cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16699481.v2
title: Orthodenticle homeobox OTX1 is a potential prognostic biomarker for bladder cancer

description: Bladder cancer (BC) is one of the most aggressive tumors worldwide. OTX1 (orthodenticle homeobox 1) is an important transcription factor involved in various diseases, such as cancers. The aim of this study was to further investigate the role of OTX1 in BC. In this study, differentially expressed genes (DEGs) were screened from tumor tissues and para-cancerous tissues by bioinformatics. The expression of protein and RNA was separately detected by western blotting and immunohistochemistry (IHC), and quantitative polymerase chain reaction (qPCR); cell viability and cell growth were determined by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and clone formation assays, respectively; cell motility was measured by transwell and wound healing assays; cell cycle was measured by flow cytometry. In this study, 9 DEGs were screened out, and OTX1 was employed as a candidate gene for subsequent study. Results found that OTX1 was highly expressed in BC cells and BC tissues, which was significantly associated with poor prognosis of patients. In addition, OTX1 silencing significantly reduced cell viability, and inhibited cell growth and motility, while OTX1 overexpression got opposite results. Moreover, OTX1 co-expressed genes were enriched in cell cycle-related pathways, suggesting that the role of OTX1 in BC may be related to cell cycle, which was confirmed by flow cytometry analysis. Furthermore, <i>in vivo</i> experiments showed that OTX1 silencing significantly inhibited tumor growth in tumor-bearing mice. Taken together, our findings suggested that OTX1 may play a promotional role in BC progression.
DOI: 10.5281/zenodo.10534482
title: Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2021/2022 – Hungary

description: This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.

Reporting authorities contributing to 2022 AMR data collection: National Food Chain Safety Office
description: HU; xlsx; zoonoses_support@efsa.europa.eu
DOI: 10.5281/zenodo.10534483
title: Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2021/2022 – Hungary

description: This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.

Reporting authorities contributing to 2022 AMR data collection: National Food Chain Safety Office
description: HU; xlsx; zoonoses_support@efsa.europa.eu
DOI: 10.6084/m9.figshare.19320938
title: Additional file 3 of The spinach YY genome reveals sex chromosome evolution, domestication, and introgression history of the species

description: Additional file 3. Supplementary tables. Table S1. Sequencing information of “Cornell-NO.9” (YY) genome. Table S2. Comparison of contig assemblies among three genomes of Spinacia accessions. Table S3. Statistics of Hi-C mapping of “Cornell-NO.9” (YY) genome. Table S4. Assembly and annotation evaluation in S. oleracea cultivar “Cornell-NO.9” (YY) and “Viroflay” (XX) genomes. Table S5. TE annotation of S. oleracea cultivar “Cornell-NO. 9” (YY) and “Viroflay” (XX) genomes. Table S6. Summary of sequencing statistics and sex phenotypes (F: female; M: male) in F1 population. Table S7. Summary of genes identified from SDR (MSY) and X-counterpart. Table S8. Gene information of sex-determination region (SDR) of Y chromosome. Table S9. Gene information of X-counterpart of SDR in X chromosome. Table S10. Statistics summary of Pacbio reads mapping onto junction regions of structure variations within SDR. Table S11. SDR genes having sex-biased expression during flower development. Table S12. Expression of genes that are mentioned in proposed sex-determination/differentiation pathway. Table S13. Accessions of spinach S. oleracea and wild sibling species used for resequencing and population genomics as well as sex-chromosome analyses. Table S14. Details of genes in a swept region with strong selection signals on Chr3:106112216-108343948. Table S15. D statistic values of ABBA-BABA tests for different geographical regions of S. oleracea and wild species. Table S16. Introgression loci with strong signals from S. tetrandra, S. turkestanica and India cultivars respectively. Table S17. Primers used in qRT-PCR for validating the expression patterns of key candidate genes found in this study [123–132].
DOI: 10.6084/m9.figshare.19320938.v2
title: Additional file 3 of The spinach YY genome reveals sex chromosome evolution, domestication, and introgression history of the species

