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Gastric cancer (GC) remains a major global health burden, with its unfavorable prognosis primarily driven by extensive tumor heterogeneity. Traditional bulk omics, while informative, are inherently limited by the averaging effect of diverse cell populations and fail to capture the critical spatial molecular disparities within the tumor and its microenvironment (TME). Single-cell omics can capture cellular heterogeneity but lack spatial context. Therefore, there is an urgent clinical need for spatial multi-omics to provide a high-definition dissection of GC heterogeneity and to optimize therapeutic efficacy. This review first outlines briefly the evolution of spatial technologies, including transcriptomics, proteomics, metabolomics, genomics and epigenomics, and their transformative applications in GC research. We further explore how these platforms refine molecular classification beyond traditional models, identify next-generation biomarkers, and decode the intricate cellular interactions governing immune evasion and metastasis. Next, we highlight the pivotal role of spatial profiling in unravelling the multidimensional mechanisms of resistance to chemotherapy, targeted therapy and immunotherapy. Finally, we address current technical bottlenecks and discuss prospects for clinical translation.

Aldehyde dehydrogenase 3A1 (ALDH3A1) is an NAD + -dependent enzyme that is closely related to tumor development. However, its role in non-small-cell lung cancer (NSCLC) has not been elucidated. This study aimed to clarify the mechanism of ALDH3A1 and identify potential therapeutic targets for NSCLC. Here, for the first time, we found that ALDH3A1 expression could be induced by a hypoxic environment in NSCLC. ALDH3A1 was highly expressed in NSCLC tissue, especially in some late-stage patients, and was associated with a poor prognosis. In mechanistic terms, ALDH3A1 enhances glycolysis and suppresses oxidative phosphorylation (OXPHOS) to promote cell proliferation by activating the HIF-1α/LDHA pathway in NSCLC. In addition, the results showed that ALDH3A1 was a target of β-elemene. ALDH3A1 can be downregulated by β-elemene to inhibit glycolysis and enhance OXPHOS, thus suppressing NSCLC proliferation in vitro and in vivo. In conclusion, hypoxia-induced ALDH3A1 is related to the energy metabolic status of tumors and the efficacy of β-elemene, providing a new theoretical basis for better clinical applications in NSCLC.

Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex electromagnetic environments characterized by strong noise interference and a high false-alarm rate. Conventional methods, which often treat data association and fusion from heterogeneous sensors as separate, offline processes, struggle with the dynamic uncertainties and real-time decision requirements of such scenarios. To address these limitations, this paper proposes a novel Evidence–Reinforcement Learning-based Decision and Control (ERL-DC) framework. It operates through a closed-loop architecture consisting of three core modules: A static assessment model for initial target prioritization, a Dempster–Shafer (D–S) evidence-based multi-source data decision generator for dynamic information fusion and uncertainty-aware target selection, and a Deep Reinforcement Learning (DRL) controller for noise-robust sensor steering. A high-fidelity simulation environment was developed to model the multi-source data stream, encompassing radar detection with clutter and false targets, as well as the physical constraints of the electro-optical (EO) servo system. Based on the averaged results from multiple Monte Carlo simulations, the proposed ERL-DC framework reduced the Average Decision Time (ADT) from 7.51 s to 4.53 s, corresponding to an absolute reduction of 2.98 s when compared to the conventional method integrating threshold logic with Model Predictive Control (MPC). Furthermore, the Net Discrimination Accuracy (NDA), derived from the statistical outcomes across all the simulation runs, exhibited an absolute increase of 37.8 percentage points, rising from 57.8% to 95.6%. These results indicate that ERL-DC achieves a more favorable trade-off in terms of scheduling efficiency, decision robustness, and resource utilization. The primary contribution is an intelligent, closed-loop architecture that tightly couples high-level evidential reasoning for multi-source data fusion with low-level adaptive control. Within the simulated environment characterized by clutter, false targets, and angular measurement noise, ERL-DC demonstrates improved target discrimination accuracy and decision efficiency compared to conventional methods. Future work will focus on online parameter adaptation and validation on physical platforms.

