bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of a startle-based warning, age, sex, and secondary task on takeover actions in critical autonomous driving scenarios
Authors: Griffith M. , Akkem R. , Maheshwari J. , Seacrist T. , Arbogast K. B. , Graci V. 
Published: March 2023

Introduction: In highly autonomous driving scenarios, it is critical to identify strategies to accelerate reaction times since drivers may take too long to take over control of the vehicle. Previous studies reported that an Acoustic Startling Pre-Stimulus (ASPS, i.e., a loud warning preceding an action) accelerated reaction times in simple ankle flexion exercises.Methods: In this study, we examined if an ASPS warning leads to shorter takeover reaction times in a sled-simulated evasive swerving maneuver. Twenty-eight participants (seven male adults, seven male teenagers, seven female adults, and seven female teenagers) were instructed to align a marker on the steering wheel with a marker on a lateral post as fast as they could as soon as the lateral sled perturbation (0.75 g) started. Four conditions were examined: with and without an ASPS (105 dB, played 250 ms before sled perturbation for 40 ms), and with and without a secondary task (i.e., texting). A catch trial (ASPS only) was used to minimize anticipation. Human kinematics were captured with an 8-camera 3D motion capture system.Results: Results showed that the drivers’ hands lifted towards the steering wheel more quickly with the ASPS (169 ± 55 ms) than without (194 ± 46 ms; p = 0.01), and that adult drivers touched the steering wheel quicker with the ASPS (435 ± 54 ms) than without (470 ± 33 ms; p = 0.01). Similar findings were not observed for the teen drivers. Additionally, female drivers were found to lift their hands towards the steering wheel faster than male drivers (166 ± 58 ms vs. 199 ± 36 ms; p = 0.009).Discussion: Our findings suggest that the ASPS may be beneficial to accelerate driver reaction times during the initiation of a correction maneuver, and that autonomous vehicle warnings may need to be tailored to the age and sex of the driver.
An immunoresponsive three-dimensional urine-tolerant human urothelial model to study urinary tract infection
Authors: Jafari Nazila V. , Rohn Jennifer L. 
Published: March 2023

IntroductionMurine models of urinary tract infection (UTI) have improved our understanding of host-pathogen interactions. However, given differences between rodent and human bladders which may modulate host and bacterial response, including certain biomarkers, urothelial thickness and the concentration of urine, the development of new human-based models is important to complement mouse studies and to provide a more complete picture of UTI in patients.MethodsWe originally developed a human urothelial three-dimensional (3D) model which was urine tolerant and demonstrated several urothelial biomarkers, but it only achieved human thickness in heterogenous, multi-layered zones and did not demonstrate the comprehensive differentiation status needed to achieve barrier function. We optimised this model by altering a variety of conditions and validated it with microscopy, flow cytometry, transepithelial electrical resistance and FITC-dextran permeability assays to confirm tissue architecture, barrier integrity and response to bacterial infection.ResultsWe achieved an improved 3D urine-tolerant human urothelial model (3D-UHU), which after 18-20 days of growth, stratified uniformly to 7-8 layers comprised of the three expected, distinct human cell types. The apical surface differentiated into large, CD227+ umbrella-like cells expressing uroplakin-1A, II, III, and cytokeratin 20, all of which are important terminal differentiation markers, and a glycosaminoglycan layer. Below this layer, several layers of intermediate cells were present, with a single underlying layer of CD271+ basal cells. The apical surface also expressed E-cadherin, ZO-1, claudin-1 and -3, and the model possessed good barrier function. Infection with both Gram-negative and Gram-positive bacterial classes elicited elevated levels of pro-inflammatory cytokines and chemokines characteristic of urinary tract infection in humans and caused a decrease in barrier function.DiscussionTaken together, 3D-UHU holds promise for studying host-pathogen interactions and host urothelial immune response.
3D seismic simulation analysis of the Longtoushan Town Basin during the 2014 Ludian earthquake, Yunnan province
Authors: Li Chengcheng , Liu Peng , Liu Zhongxian , Yuan Xiaoming , Tian Yuan , Zhang Hai , Cao Zhenzhong 
Published: March 2023