description: Additional file 3. Supplementary tables. Table S1. Sequencing information of “Cornell-NO.9” (YY) genome. Table S2. Comparison of contig assemblies among three genomes of Spinacia accessions. Table S3. Statistics of Hi-C mapping of “Cornell-NO.9” (YY) genome. Table S4. Assembly and annotation evaluation in S. oleracea cultivar “Cornell-NO.9” (YY) and “Viroflay” (XX) genomes. Table S5. TE annotation of S. oleracea cultivar “Cornell-NO. 9” (YY) and “Viroflay” (XX) genomes. Table S6. Summary of sequencing statistics and sex phenotypes (F: female; M: male) in F1 population. Table S7. Summary of genes identified from SDR (MSY) and X-counterpart. Table S8. Gene information of sex-determination region (SDR) of Y chromosome. Table S9. Gene information of X-counterpart of SDR in X chromosome. Table S10. Statistics summary of Pacbio reads mapping onto junction regions of structure variations within SDR. Table S11. SDR genes having sex-biased expression during flower development. Table S12. Expression of genes that are mentioned in proposed sex-determination/differentiation pathway. Table S13. Accessions of spinach S. oleracea and wild sibling species used for resequencing and population genomics as well as sex-chromosome analyses. Table S14. Details of genes in a swept region with strong selection signals on Chr3:106112216-108343948. Table S15. D statistic values of ABBA-BABA tests for different geographical regions of S. oleracea and wild species. Table S16. Introgression loci with strong signals from S. tetrandra, S. turkestanica and India cultivars respectively. Table S17. Primers used in qRT-PCR for validating the expression patterns of key candidate genes found in this study [123–132].
DOI: 10.6084/m9.figshare.19320938.v1
title: Additional file 3 of The spinach YY genome reveals sex chromosome evolution, domestication, and introgression history of the species

description: Additional file 3. Supplementary tables. Table S1. Sequencing information of “Cornell-NO.9” (YY) genome. Table S2. Comparison of contig assemblies among three genomes of Spinacia accessions. Table S3. Statistics of Hi-C mapping of “Cornell-NO.9” (YY) genome. Table S4. Assembly and annotation evaluation in S. oleracea cultivar “Cornell-NO.9” (YY) and “Viroflay” (XX) genomes. Table S5. TE annotation of S. oleracea cultivar “Cornell-NO. 9” (YY) and “Viroflay” (XX) genomes. Table S6. Summary of sequencing statistics and sex phenotypes (F: female; M: male) in F1 population. Table S7. Summary of genes identified from SDR (MSY) and X-counterpart. Table S8. Gene information of sex-determination region (SDR) of Y chromosome. Table S9. Gene information of X-counterpart of SDR in X chromosome. Table S10. Statistics summary of Pacbio reads mapping onto junction regions of structure variations within SDR. Table S11. SDR genes having sex-biased expression during flower development. Table S12. Expression of genes that are mentioned in proposed sex-determination/differentiation pathway. Table S13. Accessions of spinach S. oleracea and wild sibling species used for resequencing and population genomics as well as sex-chromosome analyses. Table S14. Details of genes in a swept region with strong selection signals on Chr3:106112216-108343948. Table S15. D statistic values of ABBA-BABA tests for different geographical regions of S. oleracea and wild species. Table S16. Introgression loci with strong signals from S. tetrandra, S. turkestanica and India cultivars respectively. Table S17. Primers used in qRT-PCR for validating the expression patterns of key candidate genes found in this study [123–132].
DOI: 10.6084/m9.figshare.16699522.v1
title: Circ_0004018 suppresses cell proliferation and migration in hepatocellular carcinoma via miR-1197/PTEN/PI3K/AKT signaling pathway

description: Hepatocellular carcinoma (HCC) is a common type of primary liver cancer. Circular RNAs (circRNAs) have been demonstrated to be a crucial player in multiple cancers. However, a large number of circRNAs remain to be explored. Our study focused on investigating hsa_circ_0004018 in HCC. Firstly, we conducted quantitative reverse transcription PCR (RT-qPCR) to find that circ_0004018 was down-regulated in HCC cells. Western blot analysis was performed to detect the protein levels of phosphatase and tensin homologue (PTEN) and related factors of PI3K/AKT signaling pathway. From the results of functional assays, we found that overexpression of circ_0004018 significantly inhibited the proliferative and migratory capacities of HCC cells. The regulatory mechanism of circ_0004018 in HCC was determined by RNA immunoprecipitation (RIP), RNA pull-down, and luciferase reporter assays, thereby we knew that circ_0004018 regulated PTEN by sequestering microRNA-1197 (miR-1197) to modulate PI3K/AKT signaling pathway. Finally, rescue assays verified that circ_0004018 participated in modulation of cell proliferation and migration in HCC via sponging miR-1197 and regulating PTEN. In conclusion, circ_0004018 suppresses the proliferation and migration of HCC cells via sponging miR-1197 to inactivate the PTEN/PI3K/AKT signaling pathway. <b>Abbreviations</b> HCC: Hepatocellular carcinoma; circRNAs: Circular RNAs; PTEN: Phosphatase and tensin homologue; miR-1197: microRNA-1197; ceRNA: competitive endogenous RNA; ATCC: American Type Culture Collection; EMEM: Eagle’s Minimum Essential Medium; RT-qPCR: Quantitative real-time PCR; EdU: 5-ethynyl-20-deoxyuridine; FISH: Fluorescent <i>in situ</i> hybridization; RIP: RNA immunoprecipitation; 3ʹ-UTR: 3ʹ-untranslated region; Wt: wild-type; Mut; mutant type; gDNA: genomic DNA; Act D: Actinomycin D; PI3K: phosphatidylinositol-3-kinase; AKT: protein kinase; lncRNAs: long non-coding RNAs
DOI: 10.6084/m9.figshare.24754497.v1
title: Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

