[This corrects the article DOI: 10.1016/j.cellin.2026.100310.].

SARS-CoV-2 infection-induced syncytia formation accelerates cell-to-cell transmission of the virus and enhances viral evasion by neutralizing antibodies. Host innate immune response plays a key role in controlling viral infection. Our present work identifies tumor necrosis factor (TNF) as a key cytokine quickly released from activated innate immune cells that suppresses SARS-CoV-2 spike-mediated cell-cell fusion. Mechanistically, TNF signals through the TNFR1-TRADD/TRAF2/RIPK1-MAPK-SDC4 axis. SDC4 further activates the RhoA/ROCK signaling pathway, which promotes cytoskeletal reorganization, leading to the formation of actin bundles at the interface between infected cell and adjacent cell. Such remodeling of actin effectively blocks further propagation of syncytia and viral spreading. These findings provide critical insights into the dynamic interplay between host antiviral factors and syncytia formation, deepening our understanding of the innate immune control of SARS-CoV-2 infection.

Ride comfort is vital for intelligent vehicle adoption, and active suspension systems are central to its improvement. Road preview perception can further enhance suspension performance, yet effectively integrating road data into control strategies remains challenging. This paper introduces DGRL-RP, a mechanism-data-driven road preview control strategy that merges differential geometry analysis with deep reinforcement learning (DRL). First, differential geometry elucidates how future road information influences suspension dynamics, enabling decomposition into mechanism and data-driven modules. In the data-driven module, an expert-guided soft-hard module—which normalizes multi-scale state inputs and enforces actuator limits through combined soft and hard constraints—is integrated with the Twin Delayed Deep Deterministic Policy (TD3-SH) algorithm and a Deterministic Experience Tracking (DET) mechanism to accelerate learning and convergence. Simulation experiments using the root mean square (RMS) of body acceleration as the performance metric demonstrate that DGRL-RP outperforms Model Predictive Control (MPC) by 71.73% and TD3-SH by 65.39%. Moreover, it maintains over 90% comfort optimization across diverse scenarios, illustrating superior control performance and strong generalization. DGRL-RP offers a novel solution for ride comfort optimization and advances active suspension control toward greater intelligence and precision.

Sampling critical scenarios from the natural driving datasets (NDD) to build a test scenario library has proven to be one of the most effective approaches. Critical scenarios typically follow a long-tail distribution with the "class imbalance" characteristic, posing a challenge in balancing between exploitation and exploration to ensure the efficiency and comprehensiveness of the test scenario library simultaneously. Researchers employed the & varepsilon;-greedy policy to extract risky scenarios and achieved encouraging progress in ensuring testing efficiency. However, due to the epsilon-probability random sampling, critical scenarios with low probability may be missed from the test scenario library, limiting the test comprehensiveness. To bridge this gap, this paper proposes the Scenario Space Partitioning-Critical Scenario Sampling Method (SSP-CSSM) to efficiently sample critical scenarios from a long-tail distribution. Firstly, the concept of Scenario Space Partitioning (SSP) is proposed to decouple exploitation and exploration as sampling the risky scenarios and the rare scenarios respectively. Next, the normalized time to collision (NTTC) and normalized Shannon Information (NSI) are designed to represent the risk and rarity of a scenario. The intersection of NTTC and NSI projections is defined as the boundary to divide the scenario space into a high-risk sub-region and a high-rarity sub-region. Finally, different sampling methods and weight functions are proposed for each sub-region to efficiently sample critical scenarios. In the high-risk subregion, the NTTC is regarded as sampling weight to sample risky scenarios for exploitation, achieving an equivalent sampling efficiency as the & varepsilon;-greedy sampling policy. In the high-rarity region, the sampling weight function based on NSI is designed to amplify the granularity of rare scenarios for exploration, capturing critical scenarios missed by the & varepsilon;-greedy policy. Simulation results showed that the SSP-CSSM method sampled a significantly larger number of crash scenarios, thereby improving the efficiency and enhancing the comprehensiveness of the test scenario library.

