Integrated Transcriptomic and Proteomic Study of the Mechanism of Action of the Novel Small-Molecule Positive Allosteric Modulator 1 in Targeting PAC1-R for the Treatment of D-Gal-Induced Aging Mice.

Small-molecule positive allosteric modulator 1 (SPAM1), which targets pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R), has been found to have a neuroprotective effect, and the underlying mechanism was explored in this study. First, using a D-galactose (D-gal)-induced aging mouse model, we confirmed that SPAM1 improves the structure of the hippocampal dentate gyrus and restores the number of neurons. Compared with D-gal model mice, SPAM1-treated mice showed up-regulated expression of Sirtuin 6 (SIRT6) and Lamin B1 and down-regulated expression of YinYang 1 (YY1) and p16. A similar tendency was observed in senescent RGC-5 cells induced by long-term culture, indicating that SPAM1 exhibits significant in vitro and in vivo anti-senescence activity in neurons. Then, using whole-transcriptome sequencing and proteomic analysis, we further explored the mechanism behind SPAM1's neuroprotective effects and found that SPAM is involved in the longevity-regulating pathway. Finally, the up-regulation of neurofilament light and medium polypeptides indicated by the proteomics results was further confirmed by Western blotting. These results help to lay a pharmacological network foundation for the use of SPAM1 as a potent anti-aging therapeutic drug to combat neurodegeneration with anti-senescence, neuroprotective, and nerve regeneration activity.

High-risk event prone driver identification considering driving behavior temporal covariate shift.

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Unveiling pre-crash driving behavior common features based upon behavior entropy.

Driving behavior is considered as the primary crash influencing factor, whereas studies claimed that over 90% crashes were attributed by behavior features. Therefore, unveil pre-crash driving behavior features is of great importance for crash prevention. Previous studies have established the correlations between features such as vehicle speed, speed variability, and the probability of crash occurrences, but these analyses have concluded inconsistent results. This is due to the varying operating characteristics among roadway facilities, where given the same driving behavior statistical features, the corresponding traffic states are not identical. In this study, a behavioral entropy index was proposed to address the abovementioned issue. First, through comparing the individual driving behavior with the group distribution, behavioral entropy index was calculated to quantify the abnormality of driving behavior. Then, crash classification models were established by comparing the behavioral entropy prior to crash events and normal driving conditions. The empirical analyses have been conducted based on 1,634,770 naturalistic driving trajectories and 1027 crash events. And models have been carried out for urban roadway sections, urban intersections, and highway sections separately. The results showed that utilizing the behavior entropy instead of the statistical features could enhance the crash classification accuracy by 11.3%. And common pre-crash features of increased behavioral entropy were identified. Moreover, the speed coefficient of variation (QCV) entropy was concluded as the most influencing factor, which can be used for real-time driving risk monitoring and enables individual-level hazard mitigation.

Bifurcation analysis of Leslie-Gower predator-prey system with harvesting and fear effect.

In the paper, a Leslie-Gower predator-prey system with harvesting and fear effect is considered. The existence and stability of all possible equilibrium points are analyzed. The bifurcation dynamic behavior at key equilibrium points is investigated to explore the intrinsic driving mechanisms of population interaction modes. It is shown that the system undergoes various bifurcations, including transcritical, saddle-node, Hopf and Bogdanov-Takens bifurcations. The numerical simulation results show that harvesting and fear effect can seriously affect the dynamic evolution trend and coexistence mode. Furthermore, it is particularly worth pointing out that harvesting not only drives changes in population coexistence mode, but also has a certain degree delay. Finally, it is anticipated that these research results will be beneficial for the vigorous development of predator-prey system.

First reported case of ANCA-associated vasculitis induced by oxaliplatin, capecitabine, and trastuzumab.

