cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.5692789.v2
title: Initial.m from Dynamics and locomotion of flexible foils in a frictional environment.

description: Over the past few decades, oscillating flexible foils have been used to study the physics of organismal propulsion in different fluid environments. Here, we extend this work to a study of flexible foils in a frictional environment. When the foil is oscillated by heaving at one end but is not free to locomote, the dynamics change from periodic to non-periodic and chaotic as the heaving amplitude increases or the bending rigidity decreases. For friction coefficients lying in a certain range, the transition passes through a sequence of <i>N</i>-periodic and asymmetric states before reaching chaotic dynamics. Resonant peaks are damped and shifted by friction and large heaving amplitudes, leading to bistable states. When the foil is free to locomote, the horizontal motion smoothes the resonant behaviours. For moderate frictional coefficients, steady but slow locomotion is obtained. For large transverse friction and small tangential friction corresponding to wheeled snake robots, faster locomotion is obtained. Travelling wave motions arise spontaneously, and move with horizontal speeds that scale as transverse friction coefficient to the coefficient to and input power that scales as the transverse friction coefficient to the power 5/12. These scalings are consistent with a boundary layer form of the solutions near the foil's leading edge.
DOI: 10.6084/m9.figshare.5696485
title: MOESM2 of MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNAâ disease association prediction

description: Additional file 2. We further applied MKRMDA to predict candidate miRNAs for all the diseases involved in HMDDv2.0. Prediction results were publicly released for further research and experimental validation.
DOI: 10.6084/m9.figshare.5696485.v1
title: MOESM2 of MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNAâ disease association prediction

description: Additional file 2. We further applied MKRMDA to predict candidate miRNAs for all the diseases involved in HMDDv2.0. Prediction results were publicly released for further research and experimental validation.
DOI: 10.6084/m9.figshare.5696518
title: MOESM3 of MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNAâ disease association prediction

description: Additional file 3. The file of codes and data used in MKRMDA.
DOI: 10.6084/m9.figshare.5696518.v1
title: MOESM3 of MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNAâ disease association prediction

description: Additional file 3. The file of codes and data used in MKRMDA.
DOI: 10.6084/m9.figshare.5696836
title: MOESM2 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 2: Table S1. Correlations among DNA methylation-related enzymes in 5 mouse tissues. The expression of 7 DNA methylation-related enzymes in 5 mouse tissues was quantified by real-time qRT-PCR. The correlations between these enzymes are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696836.v1
title: MOESM2 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 2: Table S1. Correlations among DNA methylation-related enzymes in 5 mouse tissues. The expression of 7 DNA methylation-related enzymes in 5 mouse tissues was quantified by real-time qRT-PCR. The correlations between these enzymes are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696866
title: MOESM4 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 4: Table S2. Correlations among DNA methylation-related enzymes in 26 human tissues. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx dataset. The correlations among the expression levels of the 7 enzymes in 26 human tissues are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696866.v1
title: MOESM4 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 4: Table S2. Correlations among DNA methylation-related enzymes in 26 human tissues. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx dataset. The correlations among the expression levels of the 7 enzymes in 26 human tissues are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696884
title: MOESM5 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 5: Table S3. Correlations among DNA methylation-related enzymes in testis and ovary tissues. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx dataset. The correlations among the expression levels of the 7 enzymes are analyzed and shown.
DOI: 10.6084/m9.figshare.5696884.v1
title: MOESM5 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 5: Table S3. Correlations among DNA methylation-related enzymes in testis and ovary tissues. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx dataset. The correlations among the expression levels of the 7 enzymes are analyzed and shown.
DOI: 10.6084/m9.figshare.5696896
title: MOESM6 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 6: Table S4. Correlations among DNA methylation-related enzymes, ADH1A and CYP4B1 after normalization. The RNA-Seq gene expression data were obtained from the GTEx dataset and normalized to β-actin mRNA expression. The correlations among the expression levels of the 7 DNA methylation-related enzymes as well as between ADH1A or CYP4B1 and DNA methylation-related enzymes in 26 human tissues are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696896.v1
title: MOESM6 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 6: Table S4. Correlations among DNA methylation-related enzymes, ADH1A and CYP4B1 after normalization. The RNA-Seq gene expression data were obtained from the GTEx dataset and normalized to β-actin mRNA expression. The correlations among the expression levels of the 7 DNA methylation-related enzymes as well as between ADH1A or CYP4B1 and DNA methylation-related enzymes in 26 human tissues are analyzed and shown. A multiple-testing correction was done by using Benjamini–Hochberg procedure. The corrected p value is shown only when the p value was changed from &lt; 0.05 to &gt; 0.05 after the correction.
DOI: 10.6084/m9.figshare.5696908
title: MOESM7 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 7: Table S5. Correlations among DNA methylation-related enzymes in blood and leukemia. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx and TCGA dataset. The correlations among the expression levels of the 7 enzymes are analyzed and shown.
DOI: 10.6084/m9.figshare.5696908.v1
title: MOESM7 of Uncoordinated expression of DNA methylation-related enzymes in human cancer