Long-standing research on vehicle comfort optimization has centered on active and semi-active suspension control using experience-based or optimization-based algorithms. However, these methods often require substantial engineering resources and pose challenges in acquiring theoretical knowledge. The emergence of advanced Artificial Intelligence (AI), particularly data-driven approaches, has transformed how engineers tackle knowledge-intensive tasks like suspension control. Yet, the interpretability challenges of data-driven methods limit their widespread use in engineering. This study proposes a mechanism-data-driven active suspension control strategy that integrates Differential Geometry (DG) and Deep Reinforcement Learning (DRL) to achieve theoretical fusion of mechanism and data models. A DRL control architecture (DGRL) based on DG theory is introduced, enabling mechanism-level analysis of suspension control and dividing the control strategy into mechanism and data models. For the data model, a DRL optimal control framework is constructed, incorporating the Twin-Delayed Deep Deterministic policy (TD3) with an expert-guided soft-hard module (TD3-SH) and the Deterministic Experience Tracing (DET) mechanism. This effectively explores and utilizes the knowledge in massive data. Simulation results show that the DGRL strategy outperforms baseline algorithms such as Deep Deterministic Policy Gradient (DDPG), TD3, Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and TD3-SH by 75.8%, 65.5%, 77.5%, 56.3%, and 46.5%, respectively. In complex environments with varying road features and considering the domain randomization of the suspension system, the DGRL strategy can improve ride comfort by up to 85%, demonstrating its robustness and significant potential for widespread application in industrial and real-world scenarios.

Innate immune responses triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection play pivotal roles in the pathogenesis of COVID-19, while host factors including proinflammatory cytokines are critical for viral containment. By utilizing quantitative and qualitative models, we discovered that soluble factors secreted by human monocytes potently inhibit SARS-CoV-2-induced cell-cell fusion in viral-infected cells. Through cytokine screening, we identified that interleukin-1β (IL-1β), a key mediator of inflammation, inhibits syncytia formation mediated by various SARS-CoV-2 strains. Mechanistically, IL-1β activates RhoA/ROCK signaling through a non-canonical IL-1 receptor-dependent pathway, which drives the enrichment of actin bundles at the cell-cell junctions, thus prevents syncytia formation. Notably, in vivo infection experiments in mice confirmed that IL-1β significantly restricted SARS-CoV-2 spread in the lung epithelium. Together, by revealing the function and underlying mechanism of IL-1β on SARS-CoV-2-induced cell-cell fusion, our study highlights an unprecedented antiviral function for cytokines during viral infection.

Measles is an acute and highly contagious viral disease that poses significant public health challenges globally. Since 2001, continuous virologic surveillance has been conducted in Shanghai, enabling a comprehensive analysis of the evolution of the nucleoprotein (N gene) and fusion gene (F gene) of the measles virus (MeV) over a 21-year period. Between 2001 and 2022, there were a total of 1405 MeV strains isolated by the Shanghai Center for Disease Control and prevention (SCDC), including 6 strains of genotype D8, 8 strains of genotype B3, 12 strains of genotype H1b, and the remaining strains of genotype H1a. Reverse transcription polymerase chain reaction (RT-PCR) was used to amplify the 3' end of the N gene (450 nt) and the complete sequence of the F gene (1622 nt) from the viral isolates. Sequencing of the RT-PCR products was followed by nucleotide and amino acid phylogenetic analyses. The substitution rates were for the F and N genes in Shanghai were determined to be 0.89 × 10-3 and 2.20 × 10-3 substitutions site/year, respectively. Globally, the nucleotide and amino acid similarities of the N gene among 13,498 MeV isolates ranged from 89.1 %-100.0 % and 90.2 %-100.0 %, respectively. Notably, the F gene exhibited 16 high-amino-acid-mutation sites, most of which differed among H1a MeV strains compared to the Shanghai-191 vaccine strain. The deletion of the glycosylation site at aa 9-11(NVS) was primarily observed in H1a and H1b of MeV strains. However, critical functional sites in the F gene remained conserved. In conclusion, the previously predominant indigenous H1a wild-type measles virus (MeV) has not been detected for over two years, with only imported MeV genotypes currently being identified. It is crucial to strengthen the surveillance of MeV genotypes to facilitate the timely identification and containment of imported measles cases, thereby preventing potential outbreaks.