On 3 August 2014, a magnitude Ms 6.5 earthquake struck Ludian County, Zhaotong City, Yunnan Province, causing grave losses of life and property in the Longtoushan Town Basin near the fault. In this study, a three-dimensional model of the Longtoushan Town Basin and the velocity structure of the surrounding area, and the Spectral Elements in Elastic Dynamics code, which combines the discontinuous Galerkin technique and the spectral element method (SEM) are used to simulate and study the entire seismic wave propagation process. The results show that due to the variations in the basin geometry and the impedance ratio of the media inside and outside the basin, the seismic waves incident on the basin edge are refracted and diffracted, further prolonging the ground motion holding time within the basin. In the bedrock outside the basin, the velocity peaks are higher at higher elevations; viceversa within the basin, the locally depressed basement produces an obvious amplification effect. The amplitude of the ground motion is not the greatest in the thickest sedimentary layers in the basin, and it is closely related to the degree of undulation at the base of the sedimentary layers, the overburden thickness, and the basin geometry. The peak ground accelerations (PGAs) of approximately 8 m/s2 in the east–west (E–W) direction and 3 m/s2 in the north–south (N–S) direction are influenced by the rupture directivity effect (the ruptured surface is the Baogunao–Xiaohe fault that is oriented in the N–W direction). The peak ground velocity with a sedimentary model is 2.6 and 1.6 times that of the non-sedimentary model in the E–W and N–S directions, respectively. The maximum amplification factor for PGA in the E–W direction is 2.8 and that in the N–S direction is approximately 2.3. The results are in agreement with the actual observed seismic station data in terms of the waveforms and peaks, and the intensity distribution map matches the actual damage distribution. This proves the accuracy and rationality of the method used in this study. The results are useful for the seismic zoning of cities, and they can help engineers predict ground motions for future large earthquakes.
Mosaicking Andean morphostructure and seismic cycle crustal deformation patterns using GNSS velocities and machine learning
Authors: Yáñez-Cuadra Vicente , Moreno Marcos , Ortega-Culaciati Francisco , Donoso Felipe , Báez Juan Carlos , Tassara Andrés 
Published: March 2023

We use unsupervised machine learning techniques to analyze continental-scale crustal motions in areas affected by the seismic cycle of large subduction earthquakes along the Chilean Trench. Specifically, we use the agglomerative clustering algorithm as an exploratory tool to investigate spatial patterns in GNSS regional velocities without the complexity of modeling a physical source. We present a continental-scale velocity field including all available GNSS data for two-time windows (pre-2014, 2018–2021) that represents two periods with different deformation patterns of the seismic cycle. We test two different pre-processing methodologies for the design of machine learning features from the GNSS-derived velocities. The first method uses the direction and magnitude of the secular rates as input features to the clustering algorithm. These results show a clustering spatially related to seismic cycle deformation, separating latitudinal segments with different velocities in the fore-arc and back-arc, as well as regions affected by postseismic relaxation. Thus, highlighting the effectiveness of this method for mapping first-order patterns of active deformation in a subduction zone, that are particularly related to variations on interplate coupling and postseismic transient deformation. In a more sophisticated approach, we use surface strain and rotational rates from GNSS velocities as features in the second methodology. Here, we develop a novel methodology to estimate strain and rotation rates accounting for the spatial heterogeneity of the GNSS-network. We determine the spatial scale at which these features are estimated by least squares inversions, by using a Bayesian model class selection method. The distribution of stations allows to identify heterogeneities in strain and rotation rates at spatial scales larger than 50 km, being particularly notorious the main features of regional deformation at scales > 100 km. Interestingly, the results show a spatial correlation between seismic segmentation in the fore-arc and geologic and structural domains in the arc and back-arc. Our results demonstrate the ability of the combination of inverse and machine learning methods to efficiently identify active deformation patterns and their relationship to the subduction seismic cycle and regional-scale geological structures. Furthermore, our analysis suggests that Andean geological structures influence the observed deformation field.
Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography
Authors: Zhang Jianlun , Liu Feng , Xu Jingxu , Zhao Qingqing , Huang Chencui , Yu Yizhou , Yuan Huishu 
Published: March 2023