Powdery mildew, caused by Blumeria graminis f. sp. tritici ( Bgt ), reduces wheat yields and grain quality, thus posing a significant threat to global food security. Wild relatives of wheat serve as valuable resources for resistance to powdery mildew. Here, the powdery mildew resistance gene Pm6Sl is cloned from the wild wheat species Aegilops longissima . It encodes a nucleotide-binding leucine-rich repeat (NLR) protein featuring a CC-BED module formed by a zinc finger BED (Znf-BED) domain integrated into the coiled-coil (CC) domain. The function of Pm6Sl is validated via mutagenesis, gene silencing, and transgenic assays. In addition, we develop a resistant germplasm harbouring Pm6Sl in a very small segment with no linkage drag along with the diagnostic gene marker pm6sl-1 to facilitate Pm6Sl deployment in wheat breeding programs. The cloning of Pm6Sl , a resistance gene with BED-NLR architecture, will increase our understanding of the molecular mechanisms underlying BED-NLR-mediated resistance to various pathogens. Powdery mildew disease seriously threatens wheat production by reducing grain yield and quality. Here, the authors clone the Znf-BED-bearing NLR protein encoding powdery mildew resistance gene Pm6Sl derived from wild wheat species Aegilops longissima and show its application in breeding resistance cultivars.

Ship–bridge collision is a common type of accident in bridge engineering which could cause heavy casualties and economic losses. To ensure the safety of both the ships and bridges during collision, a novel steel box-soft body combination was proposed in this work. The time history curves of the impact force of three downscaled facility specimens were obtained through the horizontal impact test. The influence of the steel box web spacing and the existence of the anti-collision facilities on the ship collision force reduction rate was investigated. The collision failure modes of ship bow and the anti-collision facilities, as well as the energy absorption behavior of the facility were analyzed. Based on ANSYS/LS-DYNA finite element (FE) analysis software, the nonlinear numerical models of the anti-collision facilities were generated. The analysis results show that the proposed anti-collision facility can not only greatly reduce the ship impact force, but the bow damage as well. The densified steel box web can improve the anti-collision performance of the whole anti-collision facilities to a certain extent. Compared with the direct impact on the steel plate, the maximum reduction rate of peak force of the proposed facility can be achieved to be 31.07%. The anti-collision facilities deformation energy absorption accounts for more than 70% of the total energy, which shows that the facility is able to absorb most of the energy and protect the bow. The FE simulation results coincide with the experimental outcomes, indicating the acceptable accuracy of the FE models.

Wheat leaf rust, caused by Puccinia triticina , is one of the most important fungal diseases of wheat in China. However, little is known about the dynamic changes of population structure and genetic diversity of P. triticina during a period of time. In this study, 247 isolates of P. triticina collected from Hebei Province from 2001 to 2010 were tested on 36 Thatcher near-isogenic lines for virulence diversity and detected by 21 pairs of Expressed Sequence Tag derived Simple Sequence Repeat (EST-SSR) primers for genetic diversity. A total of 204 isolates were successfully identified as 164 races, and THTT, THST, PHRT, THTS, and PHTT were the most common races in Hebei Province from 2001 to 2010. The cluster analysis based on virulence showed that P. triticina has a rich virulence polymorphism, which had a certain correlation with the years, while the cluster analysis based on EST-SSR showed that the genetic diversity of the P. triticina population was significantly different between years in Hebei Province from 2001 to 2010. In addition, the population structure of P. triticina may have changed greatly in 2007 and 2009, which was significantly different from that of 2001–2006 on either virulence or genetic characteristics. The variation frequency of the population structure had an increasing trend during this period. From 2001 to 2010, there was a certain degree of gene flow among the P. triticina populations. No significant correlation was found between virulence and molecular polymorphism. The genetic differentiation analysis of the 10 tested populations (each year as a population) showed that the coefficient of genetic differentiation (Gst) was 0.27, indicating that there was a certain genetic differentiation among or within populations of P. triticina in Hebei Province. The genetic variation within populations (73.08%) was higher than that among populations (26.92%), which indicated that the genetic variations were mainly found within populations. Our study provides the foundation for a better understanding of the population structure change and genetic diversity of P. triticina over a period in Hebei Province of China.