Estimating the depth of the 3D world from 2D images is a classic and important issue in computer vision, which has been widely studied for decades. With the remarkable effect of deep learning on various computer vision tasks, scholars have become increasingly interested in exploring stereo matching (i.e., disparity estimation) with deep learning. We reviewed related studies through bibliometrics, and especially extracted research hotspots and evolution context in the field, aiming to facilitate researchers in clarifying the positioning of their research and finding new inspiration. Specifically, we summarized the distribution of publication years, countries/regions, institutions, authors, research areas, document types, etc. of the research in this field. According to the analysis of information from these publications, we presented an overview of this field and divided its development into three stages: the preliminary exploration period (1999–2011), the gradual awakening period (2012–2016), and the vigorous development period (2017- present). Finally, we analyzed and predicted the future research directions.

As the challenges in autonomous driving become more complex and changing, traditional methods are struggling to cope. As a result, artificial intelligence (AI) techniques have gained widespread attention due to their potential in addressing these challenges. To investigate the application and performance of deep reinforcement learning (DRL) techniques in vertical control of autonomous vehicles, we propose an active suspension control algorithm that incorporates deterministic experience tracing (DET). The agent explores and learns deterministic policies by interacting with the environment and continuously exploring and exploiting the generated data. During this process, DET stores state and action data in a separate experience memory buffer over time. Additionally, DET processes this information into auxiliary rewards that decay based on temporal logic. This drives the agent to self-iterate and rapidly improve. DET allows AI techniques to incorporate temporal robustness into data-driven learning, resulting in improved generalization performance and optimized ride comfort in engineering applications. Simulation results demonstrated that DET improved control performance by 74.92%, 64.20%, and 54.64% compared to the deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and model predictive control (MPC) baselines, respectively. Furthermore, it achieved nearly a 90% improvement in ride comfort on random roads in classes A, B, and C across different speeds. Even on class D roads, the optimization remained around 85%, demonstrating its excellent generalization performance.

ObjectiveTo analyze the epidemic characteristics of measles and rubella in Pudong New Area of Shanghai from 2013 to 2022， and to provide data support for the elimination of measles and rubella.MethodsEnzyme linked immunosorbent assay was used to detect IgM antibodies in serum samples. The sequence of 630 nucleotides at the C-terminal of N gene of measles virus was amplified by reverse transcription-polymerase chain reaction and the phylogenic tree was constructed.ResultsA total of 1 529 suspected cases of measles were detected from 2013 to 2022， among which the positive rate of measles IgM antibody was 33.55% （513/1 529）. The highest positive rate （20.73%） was from March to May ， and the positive rate of rubella IgM antibody was 6.80% （104/1 529）. The positive rate of both IgM was higher in males than that in females （P<0.05）. The IgM against measles was mainly detected in 0‒ years old （63.16%， 96/152） and 20‒ years old （45.61%， 161/353）. The IgM against rubella was mainly detected in 10‒20 years old （27.27%， 18/66）. The IgM antibody could be detected more easily from 4 to 28 days after eruption， and the IgM antibody positive rate of measles/rubella from 2020 to 2022 was significantly lower than previous years （2013‒2019）. There were 2 D8 genotype strains， and the rest were H1a gene subtypes.ConclusionThe positive rate of IgM antibodies against measles/rubella in Pudong New Area of Shanghai decreased significantly. People aged 0‒ years and 20‒ years old are more susceptible to measles， and rubella is concentrated in 10‒ years old. It is necessary to strengthen the vaccination of school-age children， in order to achieve the goal of eliminating measles. The age group with high risk of exposure should be checked for vaccination status to ensure the enhanced immunization， and the surveillance of imported measles cases should be strengthened.