description: Additional file 7: Table S5. Correlations among DNA methylation-related enzymes in blood and leukemia. The RNA-Seq gene expression data of 7 DNA methylation-related enzymes were obtained from the GTEx and TCGA dataset. The correlations among the expression levels of the 7 enzymes are analyzed and shown.
DOI: 10.6084/m9.figshare.5697121
title: Additional file 2: Table S2. of CRISPR/Cas9-mediated targeted chromosome elimination

description: Unique and multiple repeated sequences for targeting by a single specific sgRNA. (XLSX 9524 kb)
DOI: 10.6084/m9.figshare.5697121.v1
title: Additional file 2: Table S2. of CRISPR/Cas9-mediated targeted chromosome elimination

description: Unique and multiple repeated sequences for targeting by a single specific sgRNA. (XLSX 9524 kb)
DOI: 10.6084/m9.figshare.5697124
title: Additional file 3: Table S5. of CRISPR/Cas9-mediated targeted chromosome elimination

description: Summary of genomic variations and filtering steps. (XLSX 19 kb)
DOI: 10.6084/m9.figshare.5697124.v1
title: Additional file 3: Table S5. of CRISPR/Cas9-mediated targeted chromosome elimination

description: Summary of genomic variations and filtering steps. (XLSX 19 kb)
DOI: 10.6084/m9.figshare.5697139
title: Additional file 4: of Anti-fibrotic, anti-VEGF or radiotherapy treatments as adjuvants for pterygium excision: a systematic review and network meta-analysis

description: Original recurrence data of this study (XLS 18 kb)
DOI: 10.6084/m9.figshare.5697139.v1
title: Additional file 4: of Anti-fibrotic, anti-VEGF or radiotherapy treatments as adjuvants for pterygium excision: a systematic review and network meta-analysis

description: Original recurrence data of this study (XLS 18 kb)
DOI: 10.6084/m9.figshare.5697151
title: Additional file 1: of Femoral nonunion with segmental bone defect treated by distraction osteogenesis with monolateral external fixation

description: This is the raw data.(XLSX 14Â kb)
DOI: 10.6084/m9.figshare.5697151.v1
title: Additional file 1: of Femoral nonunion with segmental bone defect treated by distraction osteogenesis with monolateral external fixation

description: This is the raw data.(XLSX 14Â kb)
DOI: 10.6084/m9.figshare.5697169
title: Additional file 2: of Optimization of the prescription isodose line for Gamma Knife radiosurgery using the shot within shot technique

description: Profile validation and patient data. (XLSX 35Â kb)
DOI: 10.6084/m9.figshare.5697169.v1
title: Additional file 2: of Optimization of the prescription isodose line for Gamma Knife radiosurgery using the shot within shot technique

description: Profile validation and patient data. (XLSX 35Â kb)
DOI: 10.6084/m9.figshare.5701465
title: Additional file 1: of Prevalence and serotype distribution of nasopharyngeal carriage of Streptococcus pneumoniae in China: a meta-analysis

description: Data base. (XLSX 25 kb)
DOI: 10.6084/m9.figshare.5701465.v1
title: Additional file 1: of Prevalence and serotype distribution of nasopharyngeal carriage of Streptococcus pneumoniae in China: a meta-analysis

description: Data base. (XLSX 25 kb)
DOI: 10.6084/m9.figshare.5701747
title: Additional file 1: Table S1. of Long non-coding RNA MALAT1 protects preterm infants with bronchopulmonary dysplasia by inhibiting cell apoptosis

description: List of differentially expressed genes in GSE25286 (XLSX 157 kb)
DOI: 10.6084/m9.figshare.5701747.v1
title: Additional file 1: Table S1. of Long non-coding RNA MALAT1 protects preterm infants with bronchopulmonary dysplasia by inhibiting cell apoptosis

description: List of differentially expressed genes in GSE25286 (XLSX 157 kb)
DOI: 10.6084/m9.figshare.5701765
title: Additional file 2: Table S2. of Long non-coding RNA MALAT1 protects preterm infants with bronchopulmonary dysplasia by inhibiting cell apoptosis

