cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7750202.v1
title: The HGF-MET axis coordinates liver cancer metabolism and autophagy for chemotherapeutic resistance

description: Notwithstanding the numerous drugs available for liver cancer, emerging evidence suggests that chemotherapeutic resistance is a significant issue. HGF and its receptor MET play critical roles in liver carcinogenesis and metastasis, mainly dependent on the activity of receptor tyrosine kinase. However, for unknown reasons, all HGF-MET kinase activity-targeted drugs have failed or have been suspended in clinical trials thus far. Macroautophagy/autophagy is a protective ‘self-eating’ process for resisting metabolic stress by recycling obsolete components, whereas the impact of autophagy-mediated reprogrammed metabolism on therapeutic resistance is largely unclear, especially in liver cancer. In the present study, we first observed that HGF stimulus facilitated the Warburg effect and glutaminolysis to promote biogenesis in multiple liver cancer cells. We then identified the pyruvate dehydrogenase complex (PDHC) and GLS/GLS1 as crucial substrates of HGF-activated MET kinase; MET-mediated phosphorylation inhibits PDHC activity but activates GLS to promote cancer cell metabolism and biogenesis. We further found that the key residues of kinase activity in MET (Y1234/1235) also constitute a conserved LC3-interacting region motif (Y1234-Y1235-x-V1237). Therefore, on inhibiting HGF-mediated MET kinase activation, Y1234/1235-dephosphorylated MET induced autophagy to maintain biogenesis for cancer cell survival. Moreover, we verified that Y1234/1235-dephosphorylated MET correlated with autophagy in clinical liver cancer. Finally, a combination of MET inhibitor and autophagy suppressor significantly improved the therapeutic efficiency of liver cancer <i>in vitro</i> and in mice. Together, our findings reveal an HGF-MET axis-coordinated functional interaction between tyrosine kinase signaling and autophagy, and establish a MET-autophagy double-targeted strategy to overcome chemotherapeutic resistance in liver cancer. <b>Abbreviations:</b> ALDO: aldolase, fructose-bisphosphate; CQ: chloroquine; DLAT/PDCE2: dihydrolipoamide S-acetyltransferase; EMT: epithelial-mesenchymal transition; ENO: enolase; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GLS/GLS1: glutaminase; GLUL/GS: glutamine-ammonia ligase; GPI/PGI: glucose-6-phosphate isomerase; HCC: hepatocellular carcinoma; HGF: hepatocyte growth factor; HK: hexokinase; LDH: lactate dehydrogenase; LIHC: liver hepatocellular carcinoma; LIR: LC3-interacting region; PDH: pyruvate dehydrogenase; PDHA1: pyruvate dehydrogenase E1 alpha 1 subunit; PDHX: pyruvate dehydrogenase complex component X; PFK: phosphofructokinase; PK: pyruvate kinase; RTK: receptor tyrosine kinase; TCGA: The Cancer Genome Atlas
DOI: 10.6084/m9.figshare.12993125
title: Contactless reversible 4D-printing for 3D-to-3D shape morphing

description: 4D printing provides a means to create dynamic structures for self-actuating devices fabricated by additive manufacturing techniques. Most existing 4D printing processes are only capable of one-way shape morphing using a single mechanism for stimulation. In the limited successful reversible 4D-printing studies, most only demonstrate 2D-to-3D shape morphing. This study introduces a contactless shape setting mechanism to empower reversible 4D printing using a combination of stimuli. The forward shape programming is realised by asymmetric swelling with heated ethanol, and the shape recovery is accomplished by dry heating. The shape morphing can be predicted using a simulation model. Several designs are 4D-printed to achieve different forms of morphing to obtain 3D-to-3D shape morphing. The entire shape morphing cycle could be completed within as short as 3 min. The capability of 3D-to-3D shape morphing and reversibility of our 4D-printing process promise great potential for different applications such as biomimetics and soft robotics.
DOI: 10.6084/m9.figshare.12993125.v1
title: Contactless reversible 4D-printing for 3D-to-3D shape morphing

description: 4D printing provides a means to create dynamic structures for self-actuating devices fabricated by additive manufacturing techniques. Most existing 4D printing processes are only capable of one-way shape morphing using a single mechanism for stimulation. In the limited successful reversible 4D-printing studies, most only demonstrate 2D-to-3D shape morphing. This study introduces a contactless shape setting mechanism to empower reversible 4D printing using a combination of stimuli. The forward shape programming is realised by asymmetric swelling with heated ethanol, and the shape recovery is accomplished by dry heating. The shape morphing can be predicted using a simulation model. Several designs are 4D-printed to achieve different forms of morphing to obtain 3D-to-3D shape morphing. The entire shape morphing cycle could be completed within as short as 3 min. The capability of 3D-to-3D shape morphing and reversibility of our 4D-printing process promise great potential for different applications such as biomimetics and soft robotics.
DOI: 10.6084/m9.figshare.11944572
title: Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

description: <i>Abstract–</i>Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approaches based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinental scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggesting the need for careful examinations of the properties of the statistical methods in practice. 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.11944572.v2
title: Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

