cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24580651.v1
title: Exosome-delivered circRNA circSYT15 contributes to cisplatin resistance in cervical cancer cells through the miR-503-5p/RSF1 axis

description: The development of chemotherapy resistance is a major obstacle for cervical cancer (CC) patients. Exosome-mediated transfer of circular RNAs (circRNAs) was found to have relevance to the CC. This study is designed to explore the role and mechanism of exosomal circRNA synaptotagmin 15 (circSYT15) on cisplatin (DDP) resistance in CC. Cell proliferation ability and apoptosis rate were detected by Cell Counting Kit-8 (CCK-8), 5-ethynyl-2’-deoxyuridine (EdU), colony formation, and flow cytometry assays. CircSYT15, microRNA-503-5p (miR-503-5p), Remodeling spacing factor 1 (RSF1) levels were detected by real-time quantitative polymerase chain reaction (RT-qPCR). Exosomes were analyzed by a transmission electron microscope and nanoparticle tracking analysis. CD63, CD81, TSC101, Bcl-2, Bax, C-caspase 3, and RSF1 protein levels were examined by western blot assay. The binding between miR-503-5p and circSYT15 or RSF1 was predicted by circBank or Starbase and then verified by a dual-luciferase reporter and RNA Immunoprecipitation (RIP). The biological role of exosomal circSYT15 in DDP resistance of CC in vivo. CircSYT15 was upregulated in the DDP-resistant CC cells and exosomes isolated from DDP-resistant CC cells. CircSYT15 knockdown repressed the proliferation and drug resistance of CC and induced apoptosis in CC cells. Exosomes shuttled circSYT15 act as a sponge to affect RSF1 expression, thereby promoting proliferation and drug resistance and repressing apoptosis of sensitive CC cells. Exosomal circSYT15 boost DDP resistance of cervical cancer in vivo. Exosome-mediated transfer of circSYT15 enhanced DDP resistance in CC partly by targeting the miR-503-5p/RSF1 axis, providing a foundation for future clinical applications of CC drug resistance.
DOI: 10.6084/m9.figshare.23720982
title: PDXNet Genetic Ancestry Reference Panel for SNPWeights

description: Novel reference panel for the genetic ancestry estimation program SNPWeights. This panel was created as part by researchers in the National Cancer Institute (NCI) supported PDX Development and Trial Centers Research Network (PDXNet).
DOI: 10.6084/m9.figshare.14619186
title: GHG_EQTP_final.csv

description: #This project is for analyzing the Carbon balance of an peatland on the Eastern Qinghai-Tibetan Plateau, details about this paper is as follows.#Methane emissions offset net carbon dioxide uptake from an alpine peatland on the Eastern Qinghai-Tibetan PlateauHaijun Peng a,d,e, Jinshu Chi b,<em>, Hu Yao a,c,d, Qian Guo a,c,d, Bing Hong a,d,e,</em>, Hanwei Ding a,c,d, Yongxuan Zhu a, Jie Wang a,c,d, Yetang Hong aa State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China b Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, 90183, Sweden c University of Chinese Academy of Sciences, Beijing 100049, China d Bayinbuluk alpine wetland carbon flux research station, Chinese Flux Observation and Research Network e CAS Center for Excellence in Quaternary Science and Global Change, Xi’an, 710061, ChinaCorrespondence to J. Chi (jinshu.chi@slu.se) and B. Hong (hongbing@mail.gyig.ac.cn)
DOI: 10.6084/m9.figshare.24580696
title: <b>Extreme rainfall events eliminate the response of GHG fluxes to hydrological alterations and fertilization in a riparian ecosystem</b>

description: The data of greenhouse gas fluxes in riparian ecosystems.
DOI: 10.6084/m9.figshare.14619228
title: EQTP_REddyProc_output.txt

