kit-8 (CCK-8) assays.
DOI: 10.6084/m9.figshare.11590545
title: HuR stabilizes <i>HTT</i> mRNA via interacting with its exon 11 in a mutant HTT-dependent manner

description: Huntington’s Disease (HD) is a monogenetic neurodegenerative disorder mainly caused by the cytotoxicity of the mutant HTT protein (mHTT) encoded by the mutant <i>HTT</i> gene. Lowering <i>HTT</i> mRNA has been extensively studied as a potential therapeutic strategy, but how its level is regulated endogenously has been unclear. Here we report that the RNA-binding protein (RBP) HuR interacts with and stabilizes <i>HTT</i> mRNA in an mHTT-dependent manner. In HD cells but not wild-type cells, siRNA knockdown or CRISPR-induced heterozygous knockout of HuR decreased <i>HTT</i> mRNA stability. HuR interacted with <i>HTT</i> mRNA at a conserved site in exon 11 rather than the 3ʹ-UTR region of the mRNA. Interestingly, this interaction was dependent on the presence of mHTT, likely <i>via</i> the activation of MAPK11, which enhanced cytosolic localization of the HuR protein. Thus, mHTT, MAPK11 and HuR may form a positive feedback loop that stabilizes <i>HTT</i> mRNA and enhances mHTT accumulation, which may contribute to HD progression. Our data reveal a novel regulatory mechanism of <i>HTT</i> mRNA <i>via</i> non-canonical binding of HuR.
DOI: 10.6084/m9.figshare.16305559
title: Additional file 11 of Identifying causal models between genetically regulated methylation patterns and gene expression in healthy colon tissue

description: Additional file 11: Table 4. haploReg results by model. Fisher exact test comparing, for chromatin states and histone marks annotation in haploReg database, the proportion of SNPs in each model with a sample of cis-SNPs. Bonferroni adjusted p-value was calculated for chromatin states and histone marks separately.
DOI: 10.6084/m9.figshare.3532145
title: Appendix C. Effects of cordgrass shoot density and height on water flow and cobble movement.

description: Effects of cordgrass shoot density and height on water flow and cobble movement.
DOI: 10.6084/m9.figshare.3532145.v1
title: Appendix C. Effects of cordgrass shoot density and height on water flow and cobble movement.

description: Effects of cordgrass shoot density and height on water flow and cobble movement.
DOI: 10.6084/m9.figshare.3532142
title: Appendix D. Patterns in the emergence of cordgrass and forbs on cobble beaches.

description: Patterns in the emergence of cordgrass and forbs on cobble beaches.
DOI: 10.6084/m9.figshare.3532142.v1
title: Appendix D. Patterns in the emergence of cordgrass and forbs on cobble beaches.

description: Patterns in the emergence of cordgrass and forbs on cobble beaches.
DOI: 10.6084/m9.figshare.3532139
title: Appendix A. Main algorithms for the four spatial point models and the steps for estimating parameters used in addressing the joint effects of habitat heterogeneity and dispersal limitation on the species–area relationships (SARs), and calculations of AIC.

description: Main algorithms for the four spatial point models and the steps for estimating parameters used in addressing the joint effects of habitat heterogeneity and dispersal limitation on the species–area relationships (SARs), and calculations of AIC.
DOI: 10.6084/m9.figshare.3532139.v1
title: Appendix A. Main algorithms for the four spatial point models and the steps for estimating parameters used in addressing the joint effects of habitat heterogeneity and dispersal limitation on the species–area relationships (SARs), and calculations of AIC.

description: Main algorithms for the four spatial point models and the steps for estimating parameters used in addressing the joint effects of habitat heterogeneity and dispersal limitation on the species–area relationships (SARs), and calculations of AIC.
DOI: 10.6084/m9.figshare.3532136
title: Supplement 1. R program code for estimating parameters of spatial point models and generating the expected SARs for each model.

description: File List main_function.r Description This program is to test the effects of environmental factors and dispersal on species–area relationships(SARs). Detail instructions for the program can be found in the file. The main algorithms used in this program are described in Appendix A.
DOI: 10.6084/m9.figshare.3532133
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3532133.v1
title: Full Archive

description: Full Archive
DOI: 10.6084/m9.figshare.3532130
title: Appendix A. Perfume differentiation in orchid bees. Fig. A1 shows a rotating, three-dimensional fragrance space of Euglossa spp. on Barro Colorado Island, Panama, in which the multidimensional scaling plot is based on the same data and analysis as the two-dimensional representation in Fig. 2a, but allows one additional dimension and thus accommodates more of the variation in the underlying similarity matrix (stress = 0.15). Fig. A2 is a full version of Fig. 4.

description: Perfume differentiation in orchid bees. Fig. A1 shows a rotating, three-dimensional fragrance space of Euglossa spp. on Barro Colorado Island, Panama, in which the multidimensional scaling plot is based on the same data and analysis as the two-dimensional representation in Fig. 2a, but allows one additional dimension and thus accommodates more of the variation in the underlying similarity matrix (stress = 0.15). Fig. A2 is a full version of Fig. 4.
DOI: 10.6084/m9.figshare.3532130.v1
title: Appendix A. Perfume differentiation in orchid bees. Fig. A1 shows a rotating, three-dimensional fragrance space of Euglossa spp. on Barro Colorado Island, Panama, in which the multidimensional scaling plot is based on the same data and analysis as the two-dimensional representation in Fig. 2a, but allows one additional dimension and thus accommodates more of the variation in the underlying similarity matrix (stress = 0.15). Fig. A2 is a full version of Fig. 4.

