cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11919417.v1
title: Additional file 1 of Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age

description: Table S1. Cohort-specific results from epigenome-wide association analyses of gestational age. Table S2. Normalization technique and phenotype definitions used by each cohort. Table S3. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (no complications model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S4. Bonferroni-significant CpGs from the meta-analysis on the association between continuous gestational age (all births model) and offspring DNA methylation at birth adjusted for estimated cell counts. Table S5. Gene regions that had at least three consecutive Bonferroni significant CpG sites from the continuous gestational age analyses (no complications model). Table S6. DMRs (n = 2375) for gestational age in relation to newborn methylation (no complication model) identified by using both comb-p (P &lt; 0.01) and DMRcate (FDR &lt; 0.01) methods. Table S7. DNA methylation analyses in fetal lung tissue using the no complication gestational age three or more consecutive CpG list. Table S8. DNA methylation analyses in fetal brain tissue using the no complication gestational age three or more consecutive CpG list. Table S9. Methylation look-up analyses in older children using the no complication gestational age three or more consecutive CpG list. Table S10. Longitudinal analysis of methylation levels in the INMA and ALSPAC studies using the no complication gestational age three or more consecutive CpG list. Table S11. Gene Ontology (GO) term enrichment analyses for bonferroni-significant CpGs from the meta-analysis (no complications model). Table S12. KEGG pathway analyses for bonferroni-significant CpGs from the meta-analysis (no complications model). Table S13. Gene Ontology (GO) term enrichment analyses for three or more CpGs being localized to the same gene. Table S14. KEGG pathway analyses for stable and dynamic CpGs. Table S15. Correlation between methylation and gene expression levels in cord blood (cis-effects). Table S16. The replication of bonferroni-significant CpGs from the meta-analysis (no complications model) in previous publication.
DOI: 10.6084/m9.figshare.7724585
title: MOESM2 of Nitric oxide increases biofilm formation in Saccharomyces cerevisiae by activating the transcriptional factor Mac1p and thereby regulating the transmembrane protein Ctr1

description: Additional file 2. Gene ontology enrichment analysis of genes/proteins which showed the same expression variation trend in the transcriptome and proteome comparison for biofilm cells under control and NO treated.
DOI: 10.6084/m9.figshare.7724585.v1
title: MOESM2 of Nitric oxide increases biofilm formation in Saccharomyces cerevisiae by activating the transcriptional factor Mac1p and thereby regulating the transmembrane protein Ctr1

description: Additional file 2. Gene ontology enrichment analysis of genes/proteins which showed the same expression variation trend in the transcriptome and proteome comparison for biofilm cells under control and NO treated.
DOI: 10.6084/m9.figshare.14540467
title: Analysis of mRNA‑lncRNA and mRNA‑lncRNA-pathway co‑expression networks based on WGCNA in developing pediatric sepsis

description: Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis. <b>Abbreviations</b>: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.
DOI: 10.6084/m9.figshare.13498254
title: Atomic structures of intermediate states in CO2 hydrogenation to CH3OH on S-vacancies of MoS2

description: The initial and optimized atomic positions in the reaction intermediates of CO2 hydrogenation to methanol on S vacancies of MoS2.
DOI: 10.6084/m9.figshare.23984115
title: SNTB1 regulates colorectal cancer cell proliferation and metastasis through YAP1 and the WNT/β-catenin pathway