description: This is the REddyProc online tool output file, which provides gap-filled and partitioned flux data for the study 'Methane emissions offset net carbon dioxide uptake from an alpine peatland on the Eastern Qinghai-Tibetan Plateau'.
DOI: 10.6084/m9.figshare.11997810
title: ANALYSIS OF THE BASELINE DRIFT ARTIFACT IN HADAMARD TRANSFORM SEPARATION TECHNIQUES

description: The baseline drift artifact (BDA), namely, the baseline bias between the Hadamard transform (HT) spectrum and the signal averaging (SA) spectrum was unfortunately found in the HT pulsed separation techniques, which makes the HT analytical chemistry face the challenge of non-stability and unreliability. In order to find out the origin and eliminate the BDA phenomenon, the multiplexing mechanism of Hadamard transform ion mobility spectrometry (HT-IMS) has been analyzed in this research. Through simulation and experimental comparisons, the non-ideal overlap defect in Hadamard multiplexing process was suggested as the cause for the BDA phenomenon. Eventually, with a correction method, the BDA phenomenon could be well addressed, which guarantees the reliability and stability of HT techniques without increasing the hardware complexity.
DOI: 10.6084/m9.figshare.11997810.v1
title: ANALYSIS OF THE BASELINE DRIFT ARTIFACT IN HADAMARD TRANSFORM SEPARATION TECHNIQUES

description: The baseline drift artifact (BDA), namely, the baseline bias between the Hadamard transform (HT) spectrum and the signal averaging (SA) spectrum was unfortunately found in the HT pulsed separation techniques, which makes the HT analytical chemistry face the challenge of non-stability and unreliability. In order to find out the origin and eliminate the BDA phenomenon, the multiplexing mechanism of Hadamard transform ion mobility spectrometry (HT-IMS) has been analyzed in this research. Through simulation and experimental comparisons, the non-ideal overlap defect in Hadamard multiplexing process was suggested as the cause for the BDA phenomenon. Eventually, with a correction method, the BDA phenomenon could be well addressed, which guarantees the reliability and stability of HT techniques without increasing the hardware complexity.
DOI: 10.6084/m9.figshare.23685024.v1
title: lncRNA expression and regulation across different brain regions in primates

description: <strong>Supplement_File1.xlsx:</strong> Orthologous lncRNA information across human, chimpanzee, gorilla, and gibbon.
<strong>Supplement_File2.xlsx: </strong>Information about all the co-expressed pairs of lncRNA-PCG from all the clusters of lncRNA and PCGs.
<strong>Supplement_File3.xlsx:</strong> Information about the hub lncRNAs regulating the co-expression of PCGs from lncRNA-PCG pairs across different clusters of lncRNAs and PCGs.
<strong>Supplement_File4.xlsx:</strong> List of commonly and uniquely regulated PCGs in C8-PCG_D co-expression network of human and chimpanzee.
<strong>Supplement_File5.docx:</strong> Represents the gffcompare class codes and its meaning during annotation of the lncRNA transcripts.
<strong>Supplement_File6.xlsx:</strong> List of primers used for qPCR of hub lncRNAs.
<strong>Details_of_predicted_lncRNAs.txt:</strong> Provides details of lncRNAs ranging from genomic coordinates, length, differential expression status, region of differential expression, nearest genes, orthologs, class code.
DEG_regions_human.txt: Differential expressed 
DEG_regions_chimp.txt: 
<strong>DElncRNAs_and_regions_human.txt: </strong>Differentially expressed lncRNA from any of the brain regions in human.
<strong>DElncRNAs_and_regions_chimp.txt: </strong>Differentially expressed lncRNA from any of the brain regions in chimpanzee.
<strong>Normalized_Read_Counts_Human.txt: </strong>Normalized read counts for chimpanzee lncRNAs.
<strong>Normalized_Read_Counts_Chimp.txt: </strong>Normalized read counts for chimpanzee lncRNAs.
<strong>Normalized_Read_Counts_Gorilla.txt:</strong> Normalized read counts for gorilla lncRNAs.
<strong>VarianceStabilizedCounts_Human.txt: </strong> Variance stabilized counts for human lncRNAs.
<strong>VarianceStabilizedCounts_Chimp.txt:</strong> Variance stabilized counts for chimpanzee lncRNAs.
DOI: 10.6084/m9.figshare.23685024
title: lncRNA expression and regulation across different brain regions in primates

