cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14173407.v2
title: Graph-Based Equilibrium Metrics for Dynamic Supply–Demand Systems With Applications to Ride-sourcing Platforms

description: How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such coherence measurement is critically important for the quantification of the market efficiency and the comparison of different platform policies, such as dispatching. The aim of this paper is to introduce a graph-based equilibrium metric (GEM) to quantify the distance between demand and supply networks based on a weighted graph structure. We formulate GEM as the optimal objective value of an unbalanced optimal transport problem, which can be formulated as an equivalent linear programming and efficiently solved. We examine how the GEM can help solve three operational tasks of ride-sourcing platforms. The first one is that GEM achieves up to 70.6% reduction in root-mean-square error over the second-best distance measurement for the prediction accuracy of order answer rate. The second one is that the use of GEM for designing order dispatching policy increases drivers’ revenue for more than 1%, representing a huge improvement in number. The third one is that GEM can serve as an endpoint for comparing different platform policies in AB test. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.24136266
title: Additional file 2 of Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics

description: Additional file 2. Supplementary tables. Table S1: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of H7-ESC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE76523 and our produced data (GSE239918). Table S2: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of hiPSC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE35671. Table S3: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of hiPSC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE81585. Table S4: The top 50 predicted logics formed by TFs and the regulated genes during the early and late stages of hESC/hiPSC-derived cardiomyocytes. Table S5:  Heart target genes co-regulated by cardiac TFs and miRNAs on hESC/hiPSC CM differentiation. Table S6:  Heart target genes co-regulated by cardiac TFs and lncRNAs on hESC/hiPSC CM differentiation. Table S7: Heart target genes of TFs, lncRNAs and miRNAs in human fetal embryo-developed atrial cardiomyocytes using single cell RNA-seq data (GSE106118). Table S8: Heart target genes of TFs, lncRNAs and miRNAs in human fetal embryo-developed ventricular cardiomyocytes using single cell RNA-seq data (GSE106118). Table S9: The top 25 predicted logics formed by TFs and the regulated genes during fetal embryo-derived atrial and ventricular CM development. Table S10: The predicted heart target genes of TFs, miRNAs and lncRNAs from fetal embryo-derived atrial and ventricular CM development.
DOI: 10.6084/m9.figshare.24136266.v1
title: Additional file 2 of Integrated modeling framework reveals co-regulation of transcription factors, miRNAs and lncRNAs on cardiac developmental dynamics

description: Additional file 2. Supplementary tables. Table S1: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of H7-ESC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE76523 and our produced data (GSE239918). Table S2: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of hiPSC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE35671. Table S3: Target genes of TFs, lncRNAs and miRNAs during the early and late stages of hiPSC-derived cardiomyocytes, with mRNA expression data extracted from NCBI GSE81585. Table S4: The top 50 predicted logics formed by TFs and the regulated genes during the early and late stages of hESC/hiPSC-derived cardiomyocytes. Table S5:  Heart target genes co-regulated by cardiac TFs and miRNAs on hESC/hiPSC CM differentiation. Table S6:  Heart target genes co-regulated by cardiac TFs and lncRNAs on hESC/hiPSC CM differentiation. Table S7: Heart target genes of TFs, lncRNAs and miRNAs in human fetal embryo-developed atrial cardiomyocytes using single cell RNA-seq data (GSE106118). Table S8: Heart target genes of TFs, lncRNAs and miRNAs in human fetal embryo-developed ventricular cardiomyocytes using single cell RNA-seq data (GSE106118). Table S9: The top 25 predicted logics formed by TFs and the regulated genes during fetal embryo-derived atrial and ventricular CM development. Table S10: The predicted heart target genes of TFs, miRNAs and lncRNAs from fetal embryo-derived atrial and ventricular CM development.
DOI: 10.6084/m9.figshare.13647119
title: Additional file 1 of The evolution of alternative splicing in glioblastoma under therapy

description: Additional file 1: Table S1. Table of specimens used in study, patient clinical data, and tumor mutational profiles.
DOI: 10.6084/m9.figshare.13124953
title: Health-related quality of life assessed by Gout Impact Scale (GIS) in Chinese patients with gout

