Exploring the potential biological significance of KDELR family genes in lung adenocarcinoma.

The Lys-Asp-Glu-Leu receptor (KDELR) family genes play critical roles in a variety of biological processes in different tumors. Our study aimed to provide a comprehensive analysis of the potential roles of KDELRs in lung adenocarcinoma (LUAD). Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, as well as clinical samples, we conducted a series of analyses and validations using R software tools and various online resources. The results showed that KDELR family genes and proteins were highly expressed and associated with a poor prognosis of LUAD. Promoter hypomethylation and the competing endogenous RNA (ceRNA) network of PCAT6/hsa-miR-326/KDELR1 might be potential causes of aberrant KDELR1 overexpression in LUAD. Three key Transcription factors (TFs) (SPI1, EP300, and MAZ) and a TFs-miRNAs-KDELRs network (involving 11 TFs) might be involved in modulating KDELRs expression abnormalities. Gene Set Enrichment Analysis (GSEA) indicated enrichment of genes highly expressing KDELR1, KDELR2, and KDELR3 in MTORC1_SIGNALING, P53_PATHWAY, and ANGIOGENESIS. Negative correlations between KDELRs expression and CD8 + T cell infiltration, as well as CTLA-4 expression. Our multiple analyses suggested that the KDELRs are important signaling molecules in LUAD. These results provided novel insights for developing prognostic markers and novel therapies of LUAD.

Rapid Membrane-Penetrating Hybrid Peptides Achieve Efficient Dual Antimicrobial and Antibiofilm Activity through a Triple Bactericidal Mechanism.

Antimicrobial peptides (AMPs) are a type of biomaterial used against multidrug resistant (MDR) bacteria. This study reports the design of a peptide family rich in tryptophan and lysine obtained by optimizing a natural AMP using single factor modification and pheromone hybridization to expedite the penetration and improve the antimicrobial activity of AMPs. S-4, L-4, and P-4 showed α-helical structures, exhibited extremely fast membrane penetration rates in vitro, and could kill MDR bacteria efficiently within 30 min. Intracellular fluorescence localization suggested rapid membrane-penetrating of AMPs within 1 min, making it more difficult for bacteria to develop resistance. Furthermore, they could effectively inhibit and destroy bacterial biofilms with dual antimicrobial and antibiofilm activity. In the treatment of skin infections caused by MDR-Acinetobacter baumannii in vivo , AMPs could effectively alleviate inflammation without toxic side effects. Additionally, the triple antimicrobial damage of AMPs was described in detail. AMPs rapidly penetrate the cell membrane, inducing cell membrane damage, triggering oxidative damage with a storm of reactive oxygen species and leading to bacterial death through leakage of cellular contents by complexing with DNA. The multiple damage is an important means by which AMPs can prevent bacterial resistance adequately.

Comparative efficacy of six programmed cell death Protein-1 inhibitors as first-line treatment for advanced non-small cell lung cancer: a multicenter retrospective cohort study.

The purpose of this study was to assess the comparative efficacy of six programmed cell death-1 inhibitors (nivolumab, pembrolizumab, sintilimab, tislelizumab, toripalimab, and camrelizumab) that have been used as first-line therapy for Chinese patients with advanced non-small cell lung cancer (NSCLC), which remains unclear. We determined the differences in efficacy by observing patient survival data, with the goal of informing future treatment options. Retrospective data analysis from June 2015 to April 2023 included 913 patients across six groups: nivolumab (123%, 13.5%), pembrolizumab (421%, 46.1%), sintilimab (239%, 26.1%), tislelizumab (64%, 7.0%), toripalimab (39%, 4.3%), and camrelizumab (27%, 3.0%). The median progression-free survival (PFS) for each group was 16.0, 16.1, 18.4, 16.9, 23.7, and 12.8 months, and the median overall survival (OS) was 33.7, 36.1, 32.5, not reached, 30.9 and 46.0 months for the nivolumab, sintilimab, pembrolizumab, tislelizumab, toripalimab, and camrelizumab groups, respectively. While differences existed in the objective response rates among groups (p < 0.05), there were no significant differences (all p > 0.05) in PFS or OS. The findings suggest comparable efficacy among these PD-1 inhibitors for NSCLC treatment, underscoring their collective suitability and aiding treatment decisions.

Formation and reactivity of a unique M⋯C-H interaction stabilized by carborane cages.