description: <b>Orthologous_lncRNA_information.xlsx:</b> Orthologous lncRNA information across human, chimpanzee, gorilla, and gibbon<b>Co-expressed_pairs_lncRNA-PCG</b><b>.xlsx: </b>Information about all the co-expressed pairs of lncRNA-PCG from all the clusters of lncRNA and PCGs.<b>Hub_lncRNAs_co-expression _of_PCGs</b><b>.</b><b>xlsx:</b> Information about the hub lncRNAs regulating the co-expression of PCGs from lncRNA-PCG pairs across different clusters of lncRNAs and PCGs.<b>Common_and_unique_PCGs_C8_PCG_D</b><b>.xlsx:</b> List of commonly and uniquely regulated PCGs in C8-PCG_D co-expression network of human and chimpanzee.<b>gffcompare_class_codes_meaning</b><b>.</b><b>docx:</b> Represents the gffcompare class codes and its meaning during annotation of the lncRNA transcripts.<b>Primers_qPCR_hub lncRNAs</b><b>.xlsx:</b> List of primers used for qPCR of hub lncRNAs.<b>Details_of_predicted_lncRNAs.txt:</b> Provides details of lncRNAs ranging from genomic coordinates, length, differential expression status, region of differential expression, nearest genes, orthologs, class code.<b>DElncRNAs_and_regions_human.txt: </b>Differentially expressed lncRNA from any of the brain regions in human.<b>DElncRNAs_and_regions_chimp.txt: </b>Differentially expressed lncRNA from any of the brain regions in chimpanzee.<b>Normalized_Read_Counts_Human.txt: </b>Normalized read counts for chimpanzee lncRNAs.<b>Normalized_Read_Counts_Chimp.txt: </b>Normalized read counts for chimpanzee lncRNAs.<b>Normalized_Read_Counts_Gorilla.txt:</b> Normalized read counts for gorilla lncRNAs.<b>VarianceStabilizedCounts_Human.txt: </b>Variance stabilized counts for human lncRNAs.<b>VarianceStabilizedCounts_Chimp.txt:</b> Variance stabilized counts for chimpanzee lncRNAs.
DOI: 10.6084/m9.figshare.23685024.v2
title: lncRNA expression and regulation across different brain regions in primates

description: <b>Orthologous_lncRNA_information.xlsx:</b> Orthologous lncRNA information across human, chimpanzee, gorilla, and gibbon<b>Co-expressed_pairs_lncRNA-PCG</b><b>.xlsx: </b>Information about all the co-expressed pairs of lncRNA-PCG from all the clusters of lncRNA and PCGs.<b>Hub_lncRNAs_co-expression _of_PCGs</b><b>.</b><b>xlsx:</b> Information about the hub lncRNAs regulating the co-expression of PCGs from lncRNA-PCG pairs across different clusters of lncRNAs and PCGs.<b>Common_and_unique_PCGs_C8_PCG_D</b><b>.xlsx:</b> List of commonly and uniquely regulated PCGs in C8-PCG_D co-expression network of human and chimpanzee.<b>gffcompare_class_codes_meaning</b><b>.</b><b>docx:</b> Represents the gffcompare class codes and its meaning during annotation of the lncRNA transcripts.<b>Primers_qPCR_hub lncRNAs</b><b>.xlsx:</b> List of primers used for qPCR of hub lncRNAs.<b>Details_of_predicted_lncRNAs.txt:</b> Provides details of lncRNAs ranging from genomic coordinates, length, differential expression status, region of differential expression, nearest genes, orthologs, class code.<b>DElncRNAs_and_regions_human.txt: </b>Differentially expressed lncRNA from any of the brain regions in human.<b>DElncRNAs_and_regions_chimp.txt: </b>Differentially expressed lncRNA from any of the brain regions in chimpanzee.<b>Normalized_Read_Counts_Human.txt: </b>Normalized read counts for chimpanzee lncRNAs.<b>Normalized_Read_Counts_Chimp.txt: </b>Normalized read counts for chimpanzee lncRNAs.<b>Normalized_Read_Counts_Gorilla.txt:</b> Normalized read counts for gorilla lncRNAs.<b>VarianceStabilizedCounts_Human.txt: </b>Variance stabilized counts for human lncRNAs.<b>VarianceStabilizedCounts_Chimp.txt:</b> Variance stabilized counts for chimpanzee lncRNAs.
DOI: 10.6084/m9.figshare.11997930
title: Mapping the tuberculosis scientific landscape among BRICS countries: a bibliometric and network analysis