A 68-year-old male, who was undergoing XELOX plus trastuzumab therapy for gastric cancer, developed proteinuria, hematuria, and progressive increase in creatinine after 3 months. Subsequently, the patient also experienced hemoptysis, nasal bleeding. Chest CT examination shown pulmonary hemorrhage. The MRI of the nasopharynx ruled out nasopharyngeal cancer recurrence. The MPO and PR3 were elevated, and renal biopsy confirmed ANCA-related vasculitis, which affected the lungs, kidneys, and nasopharynx. Based on the review of the patient''s medical history and medication, it is believed that ANCA-related vasculitis was caused by XELOX plus trastuzumab chemotherapy, but it is difficult to confirm which specific drug caused it. After stopping XELOX plus trastuzumab chemotherapy, glucocorticoids and cyclophosphamide was given, the patient''s pulmonary hemorrhage and nasal bleeding stopped, and the lung lesions were absorbed. The renal function also improved. The patient later experienced pulmonary infection again, and tNGS indicated Legionella pneumophila and pulmonary tuberculosis infection. Despite anti-infection treatment, steroid dose was rapidly reduced. Ultimately, the patient gave up on treatment and eventually died.

Critical safety management driver identification based upon temporal variation characteristics of driving behavior.

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers' driving behavior heterogeneity on model performance, "traffic entropy" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

Counterfactual safety benefits quantification method for en-route driving behavior interventions.

Driving behavior intervention is a dominant traffic safety countermeasure being implemented that has substantially reduced crash occurrence. However, during implementation, the intervention strategy faces the curse of dimensionality as there are multiple candidate intervention locations with various intervention measures and options. Quantifying the interventions' safety benefits and further implementing the most effective ones could avoid too frequent interventions which may lead to counterproductive safety impacts. Traditional intervention effects quantification approaches rely on observational data, thus failing to control confounding variables and leading to biased results. In this study, a counterfactual safety benefits quantification method for en-route driving behavior interventions was proposed. Empirical data from online ride-hailing services were employed to quantify the safety benefits of en-route safety broadcasting to speed maintenance behavior. Specifically, to effectively control the impacts of confounding variables on the quantification results of interventions, the "if without intervention" case of the intervention case is inferred based on the structural causality model according to the Theory of Planned Behavior (TPB). Then, a safety benefits quantification method based on Extreme Value Theory (EVT) was developed to connect changes of speed maintenance behavior with crash occurrence probabilities. Furthermore, a closed-loop evaluation and optimization framework for the various behavior interventions was established and applied to a subset of Didi's online ride-hailing service drivers (more than 1.35 million). Analyses results indicated safety broadcasting could effectively reduce driving speed by approximately 6.30 km/h and contribute to an approximate 40% reduction in speeding-related crashes. Besides, empirical application results showed that the whole framework contributed to a remarkable reduction in the fatality rate per 100 million km, from an average of 0.368 to 0.225. Finally, directions for future research in terms of data, counterfactual inference methodology, and research subjects have been discussed.

Identification of potential biomarkers and pathways for sepsis using RNA sequencing technology and bioinformatic analysis.

Long non-coding RNAs (lncRNAs) has been proven by many to play a crucial part in the process of sepsis. To obtain a better understanding of sepsis, the molecular biomarkers associated with it, and its possible pathogenesis, we obtained data from RNA-sequencing analysis using serum from three sepsis patients and three healthy controls (HCs). Using edgeR (one of the Bioconductor software package), we identified 1118 differentially expressed mRNAs (DEmRNAs) and 1394 differentially expressed long noncoding RNAs (DElncRNAs) between sepsis patients and HCs. We identified the biological functions of these disordered genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses. The GO analysis showed that the homophilic cell adhesion via plasma membrane adhesion molecules was the most significantly enriched category. The KEGG signaling pathway analysis indicated that the differentially expressed genes (DEGs) were most significantly enriched in retrograde endocannabinoid signaling. Using STRING, a protein-protein interaction network was also created, and Cytohubba was used to determine the top 10 hub genes. To examine the relationship between the hub genes and sepsis, we examined three datasets relevant to sepsis that were found in the gene expression omnibus (GEO) database. PTEN and HIST2H2BE were recognized as hub gene in both GSE4607, GSE26378, and GSE9692 datasets. The receiver operating characteristic (ROC) curves indicate that PTEN and HIST2H2BE have good diagnostic value for sepsis. In conclusion, this two hub genes may be biomarkers for the early diagnosis of sepsis, our findings should deepen our understanding of the pathogenesis of sepsis.

