cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1291722.v1
title: TXNDC17 promotes paclitaxel resistance via inducing autophagy in ovarian cancer

description: Paclitaxel is recommended as a first-line chemotherapeutic agent against ovarian cancer, but drug resistance becomes a major limitation of its success clinically. The key molecule or mechanism associated with paclitaxel resistance in ovarian cancer still remains unclear. Here, we showed that TXNDC17 screened from 356 differentially expressed proteins by LC-MS/MS label-free quantitative proteomics was more highly expressed in paclitaxel-resistant ovarian cancer cells and tissues, and the high expression of TXNDC17 was associated with poorer prognostic factors and exhibited shortened survival in 157 ovarian cancer patients. Moreover, paclitaxel exposure induced upregulation of TXNDC17 and BECN1 expression, increase of autophagosome formation, and autophagic flux that conferred cytoprotection for ovarian cancer cells from paclitaxel. <i>TXNDC17</i> inhibition by siRNA or enforced overexpression by a pcDNA3.1(+)-TXNDC17 plasmid correspondingly decreased or increased the autophagy response and paclitaxel resistance. Additionally, the downregulation of <i>BECN1</i> by siRNA attenuated the activation of autophagy and cytoprotection from paclitaxel induced by TXNDC17 overexpression in ovarian cancer cells. Thus, our findings suggest that TXNDC17, through participation of BECN1, induces autophagy and consequently results in paclitaxel resistance in ovarian cancer. TXNDC17 may be a potential predictor or target in ovarian cancer therapeutics.
DOI: 10.6084/m9.figshare.25391476.v1
title: The coupling coordination relationship between sports industry agglomeration and economic resilience in the Yangtze River Delta region raw data.xlsx

description: This study takes three provinces and one city in the YRD region (provinces: Jiangsu, Zhejiang, and Anhui; and city: Shanghai) as the object of study. The period of study is 2010-2020. Specific data are mainly derived from China Statistical Yearbook (2011-2021), Report on the Development of the Sports Industry in the YRD region (2014-2021), as well as the statistical yearbooks, statistical bulletins, and official websites of the sports bureaus of these geographies in the YRD region.
DOI: 10.6084/m9.figshare.1292986
title: Sliced Orthogonal Array Based Latin Hypercube Designs

description: We propose an approach for constructing a new type of design, called a sliced orthogonal array based Latin hypercube design. This approach exploits a slicing structure of orthogonal arrays with strength two and makes use of sliced random permutations. Such a design achieves one- and two-dimensional uniformity and can be divided into smaller Latin hypercube designs with one-dimensional uniformity. Sampling properties of the proposed designs are derived. Examples are given for illustrating the construction method and corroborating the derived theoretical results. Potential applications of the constructed designs include uncertainty quantification of computer models, computer models with qualitative and quantitative factors, cross-validation and efficient allocation of computing resources.
DOI: 10.6084/m9.figshare.1292986.v1
title: Sliced Orthogonal Array Based Latin Hypercube Designs

description: We propose an approach for constructing a new type of design, called a sliced orthogonal array based Latin hypercube design. This approach exploits a slicing structure of orthogonal arrays with strength two and makes use of sliced random permutations. Such a design achieves one- and two-dimensional uniformity and can be divided into smaller Latin hypercube designs with one-dimensional uniformity. Sampling properties of the proposed designs are derived. Examples are given for illustrating the construction method and corroborating the derived theoretical results. Potential applications of the constructed designs include uncertainty quantification of computer models, computer models with qualitative and quantitative factors, cross-validation and efficient allocation of computing resources.
DOI: 10.6084/m9.figshare.1293021
title: Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1293021.v1
title: Survival Analysis of Loblolly Pine Trees with Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980/81. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the Expectation-Maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large scale loblolly pine tree survival data and interpret the results. We conclude this paper with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. This paper has online supplementary material.
DOI: 10.6084/m9.figshare.1293021.v2
title: Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1293021.v3
title: Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1293021.v4
title: Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1293021.v5
title: Survival Analysis of Loblolly Pine Trees With Spatially Correlated Random Effects

description: Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1294143
title: A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

description: Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification <i>accuracy</i> of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.
DOI: 10.6084/m9.figshare.1294143.v1
title: A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

description: Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification <i>accuracy</i> of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.
DOI: 10.6084/m9.figshare.1294143.v2
title: A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

description: Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification <i>accuracy</i> of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.
DOI: 10.6084/m9.figshare.1294143.v3
title: A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

description: Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification <i>accuracy</i> of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.
DOI: 10.6084/m9.figshare.1294143.v4
title: A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

description: Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification <i>accuracy</i> of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.