cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4015344
title: Covid-19 airline data

description: These data provide information on determinants of airline technical efficiency and productivity for 17 airline companies in the Asia Pacific region.
DOI: 10.5281/zenodo.4015343
title: Covid-19 airline data

description: These data provide information on determinants of airline technical efficiency and productivity for 17 airline companies in the Asia Pacific region.
DOI: 10.6084/m9.figshare.1436192.v1
title: Development of a Method to Predict Crash Risk using Trend Analysis of Driver Behavior Changes over Time

description: <i><b>Objective</b></i>: This study aimed at identifying and predicting in advance the point in time with high risk of virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure. <i><b>Methods</b></i>:The behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted by using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with high risk of virtual accident (in real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of virtual accident). <i><b>Results</b></i>: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with high risk of virtual accident before the point in time of virtual accident. <i><b>Conclusion</b></i>: The proposed method is one of the promising techniques for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving, and for warning drivers of such a drowsy and risky state.
DOI: 10.6084/m9.figshare.1436192
title: Development of a method to predict crash risk using trend analysis of driver behavior changes over time

description: <b>Objective</b>: This study aimed at identifying and predicting in advance the point in time with a high risk of a virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure. <b>Methods</b>: Behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with a high risk of a virtual accident (in a real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of a virtual accident). <b>Results</b>: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with a high risk of a virtual accident before the point in time of a virtual accident. <b>Conclusion</b>: The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state.
DOI: 10.6084/m9.figshare.1436188
title: A plastic SQSTM1/p62-dependent autophagic reserve maintains proteostasis and determines proteasome inhibitor susceptibility in multiple myeloma cells

description: Multiple myeloma (MM) is the paradigmatic proteasome inhibitor (PI) responsive cancer, but many patients fail to respond. An attractive target to enhance sensitivity is (macro)autophagy, recently found essential to bone marrow plasma cells, the normal counterpart of MM. Here, integrating proteomics with hypothesis-driven strategies, we identified the autophagic cargo receptor and adapter protein, SQSTM1/p62 as an essential component of an autophagic reserve that not only synergizes with the proteasome to maintain proteostasis, but also mediates a plastic adaptive response to PIs, and faithfully reports on inherent PI sensitivity. Lentiviral engineering revealed that SQSTM1 is essential for MM cell survival and affords specific PI protection. Under basal conditions, SQSTM1-dependent autophagy alleviates the degradative burden on the proteasome by constitutively disposing of substantial amounts of ubiquitinated proteins. Indeed, its inhibition or stimulation greatly sensitized to, or protected from, PI-induced protein aggregation and cell death. Moreover, under proteasome stress, myeloma cells selectively enhanced SQSTM1 de novo expression and reset its vast endogenous interactome, diverting SQSTM1 from signaling partners to maximize its association with ubiquitinated proteins. Saturation of such autophagic reserve, as indicated by intracellular accumulation of undigested SQSTM1-positive aggregates, specifically discriminated patient-derived myelomas inherently susceptible to PIs from primarily resistant ones. These aggregates correlated with accumulation of the endoplasmic reticulum, which comparative proteomics identified as the main cell compartment targeted by autophagy in MM. Altogether, the data integrate autophagy into our previously established proteasome load-versus-capacity model, and reveal SQSTM1 aggregation as a faithful marker of defective proteostasis, defining a novel prognostic and therapeutic framework for MM.
DOI: 10.6084/m9.figshare.1436192.v2
title: Development of a Method to Predict Crash Risk using Trend Analysis of Driver Behavior Changes over Time

description: <i><b>Objective</b></i>: This study aimed at identifying and predicting in advance the point in time with high risk of virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure. <i><b>Methods</b></i>:The behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted by using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with high risk of virtual accident (in real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of virtual accident). <i><b>Results</b></i>: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with high risk of virtual accident before the point in time of virtual accident. <i><b>Conclusion</b></i>: The proposed method is one of the promising techniques for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving, and for warning drivers of such a drowsy and risky state.
DOI: 10.6084/m9.figshare.1436187.v1
title: Evaluation of Developmental Metrics for Utilization in a Pediatric Advanced Automatic Crash Notification Algorithm

