cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19200204
title: Additional file 1 of Genetic diversity and multidrug resistance of phylogenic groups B2 and D in InPEC and ExPEC isolated from chickens in Central China

description: Additional file 1 Supplement Table 1. The results of resistance phenotypes and antimicrobial resistance genes detected in 58 E. coli isolates.
DOI: 10.6084/m9.figshare.19200204.v1
title: Additional file 1 of Genetic diversity and multidrug resistance of phylogenic groups B2 and D in InPEC and ExPEC isolated from chickens in Central China

description: Additional file 1 Supplement Table 1. The results of resistance phenotypes and antimicrobial resistance genes detected in 58 E. coli isolates.
DOI: 10.6084/m9.figshare.19200253
title: Additional file 2 of Revealing potential anti-fibrotic mechanism of Ganxianfang formula based on RNA sequence

description: Additional file 2: Table S2. Detailed information and detection map of the main compounds.
DOI: 10.6084/m9.figshare.19200253.v1
title: Additional file 2 of Revealing potential anti-fibrotic mechanism of Ganxianfang formula based on RNA sequence

description: Additional file 2: Table S2. Detailed information and detection map of the main compounds.
DOI: 10.6084/m9.figshare.19200256
title: Additional file 3 of Revealing potential anti-fibrotic mechanism of Ganxianfang formula based on RNA sequence

description: Additional file 3: Table S3. KEGG pathways of DEGs with opposite trends in GXF and CCl4 group.
DOI: 10.6084/m9.figshare.19200256.v1
title: Additional file 3 of Revealing potential anti-fibrotic mechanism of Ganxianfang formula based on RNA sequence

description: Additional file 3: Table S3. KEGG pathways of DEGs with opposite trends in GXF and CCl4 group.
DOI: 10.6084/m9.figshare.19200307
title: Comparison of genetically modified insect-resistant maize and non-transgenic maize revealed changes in soil metabolomes but not in rhizosphere bacterial community

description: The deliberate introduction of the beneficial gene in crop plants through transgenic technology can provide enormous agricultural and economic benefits. However, the impact of commercialization of these crops on the ecosystem particularly on belowground soil biodiversity is still uncertain. Here, we examined and compared the effects of a non-transgenic maize cultivar and an insect-resistant transgenic maize cultivar genetically engineered with <i>cry1Ah</i> gene from <i>Bacillus thuringiensis</i>, on the rhizosphere bacterial community using 16S rDNA amplicon sequencing and soil metabolome profile using UPLC/MS analysis at six different growth stages. We found no significant differences in bacterial community composition and diversity at all growth stages between the two cultivars. The analysis of bacterial beta-diversity showed an evident difference in community structure attributed to plant different growth stages but not to the plant type. In contrast, the soil metabolic profile of transgenic maize differed from that of the non-transgenic plant at some growth stages, and most of the altered metabolites were usually related to the metabolism but not to the plant-microbe interaction related pathways. These results suggest that genetic modification with the <i>cry1Ah</i> gene-altered maize soil metabolism but had no obvious effect on the rhizosphere bacterial community.
DOI: 10.6084/m9.figshare.10287442
title: CEY_annotation