BackgroundAcute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists.PurposeTo design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography.Materials and MethodsThe CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types.ResultsThe mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4.ConclusionThe multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.
Crafting a prognostic nomogram for the overall survival rate of cutaneous verrucous carcinoma using the surveillance, epidemiology, and end results database
Authors: Chong Siomui , Huang Liying , Yu Hai , Huang Hui , Ming Wai-kit , Ip Cheong Cheong , Mu Hsin-Hua , Li Kexin , Zhang Xiaoxi , Lyu Jun , Deng Liehua 
Published: March 2023

BackgroundThe aim of this study was to establish and verify a predictive nomogram for patients with cutaneous verrucous carcinoma (CVC) who will eventually survive and to determine the accuracy of the nomogram relative to the conventional American Joint Committee on Cancer (AJCC) staging system.MethodsAssessments were performed on 1125 patients with CVC between 2004 and 2015, and the results of those examinations were recorded in the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided at a ratio of 7:3 into the training (n = 787) and validation (n = 338) cohorts. Predictors were identified using stepwise regression analysis in the COX regression model for create a nomogram to predict overall survival of CVC patients at 3-, 5-, and 8-years post-diagnosis. We compared the performance of our model with that of the AJCC prognosis model using several evaluation metrics, including C-index, NRI, IDI, AUC, calibration plots, and DCAs.ResultsMultivariate risk factors including sex, age at diagnosis, marital status, AJCC stage, radiation status, and surgery status were employed to determine the overall survival (OS) rate (P&lt;0.05). The C-index nomogram performed better than the AJCC staging system variable for both the training (0.737 versus 0.582) and validation cohorts (0.735 versus 0.573), which AUC (&gt; 0.7) revealed that the nomogram exhibited significant discriminative ability. The statistically significant NRI and IDI values at 3-, 5-, and 8-year predictions for overall survival (OS) in the validation cohort (55.72%, 63.71%, and 78.23%, respectively and 13.65%, 20.52%, and 23.73%, respectively) demonstrate that the established nomogram outperforms the AJCC staging system (P &lt; 0.01) in predicting OS for patients with cutaneous verrucous carcinoma (CVC). The calibration plots indicate good performance of the nomogram, while decision curve analyses (DCAs) show that the predictive model could have a favorable clinical impact.ConclusionThis study constructed and validated a nomogram for predicting the prognosis of patients with CVC in the SEER database and assessed it using several variables. This nomogram model can assist clinical staff in making more-accurate predictions than the AJCC staging method about the 3-, 5-, and 8-year OS probabilities of patients with CVC.
Identification of disease-related genes and construction of a gene co-expression database in non-alcoholic fatty liver disease
Authors: Ye Hua , Sun Mengxia , Su Mingli , Chen Dahua , Liu Huiwei , Ma Yanyan , Luo Wenjing , Li Hong , Xu Feng 
Published: March 2023