description: BACKGROUND The five BRICS (Brazil, Russian, Indian, China, and South Africa) countries bear 49% of the world’s tuberculosis (TB) burden and they are committed to ending tuberculosis. OBJECTIVES The aim of this paper is to map the scientific landscape related to TB research in BRICS countries. METHODS Were combined bibliometrics and social network analysis techniques to map the scientific publications related to TB produced by the BRICS. Was made a descriptive statistical data covering the full period of analysis (1993-2016) and the research networks were made for 2007-2016 (8,366 records). The bubble charts were generated by VantagePoint and the networks by the Gephi 0.9.1 software (Gephi Consortium 2010) from co-occurrence matrices produced in VantagePoint. The Fruchterman-Reingold algorithm provided the networks’ layout. FINDINGS During the period 1993-2016, there were 38,315 peer-reviewed, among them, there were 11,018 (28.7%) articles related by one or more authors in a BRICS: India 38.7%; China 23.8%; South Africa 21.1%; Brazil 13.0%; and Russia 4.5% (The total was greater than 100% because our criterion was all papers with at least one author in a BRICS). Among the BRICS, there was greater interaction between India and South Africa and organisations in India and China had the highest productivity; however, South African organisations had more interaction with countries outside the BRICS. Publications by and about BRICS generally covered all research areas, especially those in India and China covered all research areas, although Brazil and South Africa prioritised infectious diseases, microbiology, and the respiratory system. MAIN CONCLUSIONS An overview of BRICS scientific publications and interactions highlighted the necessity to develop a BRICS TB research plan to increase efforts and funding to ensure that basic science research successfully translates into products and policies to help end the TB epidemic.
DOI: 10.6084/m9.figshare.11997930.v1
title: Mapping the tuberculosis scientific landscape among BRICS countries: a bibliometric and network analysis

description: BACKGROUND The five BRICS (Brazil, Russian, Indian, China, and South Africa) countries bear 49% of the world’s tuberculosis (TB) burden and they are committed to ending tuberculosis. OBJECTIVES The aim of this paper is to map the scientific landscape related to TB research in BRICS countries. METHODS Were combined bibliometrics and social network analysis techniques to map the scientific publications related to TB produced by the BRICS. Was made a descriptive statistical data covering the full period of analysis (1993-2016) and the research networks were made for 2007-2016 (8,366 records). The bubble charts were generated by VantagePoint and the networks by the Gephi 0.9.1 software (Gephi Consortium 2010) from co-occurrence matrices produced in VantagePoint. The Fruchterman-Reingold algorithm provided the networks’ layout. FINDINGS During the period 1993-2016, there were 38,315 peer-reviewed, among them, there were 11,018 (28.7%) articles related by one or more authors in a BRICS: India 38.7%; China 23.8%; South Africa 21.1%; Brazil 13.0%; and Russia 4.5% (The total was greater than 100% because our criterion was all papers with at least one author in a BRICS). Among the BRICS, there was greater interaction between India and South Africa and organisations in India and China had the highest productivity; however, South African organisations had more interaction with countries outside the BRICS. Publications by and about BRICS generally covered all research areas, especially those in India and China covered all research areas, although Brazil and South Africa prioritised infectious diseases, microbiology, and the respiratory system. MAIN CONCLUSIONS An overview of BRICS scientific publications and interactions highlighted the necessity to develop a BRICS TB research plan to increase efforts and funding to ensure that basic science research successfully translates into products and policies to help end the TB epidemic.
DOI: 10.6084/m9.figshare.14619370
title: lncRNA Sequencing of Antler Mesenchymal Tissue Revealed that the Regulatory Network of Antler Cell Proliferation and Differentiation