description: ABSTRACT<b>Objective</b>: Appropriate treatment at designated trauma centers (TCs) improves outcomes among injured children after motor vehicle crashes (MVCs). Advanced Automatic Crash Notification (AACN) has shown promise in improving triage to appropriate TCs. Pediatric-specific AACN algorithms have not yet been created. To create such an algorithm, it will be necessary to include some metric of development (age, height or weight) as a covariate in the injury risk algorithm. This study sought to determine which marker of development should serve as a covariate in such an algorithm and to quantify injury risk at different levels of this metric. <b>Methods</b>: Retrospective review of occupants &lt;19yrs within the MVC dataset NASS-CDS 2000–2011 was performed. R<sup>2</sup> values of logistic regression models using age, height or weight to predict 18 key injury types were compared to determine which metric should be used as a covariate in a pediatric AACN algorithm. Clinical judgment, literature review and Chi Square analysis were used to create groupings of the chosen metric that would discriminate injury patterns. Adjusted odds of particular injury types at the different levels of this metric were calculated from logistic regression while controlling for gender, vehicle velocity change (delta V), belted status (optimal, sub-optimal or unrestrained) and crash mode (rollover, rear, frontal, near-side or far-side). <b>Results</b>: NASS-CDS analysis produced 11,541 occupants &lt;19yrs with non-missing data. Age, height and weight were correlated with one another and with injury patterns. Age demonstrated the best predictive power in injury patterns and was categorized into bins of 0–4 years, 5–9 years, 10–14 years and 15–18 years. Age was a significant predictor of all 18 injury types evaluated even when controlling for all other confounders and when controlling for age and gender specific BMI classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining AIS 2+ and 3+ head injuries and AIS 3+ spinal injuries, while older children were at increased odds of sustaining thoracic fractures, AIS 3+ abdominal injuries, and AIS 2+ upper and lower extremity injuries. <b>Conclusions</b>: The injury patterns observed across developmental metrics in this study mirror those previously described among children with blunt trauma. This study identifies age as the metric best suited for use in a pediatric AACN algorithm and utilizes 12 years of data to provide quantifiable risks of particular injuries at different levels of this metric. This risk quantification will have important predictive purposes in a pediatric-specific AACN algorithm.
DOI: 10.6084/m9.figshare.1436187
title: Evaluation of developmental metrics for utilization in a pediatric advanced automatic crash notification algorithm

description: <b>Objective:</b> Appropriate treatment at designated trauma centers (TCs) improves outcomes among injured children after motor vehicle crashes (MVCs). Advanced Automatic Crash Notification (AACN) has shown promise in improving triage to appropriate TCs. Pediatric-specific AACN algorithms have not yet been created. To create such an algorithm, it will be necessary to include some metric of development (age, height, or weight) as a covariate in the injury risk algorithm. This study sought to determine which marker of development should serve as a covariate in such an algorithm and to quantify injury risk at different levels of this metric. <b>Methods:</b> A retrospective review of occupants age &lt; 19 years within the MVC data set NASS-CDS 2000–2011 was performed. <i>R</i><sup>2</sup> values of logistic regression models using age, height, or weight to predict 18 key injury types were compared to determine which metric should be used as a covariate in a pediatric AACN algorithm. Clinical judgment, literature review, and chi-square analysis were used to create groupings of the chosen metric that would discriminate injury patterns. Adjusted odds of particular injury types at the different levels of this metric were calculated from logistic regression while controlling for gender, vehicle velocity change (delta <i>V</i>), belted status (optimal, suboptimal, or unrestrained), and crash mode (rollover, rear, frontal, near-side, or far-side). <b>Results:</b> NASS-CDS analysis produced 11,541 occupants age &lt; 19 years with nonmissing data. Age, height, and weight were correlated with one another and with injury patterns. Age demonstrated the best predictive power in injury patterns and was categorized into bins of 0–4 years, 5–9 years, 10–14 years, and 15–18 years. Age was a significant predictor of all 18 injury types evaluated even when controlling for all other confounders and when controlling for age- and gender-specific body mass index (BMI) classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining Abbreviated Injury Scale (AIS) 2+ and 3+ head injuries and AIS 3+ spinal injuries, whereas older children were at increased odds of sustaining thoracic fractures, AIS 3+ abdominal injuries, and AIS 2+ upper and lower extremity injuries. <b>Conclusions:</b> The injury patterns observed across developmental metrics in this study mirror those previously described among children with blunt trauma. This study identifies age as the metric best suited for use in a pediatric AACN algorithm and utilizes 12 years of data to provide quantifiable risks of particular injuries at different levels of this metric. This risk quantification will have important predictive purposes in a pediatric-specific AACN algorithm.
DOI: 10.6084/m9.figshare.1436188.v1
title: A Plastic SQSTM1/p62-Dependent Autophagic Reserve Maintains Proteostasis and Determines Proteasome Inhibitor Susceptibility in Multiple Myeloma Cells