description: The Gout Impact Scale (GIS) is a disease-specific health-related quality of life (HR-QoL) measurement for patients with gout. This study aimed to investigate the quality of life in Chinese patients with gout and potential risk factors for poorer HR-QoL by GIS. Adults with gout from February 2017 to February 2019 were invited to complete a questionnaire containing the GIS, social demographic characteristics, clinical information and gout-specific questions. Pearson/Spearman correlation and linear regression were used to analyze the data. A total of 413 patients were included in the analysis (mean age, 51.85 years; 96.4% male). The mean (SD) score of GIS total was 56.79 ± 15.45. Worse gout-specific HR-QoL was associated with anxiety (<i>p</i> &lt; .05), depression (<i>p</i> &lt; .05) and fatigue (<i>p</i> &lt; .05). The effectiveness and global satisfaction of Treatment Satisfaction Questionnaire for Medication (TSQM) were negatively related to each dimension of GIS. Age (<i>B</i> = −0.251, <i>p</i> = .013), fatigue (<i>B</i> = 1.850, <i>p</i> &lt; .001) and depression (<i>B</i> = 9.068, <i>p</i> = .042) were independent predictors of GIS total score. Gout-specific HR-QoL is impaired by social demographic and clinical characteristics, highlighting the importance of psychological factors (fatigue and depression) and patient-reported outcomes (patients’ satisfaction and confidence in gout treatment). These findings suggest that more studies should focus on disease-specific HR-QoL.
DOI: 10.6084/m9.figshare.13647047
title: Additional file 5 of Hypoxia promotes the metastasis of pancreatic cancer through regulating NOX4/KDM5A-mediated histone methylation modification changes in a HIF1A-independent manner

description: Additional file 5. Supplementary Table 2. EMT score of the pancreatic cancer samples in TCGA.
DOI: 10.6084/m9.figshare.13124953.v2
title: Health-related quality of life assessed by Gout Impact Scale (GIS) in Chinese patients with gout

description: The Gout Impact Scale (GIS) is a disease-specific health-related quality of life (HR-QoL) measurement for patients with gout. This study aimed to investigate the quality of life in Chinese patients with gout and potential risk factors for poorer HR-QoL by GIS. Adults with gout from February 2017 to February 2019 were invited to complete a questionnaire containing the GIS, social demographic characteristics, clinical information and gout-specific questions. Pearson/Spearman correlation and linear regression were used to analyze the data. A total of 413 patients were included in the analysis (mean age, 51.85 years; 96.4% male). The mean (SD) score of GIS total was 56.79 ± 15.45. Worse gout-specific HR-QoL was associated with anxiety (<i>p</i> &lt; .05), depression (<i>p</i> &lt; .05) and fatigue (<i>p</i> &lt; .05). The effectiveness and global satisfaction of Treatment Satisfaction Questionnaire for Medication (TSQM) were negatively related to each dimension of GIS. Age (<i>B</i> = −0.251, <i>p</i> = .013), fatigue (<i>B</i> = 1.850, <i>p</i> &lt; .001) and depression (<i>B</i> = 9.068, <i>p</i> = .042) were independent predictors of GIS total score. Gout-specific HR-QoL is impaired by social demographic and clinical characteristics, highlighting the importance of psychological factors (fatigue and depression) and patient-reported outcomes (patients’ satisfaction and confidence in gout treatment). These findings suggest that more studies should focus on disease-specific HR-QoL.
DOI: 10.6084/m9.figshare.13647044.v1
title: Additional file 4 of Hypoxia promotes the metastasis of pancreatic cancer through regulating NOX4/KDM5A-mediated histone methylation modification changes in a HIF1A-independent manner

description: Additional file 4. Supplementary Table 1. Hypoxia score of the pancreatic cancer samples in TCGA.
DOI: 10.6084/m9.figshare.13650556
title: Effect of anthracyclines/ifosfamide-based adjuvant chemotherapy for soft tissue sarcoma: a conventional and network Meta-analysis

description: The objective of this study is to assess the effect of anthracyclines/ifosfamide-based adjuvant chemotherapy for soft tissue sarcoma (STS) and provide a relative ranking of regimens for STS. We pooled the hazard ratios of overall survival (OS) and relapse free survival (RFS) by conventional meta-analysis to appraise whether adjuvant chemotherapy benefits STS and performed a network meta-analysis using a Bayesian model to establish the relative ranking of regimens. Nine studies were included in our meta-analysis. The pooled hazard ratios were 0.68 (95%CI: 0.53–0.86) and 0.65 (95%CI: 0.52–0.83) for OS and RFS, respectively. Doxorubicin was indicated as best regimen to benefit OS (probability: 30.2%), while cyclophosphamide + vincristine + doxorubicin + dactinomycin was indicated as the best regimen for RFS (probability: 37.1%). This meta-analysis confirms the positive effect of anthracyclines/ifosfamide-based adjuvant chemotherapy in STS for both OS and RFS.
DOI: 10.6084/m9.figshare.13647047.v1
title: Additional file 5 of Hypoxia promotes the metastasis of pancreatic cancer through regulating NOX4/KDM5A-mediated histone methylation modification changes in a HIF1A-independent manner