The COVID pandemic fueled by emerging SARS-CoV-2 new variants of concern remains a major global health concern, and the constantly emerging mutations present challenges to current therapeutics. The spike glycoprotein is not only essential for the initial viral entry, but is also responsible for the transmission of SARS-CoV-2 components via syncytia formation. Spike-mediated cell-cell transmission is strongly resistant to extracellular therapeutic and convalescent antibodies via an unknown mechanism. Here, we describe the antibody-mediated spike activation and syncytia formation on cells displaying the viral spike. We found that soluble antibodies against receptor binding motif (RBM) are capable of inducing the proteolytic processing of spike at both the S1/S2 and S2' cleavage sites, hence triggering ACE2-independent cell-cell fusion. Mechanistically, antibody-induced cell-cell fusion requires the shedding of S1 and exposure of the fusion peptide at the cell surface. By inhibiting S1/S2 proteolysis, we demonstrated that cell-cell fusion mediated by spike can be re-sensitized towards antibody neutralization in vitro. Lastly, we showed that cytopathic effect mediated by authentic SARS-CoV-2 infection remain unaffected by the addition of extracellular neutralization antibodies. Hence, these results unveil a novel mode of antibody evasion and provide insights for antibody selection and drug design strategies targeting the SARS-CoV-2 infected cells.

Currently, the research on controlling vehicle ride comfort primarily revolves around utilizing traditional algorithms for active or semi-active control of suspension systems. However, these methods often lack adaptability and necessitate a substantial allocation of human and material resources for system calibration and parameter tuning. With the advancement of cutting-edge computational methods, such as artificial intelligence (AI), being applied in engineering, new opportunities have arisen to tackle knowledge-intensive tasks like suspension control. This study aims to enhance vehicle ride comfort by proposing an active suspension control method that integrates deep reinforcement learning (DRL) while considering system characteristics. Firstly, we construct a Twin Delayed Deep Deterministic Policy Gradient (TD3) architecture to systematically explore control policies. Secondly, we propose an expert-guided soft-hard constraints model (TD3-SH) that synergistically incorporates multi-scale information such as displacement, velocity, acceleration, and control force. Additionally, in practical engineering applications, we introduce action delay mechanisms and hard constraint modules to address time delay and actuator dynamic constraints, thereby alleviating the challenges associated with subsequent parameter adjustments and other knowledge-intensive tasks. Finally, simulations demonstrate the effective mitigation of body vibrations in the low-frequency range and the subsequent improvement of ride comfort by TD3-SH. In comparison to the deep deterministic policy gradient (DDPG), TD3, and model predictive control (MPC) baselines, the proposed method showcases control performance improvements of 54.8%, 35.5%, and 18.3%, respectively. Moreover, the method exhibits ride comfort optimization exceeding 85% across diverse road conditions, showcasing its exceptional generalization and adaptive capacity. Furthermore, the optimization amount exceeding 58% can be sustained despite the constraints of time delay and actuator dynamics. Evidently, the proposed algorithm holds significant potential for engineering applications and is uniquely suited for complex tasks in the vehicle industry characterized by high uncertainty.

To better understand the importation and circulation patterns of rubella virus lineages 1E-L2 and 2B-L2c circulating in China since 2018, 3,312 viral strains collected from 27 out of 31 provinces in China between 2018 and 2021 were sequenced and analyzed with the representative international strains of lineages 1E-L2 and 2B-L2c based on genotyping region. Time-scale phylogenetic analysis revealed that the global lineages 1E-L2 and 2B-L2c presented distinct evolutionary patterns. Lineage 1E-L2 circulated in relatively limited geographical areas (mainly Asia) and showed geographical and temporal clustering, while lineage 2B-L2c strains circulated widely throughout the world and exhibited a complicated topology with several independently evolved branches. Furthermore, both lineages showed extensive international transmission activities, and phylogeographic inference provided evidence that lineage 1E-L2 strains circulating in China possibly originated from Japan, while the source of lineage 2B-L2c isolated since 2018 is still unclear. After importation into China in 2018, the spread of lineage 1E-L2 presented a three-stage transmission pattern from southern to northern China, whereas lineage 2B-L2c spread from a single point in western China to all the other four regions. These two transmission patterns allowed both imported lineages to spread rapidly across China during the 2018-9 rubella epidemic and eventually established endemic circulations. This study provides critical scientific data for rubella control and elimination in China and worldwide.