Novel Small Molecule Positive Allosteric Modulator SPAM1 Triggers the Nuclear Translocation of PAC1-R to Exert Neuroprotective Effects through Neuron-Restrictive Silencer Factor.

The neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) exerts effective neuroprotective activity through its specific receptor, PAC1-R. We accidentally discovered that as a positive allosteric modulator (PAM) of PAC1-R, the small-molecule PAM (SPAM1) has a hydrazide-like structure, but different binding characteristics, from hydrazide for the N-terminal extracellular domain of PAC1-R (PAC1-R-EC1). SPAM1 had a significant neuroprotective effect against oxidative stress, both in a cell model treated with hydrogen peroxide (H2O2) and an aging mouse model induced by D-galactose (D-gal). SPAM1 was found to block the decrease in PACAP levels in brain tissues induced by D-gal and significantly induced the nuclear translocation of PAC1-R in PAC1R-CHO cells and mouse retinal ganglion cells. Nuclear PAC1-R was subjected to fragmentation and the nuclear 35 kDa, but not the 15 kDa fragments, of PAC1-R interacted with SP1 to upregulate the expression of Huntingtin (Htt), which then exerted a neuroprotective effect by attenuating the binding availability of the neuron-restrictive silencer factor (NRSF) to the neuron-restrictive silencer element (NRSE). This resulted in an upregulation of the expression of NRSF-related neuropeptides, including PACAP, the brain-derived neurotrophic factor (BDNF), tyrosine hydroxylase (TH), and synapsin-1 (SYN1). The novel mechanism reported in this study indicates that SPAM1 has potential use as a drug, as it exerts a neuroprotective effect by regulating NRSF.

A novel small positive allosteric modulator of neuropeptide receptor PAC1-R exerts neuroprotective effects in MPTP mouse Parkinson's disease model.

As a neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP)-preferring receptor, PAC1-R mediates effective neuroprotective activity. Based on the finding that the antibiotic doxycycline (DOX) with clinical neuroprotective activity functions as a positive allosteric modulator (PAM) of neuropeptide PACAP receptor 1 (PAC1-R), we use virtual and laboratory screening to search for novel small molecule PAMs of PAC1-R. Virtual screening is carried out using a small-molecule library TargetMol. After two-level precision screening with Glide, the top five compounds with the best predicted affinities for PAC1-R are selected and named small positive allosteric modulator 1‒5 (SPAM1‒5). Our results show that only 4-{[4-(4-Oxo-3,4-2-yl)butanamido]methyl}benzoic acid (SPAM1) has stronger neuroprotective activity than DOX in the MPP+ PD cell model and MPTP PD mouse model. SPAM1 has a higher affinity for PAC1-R than DOX, but has no antibiotic activity. Moreover, both SPAM1 and DOX block the decrease of PAC1-R level in mouse brain tissues induced by MPTP. The successful screening of SPAM1 offers a novel drug for the treatment of neurodegenerative disease targeting the PAC1-R.

Eosinophilic Granulomatosis With Polyangiitis Presenting With Oral Granuloma as the Initial Symptom: A Case Report.

Antineutrophil cytoplasmic antibody associated vasculitis includes granulomatosis with polyangiitis, eosinophilic granulomatosis with polyangiitis (EGPA), and microscopic polyangiitis. While EGPA has no specific symptoms, it usually presents as necrotizing vasculitis, eosinophil infiltration of the tissues and organs, and extravascular granuloma formation. Here, we report a patient who had a rare initial presentation of oral granuloma and had been previously misdiagnosed several times at other hospitals. He was finally diagnosed with EGPA and recovered after methylprednisolone and cyclophosphamide treatment. The disease EGPA can present with a rare initial presentation of oral granuloma, methylprednisolone, and cyclophosphamide can be a suitable choice of treatment.