description: Gene structure
annotation, repeat predictions and gene functional annotation<br>
DOI: 10.6084/m9.figshare.8190329
title: Additional file 1: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Primers sequence (xlsx 13â kb). Information of primers used in RT-qPCR experiments. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.8190329.v1
title: Additional file 1: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Primers sequence (xlsx 13â kb). Information of primers used in RT-qPCR experiments. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.8190332
title: Additional file 2: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RNA-seq reads of cervical cancer samples (xlsx 13â kb). Summary of RNA-seq reads of cervical cancer samples obtained from TCGA database used in alternative splicing analysis. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.8190332.v1
title: Additional file 2: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RNA-seq reads of cervical cancer samples (xlsx 13â kb). Summary of RNA-seq reads of cervical cancer samples obtained from TCGA database used in alternative splicing analysis. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.8190341
title: Additional file 4: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RNA-seq reads of HeLa cells samples (xlsx 12â kb). Summary of RNA-seq reads of HeLa cells samples used in alternative splicing analysis. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.8190341.v1
title: Additional file 4: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RNA-seq reads of HeLa cells samples (xlsx 12â kb). Summary of RNA-seq reads of HeLa cells samples used in alternative splicing analysis. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.8190344
title: Additional file 5: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Expressed gene number in HeLa cells (xlsx 10â kb). Statistics of expressed gene number from RNA-seq data of HeLa cells samples. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.8190344.v1
title: Additional file 5: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Expressed gene number in HeLa cells (xlsx 10â kb). Statistics of expressed gene number from RNA-seq data of HeLa cells samples. (XLSX 9 kb)
DOI: 10.6084/m9.figshare.8190347
title: Additional file 6: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: CRKL-KD vs Ctrl DEGs (xlsx 114â kb). Information of DEGs (differently expressed genes) between CRKL-knockdown and control samples of HeLa cells. (XLSX 113 kb)
DOI: 10.6084/m9.figshare.8190347.v1
title: Additional file 6: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: CRKL-KD vs Ctrl DEGs (xlsx 114â kb). Information of DEGs (differently expressed genes) between CRKL-knockdown and control samples of HeLa cells. (XLSX 113 kb)
DOI: 10.6084/m9.figshare.8190350
title: Additional file 7: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Alternative splicing events (xlsx 11â kb). Statistics of various types of alternative splicing events detected in CRKL-knockdown and control samples of HeLa cells. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8190350.v1
title: Additional file 7: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: Alternative splicing events (xlsx 11â kb). Statistics of various types of alternative splicing events detected in CRKL-knockdown and control samples of HeLa cells. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.8190353
title: Additional file 8: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: shCRKL_vs_Ctrl_RAS_p0.05. Information of RASEs (regulated alternative splicing events) between CRKL-knockdown and control samples of HeLa cells. (XLSX 136 kb)
DOI: 10.6084/m9.figshare.8190353.v1
title: Additional file 8: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: shCRKL_vs_Ctrl_RAS_p0.05. Information of RASEs (regulated alternative splicing events) between CRKL-knockdown and control samples of HeLa cells. (XLSX 136 kb)
DOI: 10.6084/m9.figshare.8190356
title: Additional file 9: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RAS GO enrichment and KEGG pathway (xlsx 45â kb). GO and KEGG pathway enrichment of RASEs (regulated alternative splicing events) between CRKL-knockdown and control samples of HeLa cells. (XLSX 44 kb)
DOI: 10.6084/m9.figshare.8190356.v1
title: Additional file 9: of CRKL regulates alternative splicing of cancer-related genes in cervical cancer samples and HeLa cell

description: RAS GO enrichment and KEGG pathway (xlsx 45â kb). GO and KEGG pathway enrichment of RASEs (regulated alternative splicing events) between CRKL-knockdown and control samples of HeLa cells. (XLSX 44 kb)
DOI: 10.6084/m9.figshare.25242952
title: Supplementary Material for: Identification of TNF-α as major susceptible risk locus for vitiligo: A systematic review and meta-analysis study in Asian population.

description: Abstract

Introduction: 
Vitiligo is a common depigmentation disorder characterized by defined white patches on the skin and affecting around 0.5 % to 2% of the general population. Genetic association studies have identified several pre-disposing genes and single nucleotide polymorphisms (SNPs) for vitiligo pathogenesis; nonetheless, the reports are often conflicting, and rarely conclusive. This comprehensive meta-analysis study was designed to evaluate the effect of the risk variants on vitiligo aetiology and covariate stratified vitiligo risk in the Asian population considering all the studies published so far.


Methods: 
We followed a systematic and comprehensive search to identify the relevant vitiligo related candidate gene association studies in PubMed using specific keywords. 
After data extraction, we calculated, for the variants involved, the study-level unadjusted odds ratio (OR), standard errors (SE), and 95% confidence intervals (95% CI) by using logistic regression with additive, dominant effect and recessive models using R software package (R, 3.4.2) ‘metafor'. Subgroup analysis was performed using logistic regression (generalized linear model; ‘glm’) of disease status on subgroup-specific genotype counts. For a better understanding of the likely biological function of vitiligo-associated variant obtained through the meta-analysis, in silico functional analyses, through standard publicly available web tools, were also conducted. 

Results: 
Thirty-one vitiligo associated case control studies on eleven SNPs were analysed in our study. In the Fixed effect meta-analysis, one variant upstream of TNF-α gene: rs1800629 was found to be associated with vitiligo risk in additive (p=.4.26E-06), dominant (p=1.65E-7) and recessive model (p=.000453). After Benjamini–Hochberg false discovery rate (FDR) correction, rs1800629/TNF α was found to be significant at 5% FDR in the dominant (padj=1.82E-6) and recessive models (padj = .0049). In silico characterization revealed the prioritized variant to be regulatory in nature and thus having potential to contribute towards vitiligo pathogenesis.