Background: The mechanism of NAFLD progression remains incompletely understood. Current gene-centric analysis methods lack reproducibility in transcriptomic studies.Methods: A compendium of NAFLD tissue transcriptome datasets was analyzed. Gene co-expression modules were identified in the RNA-seq dataset GSE135251. Module genes were analyzed in the R gProfiler package for functional annotation. Module stability was assessed by sampling. Module reproducibility was analyzed by the ModulePreservation function in the WGCNA package. Analysis of variance (ANOVA) and Student’s t-test was used to identify differential modules. The receiver operating characteristic (ROC) curve was used to illustrate the classification performance of modules. Connectivity Map was used to mine potential drugs for NAFLD treatment.Results: Sixteen gene co-expression modules were identified in NAFLD. These modules were associated with multiple functions such as nucleus, translation, transcription factors, vesicle, immune response, mitochondrion, collagen, and sterol biosynthesis. These modules were stable and reproducible in the other 10 datasets. Two modules were positively associated with steatosis and fibrosis and were differentially expressed between non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver (NAFL). Three modules can efficiently separate control and NAFL. Four modules can separate NAFL and NASH. Two endoplasmic reticulum related modules were both upregulated in NAFL and NASH compared to normal control. Proportions of fibroblasts and M1 macrophages are positively correlated with fibrosis. Two hub genes Aebp1 and Fdft1 may play important roles in fibrosis and steatosis. m6A genes were strongly correlated with the expression of modules. Eight candidate drugs for NAFLD treatment were proposed. Finally, an easy-to-use NAFLD gene co-expression database was developed (available at https://nafld.shinyapps.io/shiny/).Conclusion: Two gene modules show good performance in stratifying NAFLD patients. The modules and hub genes may provide targets for disease treatment.
Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes
Authors: Chen Chen , Wang Junxiao , Dong Chao , Lim David , Feng Zhihui 
Published: March 2023

Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.
The genomic landscape of ANCA-associated vasculitis: Distinct transcriptional signatures, molecular endotypes and comparison with systemic lupus erythematosus
Authors: Banos Aggelos , Thomas Konstantinos , Garantziotis Panagiotis , Filia Anastasia , Malissovas Nikolaos , Pieta Antigone , Nikolakis Dimitrios , Panagiotopoulos Alexandros G. , Chalkia Aglaia , Petras Dimitrios , Bertsias George , Boumpas Dimitrios T. , Vassilopoulos Dimitrios 
Published: March 2023

IntroductionAnti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAVs) present with a complex phenotype and are associated with high mortality and multi-organ involvement. We sought to define the transcriptional landscape and molecular endotypes of AAVs and compare it to systemic lupus erythematosus (SLE).MethodsWe performed whole blood mRNA sequencing from 30 patients with AAV (granulomatosis with polyangiitis/GPA and microscopic polyangiitis/MPA) combined with functional enrichment and network analysis for aberrant pathways. Key genes and pathways were validated in an independent cohort of 18 AAV patients. Co-expression network and hierarchical clustering analysis, identified molecular endotypes. Multi-level transcriptional overlap analysis to SLE was based on our published data from 142 patients.ResultsWe report here that “Pan-vasculitis” signature contained 1,982 differentially expressed genes, enriched in leukocyte differentiation, cytokine signaling, type I and type II IFN signaling and aberrant B-T cell immunity. Active disease was characterized by signatures linked to cell cycle checkpoints and metabolism pathways, whereas ANCA-positive patients exhibited a humoral immunity transcriptional fingerprint. Differential expression analysis of GPA and MPA yielded an IFN-g pathway (in addition to a type I IFN) in the former and aberrant expression of genes related to autophagy and mRNA splicing in the latter. Unsupervised molecular taxonomy analysis revealed four endotypes with neutrophil degranulation, aberrant metabolism and B-cell responses as potential mechanistic drivers. Transcriptional perturbations and molecular heterogeneity were more pronounced in SLE. Molecular analysis and data-driven clustering of AAV uncovered distinct transcriptional pathways that could be exploited for targeted therapy.DiscussionWe conclude that transcriptomic analysis of AAV reveals distinct endotypes and molecular pathways that could be targeted for therapy. The AAV transcriptome is more homogenous and less fragmented compared to the SLE which may account for its superior rates of response to therapy.