Positive allosteric regulation of PAC1-R up-regulates PAC1-R and its specific ligand PACAP.

PAC1-R is a recognized preferential receptor for the neuropeptide of pituitary adenylate cyclase-activating polypeptide (PACAP), which mediates neuroprotective and nerve regenerative activities of PACAP. In this study, we found that in both PAC1R-CHO cells with high expression of PAC1R-eGFP and retinal ganglion cells (RGC-5) with the natural expression of PAC1-R, oligo-peptide PACAP(28-38) and the positively charged arginine-rich penetrating peptide TAT, as positive allosteric modulators of PAC1-R, significantly trigger the nuclear translocation of PAC1-R. The chromatin immunoprecipitation (ChIP)-PCR results show that the nuclear translocated PAC1-R binds with the promoter regions of PAC1-R and its specific ligand PACAP. The up-regulated promoter activities of PAC1-R and PACAP induced by PACAP(28-38) or TAT are positively correlative with the increase of the expression levels of PAC1-R and PACAP. Moreover, the nuclear translocation of PAC1-R induced by PACAP(28-38) or TAT is significantly inhibited by the mutation of PAC1-R on Cys25 and the palmitoylation inhibitor 2-bromopalmitate. Meanwhile, the increase in both PAC1-R and PACAP levels and the neuroprotective activities of PACAP(28-38) and TAT in MPP-induced cell model of Parkinson ' s disease are synchronously inhibited by 2-bromopalmitate, which are positively correlated with the nuclear translocation of PAC1-R induced by PACAP(28-38) or TAT. Bioinformatics analysis and motif enrichment analysis following ChIP-sequencing show that the transcription factors including SP1, Zic2, GATA1, REST and YY1 may be recruited by nuclear PAC1-R and involved in regulating the promoter activities of PAC1-R and PACAP. ChIP-sequencing and related bioinformatics analysis show that the downstream target genes regulated by the nuclear PAC1-R are mostly involved in the process of cellular stress and related to neuroprotection, neuronal genesis and development.

Personalized driving assistance algorithms: Case study of federated learning based forward collision warning.

Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers' trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers' data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed.

Exploring the associations between driving volatility and autonomous vehicle hazardous scenarios: Insights from field operational test data.

With the promising development and deployment trends of autonomous vehicles (AVs), AVs' operation safety has become a key issue worldwide. Studies have been conducted to reveal the risk factors of AV operation safety based upon AV-involved crash reports. However, the crash data sample size was limited and the crash reports only recorded static information, thus it failed to identify crash contributing factors and further provide feedbacks to AV algorithm development. In this study, the risk factors were investigated based upon hazardous scenarios, which were claimed to possess consistent causal mechanisms with crash events. First, contributing factors were extracted from both vehicle kinematics and traffic environment aspects, and their volatility features were obtained. Then, path analysis models were developed to reveal the concurrent relationships between scenario volatility and hazardous scenario occurrence probability. Besides, to understand the varying risk factors for hazardous scenarios caused by human drivers and AVs, a logit regression model was further established. The modeling results showed that large volatility of space headway held direct impacts on increasing the AV driving risks. And the volatility of the drivable road area had no significant impacts on AV driving risks while it indirectly influenced human driving risks. Finally, result implications for AV driving behavior improvements have been discussed.

Blue light induces the nuclear translocation of neuropeptide receptor PAC1-R associated with the up-regulation of PAC1-R its own in reactive oxygen species associated way.