description: This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications. Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein–protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed. A total of 686 DEGs were identified between the two groups—of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA–mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs. FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia <i>via</i> its action potential function.
DOI: 10.6084/m9.figshare.24754497
title: Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

description: This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications. Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein–protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed. A total of 686 DEGs were identified between the two groups—of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA–mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs. FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia <i>via</i> its action potential function.
DOI: 10.6084/m9.figshare.11980941
title: Supporting Information-Al2O3-MoS2-on-GaN-Photonics Research ref.doc

description: Raman and Absorbance of multilayer MoS2
DOI: 10.6084/m9.figshare.11980941.v1
title: Supporting Information-Al2O3-MoS2-on-GaN-Photonics Research ref.doc

description: Raman and Absorbance of multilayer MoS2
DOI: 10.6084/m9.figshare.7262363
title: Additional file 11: of OsWRKY67 positively regulates blast and bacteria blight resistance by direct activation of PR genes in rice

description: Table S3. Gene ontology analysis of up- and down-regulated genes in OsWRKY67-overexpressing plants detected by RNA-seq analysis. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.7262363.v1
title: Additional file 11: of OsWRKY67 positively regulates blast and bacteria blight resistance by direct activation of PR genes in rice

description: Table S3. Gene ontology analysis of up- and down-regulated genes in OsWRKY67-overexpressing plants detected by RNA-seq analysis. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.7262372
title: Additional file 12: of OsWRKY67 positively regulates blast and bacteria blight resistance by direct activation of PR genes in rice

description: Table S4. A statistic pathway enrichment analysis of differentially expressed genes between OsWRKY67-overexpressing plants and Nipponbare plants. (XLSX 11 kb)
DOI: 10.5281/zenodo.10534463
title: Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2021/2022 – Belgium

description: This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.

Reporting authorities contributing to 2022 AMR data collection SCIENSANO
description: BE; xlsx; zoonoses_support@efsa.europa.eu
DOI: 10.5281/zenodo.10534464
title: Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2021/2022 – Belgium

description: This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision (EU) 2020/1729. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision (EU) 2020/1729. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.

Reporting authorities contributing to 2022 AMR data collection SCIENSANO
description: BE; xlsx; zoonoses_support@efsa.europa.eu
DOI: 10.6084/m9.figshare.7262372.v1
title: Additional file 12: of OsWRKY67 positively regulates blast and bacteria blight resistance by direct activation of PR genes in rice

description: Table S4. A statistic pathway enrichment analysis of differentially expressed genes between OsWRKY67-overexpressing plants and Nipponbare plants. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.25303960
title: Experimental and simulation data used to produce the manuscript figures

description: This Microsoft Excel file tabulates all the <i>in vivo</i> and <i>in silico</i> results that were produced in this study.
DOI: 10.6084/m9.figshare.25303960.v1
title: Experimental and simulation data used to produce the manuscript figures

description: This Microsoft Excel file tabulates all the <i>in vivo</i> and <i>in silico</i> results that were produced in this study.
DOI: 10.6084/m9.figshare.11980971
title: Input data

description: Raw input data from wearable sensors.
DOI: 10.6084/m9.figshare.19317015
title: Different thresholding methods on Nearest Shrunken Centroid algorithm

description: This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features.
DOI: 10.6084/m9.figshare.19317015.v1
title: Different thresholding methods on Nearest Shrunken Centroid algorithm

description: This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features.
DOI: 10.6084/m9.figshare.19493550
title: Numerical characterization of support recovery in sparse regression with correlated design

description: Sparse regression is employed in diverse scientific settings as a feature selection method. A pervasive aspect of scientific data is the presence of correlations between predictive features. These correlations hamper both feature selection and estimation and jeopardize conclusions drawn from estimated models. On the other hand, theoretical results on sparsity-inducing regularized regression have largely addressed conditions for selection consistency via asymptotics, and disregard the problem of model selection, whereby regularization parameters are chosen. In this numerical study, we address these issues through exhaustive characterization of the performance of several regression estimators, coupled with a range of model selection strategies. These estimators and selection criteria were examined across correlated regression problems with varying degrees of signal to noise, distributions of non-zero model coefficients, and model sparsity. Our results reveal a fundamental tradeoff between false positive and false negative control in all regression estimators and model selection criteria examined. Additionally, we numerically explore a transition point modulated by the signal-to-noise ratio and spectral properties of the design covariance matrix at which the selection accuracy of all considered algorithms degrades. Overall, we find that SCAD coupled with BIC or empirical Bayes model selection performs the best feature selection across the regression problems considered.