description: Antlers have been widely studied due to their unique physiological characteristics, such as rapid growth, periodic shedding and regeneration. However, little is known about how antler growth is regulated by long non-coding RNA (lncRNA). The aim of the present study was to identify the lncRNAs expression profile and explore the function of lncRNAs during the antler growth. Herein, RNA-sequencing technology (RNA-seq) was performed on the three growth periods (early developmental period: EP, middle developmental period: MP, later developmental period: LP) of male sika deer (<i>Cervus nippon</i>) antler, 16 differentially expressed lncRNAs (DE lncRNAs) and 11 DE lncRNAs were identified in EP vs MP and MP vs LP related to cell proliferation and cell differentiation, respectively. Finally, lncRNAs-mRNAs co-expression networks were constructed based on the identified DE lncRNAs and their potential trans-target genes. The result reveals that lncRNAs may play diverse roles in different periods of antler growth. It provides a novel perspective for revealing the molecular mechanism of antler growth.
DOI: 10.6084/m9.figshare.14619370.v1
title: lncRNA Sequencing of Antler Mesenchymal Tissue Revealed that the Regulatory Network of Antler Cell Proliferation and Differentiation

description: Antlers have been widely studied due to their unique physiological characteristics, such as rapid growth, periodic shedding and regeneration. However, little is known about how antler growth is regulated by long non-coding RNA (lncRNA). The aim of the present study was to identify the lncRNAs expression profile and explore the function of lncRNAs during the antler growth. Herein, RNA-sequencing technology (RNA-seq) was performed on the three growth periods (early developmental period: EP, middle developmental period: MP, later developmental period: LP) of male sika deer (<i>Cervus nippon</i>) antler, 16 differentially expressed lncRNAs (DE lncRNAs) and 11 DE lncRNAs were identified in EP vs MP and MP vs LP related to cell proliferation and cell differentiation, respectively. Finally, lncRNAs-mRNAs co-expression networks were constructed based on the identified DE lncRNAs and their potential trans-target genes. The result reveals that lncRNAs may play diverse roles in different periods of antler growth. It provides a novel perspective for revealing the molecular mechanism of antler growth.
DOI: 10.6084/m9.figshare.14619373
title: A further look at quantitative trait loci for growth and fatness traits in a White Duroc × Erhualian F<sub>3</sub> intercross population

description: Genetic analysis of porcine growth and fatness traits is beneficial to the swine industry and provides a reference to understand human obesity. Here, we obtained 29 growth and fatness traits for 473 individuals from a White Duroc × Erhualian F<sub>3</sub> intercross population. Basic statistical analyses showed that: (1) Positive correlations between different-stage body weights were detected, the shorter the time interval the stronger the correlation. (2) Strong correlations existed in the paired fatness traits. (3) With the growth of age, the correlation between fatness and body weight was increasing. All pigs were genotyped by Illumina 50 K SNP chips and their whole-genome genotypes were imputed referred to 109 re-sequencing data. We performed common and imputation-based GWASs for these traits. Two genome-wide significant loci on swine chromosome (SSC) 4 and 7 were repeatedly detected. The strongest association (<i>P</i> = 3.24 × 10<sup>−19</sup>) was detected at 31.96 Mb on SSC7 for leaf fat weight. On this locus, seven major haplotypes were identified, of which two were novel and had an increasing-fatness effect. In the imputation-based GWAS, three new loci were identified. Our findings provide further insights into and enhance our understanding of genetic mechanism of porcine growth and fat deposition.
DOI: 10.6084/m9.figshare.14619373.v1
title: A further look at quantitative trait loci for growth and fatness traits in a White Duroc × Erhualian F<sub>3</sub> intercross population