PAC1-R is neuropeptide PACAP (pituitary adenylate cyclase activating polypeptide) preferring receptor mediates the antioxidant and cytoprotective effects of PACAP. It was found in this research that in both PAC1R-CHO cells with high expression of PAC1R-eGFP and retinal ganglion cells (RGC-5) with natural expression of PAC1-R, blue light and hydrogen peroxide (H2O2) trigger the significant nuclear translocation of PAC1-R, and the nuclear translocation of PAC1-R was positive correlation with the up-regulation of expression level and promoter activity of PAC1-R its own, while red light worked much less efficiently than blue light. Reactive oxygen species (ROS) scavenger NAC (N-acetyl-L-cysteine) and palmitoylation inhibitor 2-bromopalmitate (2-BP) disturbed the nuclear shifting associated with the correlative up-regulation of PAC1 significantly, and PAC1-R mutant (M-PAC1-R) on Cys25/Ala25 displayed the significant decreased nuclear trafficking efficiency. Furthermore, the Western Blot results with the antibody raised against the C-terminal of PAC1-R showing PAC1-R in the nucleus was fragmentation hinting that C-terminal of PAC1-R with theoretical nuclear location signal (NLS) may be involved in activation of PAC1-R promoter in the nucleus. All above results indicated that PAC1-R makes the nuclear translocation to trigger the activation of PAC1-R itself promoter resulting into the up-regulation of of PAC1-R in response to the oxidative stress induced by blue light and ROS such as H2O2 .

Trajectory data based freeway high-risk events prediction and its influencing factors analyses.

The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed.

Risk factors and prognosis for COVID-19-induced acute kidney injury: a meta-analysis.

OBJECTIVE: To analyse the incidence, risk factors and impact of acute kidney injury (AKI) on the prognosis of patients with COVID-19. DESIGN: Meta-analysis. DATA SOURCES: PubMed, Embase, CNKI and MedRxiv of Systematic Reviews from 1 January 2020 to 15 May 2020. STUDY SELECTION: Studies examining the following demographics and outcomes were included: patients' age; sex; incidence of and risk factors for AKI and their impact on prognosis; COVID-19 disease type and incidence of continuous renal replacement therapy (CRRT) administration during COVID-19 infection. RESULTS: A total of 79 research articles, including 49 692 patients with COVID-19, met the systemic evaluation criteria. The mortality rate and incidence of AKI in patients with COVID-19 in China were significantly lower than those in patients with COVID-19 outside China. A significantly higher proportion of patients with COVID-19 from North America were aged ≥65 years and also developed AKI. European patients with COVID-19 had significantly higher mortality and a higher CRRT rate than patients from other regions. Further analysis of the risk factors for COVID-19 combined with AKI showed that age ≥60 years and severe COVID-19 were independent risk factors for AKI, with an OR of 3.53, 95% CI (2.92-4.25) and an OR of 6.07, 95% CI (2.53-14.58), respectively. The CRRT rate in patients with severe COVID-19 was significantly higher than in patients with non-severe COVID-19, with an OR of 6.60, 95% CI (2.83-15.39). The risk of death in patients with COVID-19 and AKI was significantly increased, with an OR of 11.05, 95% CI (9.13-13.36). CONCLUSION: AKI was a common and serious complication of COVID-19. Older age and having severe COVID-19 were independent risk factors for AKI. The risk of in-hospital death was significantly increased in patients with COVID-19 complicated by AKI.

A Bayesian Tobit quantile regression approach for naturalistic longitudinal driving capability assessment.