description: <b><i>Abstract–</i>Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approaches based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinental scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggesting the need for careful examinations of the properties of the statistical methods in practice. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</b>
DOI: 10.6084/m9.figshare.11944572.v4
title: Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

description: <i>Abstract–</i>Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approaches based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinental scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggesting the need for careful examinations of the properties of the statistical methods in practice. 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.11944572.v3
title: Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

description: <i>Abstract–</i>Detection and attribution of climate change plays a central role in establishing the causal relationship between the observed changes in the climate and their possible causes. Optimal fingerprinting has been widely used as a standard method for detection and attribution analysis for mean climate conditions, but there has been no satisfactory analog for climate extremes. Here, we turn an intuitive concept, which incorporates the expected climate responses to external forcings into the location parameters of the marginal generalized extreme value (GEV) distributions of the observed extremes, to a practical and better-understood method. Marginal approaches based on a weighted sum of marginal GEV score equations are promising for no need to specify the dependence structure. The computational efficiency makes them feasible in handling multiple forcings simultaneously. The method under working independence is recommended because it produces robust results where there are errors-in-variables. Our analyses show human influences on temperature extremes at the subcontinental scale. Compared with previous studies, we detected human influences in a slightly smaller number of regions. This is possibly due to the under-coverage of the confidence intervals in existing works, suggesting the need for careful examinations of the properties of the statistical methods in practice. 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.8274566.v1
title: Additional file 10: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of Queensland guideline. The Queensland guideline was developed based on evidence, and there were 8 relevant recommendations being extracted, which were displayed and appraised in Additional file 10. (XLSX 14 kb)
DOI: 10.6084/m9.figshare.8274566
title: Additional file 10: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of Queensland guideline. The Queensland guideline was developed based on evidence, and there were 8 relevant recommendations being extracted, which were displayed and appraised in Additional file 10. (XLSX 14 kb)
DOI: 10.6084/m9.figshare.8274569
title: Additional file 11: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of HKCOG guideline. The HKCOG guideline was an expert consensus. It was updated taking reference to the recent evidence, WHO, NICE guideline and recommendations of other international bodies. There were 13 relevant recommendations being extracted, which were displayed and appraised in Additional file 11. (XLSX 14 kb)
DOI: 10.6084/m9.figshare.8274569.v1
title: Additional file 11: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of HKCOG guideline. The HKCOG guideline was an expert consensus. It was updated taking reference to the recent evidence, WHO, NICE guideline and recommendations of other international bodies. There were 13 relevant recommendations being extracted, which were displayed and appraised in Additional file 11. (XLSX 14 kb)
DOI: 10.6084/m9.figshare.11944581
title: Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

description: Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. 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.11944581.v2
title: Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

description: Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. 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.11944581.v4
title: Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

description: Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. 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.11944581.v3
title: Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection

description: Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, “guilt by association” methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to affect the gene expression’s covariance, previous research has focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called covariance-based sample selection (COBS) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBSs effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWNs ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input. 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.11944590
title: Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression

description: Analysis of high-dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, and the remaining variables are nuisance. To this end, we propose the nuisance penalized regression which does not penalize the parameters of interest. When the coherence between interest parameters and nuisance parameters is negligible, we show that resulting estimator can be directly used for inference without any correction. When the coherence is not negligible, we propose an iterative procedure to further refine the estimate of interest parameters, based on which we propose a modified profile likelihood based statistic for hypothesis testing. The utilities of our general results are demonstrated in three specific examples. Numerical studies lend further support to our method.
DOI: 10.6084/m9.figshare.11944590.v2
title: Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression

description: Analysis of high-dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, and the remaining variables are nuisance. To this end, we propose the nuisance penalized regression which does not penalize the parameters of interest. When the coherence between interest parameters and nuisance parameters is negligible, we show that resulting estimator can be directly used for inference without any correction. When the coherence is not negligible, we propose an iterative procedure to further refine the estimate of interest parameters, based on which we propose a modified profile likelihood based statistic for hypothesis testing. The utilities of our general results are demonstrated in three specific examples. Numerical studies lend further support to our method.
DOI: 10.6084/m9.figshare.8274578
title: Additional file 12: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of A.N.D. guideline. The guideline focused on nutrition practiece during the treatment of women with GDM. There were 15 relevant recommendations being extracted, which were displayed and appraised in Additional file 12. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8274578.v1
title: Additional file 12: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of A.N.D. guideline. The guideline focused on nutrition practiece during the treatment of women with GDM. There were 15 relevant recommendations being extracted, which were displayed and appraised in Additional file 12. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8274587
title: Additional file 13: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of DDG guideline. The recommendations of DDG guideline were based on the evidence from the literature, which was selected through a systematic external literature search. There were 21 relevant recommendations being extracted, which were displayed and appraised in Additional file 13. (XLSX 15 kb)
DOI: 10.6084/m9.figshare.8274587.v1
title: Additional file 13: of Current guidelines on the management of gestational diabetes mellitus: a content analysis and appraisal

description: Recommendations Extraction of DDG guideline. The recommendations of DDG guideline were based on the evidence from the literature, which was selected through a systematic external literature search. There were 21 relevant recommendations being extracted, which were displayed and appraised in Additional file 13. (XLSX 15 kb)