Although the highly effective measles vaccine has dramatically reduced the incidence of measles, measles, and outbreaks continue to occur in individuals who received the measles vaccine because of immunization failure. In this study, patients who have definite records of immunization were enrolled based on measles surveillance in Shanghai, China, from 2009 to 2017, and genomic characteristics regarding viruses retrieved from these cases provided insights into immunization failure. A total of 147 complete genomes of measles virus (MV) were obtained from the laboratory-confirmed cases through Illumina MiSeq. Epidemiological, and genetic characteristics of the MV were focused on information about age, gender, immunization record, variation, and evolution of the whole genome. Furthermore, systematic genomics using phylogeny and selection pressure approaches were analyzed. Our analysis based on the whole genome of 147 isolates revealed 4 clusters: 2 for the genotype H1 (clusters named H1-A, including 73 isolates; H1-B, including 72 isolates) and the other 2 for D8 and B3, respectively. Estimated nucleotide substitution rates of genotype H1 MV derived using genome and individual genes are lower than other genotypes. Our study contributes to global measles epidemiology and proves that whole-genome sequencing was a useful tool for more refined genomic characterization. The conclusion indicates that vaccination may have an effect on virus evolution. However, no major impact was found on the antigenicity in Shanghai isolates.

ObjectiveTo isolate and study the biological characteristics of severe acute respiratory syndrome coronavirus 2 （SARS-CoV-2） from feces of coronavirus disease 2019 （COVID-19） patients.MethodsVero E6 cells were used for virus isolation and the isolated strains were tested by nucleic acid test， immunofluorescence test， virulence test and whole genome sequencing. 50% tissue culture infective dose （TCID50） was calculated after the cell cultures of each generation were collectedResultsEight fecal specimens were inoculated with Vero E6 cells after treatment and cultured for 48 h. One specimen showed obvious cytopathic effect on Vero E6 cells. One SARS-CoV-2 out of 8 fecal samples from COVID-19 patients were isolated， and separation rate was 12.5%. The TCID50 of P1， P2 and P3 were 104.0/0.2 mL， 104.5/0.2 mL and 104.75/0.2 mL， respectively. Only one of the 8 stool samples had SARS-CoV-2 virus replication and amplification， and the Ct value of the nucleic acid detection was about 10. The sequence of the isolation was more than 99.99% homologous with that of Wuhan-Hu-1（GenBank MN908947）.ConclusionThe SARS-CoV-2 strain is isolated from the fecal samples of COVID-19 cases and is confirmed by genomic sequencing and immunofluorescence test， which indicates the presence of live virus in feces of COVID-19 cases.

The global spread of SARS-CoV-2 is currently continuing, and the World Health Organization has announced the risk assessment of the viruses as high. In this study, we analyzed virology features of SARS-CoV-2 causing a family cluster outbreak. Among the six family members, five have been laboratory-confirmed infection of SARS-CoV-2 viruses. A total of five SARS-CoV-2 viruses have been isolated from the nasopharyngeal swabs. The complete genome of the viruses exhibited 100% nucleotide identity with each other. Only two nucleotide differences have been observed between genomes of the isolated viruses and the HCoV/Wuhan/ IVDC-HB-01/2019 strain. Therefore, SARS-CoV-2 has been confirmed as the causation of the family cluster infections.

[目的]观察不同温度保存条件下细胞培养物中新型冠状病毒(简称"新冠病毒")存活情况,判断温度对病毒稳定性的影响,为新冠病毒肺炎疫情趋势研判及防控提供基础数据.[方法]将病毒接种于Vero E6细胞适应培养后,收获病毒液,根据所测得病毒半数组织培养感染剂量(TCID50)将不同稀释度(10-1、10-3、10-5、10-6)的病毒在不同温度下(4℃、22.5℃、37℃)保存1～7 d,并分别感染细胞,通过观察细胞病变效应(CPE)、实时荧光定量检测病毒核酸确定病毒感染性,以评价病毒在不同温度条件下的稳定性.[结果]不同浓度的新冠病毒在4℃条件下保存较为稳定,均具有感染性;22.5℃条件下,高浓度(10-1稀释度)病毒放置7d感染性逐渐下降,其他较低浓度病毒放置1d则完全失去感染性;37℃保存超过1d病毒即失去感染性.[结论]在细胞培养环境中,新冠病毒在4℃条件下高度稳定,对热敏感,且与病毒浓度相关,高浓度病毒室温22.5℃条件下仍可存活7d,37℃条件下放置1d病毒完全失活.