Broadening carborane applications has consistently been the goal of chemists in this field. Herein, compared to alkyl or aryl groups, a carborane cage demonstrates an advantage in stabilizing a unique bonding interaction: M⋯C-H interaction. Experimental results and theoretical calculations have revealed the characteristic of this two-center, two-electron bonding interaction, in which the carbon atom in the arene ring provides two electrons to the metal center. The reduced aromaticity of the benzene moiety, long distance between the metal and carbon atom in arene, and the upfield shift of the signal of M⋯C-H in the nuclear magnetic resonance spectrum distinguished this interaction from metal⋯C π interaction and metal-C(H) σ bonds. Control experiments demonstrate the unique electronic effects of carborane in stabilizing the M⋯C-H bonding interaction in organometallic chemistry. Furthermore, the M⋯C-H interaction can convert into C-H bond metallization under acidic conditions or via treatment with t-butyl isocyanide. These findings deepen our understanding regarding the interactions between metal centers and carbon atoms and provide new opportunities for the use of carboranes.

Does pasteurization inactivate bird flu virus in milk?

Recently, an outbreak of highly pathogenic avian influenza A (H5N1), which carries the clade 2.3.4.4b hemagglutinin (HA) gene and has been prevalent among North American bird populations since the winter of 2021, was reported in dairy cows in the United States. As of 24 May 2024, the virus has affected 63 dairy herds across nine states and has resulted in two human infections. The virus causes unusual symptoms in dairy cows, including an unexpected drop in milk production, and thick colostrum-like milk. Notably, The US Food and Drug Administration reported that around 20% of tested retail milk samples contained H5N1 viruses, with a higher percentage of positive results from regions with infected cattle herds. Data are scant regarding how effectively pasteurization inactivates the H5N1 virus in milk. Therefore, in this study, we evaluated the thermal stability of the H5 clade 2.3.4.4b viruses, along with one human H3N2 virus and other influenza subtype viruses, including H1, H3, H7, H9, and H10 subtype viruses. We also assessed the effectiveness of pasteurization in inactivating these viruses. We found that the avian H3 virus exhibits the highest thermal stability, whereas the H5N1 viruses that belong to clade 2.3.4.4b display moderate thermal stability. Importantly, our data provide direct evidence that the standard pasteurization methods used by dairy companies are effective in inactivating all tested subtypes of influenza viruses in raw milk. Our findings indicate that thermally pasteurized milk products do not pose a safety risk to consumers.

Amorphous curcumin-based hydrogels to reduce the incidence of post-surgical intrauterine adhesions.

The incidence of intrauterine adhesions (IUA) has increased with the rising utilization of intrauterine surgery. The postoperative physical barrier methods commonly used, such as balloons and other fillers, have limited effectiveness and may even cause further damage to the remaining endometrial tissue. Herein, we developed an injectable thermosensitive hydrogel using Pluronic F127/F68 as pharmaceutical excipients and curcumin as a natural active molecule. The hydrogel effectively addresses solubility and low bioavailability issues associated with curcumin. In vitro, drug release assays revealed that the amorphous curcumin hydrogel promotes dissolution and sustained release of curcumin. In vitro experiments reveal high biocompatibility of the hydrogel and its ability to enhance vascular formation while inhibiting the expression of fibrotic factor TGF-ß1. To assess the effectiveness of preventing IUAs, in vivo experiments were conducted using IUA rats and compared with a class III medical device, a new-crosslinked hyaluronic acid (NCHA) gel. According to the study, curcumin hydrogel is more effective than the NCHA group in improving the regeneration of the endometrium, increasing the blood supply to the endometrium and reducing the abnormal deposition of fibrin, thus preventing IUA more effectively. This study provides a promising strategy for treating and preventing IUA.

Albumin Nanoparticles Increase the Efficacy of Doxorubicin Hydrochloride Liposome Injection Based on Threshold Theory.

