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.

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.

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.

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.

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.

The allosteric modulation effects of doxycycline, minocycline, and their derivatives on the neuropeptide receptor PAC1-R.

Class B G-protein coupled receptors (GPCR) PAC1-R is a neuropeptide pituitary adenylate cyclase activating polypeptide (PACAP)-preferring receptor that mediates the effective neuroprotective activity. Based on our previous data showing that doxycycline and minocycline work as the positive allosteric modulator (PAM) of PAC1-R, we used computer molecular docking and isothermal titration calorimetry assay to further determine the bindings of doxycycline/minocycline's derivatives including tetracycline/tigecycline with the N-terminal extracellular domain of PAC1-R (PAC1-EC1). Then the cAMP assay combined with the PAC1-R natural agonist PACAP27 was used to confirm the possible PAM roles of the small-molecule antibiotics. The results showed that tetracycline/tigecycline had significant lower affinity to PAC1-EC1 than doxycycline/minocycline, which was consistent with their non-positive allosteric modulation activity on PAC1-R. Furthermore, by comparing the key residues contributing to the PAM binding with the predicted allosteric site in PAC1-EC1, we characterized four motifs contributing to PAM binding in PAC1-EC1. The site-directed mutation results showed that ASN60 played the most important role in the PAM binding of the small-molecule antibiotics, while ASP116 played a sensitive marginal role in the PAM binding. These results not only help to explain the clinical and experimental neuroprotective effects of doxycycline/minocycline, but also help to characterize the PAM binding site in PAC1-EC1, which will promote the screening and characterization of novel small-molecule PAMs targeting PAC1-EC1 with drug development potency in nerve system disease.

Curcumin Suppresses Epithelial-Mesenchymal Transition of Renal Tubular Epithelial Cells through the Inhibition of Akt/mTOR Pathway.

Curcumin has exhibited a protective effect against development of renal fibrosis in animal models, however, its underlying molecular mechanisms are largely unclear. Therefore, we investigated the anti-fibrosis effects of curcumin in transforming growth factor-ß1 (TGF-ß1)-induced epithelial-to-mesenchymal transition (EMT), and the mechanism by which it mediates the phosphatidylinositol 3-kinase (PI3K)/Akt pathway. Human kidney tubular epithelial cells (HKCs) were treated with TGF-ß1 or curcumin alone, or TGF-ß1 in combination with curcumin. The effect of curcumin on cell proliferation was measured by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Expression of E-cadherin, cytokeratin, vimentin, alpha smooth muscle actin (α-SMA), fibroblast-specific protein 1 (FSP1) and key proteins of Akt/mammalian target of rapamycin (mTOR) pathway were analyzed by immunocytochemistry, real-time PCR and Western blot. Low dose curcumin (3.125 and 25 µmol/L) effectively promoted HKC proliferation. When HKCs were co-incubated with TGF-ß1 and curcumin for 72 h, curcumin maintained the epithelial morphology in a dose-dependent manner, decreased expression of vimentin, α-SMA and FSP1 normally induced by TGF-ß1, and increased expression of E-cadherin, cytokeratin. Importantly, we found that curcumin reduced Akt, mTOR and P70S6K phosphorylation, effectively suppressing the activity of the Akt/mTOR pathway in HKCs. Curcumin also promoted HKC proliferation, and antagonized TGF-ß1-driven EMT through the inhibition of Akt/mTOR pathway activity, which may suggest an alternative therapy for renal fibrosis.

PAC1R agonist maxadilan enhances hADSC viability and neural differentiation potential.

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a structurally endogenous peptide with many biological roles. However, little is known about its presence or effects in human adipose-derived stem cells (hADSCs). In this study, the expression of PACAP type I receptor (PAC1R) was first confirmed in hADSCs. Maxadilan, a specific agonist of PAC1R, could increase hADSC proliferation as determined by Cell Counting Kit-8 and cell cycle analysis and promote migration as shown in wound-healing assays. Maxadilan also showed anti-apoptotic activity in hADSCs against serum withdrawal-induced apoptosis based on Annexin V/propidium iodide analysis and mitochondrial membrane potential assays. The anti-apoptotic effects of maxadilan correlated with the down-regulation of Cleaved Caspase 3 and Caspase 9 as well as up-regulation of Bcl-2. The chemical neural differentiation potential could be enhanced by maxadilan as indicated through quantitative PCR, Western blot and cell morphology analysis. Moreover, cytokine neural redifferentiation of hADSCs treated with maxadilan acquired stronger neuron-like functions with higher voltage-dependent tetrodotoxin-sensitive sodium currents, higher outward potassium currents and partial electrical impulses as determined using whole-cell patch clamp recordings. Maxadilan up-regulated the Wnt/ß-catenin signalling pathway associated with dimer-dependent activity of PAC1R, promoting cell viability that was inhibited by XAV939, and it also activated the protein kinase A (PKA) signalling pathway associated with ligand-dependent activity of PAC1R, enhancing cell viability and neural differentiation potential that was inhibited by H-89. In summary, these results demonstrated that PAC1R is present in hADSCs, and maxadilan could enhance hADSC viability and neural differentiation potential in neural differentiation medium.

Transduced protein transduction domain linked HSP27 protected LECs against UVB radiation-induced damage.