description: ABSTRACTMultiple myeloma (MM) is the paradigmatic proteasome inhibitor (PI) responsive cancer, but many patients fail to respond. An attractive target to enhance sensitivity is (macro)autophagy, recently found essential to bone marrow plasma cells, the normal counterpart of MM. Here, integrating proteomics with hypothesis-driven strategies, we identified the autophagic cargo receptor and adapter protein, SQSTM1/p62 as an essential component of an autophagic reserve that not only synergizes with the proteasome to maintain proteostasis, but also mediates a plastic adaptive response to PIs, and faithfully reports on inherent PI sensitivity. Lentiviral engineering revealed that SQSTM1 is essential for MM cell survival and affords specific PI protection. Under basal conditions, SQSTM1-dependent autophagy alleviates the degradative burden on the proteasome by constitutively disposing of substantial amounts of ubiquitinated proteins. Indeed, its inhibition or stimulation greatly sensitized to, or protected from, PI-induced protein aggregation and cell death. Moreover, under proteasome stress, myeloma cells selectively enhanced SQSTM1 <i>de novo</i> expression and reset its vast endogenous interactome, diverting SQSTM1 from signaling partners to maximize its association with ubiquitinated proteins. Saturation of such autophagic reserve, as indicated by intracellular accumulation of undigested SQSTM1-positive aggregates, specifically discriminated patient-derived myelomas inherently susceptible to PIs from primarily resistant ones. These aggregates correlated with accumulation of the endoplasmic reticulum, which comparative proteomics identified as the main cell compartment targeted by autophagy in MM. Altogether, the data integrate autophagy into our previously established proteasome <i>load-versus-capacity</i> model, and reveal SQSTM1 aggregation as a faithful marker of defective proteostasis, defining a novel prognostic and therapeutic framework for MM.
DOI: 10.6084/m9.figshare.1436185
title: PLOSOne supplementary data for fMRI of preparation

description: Raw group data and values from correlation analyses. See README file in zip download.
DOI: 10.6084/m9.figshare.1436187.v2
title: Evaluation of Developmental Metrics for Utilization in a Pediatric Advanced Automatic Crash Notification Algorithm

description: ABSTRACT<b>Objective</b>: Appropriate treatment at designated trauma centers (TCs) improves outcomes among injured children after motor vehicle crashes (MVCs). Advanced Automatic Crash Notification (AACN) has shown promise in improving triage to appropriate TCs. Pediatric-specific AACN algorithms have not yet been created. To create such an algorithm, it will be necessary to include some metric of development (age, height or weight) as a covariate in the injury risk algorithm. This study sought to determine which marker of development should serve as a covariate in such an algorithm and to quantify injury risk at different levels of this metric. <b>Methods</b>: Retrospective review of occupants &lt;19yrs within the MVC dataset NASS-CDS 2000–2011 was performed. R<sup>2</sup> values of logistic regression models using age, height or weight to predict 18 key injury types were compared to determine which metric should be used as a covariate in a pediatric AACN algorithm. Clinical judgment, literature review and Chi Square analysis were used to create groupings of the chosen metric that would discriminate injury patterns. Adjusted odds of particular injury types at the different levels of this metric were calculated from logistic regression while controlling for gender, vehicle velocity change (delta V), belted status (optimal, sub-optimal or unrestrained) and crash mode (rollover, rear, frontal, near-side or far-side). <b>Results</b>: NASS-CDS analysis produced 11,541 occupants &lt;19yrs with non-missing data. Age, height and weight were correlated with one another and with injury patterns. Age demonstrated the best predictive power in injury patterns and was categorized into bins of 0–4 years, 5–9 years, 10–14 years and 15–18 years. Age was a significant predictor of all 18 injury types evaluated even when controlling for all other confounders and when controlling for age and gender specific BMI classifications. Adjusted odds of key injury types with respect to these age categorizations revealed that younger children were at increased odds of sustaining AIS 2+ and 3+ head injuries and AIS 3+ spinal injuries, while older children were at increased odds of sustaining thoracic fractures, AIS 3+ abdominal injuries, and AIS 2+ upper and lower extremity injuries. <b>Conclusions</b>: The injury patterns observed across developmental metrics in this study mirror those previously described among children with blunt trauma. This study identifies age as the metric best suited for use in a pediatric AACN algorithm and utilizes 12 years of data to provide quantifiable risks of particular injuries at different levels of this metric. This risk quantification will have important predictive purposes in a pediatric-specific AACN algorithm.
DOI: 10.6084/m9.figshare.1436188.v2
title: A Plastic SQSTM1/p62-Dependent Autophagic Reserve Maintains Proteostasis and Determines Proteasome Inhibitor Susceptibility in Multiple Myeloma Cells