One of the most significant reasons hindering the clinical translation of nanomedicines is the rapid clearance of intravenously injected nanoparticles by the mononuclear phagocyte system, particularly by Kupffer cells in the liver, leading to an inefficient delivery of nanomedicines for tumor treatment. The threshold theory suggests that the liver's capacity to clear nanoparticles is limited, and a single high dose of nanoparticles can reduce the hepatic clearance efficiency, allowing more nanomedicines to reach tumor tissues and enhance therapeutic efficacy. Building upon this theory, researchers have conducted numerous validation studies based on the same nanoparticle carrier systems. These studies involve the use of albumin nanoparticles to improve the therapeutic efficacy of albumin nanomedicines as well as polyethylene glycol (PEG)-modified liposomal nanoparticles to enhance the efficacy of PEGylated liposomal nanomedicines. However, there is no research indicating the feasibility of the threshold theory when blank nanoparticles and nanomedicine belong to different nanoparticle carrier systems currently. In this study, we prepared two different sizes of albumin nanoparticles by using bovine serum albumin. We used the marketed nanomedicine liposomal doxorubicin hydrochloride injection (trade name: LIBOD, manufacturer: Shanghai Fudan-zhangjiang Biopharmaceutical Co., Ltd.), as the representative nanomedicine. Through in vivo experiments, we found that using threshold doses of albumin nanoparticles still can reduce the clearance rate of LIBOD, prolong its time in vivo, increase the area under the plasma concentration-time curve (AUC), and also lead to an increased accumulation of the drug at the tumor site. Furthermore, evaluation of in vivo efficacy and safety further indicates that threshold doses of 100 nm albumin nanoparticles can enhance the antitumor effect of LIBOD without causing harm to the animals. During the study, we found that the particle size of albumin nanoparticles influenced the in vivo distribution of the nanomedicine at the same threshold dose. Compared with 200 nm albumin nanoparticles, 100 nm albumin nanoparticles more effectively reduce the clearance efficiency of LIBOD and enhance nanomedicine accumulation at the tumor site, warranting further investigation. This study utilized albumin nanoparticles to reduce hepatic clearance efficiency and enhance the delivery efficiency of nonalbumin nanocarrier liposomal nanomedicine, providing a new avenue to improve the efficacy and clinical translation of nanomedicines with different carrier systems.

Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

Metabolomics-driven discovery of therapeutic targets for cancer cachexia.

Cancer cachexia (CC) is a devastating metabolic syndrome characterized by skeletal muscle wasting and body weight loss, posing a significant burden on the health and survival of cancer patients. Despite ongoing efforts, effective treatments for CC are still lacking. Metabolomics, an advanced omics technique, offers a comprehensive analysis of small-molecule metabolites involved in cellular metabolism. In CC research, metabolomics has emerged as a valuable tool for identifying diagnostic biomarkers, unravelling molecular mechanisms and discovering potential therapeutic targets. A comprehensive search strategy was implemented to retrieve relevant articles from primary databases, including Web of Science, Google Scholar, Scopus and PubMed, for CC and metabolomics. Recent advancements in metabolomics have deepened our understanding of CC by uncovering key metabolic signatures and elucidating underlying mechanisms. By targeting crucial metabolic pathways including glucose metabolism, amino acid metabolism, fatty acid metabolism, bile acid metabolism, ketone body metabolism, steroid metabolism and mitochondrial energy metabolism, it becomes possible to restore metabolic balance and alleviate CC symptoms. This review provides a comprehensive summary of metabolomics studies in CC, focusing on the discovery of potential therapeutic targets and the evaluation of modulating specific metabolic pathways for CC treatment. By harnessing the insights derived from metabolomics, novel interventions for CC can be developed, leading to improved patient outcomes and enhanced quality of life.

Evolution of H7N9 highly pathogenic avian influenza virus in the context of vaccination.

Human infections with the H7N9 influenza virus have been eliminated in China through vaccination of poultry; however, the H7N9 virus has not yet been eradicated from poultry. Carefully analysis of H7N9 viruses in poultry that have sub-optimal immunity may provide a unique opportunity to witness the evolution of highly pathogenic avian influenza virus in the context of vaccination. Between January 2020 and June 2023, we isolated 16 H7N9 viruses from samples we collected during surveillance and samples that were sent to us for disease diagnosis. Genetic analysis indicated that these viruses belonged to a single genotype previously detected in poultry. Antigenic analysis indicated that 12 of the 16 viruses were antigenically close to the H7-Re4 vaccine virus that has been used since January 2022, and the other four viruses showed reduced reactivity with the vaccine. Animal studies indicated that all 16 viruses were nonlethal in mice, and four of six viruses showed reduced virulence in chickens upon intranasally inoculation. Importantly, the H7N9 viruses detected in this study exclusively bound to the avian-type receptors, having lost the capacity to bind to human-type receptors. Our study shows that vaccination slows the evolution of H7N9 virus by preventing its reassortment with other viruses and eliminates a harmful characteristic of H7N9 virus, namely its ability to bind to human-type receptors.

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.

Correction: Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

[This corrects the article DOI: 10.1371/journal.ppat.1006851.].

Green synthesis of epigallocatechin gallate-ferric complex nanoparticles for photothermal enhanced antibacterial and wound healing.