PTD-fusion protein technology was used to transduce heat shock protein 27 (HSP27), an anti-apoptotic protein, into human lens epithelial cells (HLECs) (SRA01/04). The protein transduction domain (PTD) of the 11-amino acid YGRKKRRQRRR was tagged at the N-terminus of HSP27. The fusion protein was purified from bacteria transformed with a pKYB-PTD-HSP27 construct. The HLECs were incubated with PTD-HSP27-FITC and the fluorescence inside HLECs was found by fluorescence microscopic examination. To test the ability of PTD-HSP27 to pass through the corneas, PTD-HSP27-FITC was dropped onto the conjunctival sacs of rabbits; fluorescent labeled PTD-HSP27 was then observed in the rabbit aqueous humor. After being incubated with the PTD-HSP27 protein and irradiated with ultraviolet-B (UVB) light, HLECs was analyzed by flow cytometry, Hoechst 33258 staining and measurement of the potential of the mitochondrial transmembrane. HLECs incubated with PTD-HSP27 had a lower apoptotic rate and a higher mitochondrial membrane potential than the control cells. PTD-HSP27 appears to be sufficient to protect HLECs against UVB-induced apoptosis.

Enhancing multi-scenario applicability of freeway variable speed limit control strategies using continual learning.

Variable speed limit (VSL) control benefits freeway operations through dynamic speed limit adjustment strategies for specific operation scenarios, such as traffic jams, secondary crash prevention, etc. To develop optimal strategies, deep reinforcement learning (DRL) has been employed to map the traffic operation status to speed limits with the corresponding control effects. Then, VSL control strategies were obtained based upon memories of these complex mapping relationships. However, under multi-scenario conditions, DRL trained VSL faces the challenge of performance decay, where the control strategy effects drop sharply for early trained "old scenarios". This so-called scenario forgetting problem is attributed to the fact that DRL would forget the learned old scenario mapping memories after new scenario trainings. To tackle this issue, a continual learning approach has been introduced in this study to enhance the multi-scenario applicability of VSL control strategies. Specifically, a gradient projection memory (GPM) based neural network parameter updating method was proposed to keep the mapping memories of old scenarios during new scenario trainings by imposing constraints on the direction of gradient updates for new tasks. The proposed method was evaluated using three typical freeway operation scenarios developed in the simulation platform SUMO. Experimental results showed that the continual learning approach has substantially reduced the performance decay in old scenarios by 17.76% (valued using backward transfer metrics). Furthermore, the multi-scenario VSL control strategies successfully reduced the speed standard deviation and average travel time by 28.77% and 7.25% respectively. Moreover, the generalization of the proposed continual learning based VSL approach were evaluated and discussed.

RiskFormer: Exploring the temporal associations between multi-type aberrant driving events and crash occurrence.

With the development of driving behavior monitoring technologies, commercial transportation enterprises have leveraged aberrant driving event detection results for evaluating crash risk and triggering proactive interventions. The state-of-the-art applications were established based upon instant associations between events and crash occurrence, which assumed crash risk surged with aberrant events. Consequently, the generated crash risk monitoring results merely contain discrete abrupt changes, failing to depict the time-varying trend of crash risk and posing challenges for interventions. Given the multiple types of aberrant events and their various temporal combinations, the key to depict crash risk time-varying trend is the analysis of multi-type events' temporal coupling influence. Existing studies employed event frequency to model combined influence, lacking the capability to differentiate the temporal sequential characteristics of events. Hence, there is an urgent need to further explore multi-type events' temporal coupling influence on crash risk. In this study, the temporal associations between multi-type aberrant driving events and crash occurrence are explored. Specifically, a contrastive learning method, fusing prior domain knowledge and empirical data, was proposed to analyze the single event temporal influence on crash risk. After that, a novel Crash Risk Evaluation Transformer (RiskFormer) was developed. In the RiskFormer, a unified encoding method for different events, as well as a self-attention mechanism, were established to learn multi-type events' temporal coupling influence. Empirical data from online ride-hailing services were employed, and the modeling results unveiled three distinct time-varying patterns of crash risk, including decay, increasing, and increasing-decay pattern. Additionally, RiskFormer exhibited remarkable crash risk evaluation performance, demonstrating a 12.8% improvement in the Area Under Curve (AUC) score compared to the conventional instant-association-based model. Furthermore, the practical utility of RiskFormer was illustrated through a crash risk monitoring sample case. Finally, applications of the proposed methods and their further investigations have been discussed.

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.

Personalized forward collision warning model with learning from human preferences.

The Forward Collision Warning (FCW) system has been widely equipped on vehicles to reduce rear-end crashes, which are considered the most common type of crash. However, existing FCW systems have the problem of low response rates, which restrict their safety improvement effects. This study aims to address this issue by building personalized FCW models based on human risk preferences. First, a warning feedback index ranks the gaps between drivers' risk perceptions and FCW models. Then, reward models are developed to characterize the risk perception preferences of each individual driver. After that, the reward models serve as guidelines to fine-tune the benchmark FCW model using the Proximal Policy Optimization (PPO) algorithm. In the empirical analyses, a total of 95,814 warning fragments collected from 74 drivers are used, and the proposed method generates pseudo warning results. By comparing the pseudo and historical warnings, it shows that the precision of pseudo warning results increases from 53.5% to 78.2%. Furthermore, the average differences between the moment of warning and the moment of braking behavior decrease from 2.4 s to 1.6 s. This demonstrates a higher synchronization level in the timing of risk perception between the personalized FCW models and individual drivers, which enhances the driver's trust in the warning system.

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.

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.

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.

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.

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.