description: Perfume differentiation in orchid bees. Fig. A1 shows a rotating, three-dimensional fragrance space of Euglossa spp. on Barro Colorado Island, Panama, in which the multidimensional scaling plot is based on the same data and analysis as the two-dimensional representation in Fig. 2a, but allows one additional dimension and thus accommodates more of the variation in the underlying similarity matrix (stress = 0.15). Fig. A2 is a full version of Fig. 4.
DOI: 10.6084/m9.figshare.3532127
title: Appendix B. Average relative abundance of fragrance compounds in hind leg extracts of 15 sympatric species of Euglossa from Barro Colorado Island, Panama.

description: Average relative abundance of fragrance compounds in hind leg extracts of 15 sympatric species of Euglossa from Barro Colorado Island, Panama.
DOI: 10.6084/m9.figshare.3532127.v1
title: Appendix B. Average relative abundance of fragrance compounds in hind leg extracts of 15 sympatric species of Euglossa from Barro Colorado Island, Panama.

description: Average relative abundance of fragrance compounds in hind leg extracts of 15 sympatric species of Euglossa from Barro Colorado Island, Panama.
DOI: 10.6084/m9.figshare.3532124
title: Appendix C. A list of 22 putative fragrance motifs inferred from intercorrelation analysis of male fragrance compounds, and their occurrence in different species of Euglossa.

description: A list of 22 putative fragrance motifs inferred from intercorrelation analysis of male fragrance compounds, and their occurrence in different species of Euglossa.
DOI: 10.6084/m9.figshare.3532121
title: Appendix A. Effect of selective fungicide treatment and source of soil inocula from one of eight Prunus serotina trees or from soil collected ≥ 20m away from the nearest Prunus serotina tree.

description: Effect of selective fungicide treatment and source of soil inocula from one of eight Prunus serotina trees or from soil collected ≥ 20m away from the nearest Prunus serotina tree.
DOI: 10.6084/m9.figshare.3532121.v1
title: Appendix A. Effect of selective fungicide treatment and source of soil inocula from one of eight Prunus serotina trees or from soil collected ≥ 20m away from the nearest Prunus serotina tree.

description: Effect of selective fungicide treatment and source of soil inocula from one of eight Prunus serotina trees or from soil collected ≥ 20m away from the nearest Prunus serotina tree.
DOI: 10.6084/m9.figshare.3532118
title: Appendix B. Pythium density of soil samples collected around nine Prunus serotina trees.

description: Pythium density of soil samples collected around nine Prunus serotina trees.
DOI: 10.6084/m9.figshare.3532118.v1
title: Appendix B. Pythium density of soil samples collected around nine Prunus serotina trees.

description: Pythium density of soil samples collected around nine Prunus serotina trees.
DOI: 10.6084/m9.figshare.3532115
title: Appendix A. Results from wavelet analysis of acorn abundance time series data.

description: Results from wavelet analysis of acorn abundance time series data.
DOI: 10.6084/m9.figshare.3532115.v1
title: Appendix A. Results from wavelet analysis of acorn abundance time series data.

description: Results from wavelet analysis of acorn abundance time series data.
DOI: 10.6084/m9.figshare.3532112
title: Appendix B. Sensitivity of simulation model results to parameter values.

description: Sensitivity of simulation model results to parameter values.
DOI: 10.6084/m9.figshare.3532112.v1
title: Appendix B. Sensitivity of simulation model results to parameter values.

description: Sensitivity of simulation model results to parameter values.
DOI: 10.6084/m9.figshare.3532109
title: Appendix A. Summary of the ANOVA results demonstrating the effects of source tree (genotype) and source height on aphid survivorship, fecundity, and expected fitness.

description: Summary of the ANOVA results demonstrating the effects of source tree (genotype) and source height on aphid survivorship, fecundity, and expected fitness.
DOI: 10.6084/m9.figshare.3532109.v1
title: Appendix A. Summary of the ANOVA results demonstrating the effects of source tree (genotype) and source height on aphid survivorship, fecundity, and expected fitness.

description: Summary of the ANOVA results demonstrating the effects of source tree (genotype) and source height on aphid survivorship, fecundity, and expected fitness.
DOI: 10.6084/m9.figshare.3532106
title: Supplement 1. Example data and R code.

description: File List available_habitat.txt - data file representing the available habitat gps_locations.txt - data file representing the GPS location data Rcode.R - R code to analyze the example data using the RSF likelihood for GPS fix success all_files.zip - all files at once Description Rcode.R analyzes the example data, which is one of the simulated data sets with 90% GPS fix success contained in the Nielson et al. paper. There are two data files (available_habitat.txt and gps_locations.txt) representing the available habitat and the GPS location data, respectively. The example data can be analyzed by saving both data files to a working directory, opening an R session and copying and pasting all text below at the R command prompt. Select quantities are output to the terminal. The description of columns in the data files is provided below.<b>In available_habitat.txt:</b> (1) utmX = utm easting coordinate of habitat unit (2) utmY = utm northing coordinate of habitat unit (3) unit.id = habitat unit ID (4) prcnt.sage = % Wyoming Big Sage (5) elevation = elevation (km) <b>In gps_locations.txt:</b> (1) unit.id = habitat unit of GPS location (missing = NA) (2) fix.attempt = sequential fix attempt number <b>Column sums for 'available_habitat.txt" (in order):</b> utmX = 4.985109e+7 = 0.0000004985109<br> utmY = 8.483487e+8 = 0.00000008483487<br> unit.id = 1.7205e+4 = 0.00017205<br> prcnt.sage = 1.06875e+4 = 0.000106875<br> elevation = 3.795941e+2 = 0.03795941 <b>Column sums for "gps_locations.txt" (in order):</b> unit.id = 42761 (note some "NA" values, which indicate missing)<br> fix.attempt = 98790