Bacterial infections are a significant global health concern, particularly in the context of skin infections and chronic wounds, which was further exacerbated by the emerging of antibiotic resistance. Therefore, there are urgent needs to develop alternative antibacterial strategies without inducing significant resistance. Photothermal therapy (PTT) is a promising alternative approach but usually faces limitations such as the need for stable and environmental-friendly PTT agents and ensuring biocompatibility with living tissues, necessitating ongoing research for its clinical advancement. Herein, in this study, with the aim to develop a green synthesized PTT agent for photothermal enhanced antibacterial and wound healing, we proposed a facile one-pot method to prepare epigallocatechin gallate-ferric (EGCG-Fe) complex nanoparticles. The obtained nanoparticles showed improved good size distribution and stability with high reproducibility. More importantly, EGCG-Fe complex nanoparticles have additional photothermal conversion ability which can give photothermal enhanced antibacterial effect on various pathogens, including Gram-positive Staphylococcus aureus (S. aureus) and Gram-negative Escherichia coli (E. coli) strains. EGCG-Fe complex nanoparticles also showed powerful biofilm prevention and destruction effects with promoted antibacterial and wound healing on mice model. In conclusion, EGCG-Fe complex nanoparticles can be a robust green material with effective and novel light controllable antibacterial properties for photothermal enhanced antibacterial and wound healing applications.

Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes.

The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.

Evolution and biological characterization of H5N1 influenza viruses bearing the clade 2.3.2.1 hemagglutinin gene.

H5N1 avian influenza viruses bearing the clade 2.3.2.1 hemagglutinin (HA) gene have been widely detected in birds and poultry in several countries. During our routine surveillance, we isolated 28 H5N1 viruses between January 2017 and October 2020. To investigate the genetic relationship of the globally circulating H5N1 viruses and the biological properties of those detected in China, we performed a detailed phylogenic analysis of 274 representative H5N1 strains and analyzed the antigenic properties, receptor-binding preference, and virulence in mice of the H5N1 viruses isolated in China. The phylogenic analysis indicated that the HA genes of the 274 viruses belonged to six subclades, namely clades 2.3.2.1a to 2.3.2.1f; these viruses acquired gene mutations and underwent complicated reassortment to form 58 genotypes, with G43 being the dominant genotype detected in eight Asian and African countries. The 28 H5N1 viruses detected in this study carried the HA of clade 2.3.2.1c (two strains), 2.3.2.1d (three strains), or 2.3.2.1f (23 strains), and formed eight genotypes. These viruses were antigenically well-matched with the H5-Re12 vaccine strain used in China. Animal studies showed that the pathogenicity of the H5N1 viruses ranged from non-lethal to highly lethal in mice. Moreover, the viruses exclusively bound to avian-type receptors and have not acquired the ability to bind to human-type receptors. Our study reveals the overall picture of the evolution of clade 2.3.2.1 H5N1 viruses and provides insights into the control of these viruses.

Emissions reduction strategy in a three-stage agrifood value chain: A dynamic differential game approach.

Agrifood systems account for 31% of global greenhouse gas emissions. Substantial emissions reduction in agrifood systems is critical to achieving the temperature goal set by the Paris Agreement. A key challenge in reducing GHG emissions in the agrifood value chain is the imbalanced allocation of benefits and costs associated with emissions reduction among agrifood value chain participants. However, only a few studies have examined agrifood emissions reduction from a value chain perspective, especially using dynamic methods to investigate participants' long-term emissions reduction strategies. This paper helps fill this gap in the existing literature by examining the impact of collaborations among agrifood value chain participants on correcting those misallocations and reducing emissions in agrifood systems. We develop a dynamic differential game model to examine participants' long-term emissions reduction strategies in a three-stage agrifood value chain. We use the Hamilton-Jacobi-Bellman equation to derive the Nash equilibrium emissions reduction strategies under non-cooperative, cost-sharing, and cooperative mechanisms. We then conduct numerical analysis and sensitivity analysis to validate our model. Our results show that collaboration among value chain participants leads to higher emissions reduction efforts and profits for the entire value chain. Specifically, based on our numerical results, the cooperative mechanism results in the greatest emissions reduction effort by the three participants, which leads to a total that is nearly three times higher than that of the non-cooperative mechanism and close to two times higher than the cost-sharing mechanism. The cooperative mechanism also recorded the highest profits for the entire value chain, surpassing the non-cooperative and cost-sharing mechanisms by around 37% and 16%, respectively. Our results provide valuable insights for policymakers and agrifood industry stakeholders to develop strategies and policies encouraging emissions reduction collaborations in the agrifood value chain and reduce emissions in the agrifood systems.