Given the severe traffic safety issue, tremendous efforts have been devoted to identify the crash contributing factors for developing and implementing safety improvement countermeasures. According to the study findings, driving behaviors have attributed to the majority crash occurrence, among which inadequate driving capability is a key factor. Therefore, a number of studies have been conducted for developing techniques associated with the driving capability assessment and its various improvement. However, the conventional assessment approaches, such as driving license exams and vehicle insurance quotes, have only focused on basic driving skill evaluations or aggregated driving style classifications, which failed to quantify driving capability from the safety perspective with respect to the complex driving scenarios. In this study, a novel longitudinal driving capacity assessment and ranking approach was developed with naturalistic driving data. Two Responsibility-Sensitive Safety (RSS) based driving capability indicators from the perspectives of risk exposure and severity were first proposed. Then, Bayesian Tobit quantile regression (BTQR) models were introduced to explore the relationships between driving capability indicators with trip level characteristics from the aspects of travel features, operational conditions, and roadway characteristics. The modeling results concluded that nighttime driving and higher average speed would lead to higher longitudinal collision risk and its severity. Besides, the BTQR models have provided varying factors significances among different quantile levels, for instance, driving duration is only significant at high quantiles for the driving capability indicators, implying that duration only affects drivers with large longitudinal risk exposures and strong close following tendencies. Furthermore, the case studies provided how to deploy the developed model to obtain the relative longitudinal driving capability rankings. Finally, the model applications from the aspects of commercial fleet safety management and comparing the autonomous vehicles' longitudinal driving behaviors with human drivers have been discussed.

Examining imbalanced classification algorithms in predicting real-time traffic crash risk.

The Active Traffic Management (ATM) system has been widely used in the United States and the European countries to improve the traffic safety of urban expressways. The accurate real-time crash risk prediction is fundamental to the system running well. Crash data are characterized by small probability, which poses a typical Imbalanced Data Classification problem. Most previous studies mainly improved the prediction methods only in data level or algorithm level, which may be inadequate to predict the crash risk accurately especially in a continuous real-time traffic data environment. The comprehensive imbalanced classification algorithm was examined in this research to build more accurate real-time traffic crash risk prediction model. At the output level, the Youden index method has been proved to be of the best ability to divide the prediction results and Probability Calibration Method was proposed to optimize the prediction results in further. At the data level, Under-sampling and Synthetic Minority Oversampling Technique(SMOTE) methods were compared to solve the imbalanced data classification problem by changing the data distribution. At the algorithm level, the cost-sensitive MLP algorithm and Adaboost algorithm were examined and finally the random sampling cost-sensitive MLP model(RCSMLP) and Rusboost model were constructed by synthesizing the optimization methods from three levels. The sensitivity of the RCSMLP model reached 78.10 % and the specificity of the model reached 81.44 %. The AUC and sensitivity of the Rusboost model reached 0.892 and 0.842 while the specificity of the model reached 0.816, which shows the better performance in dealing with the imbalanced traffic crash risk prediction problem compared to existed prediction models. The proposed method of improving prediction accuracy in this study is universal and can be applied to many other prediction models to predict real-time traffic crash risk.

Membranous nephropathy and thymoma in a patient with ankylosing spondylitis: A case report.

RATIONALE: We report a rare case with ankylosing spondylitis (AS), thymoma, and membranous glomerulonephritis. The pathogenic mechanisms of these 3 diseases may be associated with each other. Here, we discuss the course of diagnosis and treatment. PATIENT CONCERNS: A 64-year-old woman with bilateral pain of the sacroiliac joints for 10 years and anasarca for 10 days. DIAGNOSES: A diagnosis of AS by HLA-B27 and pelvic X-ray tests, thymoma based on computed tomography and pathological diagnosis, and membranous glomerulonephritis based on renal biopsy. INTERVENTIONS: We administered methylprednisolone 500 mg/d for 3 consecutive days, followed by methylprednisolone 40 mg oral QD, for a month. OUTCOMES: The patient was followed up once a month. In the sixth month, the patient's serum creatinine had decreased to 0.96 mg/dL, urine microalbumin/creatinine decreased to 173.3 mg/g, and albumin had risen to 33.1 g/L. Pain and morning stiffness were relieved, and the Bath Ankylosing Spondylitis Disease Activity Index score dropped to 4.0. LESSONS: Although the causal relationship between AS, thymoma, and membranous nephropathy in this patient still needs to be established, the pathogenesis between the 3 diseases may have some association. In clinical practice, patients with AS need to be screened for tumors and renal complications.