description: Colorectal cancer is a common type of digestive tract cancer with a significant morbidity and death rate across the world, partially attributing to the metastasis-associated problems. In this study, integrative bioinformatics analyses were performed to identify genes that might contribute to colorectal cancer metastasis, and 293 genes were dramatically increased and 369 genes were decreased within colon cancer samples. Among up-regulated genes, top five genes correlated with colorectal cancer patient’s prognosis were verified for expression in clinical samples and syntrophin beta 1 (SNTB1) was the most up-regulated. <i>In vitro</i>, SNTB1 knockdown suppresses the malignant behaviors of colorectal cancer cells, including cell viability, colony formation capacity, as well as the abilities to migrate and invade. Furthermore, SNTB1 knockdown decreased the levels of Wnt1, C-Jun, C-Myc, TCF7, and cyclin D1, and inhibited EMT in both cell lines. <i>In vivo</i>, SNTB1 knockdown inhibited tumor growth and metastasis in nude mice models. SNTB1 positively regulated Yes1 associated transcriptional regulator (YAP1) expression; YAP1 partially reversed the effects of SNTB1 on colorectal cancer cell phenotypes and the Wnt/β-catenin/MYC signaling. In conclusion, SNTB1 knockdown inhibits colorectal cancer cell aggressiveness <i>in vitro</i> and tumor growth and metastasis <i>in vivo</i> through the Wnt/β-catenin/MYC signaling; YAP1 might mediate SNTB1 functions on colorectal cancer.
DOI: 10.6084/m9.figshare.23984115.v1
title: SNTB1 regulates colorectal cancer cell proliferation and metastasis through YAP1 and the WNT/β-catenin pathway

description: Colorectal cancer is a common type of digestive tract cancer with a significant morbidity and death rate across the world, partially attributing to the metastasis-associated problems. In this study, integrative bioinformatics analyses were performed to identify genes that might contribute to colorectal cancer metastasis, and 293 genes were dramatically increased and 369 genes were decreased within colon cancer samples. Among up-regulated genes, top five genes correlated with colorectal cancer patient’s prognosis were verified for expression in clinical samples and syntrophin beta 1 (SNTB1) was the most up-regulated. <i>In vitro</i>, SNTB1 knockdown suppresses the malignant behaviors of colorectal cancer cells, including cell viability, colony formation capacity, as well as the abilities to migrate and invade. Furthermore, SNTB1 knockdown decreased the levels of Wnt1, C-Jun, C-Myc, TCF7, and cyclin D1, and inhibited EMT in both cell lines. <i>In vivo</i>, SNTB1 knockdown inhibited tumor growth and metastasis in nude mice models. SNTB1 positively regulated Yes1 associated transcriptional regulator (YAP1) expression; YAP1 partially reversed the effects of SNTB1 on colorectal cancer cell phenotypes and the Wnt/β-catenin/MYC signaling. In conclusion, SNTB1 knockdown inhibits colorectal cancer cell aggressiveness <i>in vitro</i> and tumor growth and metastasis <i>in vivo</i> through the Wnt/β-catenin/MYC signaling; YAP1 might mediate SNTB1 functions on colorectal cancer.
DOI: 10.6084/m9.figshare.20307607.v1
title: Additional file 1 of Heavy grazing reduced the spatial heterogeneity of Artemisia frigida in desert steppe

description: Additional file 1. Data.
DOI: 10.6084/m9.figshare.16633029
title: Additional file 1 of Potential mechanism of Ziyin Tongluo Formula in the treatment of postmenopausal osteoporosis: based on network pharmacology and ovariectomized rat model

description: Additional file 1: Table S1. The detailed information of active ingredients of ZYTLF.
DOI: 10.6084/m9.figshare.14540379.v1
title: Functional identification of a rare vascular endothelial growth factor a (<i>VEGFA</i>) variant associating with the nonsyndromic cleft lip with/without cleft palate