description: Additional file 5. Supplementary Table 2. EMT score of the pancreatic cancer samples in TCGA.
DOI: 10.6084/m9.figshare.24136359
title: Additional file 2 of Genome-wide analysis of the AP2/ERF gene family in Tritipyrum and the response of TtERF_B2-50 in salt-tolerance

description: Additional file 2.
DOI: 10.6084/m9.figshare.25150919.v2
title: The statistics for the factor analysis of the IFRS transition

description: The research is based on the recent IMD statistics (2023). In general, the data studied belong to the period 2020–2023. The sample includes 52 countries for which the values of all indicators are given in the IMD (2023) materials, which is necessary for forming a data array without gaps, which is a prerequisite for regression analysis.
DOI: 10.6084/m9.figshare.24136359.v1
title: Additional file 2 of Genome-wide analysis of the AP2/ERF gene family in Tritipyrum and the response of TtERF_B2-50 in salt-tolerance

description: Additional file 2.
DOI: 10.6084/m9.figshare.6834860
title: Covariate-Adjusted Tensor Classification in High Dimensions

description: In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is used to achieve easy interpretation and accurate prediction. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a penalized approach to select a subset of the tensor predictor entries that affect classification after adjustment for the covariates. An efficient algorithm is developed to take advantage of the tensor structure in the penalized estimation. Theoretical results confirm that the proposed method achieves variable selection and prediction consistency, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulated and real data examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6834860.v1
title: Covariate-Adjusted Tensor Classification in High dimensions

description: In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e. multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (in short for Covariate-Adjusted Tensor Classification in High-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is utilized to achieve easy interpretation and accurate prediction. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a penalized approach to select a subset of the tensor predictor entries that affect classification after adjustment for the covariates. An efficient algorithm is developed to take advantage of the tensor structure in the penalized estimation. Theoretical results confirm that the proposed method achieves variable selection and prediction consistency, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulated and real data examples.
DOI: 10.6084/m9.figshare.6834860.v2
title: Covariate-Adjusted Tensor Classification in High Dimensions

description: In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is used to achieve easy interpretation and accurate prediction. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a penalized approach to select a subset of the tensor predictor entries that affect classification after adjustment for the covariates. An efficient algorithm is developed to take advantage of the tensor structure in the penalized estimation. Theoretical results confirm that the proposed method achieves variable selection and prediction consistency, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulated and real data examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.6834860.v3
title: Covariate-Adjusted Tensor Classification in High Dimensions

description: In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is used to achieve easy interpretation and accurate prediction. To tackle the new computational and statistical challenges arising from the intimidating tensor dimensions, we propose a penalized approach to select a subset of the tensor predictor entries that affect classification after adjustment for the covariates. An efficient algorithm is developed to take advantage of the tensor structure in the penalized estimation. Theoretical results confirm that the proposed method achieves variable selection and prediction consistency, even when the tensor dimension is much larger than the sample size. The superior performance of our method over existing methods is demonstrated in extensive simulated and real data examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24136368
title: Additional file 5 of Genome-wide analysis of the AP2/ERF gene family in Tritipyrum and the response of TtERF_B2-50 in salt-tolerance

description: Additional file 5.
DOI: 10.6084/m9.figshare.24136368.v1
title: Additional file 5 of Genome-wide analysis of the AP2/ERF gene family in Tritipyrum and the response of TtERF_B2-50 in salt-tolerance

description: Additional file 5.
DOI: 10.6084/m9.figshare.6832442.v1
title: Performance of probe polymerization-conjunction-agarose gel electrophoresis in the rapid detection of KRAS gene mutation