Conclusion: 
Our study constitutes the first comprehensive meta-analysis of candidate gene-based association studies reported in the whole of Asian population, followed by an in silico analysis of the vitiligo associated variant. According to the findings of our study, TNF-α single nucleotide variant, rs1800629G&gt;A has a risk association, potentially contributing to vitiligo pathogenesis in Asian population.
DOI: 10.6084/m9.figshare.25242952.v1
title: Supplementary Material for: Identification of TNF-α as major susceptible risk locus for vitiligo: A systematic review and meta-analysis study in Asian population.

description: Abstract

Introduction: 
Vitiligo is a common depigmentation disorder characterized by defined white patches on the skin and affecting around 0.5 % to 2% of the general population. Genetic association studies have identified several pre-disposing genes and single nucleotide polymorphisms (SNPs) for vitiligo pathogenesis; nonetheless, the reports are often conflicting, and rarely conclusive. This comprehensive meta-analysis study was designed to evaluate the effect of the risk variants on vitiligo aetiology and covariate stratified vitiligo risk in the Asian population considering all the studies published so far.


Methods: 
We followed a systematic and comprehensive search to identify the relevant vitiligo related candidate gene association studies in PubMed using specific keywords. 
After data extraction, we calculated, for the variants involved, the study-level unadjusted odds ratio (OR), standard errors (SE), and 95% confidence intervals (95% CI) by using logistic regression with additive, dominant effect and recessive models using R software package (R, 3.4.2) ‘metafor'. Subgroup analysis was performed using logistic regression (generalized linear model; ‘glm’) of disease status on subgroup-specific genotype counts. For a better understanding of the likely biological function of vitiligo-associated variant obtained through the meta-analysis, in silico functional analyses, through standard publicly available web tools, were also conducted. 

Results: 
Thirty-one vitiligo associated case control studies on eleven SNPs were analysed in our study. In the Fixed effect meta-analysis, one variant upstream of TNF-α gene: rs1800629 was found to be associated with vitiligo risk in additive (p=.4.26E-06), dominant (p=1.65E-7) and recessive model (p=.000453). After Benjamini–Hochberg false discovery rate (FDR) correction, rs1800629/TNF α was found to be significant at 5% FDR in the dominant (padj=1.82E-6) and recessive models (padj = .0049). In silico characterization revealed the prioritized variant to be regulatory in nature and thus having potential to contribute towards vitiligo pathogenesis.

Conclusion: 
Our study constitutes the first comprehensive meta-analysis of candidate gene-based association studies reported in the whole of Asian population, followed by an in silico analysis of the vitiligo associated variant. According to the findings of our study, TNF-α single nucleotide variant, rs1800629G&gt;A has a risk association, potentially contributing to vitiligo pathogenesis in Asian population.
DOI: 10.6084/m9.figshare.19200426
title: Auditory predictions are phonological when phonetic information is variable

description: An emerging body of studies has claimed that exposure to phonetically varying speech sounds results in strictly phonological representations of speech sounds by the brain’s auditory prediction system (Cornell et al., 2011; Eulitz & Lahiri, 2004; Hestvik et al., 2020; Hestvik & Durvasula, 2016; Phillips et al., 2000). We test this claim by measuring mismatch negativity (MMN) to a subcategorical contrast with two sets of phonetically varying standards. When controlling for non-prediction related contributors to the MMN, we find a mismatch in a late time window. However, the mismatch effect is only significant for participants who perceived the contrast as an across-category contrast (/t/ vs /d/). Additionally, we find no modulation of mismatch amplitude predicated on phonetic distance between standards and deviant. Taken together, this indicates that the prediction generated by the auditory system in response to phonetically varying speech sounds is indeed phonological and lacks any fine-grained phonetic content.
DOI: 10.6084/m9.figshare.19200426.v1
title: Auditory predictions are phonological when phonetic information is variable

description: An emerging body of studies has claimed that exposure to phonetically varying speech sounds results in strictly phonological representations of speech sounds by the brain’s auditory prediction system (Cornell et al., 2011; Eulitz & Lahiri, 2004; Hestvik et al., 2020; Hestvik & Durvasula, 2016; Phillips et al., 2000). We test this claim by measuring mismatch negativity (MMN) to a subcategorical contrast with two sets of phonetically varying standards. When controlling for non-prediction related contributors to the MMN, we find a mismatch in a late time window. However, the mismatch effect is only significant for participants who perceived the contrast as an across-category contrast (/t/ vs /d/). Additionally, we find no modulation of mismatch amplitude predicated on phonetic distance between standards and deviant. Taken together, this indicates that the prediction generated by the auditory system in response to phonetically varying speech sounds is indeed phonological and lacks any fine-grained phonetic content.
DOI: 10.6084/m9.figshare.21822066
title: Modeling the Morphological Responses of the Yellow River Delta to the Water-Sediment Regulation Scheme (WSRS): The Role of Impulsive River Floods and Density-driven Flows