description: ABSTRACTMultiple myeloma (MM) is the paradigmatic proteasome inhibitor (PI) responsive cancer, but many patients fail to respond. An attractive target to enhance sensitivity is (macro)autophagy, recently found essential to bone marrow plasma cells, the normal counterpart of MM. Here, integrating proteomics with hypothesis-driven strategies, we identified the autophagic cargo receptor and adapter protein, SQSTM1/p62 as an essential component of an autophagic reserve that not only synergizes with the proteasome to maintain proteostasis, but also mediates a plastic adaptive response to PIs, and faithfully reports on inherent PI sensitivity. Lentiviral engineering revealed that SQSTM1 is essential for MM cell survival and affords specific PI protection. Under basal conditions, SQSTM1-dependent autophagy alleviates the degradative burden on the proteasome by constitutively disposing of substantial amounts of ubiquitinated proteins. Indeed, its inhibition or stimulation greatly sensitized to, or protected from, PI-induced protein aggregation and cell death. Moreover, under proteasome stress, myeloma cells selectively enhanced SQSTM1 <i>de novo</i> expression and reset its vast endogenous interactome, diverting SQSTM1 from signaling partners to maximize its association with ubiquitinated proteins. Saturation of such autophagic reserve, as indicated by intracellular accumulation of undigested SQSTM1-positive aggregates, specifically discriminated patient-derived myelomas inherently susceptible to PIs from primarily resistant ones. These aggregates correlated with accumulation of the endoplasmic reticulum, which comparative proteomics identified as the main cell compartment targeted by autophagy in MM. Altogether, the data integrate autophagy into our previously established proteasome <i>load-versus-capacity</i> model, and reveal SQSTM1 aggregation as a faithful marker of defective proteostasis, defining a novel prognostic and therapeutic framework for MM.
DOI: 10.6084/m9.figshare.1436179
title: transcript GTF annotation of smooth muscle cells in colon

description: trascript annotation of smooth muscle cells in colon
DOI: 10.6084/m9.figshare.1436185.v1
title: PLOSOne supplementary data for fMRI of preparation

description: Raw group data and values from correlation analyses. See README file in zip download.
DOI: 10.6084/m9.figshare.1436178
title: transcript GTF annotation of smooth muscle cells in jejunum

description: transcript annotation of smooth muscle cells in jejunum
DOI: 10.6084/m9.figshare.1436188.v3
title: A plastic SQSTM1/p62-dependent autophagic reserve maintains proteostasis and determines proteasome inhibitor susceptibility in multiple myeloma cells

description: Multiple myeloma (MM) is the paradigmatic proteasome inhibitor (PI) responsive cancer, but many patients fail to respond. An attractive target to enhance sensitivity is (macro)autophagy, recently found essential to bone marrow plasma cells, the normal counterpart of MM. Here, integrating proteomics with hypothesis-driven strategies, we identified the autophagic cargo receptor and adapter protein, SQSTM1/p62 as an essential component of an autophagic reserve that not only synergizes with the proteasome to maintain proteostasis, but also mediates a plastic adaptive response to PIs, and faithfully reports on inherent PI sensitivity. Lentiviral engineering revealed that SQSTM1 is essential for MM cell survival and affords specific PI protection. Under basal conditions, SQSTM1-dependent autophagy alleviates the degradative burden on the proteasome by constitutively disposing of substantial amounts of ubiquitinated proteins. Indeed, its inhibition or stimulation greatly sensitized to, or protected from, PI-induced protein aggregation and cell death. Moreover, under proteasome stress, myeloma cells selectively enhanced SQSTM1 de novo expression and reset its vast endogenous interactome, diverting SQSTM1 from signaling partners to maximize its association with ubiquitinated proteins. Saturation of such autophagic reserve, as indicated by intracellular accumulation of undigested SQSTM1-positive aggregates, specifically discriminated patient-derived myelomas inherently susceptible to PIs from primarily resistant ones. These aggregates correlated with accumulation of the endoplasmic reticulum, which comparative proteomics identified as the main cell compartment targeted by autophagy in MM. Altogether, the data integrate autophagy into our previously established proteasome load-versus-capacity model, and reveal SQSTM1 aggregation as a faithful marker of defective proteostasis, defining a novel prognostic and therapeutic framework for MM.