description: Abstract This study aimed to develop a simple and rapid method to detect KRAS gene mutations for conventional clinical applications under laboratory conditions. The genotype of mutation sites was determined based on the occurrence of target bands in the corresponding lanes of the reaction tubes through polymerization-conjunction of the probes, probe purification and amplification, and agarose gel electrophoresis. Circulating DNA samples were obtained from the plasma of 72 patients with lung cancer, which were identified based on six mutation sites (G12S, G12R, G12C, G12D, G12A, and G12V) of codon 12 of the KRAS gene. The detection results were compared with direct sequencing data. The proposed detection method is characterized by simple operation, high specificity, and high sensitivity (2%). This method can detect the mutations of three samples at G12S, G12R, and G12A. In the direct sequencing spectra of these samples, the genotype could not be determined due to the lack of evident sequencing peaks that correspond to the basic group of mutations. In conclusion, a simple and rapid method was established based on probe polymerization-conjunction-agarose gel electrophoresis for detecting KRAS gene mutations. This method can be applied to the conventional mutation detection of inhomogeneous samples.
DOI: 10.6084/m9.figshare.14173407.v1
title: Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms*

description: How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such coherence measurement is critically important for the quantification of the market efficiency and the comparison of different platform policies, such as dispatching. The aim of this paper is to introduce a graph-based equilibrium metric (GEM) to quantify the distance between demand and supply networks based on a weighted graph structure. We formulate GEM as the optimal objective value of an unbalanced transport problem, which can be efficiently solved by optimizing an equivalent linear programming. We examine how the GEM can help solve three operational tasks of ride-sourcing platforms. The first one is that GEM achieves up to 70.6% reduction in root-mean-square error over the second-best distance measurement for the prediction accuracy. The second one is that the use of GEM for designing order dispatching policy increases answer rate and drivers’ revenue for more than 1%, representing a huge improvement in number. The third one is that GEM is to serve as an endpoint for comparing different platform policies in AB test.
DOI: 10.6084/m9.figshare.6832538
title: Nondestructive ultrasonic testing in rod structure with a novel numerical Laplace based wavelet finite element method

description: Abstract Rod structure has been widely used in aerospace engineering and civil engineering. Nondestructive testing is a very important method applied to detect unseen flaws in structures, ultrasonic wave nondestructive testing has been used in many areas. Finite Element Method is one of the most widely used numerical methods but would have a high cost when doing simulation on ultrasonic wave due to the requirement of small time interval and element size. Wavelet based finite element method could improve the spatial resolution with fewer elements needed but still needs very small time interval. Laplace transform could easily convert the time domain into frequency and then inverse to time domain. This paper presents an innovative method combining Laplace transform and B-spline wavelet on interval (BSWI) finite element method, which could not only decrease the element number but also increase the time integration interval. Moreover, this innovative method is applied to simulate the ultrasonic wave propagation in 1D rod structure as well as used for nondestructive testing of damages in rod structures.
DOI: 10.6084/m9.figshare.13124953.v1
title: Health-related quality of life assessed by gout impact scale (GIS) in Chinese patients with gout

description: <b><i>Objective:</i></b> The Gout Impact Scale (GIS) is a disease-specific health-related quality of life (HR-QoL) measurement for patients with gout. This study aimed to investigate the quality of life in Chinese patients with gout and potential risk factors for poorer HR-QoL by GIS. <b><i>Methods:</i></b> Adults with gout from February 2017 to February 2019 were invited to complete a questionnaire containing the GIS, social demographic characteristics, clinical information and gout-specific questions. Pearson/Spearman correlation and linear regression were used to analyze the data. <b><i>Results:</i></b> A total of 413 patients were included in the analysis (mean age, 51.85 years; 96.4% male). The mean (SD) score of GIS total was 56.79 ± 15.45. Worse gout-specific HR-QoL was associated with anxiety (<i>P</i> &lt; 0.05), depression (<i>P</i> &lt; 0.05) and fatigue (<i>P</i> &lt; 0.05). The effectiveness and global satisfaction of Treatment Satisfaction Questionnaire for Medication (TSQM) were negatively related to each dimension of GIS. Age (B=-0.251, <i>P</i> = 0.013), fatigue (B = 1.850, <i>P</i> &lt; 0.001) and depression (B = 9.068, <i>P</i> = 0.042) were independent predictors of GIS total score. <b><i>Conclusion:</i></b> Gout-specific HR-QoL is impaired by social demographic and clinical characteristics, highlighting the importance of psychological factors (fatigue and depression) and patient-reported outcomes (patients’ satisfaction and confidence in gout treatment). These findings suggest that more studies should focus on disease-specific HR-QoL.