description:  
(1) Observed data for model validation
(2) Modeling results (river boundary conditions, topography, SSC, salinity, sediment fraction)
DOI: 10.6084/m9.figshare.8190608
title: Additional file 4: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S1. Pathway annotation of unigenes and the number of pathway from cracking atemoya pericarp. (XLSX 162 kb)
DOI: 10.6084/m9.figshare.8190608.v1
title: Additional file 4: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S1. Pathway annotation of unigenes and the number of pathway from cracking atemoya pericarp. (XLSX 162 kb)
DOI: 10.6084/m9.figshare.8190614
title: Additional file 5: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S2. RPKM of the all of the unigenes in cracking pericarp of atemoya. (XLS 11116 kb)
DOI: 10.6084/m9.figshare.8190614.v1
title: Additional file 5: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S2. RPKM of the all of the unigenes in cracking pericarp of atemoya. (XLS 11116 kb)
DOI: 10.6084/m9.figshare.8190629
title: Additional file 7: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S3. Primer sequences of the reference gene and candidate unigenes used in RT-qPCR analysis. (XLS 64 kb)
DOI: 10.6084/m9.figshare.8190629.v1
title: Additional file 7: of Transcriptome analysis of atemoya pericarp elucidates the role of polysaccharide metabolism in fruit ripening and cracking after harvest

description: Table S3. Primer sequences of the reference gene and candidate unigenes used in RT-qPCR analysis. (XLS 64 kb)
DOI: 10.6084/m9.figshare.19759868
title: Additional file 11 of Comparative whole-genome transcriptome analysis in renal cell populations reveals high tissue specificity of MAPK/ERK targets in embryonic kidney

description: Additional file 11: Table S10. qRT-PCR primer sequences.
DOI: 10.6084/m9.figshare.19759868.v1
title: Additional file 11 of Comparative whole-genome transcriptome analysis in renal cell populations reveals high tissue specificity of MAPK/ERK targets in embryonic kidney

description: Additional file 11: Table S10. qRT-PCR primer sequences.
DOI: 10.6084/m9.figshare.19759874
title: Additional file 2 of Comparative whole-genome transcriptome analysis in renal cell populations reveals high tissue specificity of MAPK/ERK targets in embryonic kidney

description: Additional file 2: Table S1. The differentially expressed genes in UBdko dataset.
DOI: 10.6084/m9.figshare.19759874.v1
title: Additional file 2 of Comparative whole-genome transcriptome analysis in renal cell populations reveals high tissue specificity of MAPK/ERK targets in embryonic kidney

description: Additional file 2: Table S1. The differentially expressed genes in UBdko dataset.
DOI: 10.6084/m9.figshare.19759877
title: Additional file 3 of Comparative whole-genome transcriptome analysis in renal cell populations reveals high tissue specificity of MAPK/ERK targets in embryonic kidney

description: Additional file 3: Table S2. The differentially expressed genes in MMdko dataset.
DOI: 10.5281/zenodo.7933223
title: Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques

description: Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.
description: Tran-SET Project: 21PLSU15
DOI: 10.5281/zenodo.7933222
title: Development of Distress Index Prediction Models for Rehabilitation Treatments in Louisiana Using Advanced Machine Learning Techniques

description: Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a machine-learning-based framework for states with a hot and humid climate that can predict the long-term field performance (for 11 years) of their asphalt (AC) overlays based on their key project conditions. Two machine learning algorithms were examined, namely Random Forest (RF) and CatBoost, and the one yielding a higher accuracy was considered. In this study, the well-known pavement condition index (PCI) was used as the pavement performance indicator. A total of 892 log miles of AC overlay data were obtained from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) database. Based on the collected data, six models were trained (for each algorithm) and validated to predict the future PCI of AC overlays for up to 11 years. Results indicated that the RF algorithm yielded higher accuracy than the CatBoost Algorithm and thus the RF-based models were considered in the proposed decision-making framework.
description: Tran-SET Project: 21PLSU15