description: List of differentially expressed genes in GSE43830 (XLSX 56 kb)
DOI: 10.6084/m9.figshare.5701765.v1
title: Additional file 2: Table S2. of Long non-coding RNA MALAT1 protects preterm infants with bronchopulmonary dysplasia by inhibiting cell apoptosis

description: List of differentially expressed genes in GSE43830 (XLSX 56 kb)
DOI: 10.6084/m9.figshare.5705335
title: Additional file 10: Table S7. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of bile acids and microbiota in control and HFD groups. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.5705335.v1
title: Additional file 10: Table S7. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of bile acids and microbiota in control and HFD groups. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.5705347
title: Additional file 12: Table S8. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Time dependent alteration of bile acids and microbes in control and HFD groups. (XLSX 33 kb)
DOI: 10.6084/m9.figshare.5705347.v1
title: Additional file 12: Table S8. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Time dependent alteration of bile acids and microbes in control and HFD groups. (XLSX 33 kb)
DOI: 10.6084/m9.figshare.5705356
title: Additional file 14: Table S9. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of bile acids and microbiota in control and controlâ +â BA groups. (XLSX 22 kb)
DOI: 10.6084/m9.figshare.5705356.v1
title: Additional file 14: Table S9. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of bile acids and microbiota in control and controlâ +â BA groups. (XLSX 22 kb)
DOI: 10.6084/m9.figshare.5705368
title: Additional file 16: Table S10. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Feed ingredients. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.5705368.v1
title: Additional file 16: Table S10. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Feed ingredients. (XLSX 10 kb)
DOI: 10.6084/m9.figshare.5705374
title: Additional file 1: Table S1. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Physiological and biochemical parameters of mice in each group. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.5705374.v1
title: Additional file 1: Table S1. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Physiological and biochemical parameters of mice in each group. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.5705386
title: Additional file 3: Table S2. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Metabolome profile of caecal content in control and HFD groups at 8Â weeks. (XLSX 39 kb)
DOI: 10.6084/m9.figshare.5705386.v1
title: Additional file 3: Table S2. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Metabolome profile of caecal content in control and HFD groups at 8Â weeks. (XLSX 39 kb)
DOI: 10.6084/m9.figshare.5705395
title: Additional file 4: Table S3. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of metabolite types and microbial phyla. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.5705395.v1
title: Additional file 4: Table S3. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Correlation of metabolite types and microbial phyla. (XLSX 16 kb)
DOI: 10.6084/m9.figshare.5705398
title: Additional file 5: Table S4. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Concentrations of bile acids in caecal content in each group. (XLSX 48 kb)
DOI: 10.6084/m9.figshare.5705398.v1
title: Additional file 5: Table S4. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Concentrations of bile acids in caecal content in each group. (XLSX 48 kb)
DOI: 10.6084/m9.figshare.5705407
title: Additional file 7: Table S5. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Microbiota at phylum levels in each group. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5705407.v1
title: Additional file 7: Table S5. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Microbiota at phylum levels in each group. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5705413
title: Additional file 9: Table S6. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Linear discriminant analysis effect size analysis of microbiota in control and HFD groups. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.5705413.v1
title: Additional file 9: Table S6. of Bile acid is a significant host factor shaping the gut microbiome of diet-induced obese mice

description: Linear discriminant analysis effect size analysis of microbiota in control and HFD groups. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.5706067
title: Genome-wide analysis of WRKY transcription factors and their response to abiotic stress in celery (<i>Apium graveolens</i> L.)

description: Celery (<i>Apium graveolens</i> L.) is rich in nutrient substances and is cultivated worldwide. WRKY protein family is one of the largest transcription factor families in plants and is involved in growth and development, signal transduction, senescence and stress resistance. In this study, we identified 69 celery WRKY family transcription factors based on conserved WRKY domain sequence from the celery genomic and transcriptomic data. Phylogenetic analysis of celery and <i>Arabidopsis</i> WRKY proteins divided these celery WRKY proteins into three groups, seven subgroups. The numbers of WRKY proteins in celery and other 16 species were compared to illustrate the evolution of WRKY transcription factors in plants. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyse the expression profiles of five selected <i>AgWRKY</i> genes under abiotic stress (abscisic acid, cold, drought and salt) treatments. The qRT-PCR results indicated that those selected <i>AgWRKY</i> family genes were upregulated under abiotic stress treatments. This study provides a basic research for further investigation of the structure and function of <i>AgWRKY</i> genes.
DOI: 10.6084/m9.figshare.5706067.v1
title: Genome-wide analysis of WRKY transcription factors and their response to abiotic stress in celery (<i>Apium graveolens</i> L.)