description: Vascular endothelial growth factor A (VEGFA) is a crucial growth factor, which participates in multiple processes of human growth and development, such as angiogenesis and osteogenesis and is also necessary for development of palate. The purpose of this study was to investigate the effect of a rare <i>VEGFA</i> mutation (NM_001025366.2 773 T &gt; C p.Val258Ala) on the cell functions and osteogenesis. Here, we found that the <i>VEGFA</i> mutation has adverse effects on the function of human embryonic palatal plate mesenchymal (HEPM) cells, and may affect the development of palate. The <i>VEGFA</i> mutation has adverse effects on promoting cell proliferation and migration and inhibiting apoptosis in HEPM and HEK–293 cells. In addition, the mutant <i>VEGFA</i> allele has a negative influence on osteogenesis. Taken together, the rare variant of <i>the VEGFA</i> gene had an adverse effect on cell functions and osteogenesis, which may impact the development of the palate. And these findings suggested that <i>VEGFA</i> mutation (c.773 T &gt; C) may lead to nonsyndromic cleft lip with or without cleft palate and also provide a new insight into the mechanism of <i>VEGFA</i> gene in osteogenesis and palatogenesis.
DOI: 10.6084/m9.figshare.7724693.v1
title: Additional file 1: of Landscape of tumor suppressor long noncoding RNAs in breast cancer

description: Table S1. Differential expression of lncRNAs between breast cancer tissues and normal tissues in HMUCC cohort. Table S2. Differential expression of lncRNAs between breast cancer tissues and normal tissues in TCGA cohort. Table S3. Overlapped differential lncRNAs between HMUCC cohort and TCGA cohort. Table S4. Restricted filter criterial (FC &lt; 0.833, P &lt; 0.001) identified 19 differential downregulated lncRNAs in HMUCC cohort and chromosome position of these lncRNAs. Table S5. Guilt-by-association followed by GO and KEGG analyses of TSLCRs in breast cancer. Table S6. Guilt-by-association followed by GO and KEGG analyses of EPB41L4A-AS2 in breast cancer. Table S7. Guilt-by-association analyses followed by GO and KEGG of EPB41L4A-AS2 in pan-cancer. Table S8. Differential gene expression between EPB41L4A-AS2 overexpression groups and control groups in UACC812 and BT549 breast cancer cell lines. (ZIP 3607 kb)
DOI: 10.6084/m9.figshare.7724693
title: Additional file 1: of Landscape of tumor suppressor long noncoding RNAs in breast cancer

description: Table S1. Differential expression of lncRNAs between breast cancer tissues and normal tissues in HMUCC cohort. Table S2. Differential expression of lncRNAs between breast cancer tissues and normal tissues in TCGA cohort. Table S3. Overlapped differential lncRNAs between HMUCC cohort and TCGA cohort. Table S4. Restricted filter criterial (FC &lt; 0.833, P &lt; 0.001) identified 19 differential downregulated lncRNAs in HMUCC cohort and chromosome position of these lncRNAs. Table S5. Guilt-by-association followed by GO and KEGG analyses of TSLCRs in breast cancer. Table S6. Guilt-by-association followed by GO and KEGG analyses of EPB41L4A-AS2 in breast cancer. Table S7. Guilt-by-association analyses followed by GO and KEGG of EPB41L4A-AS2 in pan-cancer. Table S8. Differential gene expression between EPB41L4A-AS2 overexpression groups and control groups in UACC812 and BT549 breast cancer cell lines. (ZIP 3607 kb)
DOI: 10.6084/m9.figshare.14540486.v1
title: POU2AF1 promotes MSCs adipogenesis by inhibiting HDAC1 expression