description: Genetic analysis of porcine growth and fatness traits is beneficial to the swine industry and provides a reference to understand human obesity. Here, we obtained 29 growth and fatness traits for 473 individuals from a White Duroc × Erhualian F<sub>3</sub> intercross population. Basic statistical analyses showed that: (1) Positive correlations between different-stage body weights were detected, the shorter the time interval the stronger the correlation. (2) Strong correlations existed in the paired fatness traits. (3) With the growth of age, the correlation between fatness and body weight was increasing. All pigs were genotyped by Illumina 50 K SNP chips and their whole-genome genotypes were imputed referred to 109 re-sequencing data. We performed common and imputation-based GWASs for these traits. Two genome-wide significant loci on swine chromosome (SSC) 4 and 7 were repeatedly detected. The strongest association (<i>P</i> = 3.24 × 10<sup>−19</sup>) was detected at 31.96 Mb on SSC7 for leaf fat weight. On this locus, seven major haplotypes were identified, of which two were novel and had an increasing-fatness effect. In the imputation-based GWAS, three new loci were identified. Our findings provide further insights into and enhance our understanding of genetic mechanism of porcine growth and fat deposition.
DOI: 10.6084/m9.figshare.11473752
title: Preterm labor is characterized by a high abundance of amniotic fluid prostaglandins in patients with intra-amniotic infection or sterile intra-amniotic inflammation

description: To distinguish between prostaglandin and prostamide concentrations in the amniotic fluid of women who had an episode of preterm labor with intact membranes through the utilisation of liquid chromatography-tandem mass spectrometry. Liquid chromatography-tandem mass spectrometry analysis of amniotic fluid of women with preterm labor and (1) subsequent delivery at term (2) preterm delivery without intra-amniotic inflammation; (3) preterm delivery with sterile intra-amniotic inflammation (interleukin (IL)-6&gt;2.6 ng/mL without detectable microorganisms); and (4) preterm delivery with intra-amniotic infection [IL-6&gt;2.6 ng/mL with detectable microorganisms]. (1) amniotic fluid concentrations of PGE<sub>2</sub>, PGF<sub>2α</sub>, and PGFM were higher in patients with intra-amniotic infection than in those without intra-amniotic inflammation; (2) PGE<sub>2</sub> and PGF<sub>2α</sub> concentrations were also greater in patients with intra-amniotic infection than in those with sterile intra-amniotic inflammation; (3) patients with sterile intra-amniotic inflammation had higher amniotic fluid concentrations of PGE<sub>2</sub> and PGFM than those without intra-amniotic inflammation who delivered at term; (4) PGFM concentrations were also greater in women with sterile intra-amniotic inflammation than in those without intra-amniotic inflammation who delivered preterm; (5) amniotic fluid concentrations of prostamides (PGE<sub>2</sub>-EA and PGF<sub>2α</sub>-EA) were not different among patients with preterm labor; (6) amniotic fluid concentrations of prostaglandins, but no prostamides, were higher in cases with intra-amniotic inflammation; and (7) the PGE<sub>2</sub>:PGE<sub>2</sub>-EA and PGF<sub>2α</sub>:PGF<sub>2α</sub>-EA ratios were higher in patients with intra-amniotic infection compared to those without inflammation. Mass spectrometric analysis of amniotic fluid indicated that amniotic fluid concentrations of prostaglandins, but no prostamides, were higher in women with preterm labor and intra-amniotic infection than in other patients with an episode of preterm labor. Yet, women with intra-amniotic infection had greater amniotic fluid concentrations of PGE<sub>2</sub> and PGF<sub>2α</sub> than those with sterile intra-amniotic inflammation, suggesting that these two clinical conditions may be differentiated by using mass spectrometric analysis of amniotic fluid.