High rate of cardiovascular disease (CVD) has been reported among patients with coronavirus disease 2019 (COVID-19). Importantly, CVD, as one of the comorbidities, could also increase the risks of the severity of COVID-19. Here we identified phospholipase A2 group VII (PLA2G7), a well-studied CVD biomarker, as a hub gene in COVID-19 though an integrated hypothesis-free genomic analysis on nasal swabs (n=486) from patients with COVID-19. PLA2G7 was further found to be predominantly expressed by proinflammatory macrophages in lungs emerging with progression of COVID-19. In the validation stage, RNA level of PLA2G7 was identified in nasal swabs from both COVID-19 and pneumonia patients, other than health individuals. The positive rate of PLA2G7 were correlated with not only viral loads but also severity of pneumonia in non-COVID-19 patients. Serum protein levels of PLA2G7 were found to be elevated and beyond the normal limit in COVID-19 patients, especially among those re-positive patients. We identified and validated PLA2G7, a biomarker for CVD, was abnormally enhanced in COVID-19 at both nucleotide and protein aspects. These findings provided indications into the prevalence of cardiovascular involvements seen in patients with COVID-19. PLA2G7 could be a potential prognostic and therapeutic target in COVID-19.

Background: Eosinophilic granulomatosis with polyangitis manifested as myocardial infarction with non-obstructed coronary arteries (MINOCA) is rarely reported.Case: We report a 43-year-old male patient without any cardiovascular risk factors presenting with acute chest pain. Electrocardiogram was suggestive of acute anterior and inferior myocardial infarction. MINOCA was confirmed based on significant elevated cardiac troponin and normal coronary arteries. Cardiac magnetic resonance (CMR) imaging revealed extended late gadolinium enhancement (LGE). Further diagnosis of eosinophilic granulomatosis with polyangitis (EGPA) was based on clinical manifestations and auxiliary examination. Subsequent immunosuppressive therapy led to regression of symptoms and significant resolution of LGE on CMR.Conclusion: Our case highlights that EGPA can be a rare cause of MINOCA. CMR is useful for differentiation diagnosis and evaluation of cardiac involvement.

Background Recent evidences had shown that loss in phosphatase and tensin homolog deleted on chromosome 10 (PTEN) was associated with immunotherapy resistance, which may be attributed to the non-T-cell-inflamed tumor microenvironment. The impact of PTEN loss on tumor microenvironment, especially regarding T cell infiltration across tumor types is not well understood. Methods Utilizing The Cancer Genome Atlas (TCGA) and publicly available dataset of immunotherapy, we explored the correlation of PTEN expressing level or genomic loss with tumor immune microenvironment and response to immunotherapy. We further investigated the involvement of PI3K-AKT-mTOR pathway activation, which is known to be the subsequent effect of PTEN loss, in the immune microenvironment modulation. Results We reveal that PTEN mRNA expression is significantly positively correlated with CD4/CD8A gene expression and T cells infiltration especially T helpers cells, central memory T cell and effector memory T cells in multiples tumor types. Genomic loss of PTEN is associated with reduced CD8+ T cells, type 1 T helper cells, and increased type 2 T helper cells, immunosuppressed genes (e.g. VEGFA) expression. Furthermore, T cell exclusive phenotype is also observed in tumor with PI3K pathway activation or genomic gain in PIK3CA or PIK3CB. PTEN loss and PI3K pathway activation correlate with immunosuppressive microenvironment, especially in terms of T cell exclusion. PTEN loss predict poor therapeutic response and worse survival outcome in patients receiving immunotherapy. Conclusion These data brings insight into the role of PTEN loss in T cell exclusion and immunotherapy resistance, and inspires further research on immune modulating strategy to augment immunotherapy.