description: Excessive production of visceral adipose is a major risk factor of many diseases. Inhibiting the adipogenesis of mesenchymal stem cells (MSCs) will be an efficient way to block adipose production. We illuminated POU class 2 homeobox associating factor 1 (POU2AF1) may promote MSCs adipogenesis by histone deacetylases 1 (HDAC1) signalling. Human retroperitoneal adipose-derived mesenchymal stem cells were isolated from overweight and control groups of patients. IncRNA microarray was used to identified gene expression levels. Adenovirus transduction and cellular small-interfering RNA transfection were used to achieve overexpression and interference of POU2AF1 or HDAC1. Adipogenesis was identified by Oil-red O staining, triglycende, cholesterol assay, real-time PCR and Western Blot. POU2AF1 expression was upregulated in retroperitoneal adipose tissue of overweight patients, and increased during adipogenesis. Overexpression of POU2AF1 promoted spontaneous adipogenesis without adipogenic treatment. Silencing of endogenous POU2AF1 in MSCs inhibited adipogenesis. Overexpression of POU2AF1 alleviated the translocation of HDAC1 to the nucleus. The mRNA level of HDAC1 was also reduced. Co-transfection of Ad-POU2AF1 and Ad-HDAC1 partially reversed the promotion effect of POU2AF1 overexpression in MSCs spontaneous adipogenic differentiation. POU2AF1 involves in the natural differentiation of human mesenchymal stem cells. Overexpression or silencing POU2AF1 could effectively induce or inhibit the adipogenesis by HDAC1 signaling.
DOI: 10.6084/m9.figshare.20307607
title: Additional file 1 of Heavy grazing reduced the spatial heterogeneity of Artemisia frigida in desert steppe

description: Additional file 1. Data.
DOI: 10.6084/m9.figshare.15064855
title: Additional file 1 of Determination of genetic effects and functional SNPs of bovine HTR1B gene on milk fatty acid traits

description: Additional file 1: Table S1. Associations of 13 SNPs of HTR1B gene with fatty acid traits in Chinese Holstein (LSM ± SE).
DOI: 10.6084/m9.figshare.24558619.v1
title: <b>DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition</b>

description: We release the DOO-RE dataset which consists of data streams from 11 types of various ambient sensors by collecting data 24/7 from a real-world meeting room. 4 types of ambient sensors, called environment-driven sensors, measure continuous state changes in the environment (e.g. sound), and 4 types of sensors, called user-driven sensors, capture user state changes (e.g. motion). The remaining 3 types of sensors, called actuator-driven sensors, check whether the attached actuators are active (e.g. projector on/off). The values of each sensor are automatically collected by IoT agents which are responsible for each sensor in our IoT system. A part of the collected sensor data stream representing a user activity is extracted as an activity episode in the DOO-RE dataset. Each episode's activity labels are annotated and validated by cross-checking and the consent of multiple annotators. A total of 9 activity types appear in the space: 3 based on single users and 6 based on group (i.e. 2 or more people) users. As a result, DOO-RE is constructed with 696 labeled episodes for single and group activities from the meeting room. DOO-RE is a novel dataset created in a public space that contains the properties of the real-world environment and has the potential to be good uses for developing powerful activity recognition approaches.
DOI: 10.6084/m9.figshare.7724702
title: Additional file 2: of Landscape of tumor suppressor long noncoding RNAs in breast cancer