description: Celery (<i>Apium graveolens</i> L.) is rich in nutrient substances and is cultivated worldwide. WRKY protein family is one of the largest transcription factor families in plants and is involved in growth and development, signal transduction, senescence and stress resistance. In this study, we identified 69 celery WRKY family transcription factors based on conserved WRKY domain sequence from the celery genomic and transcriptomic data. Phylogenetic analysis of celery and <i>Arabidopsis</i> WRKY proteins divided these celery WRKY proteins into three groups, seven subgroups. The numbers of WRKY proteins in celery and other 16 species were compared to illustrate the evolution of WRKY transcription factors in plants. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to analyse the expression profiles of five selected <i>AgWRKY</i> genes under abiotic stress (abscisic acid, cold, drought and salt) treatments. The qRT-PCR results indicated that those selected <i>AgWRKY</i> family genes were upregulated under abiotic stress treatments. This study provides a basic research for further investigation of the structure and function of <i>AgWRKY</i> genes.
DOI: 10.6084/m9.figshare.5708752
title: Additional file 1: of High mobility group box 1 promotes sorafenib resistance in HepG2 cells and in vivo

description: FCM of Figs.Â 2C and 3C. The Excel spreadsheet shows the flow cytometry results of the shHMGB1 HepG2, HMGB1-overexpressing HepG2 cells and corresponding control cells with or without the sorafenib treatment. (CSV 1Â kb)
DOI: 10.6084/m9.figshare.5708752.v1
title: Additional file 1: of High mobility group box 1 promotes sorafenib resistance in HepG2 cells and in vivo

description: FCM of Figs.Â 2C and 3C. The Excel spreadsheet shows the flow cytometry results of the shHMGB1 HepG2, HMGB1-overexpressing HepG2 cells and corresponding control cells with or without the sorafenib treatment. (CSV 1Â kb)
DOI: 10.6084/m9.figshare.5708854
title: Additional file 1: of Learning effects of different training models for border molding from the perspective of dental students

description: The questionnaire in Japanese and English: the questionnaire consisted of 21 items regarding their overall practical training and their clinical comprehension of border molding. (XLS 42 kb)
DOI: 10.6084/m9.figshare.5708854.v1
title: Additional file 1: of Learning effects of different training models for border molding from the perspective of dental students

description: The questionnaire in Japanese and English: the questionnaire consisted of 21 items regarding their overall practical training and their clinical comprehension of border molding. (XLS 42 kb)
DOI: 10.6084/m9.figshare.5708878
title: Additional file 2: of Learning effects of different training models for border molding from the perspective of dental students

description: The questionnaire results: The raw data used in this study. Each subject rated on a five-point Likert scale. (XLSX 22 kb)
DOI: 10.6084/m9.figshare.5708878.v1
title: Additional file 2: of Learning effects of different training models for border molding from the perspective of dental students

description: The questionnaire results: The raw data used in this study. Each subject rated on a five-point Likert scale. (XLSX 22 kb)
DOI: 10.6084/m9.figshare.5709241
title: Additional file 4: Table S1. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Module identified by CoReg in yeast-1-hybrid network. 0 means no module assignment (XLSX 18 kb)
DOI: 10.6084/m9.figshare.5709241.v1
title: Additional file 4: Table S1. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Module identified by CoReg in yeast-1-hybrid network. 0 means no module assignment (XLSX 18 kb)
DOI: 10.6084/m9.figshare.5709250
title: Additional file 5: Table S2. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Edges connecting each module member with their first neighbors (XLSX 193 kb)
DOI: 10.6084/m9.figshare.5709250.v1
title: Additional file 5: Table S2. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Edges connecting each module member with their first neighbors (XLSX 193 kb)
DOI: 10.6084/m9.figshare.5709262
title: Additional file 6: Table S3. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Experiments used in co-expression analysis of DAP-seq network (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5709262.v1
title: Additional file 6: Table S3. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Experiments used in co-expression analysis of DAP-seq network (XLSX 12 kb)
DOI: 10.6084/m9.figshare.5709274
title: Additional file 7: Table S4. of Identification of regulatory modules in genome scale transcription regulatory networks

description: Co-expression levels (PCC) for each CoReg modules. This table is ordered in the same way as the heatmap (XLSX 92 kb)