Wheat relatives harbour abundant variations that can be used for genetic improvement against diseases through complex domestication processes. Here, we report the isolation of the powdery mildew resistance gene Pm37 in common wheat through a strategy integrating map-based cloning, PacBio SMRT long-read genome sequencing, mutagenesis, barley stripe mosaic virus -induced gene silencing and stable transformation. Pm37 encodes a coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) receptor. The cell death-inducing activity of PM37 in Nicotiana benthamiana is mainly mediated by its CC domain. De novo genome sequencing and haplotype analysis reveal that Pm37 may be derived from Triticum monococcum , and transferred into common wheat through T. timopheevii . Interestingly, Pm37 is a susceptible allele of the stem rust resistance locus Sr22 , and also confers moderate resistance to leaf rust. Our findings identify a gene characterized by allelic variation-driven functional divergence in response to different wheat diseases. Powdery mildew and stem rust are two major wheat pathogens. Here, the authors report the cloning of powdery mildew resistance gene Pm37 , reveal that it’s a susceptible allele of stem rust resistance gene Sr22 , and speculate the allelic variation causing functional divergence.

The HFO-1234yf/HFO-1216 mixture is a promising alternative refrigerant to HFC-134a due to its excellent environmental performance. Experimental investigation of the phase equilibrium between the mixture and lubricating oils is essential for screening suitable oils in practical refrigeration systems. In this work, an HFO-1234yf/HFO-1216 (80/20) composition was selected based on its overall performance and COP. The solubility of the mixture was measured in three linear pentaerythritol esters (PEC5, PEC7, and PEC9) over a temperature range of 283.15–343.15 K. The experimental data were correlated using the fugacity balance equation combined with the NRTL model. The calculated values show satisfactory agreement with the experimental data.

Immunotherapy, particularly chimeric antigen receptor T cell (CAR‐T) therapy, has revolutionized the treatment of hematological malignancies and autoimmune diseases. However, its efficacy in solid tumors remains limited due to challenges such as tumor heterogeneity, an immunosuppressive microenvironment, and poor T cell infiltration. This review first summarizes the primary causes and challenges that restrict CAR‐T therapy in the treatment of solid tumors, followed by an overview of recent advancements in gastric cancer, liver cancer, and glioma, where early trials have demonstrated promising clinical potential. Advances in CRISPR‐edited and “off‐the‐shelf” allogeneic CAR‐T cells seek to improve scalability, while artificial intelligence (AI)‐driven target discovery, synthetic biology, and cytokine armoring strategies aim to enhance tumor specificity and T‐cell persistence. Additionally, the flexible utilization of combination strategies in clinical surgical and medical trials, such as combining CAR‐T therapy with immune checkpoint inhibitors, oncolytic viruses, chimeric antigen receptor NK cells (CAR‐NK), or chimeric antigen receptor macrophage cells (CAR‐M) may further enhance antitumor efficacy. The evolution of CAR‐T therapy highlights its potential to reshape precision oncology, offering hope to patients with aggressive solid tumors through ongoing basic research, technological optimization, and clinical refinement. This review discusses emerging paradigms in CAR‐T cell therapy for solid tumors, emphasizing strategies to overcome therapeutic barriers through synthetic biology, immune engineering, and combinatorial approaches. It highlights advancements in logic gating, modulation of the immune microenvironment, and innovative cell designs, providing valuable insights into the translational progress from bench to bedside (Created by BioRender).

Bridge train derailments are among the most common and severe consequences of ship collisions with bridges. This study simulates the dynamic response of bridges and trains under such collisions using finite element analysis, integrating practical engineering considerations. The paper analyzes and calculates the dynamic response of both the train and the bridge structure under ship collision by establishing a ship-train-bridge coupled model. Building upon previous numerical simulations of ship-pier collisions, this paper closely focuses on the Chongqi Railway-Highway Bridge, constructing a detailed simulation model that closely mirrors the actual structure. Simulations and verifications of sea-river ship’s impacts were performed and a system model for ship-vehicle-bridge dynamic coupling was established. Through finite element analysis, an in-depth examination of this coupled system was conducted. The numerical results indicate that ship collision significantly intensifies the dynamic response of the train-bridge system: the risk of train derailment rises sharply, with transverse vibrations far exceeding those in a no-impact scenario, challenging system stability. At the same time, the transverse displacement and acceleration in the mid-span region of the bridge show a significant increase, which further highlights the profound impact of the impact on the transverse dynamic response of the bridge structure. Additionally, the train's operating speed and its specific position on the bridge significantly impact the safe train operation, while the train's location on the bridge directly affects the lateral dynamic response of the bridge structure.