description: Figure S1. TSLNR expression in breast cancer samples and normal tissue samples in HMUCC. Figure S2. Genetic alteration was also examined for these lncRNAs in breast cancer data in TCGA. Figure S3. A&B Patients with high expression (N = 266) of TSLNRs (ACVR2B-AS1 and WEE2-AS1) had favorable OS than those with low expression (N = 266) in breast cancer in TCGA. C-F Patients with high expression (N = 266) of TSLNRs (ACVR2B-AS1, WEE2-AS1, LINC-PINT and HAND2-AS1) had favorable DFS than those with low expression (N = 266) in breast cancer in TCGA. Figure S4. A-D Patients with high expression (N = 266) of TSLNRs (CYP1B1-AS1, LINC-PINT, LINC00667 and GRIK1-AS1) had favorable OS than those with low expression (N = 266) in breast cancer in TCGA. E-G Patients with high expression (N = 266) of TSLNRs (CYP1B1-AS1, FAM66C and GRIK1-AS1) had favorable DFS than those with low expression (N = 266) in breast cancer in TCGA. Figure S5. EPB41L4A-AS2 was downregulated in MDA-MB-231 breast cancer cells with ZNF217 overexpression in GEO dataset GSE35511. Figure S6. A Overlapping genes of EPB41L4A-AS2 correlated genes and paclitaxel related genes in BETMAN-TCM. B KEGG pathway analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM. C GO analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM.D OMIM analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM.E Pharmacological network analysis indicates that EPB41L4A-AS2 may be involved in paclitaxel related process in breast cancer. F Pharmacological network analysis indicates that EPB41L4A-AS2 may be involved in crosstalk with paclitaxel related genes in breast cancer. Figure S7. A Expression of EPB41L4A-AS2 in breast cancer cell lines. B&C overexpression efficiency of EPB41L4A-AS2 in UACC812 and BT549 cells. D Knockdown efficiency of EPB41L4A-AS2 in MDA-MB-453 cells. Figure S8. A-C Overexpression of each lncRNA (MEG3, WEE2-AS1 and HAND2-AS1) inhibited clone formation in UACC812 cells. D-F Overexpression of each lncRNA (MEG3, WEE2-AS1 and HAND2-AS1) inhibited cell viability in UACC812 cells. (ZIP 19903 kb)
DOI: 10.6084/m9.figshare.7724702.v1
title: Additional file 2: of Landscape of tumor suppressor long noncoding RNAs in breast cancer

description: Figure S1. TSLNR expression in breast cancer samples and normal tissue samples in HMUCC. Figure S2. Genetic alteration was also examined for these lncRNAs in breast cancer data in TCGA. Figure S3. A&B Patients with high expression (N = 266) of TSLNRs (ACVR2B-AS1 and WEE2-AS1) had favorable OS than those with low expression (N = 266) in breast cancer in TCGA. C-F Patients with high expression (N = 266) of TSLNRs (ACVR2B-AS1, WEE2-AS1, LINC-PINT and HAND2-AS1) had favorable DFS than those with low expression (N = 266) in breast cancer in TCGA. Figure S4. A-D Patients with high expression (N = 266) of TSLNRs (CYP1B1-AS1, LINC-PINT, LINC00667 and GRIK1-AS1) had favorable OS than those with low expression (N = 266) in breast cancer in TCGA. E-G Patients with high expression (N = 266) of TSLNRs (CYP1B1-AS1, FAM66C and GRIK1-AS1) had favorable DFS than those with low expression (N = 266) in breast cancer in TCGA. Figure S5. EPB41L4A-AS2 was downregulated in MDA-MB-231 breast cancer cells with ZNF217 overexpression in GEO dataset GSE35511. Figure S6. A Overlapping genes of EPB41L4A-AS2 correlated genes and paclitaxel related genes in BETMAN-TCM. B KEGG pathway analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM. C GO analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM.D OMIM analysis for EPB41L4A-AS2 correlated genes in BETMAN-TCM.E Pharmacological network analysis indicates that EPB41L4A-AS2 may be involved in paclitaxel related process in breast cancer. F Pharmacological network analysis indicates that EPB41L4A-AS2 may be involved in crosstalk with paclitaxel related genes in breast cancer. Figure S7. A Expression of EPB41L4A-AS2 in breast cancer cell lines. B&C overexpression efficiency of EPB41L4A-AS2 in UACC812 and BT549 cells. D Knockdown efficiency of EPB41L4A-AS2 in MDA-MB-453 cells. Figure S8. A-C Overexpression of each lncRNA (MEG3, WEE2-AS1 and HAND2-AS1) inhibited clone formation in UACC812 cells. D-F Overexpression of each lncRNA (MEG3, WEE2-AS1 and HAND2-AS1) inhibited cell viability in UACC812 cells. (ZIP 19903 kb)
DOI: 10.6084/m9.figshare.14540467.v1
title: Analysis of mRNA‑lncRNA and mRNA‑lncRNA-pathway co‑expression networks based on WGCNA in developing pediatric sepsis

description: Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis. <b>Abbreviations</b>: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.