Key Amino Acid Residues That Determine the Antigenic Properties of Highly Pathogenic H5 Influenza Viruses Bearing the Clade 2.3.4.4 Hemagglutinin Gene.

The H5 subtype highly pathogenic avian influenza viruses bearing the clade 2.3.4.4 HA gene have been pervasive among domestic poultry and wild birds worldwide since 2014, presenting substantial risks to human and animal health. Continued circulation of clade 2.3.4.4 viruses has resulted in the emergence of eight subclades (2.3.4.4a-h) and multiple distinct antigenic groups. However, the key antigenic substitutions responsible for the antigenic change of these viruses remain unknown. In this study, we analyzed the HA gene sequences of 5713 clade 2.3.4.4 viruses obtained from a public database and found that 23 amino acid residues were highly variable among these strains. We then generated a series of single-amino-acid mutants based on the H5-Re8 (a vaccine seed virus) background and tested their reactivity with a panel of eight monoclonal antibodies (mAbs). Six mutants bearing amino acid substitutions at positions 120, 126, 141, 156, 185, or 189 (H5 numbering) led to reduced or lost reactivity to these mAbs. Further antigenic cartography analysis revealed that the amino acid residues at positions 126, 156, and 189 acted as immunodominant epitopes of H5 viruses. Collectively, our findings offer valuable guidance for the surveillance and early detection of emerging antigenic variants.

Marine biomaterials in biomedical nano/micro-systems.

Marine resources in unique marine environments provide abundant, cost-effective natural biomaterials with distinct structures, compositions, and biological activities compared to terrestrial species. These marine-derived raw materials, including polysaccharides, natural protein components, fatty acids, and marine minerals, etc., have shown great potential in preparing, stabilizing, or modifying multifunctional nano-/micro-systems and are widely applied in drug delivery, theragnostic, tissue engineering, etc. This review provides a comprehensive summary of the most current marine biomaterial-based nano-/micro-systems developed over the past three years, primarily focusing on therapeutic delivery studies and highlighting their potential to cure a variety of diseases. Specifically, we first provided a detailed introduction to the physicochemical characteristics and biological activities of natural marine biocomponents in their raw state. Furthermore, the assembly processes, potential functionalities of each building block, and a thorough evaluation of the pharmacokinetics and pharmacodynamics of advanced marine biomaterial-based systems and their effects on molecular pathophysiological processes were fully elucidated. Finally, a list of unresolved issues and pivotal challenges of marine-derived biomaterials applications, such as standardized distinction of raw materials, long-term biosafety in vivo, the feasibility of scale-up, etc., was presented. This review is expected to serve as a roadmap for fundamental research and facilitate the rational design of marine biomaterials for diverse emerging applications.

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.

Analysis of avian influenza A (H3N8) viruses in poultry and their zoonotic potential, China, September 2021 to May 2022.

BackgroundTwo human cases of avian influenza A (H3N8) virus infection were reported in China in 2022.AimTo characterise H3N8 viruses circulating in China in September 2021-May 2022.MethodsWe sampled poultry and poultry-related environments in 25 Chinese provinces. After isolating H3N8 viruses, whole genome sequences were obtained for molecular and phylogenetic analyses. The specificity of H3N8 viruses towards human or avian receptors was assessed in vitro. Their ability to replicate in chicken and mice, and to transmit between guinea pigs was also investigated.ResultsIn total, 98 H3N8 avian influenza virus isolates were retrieved from 38,639 samples; genetic analysis of 31 representative isolates revealed 17 genotypes. Viruses belonging to 10 of these genotypes had six internal genes originating from influenza A (H9N2) viruses. These reassorted viruses could be found in live poultry markets and comprised the strains responsible for the two human infections. A subset of nine H3N8 viruses (including six reassorted) that replicated efficiently in mice bound to both avian-type and human-type receptors in vitro. Three reassorted viruses were shed by chickens for up to 9 days, replicating efficiently in their upper respiratory tract. Five reassorted viruses tested on guinea pigs were transmissible among these by respiratory droplets.ConclusionAvian H3N8 viruses with H9N2 virus internal genes, causing two human infections, occurred in live poultry markets in China. The low pathogenicity of H3N8 viruses in poultry allows their continuous circulation with potential for reassortment. Careful monitoring of spill-over infections in humans is important to strengthen early-warning systems and maintain influenza pandemic preparedness.