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Surface tubular elements (STEs) are critical components of poxvirus mature virions and play a role in suppressing host cell protein synthesis. In this study, we isolated and purified STEs from native poxvirus virions and subsequently determined their core composition and high-resolution architecture. We identified that STE is mainly composed of membrane proteins A14 and A17, along with phospholipid molecules. Within the repeat structural unit of STE, A14 proteins form two homodimers within the repeating unit, with A17 monomers flanking either side. Phospholipid molecules are distributed within the A14-A14 and A14-A17 interfaces. Our study not only revealed the molecular structures of A14 and A17 but also further emphasized that the reticulon-like and highly oligomerized characteristics of A17 provide membrane curvature, while the A14-A17-phospholipid network stabilizes the tubular structure. We proposed a hypothetical model that A17 drives changes in viral membrane curvature during maturation. These findings enhance our understanding of poxvirus biology and may guide therapeutic strategies against poxvirus infections.

Oxidative protein folding in the endoplasmic reticulum (ER) is essential for all eukaryotic cells yet generates hydrogen peroxide (H O ), a reactive oxygen species (ROS). The ER-transmembrane protein that provides reducing equivalents to ER and guards the cytosol for antioxidant defense remains unidentified. Here we combine AlphaFold2-based and functional reporter screens in to discover a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting ERGU-1 causes excessive H O and transcriptional gene up-regulation through SKN-1, homolog of mammalian antioxidant master regulator NRF2. ERGU-1 deficiency also impairs organismal reproduction and behavioral responses to H O . Both and human ERGU-1 proteins localize to ER membranes and form network reticulum structures. Human and homologs of ERGU-1 can rescue mutant phenotypes, demonstrating evolutionarily ancient and conserved functions. In addition, purified ERGU-1 and human homolog TMEM161B exhibit redox-modulated oligomeric states. Together, our results reveal an ER-membrane-specific protein machinery for peroxide detoxification and suggest a previously unknown and conserved mechanisms for antioxidant defense in animal cells.

Most existing quasi-zero stiffness (QZS) vibration isolators with excellent vibration isolation performance focus on suppressing single-directional vibration. There are limitations to applications. In this paper, a multidirectional vibration isolation system with the QZS effect is proposed based on the designed geometrical relationship between the torsion spring and the linear spring. The effect of the mechanism’s structural parameters on its static as well as dynamic characteristics is analyzed. The results reveal that (a) consisting only of elastic elements and connecting rods, the compact mechanism has ideal vibration isolation performance in all three directions; (b) the mechanism characteristics can be flexibly adjusted by tuning the structural parameters; (c) when the excitation amplitudes are large, the vibration isolator remains preferable vibration attenuation performance; (d) compared with the traditional QZS structure, the typical X-shaped QZS structure, and the proposed structure I, the designed structure has a larger QZS area and a wider vibration attenuation area in the main vibration isolation direction.

As 3D acquisition equipment picks up steam, point cloud registration has been applied in ever-increasing fields. This paper provides an exhaustive survey of the field of point cloud registration for laser scanners and examines its application in large-scale aircraft measurement. We first researched the existing representative point cloud registration algorithms, such as hierarchical optimization, stochastic and probability distribution, and feature-based methods, for analysis. These methods encompass as many point cloud registration algorithms as possible; typical algorithms of each method are suggested respectively, and their strengths and weaknesses are compared. Lastly, the application of point cloud registration algorithms in large-scale aircraft measurement is introduced. We discovered that despite the significant progress of point cloud registration combining deep learning and traditional methods, it is still difficult to meet realistic needs, and the main challenges are in the direction of robustness and generalization. Furthermore, it is impossible to extract accurate and comparable features for alignment from large-scale aircraft surfaces due to their relative smoothness, lack of obvious features, and abundance of point clouds. It is necessary to develop lightweight and effective dedicated algorithms for particular application scenarios. As a result, with the development of point cloud registration technology and the deepening into the aerospace field, the particularity of the aircraft shape and structure poses higher challenges to point cloud registration technology.

Facial expression recognition has wide application prospects in many occasions. Due to the complexity and variability of facial expressions, facial expression recognition has become a very challenging research topic. This paper proposes a Vision Transformer expression recognition method based on hybrid local attention (HLA-ViT). The network adopts a dual-stream structure. One stream extracts the hybrid local features and the other stream extracts the global contextual features. These two streams constitute a global–local fusion attention. The hybrid local attention module is proposed to enhance the network’s robustness to face occlusion and head pose variations. The convolutional neural network is combined with the hybrid local attention module to obtain feature maps with local prominent information. Robust features are then captured by the ViT from the global perspective of the visual sequence context. Finally, the decision-level fusion mechanism fuses the expression features with local prominent information, adding complementary information to enhance the network’s recognition performance and robustness against interference factors such as occlusion and head posture changes in natural scenes. Extensive experiments demonstrate that our HLA-ViT network achieves an excellent performance with 90.45% on RAF-DB, 90.13% on FERPlus, and 65.07% on AffectNet.

Background Gastric cancer (GC) is a common cancer in Asia and currently lacks a targeted therapy approach. Mesothelin (MSLN) has been reported to be expressed in GC tissue and could be targeted by chimeric antigen receptor (CAR) T cells. Mesothelin targeting CAR-T has been reported in mesothelioma, lung cancer, breast cancer, and pancreas cancer. However, the feasibility of using anti-MSLN CAR T cells to treat GC remains to be studied. Methods We verified MSLN expression in primary human GC tissues and GC cell lines and then redirected T cells with a CAR containing the MSLN scFv (single-chain variable fragment), CD3ζ, CD28, and DAP10 intracellular signaling domain (M28z10) to target MSLN. We evaluated the function of these CAR T cells in vitro in terms of cytotoxicity, cytokine secretion, and surface phenotype changes when they encountered MSLN+ GC cells. We also established four different xenograft GC mouse models to assess in vivo antitumor activity. Results M28z10 T cells exhibited strong cytotoxicity and cytokine-secreting ability against GC cells in vitro. In addition, cell surface phenotyping suggested significant activation of M28z10 T cells upon target cell stimulation. M28z10 T cells induced GC regression in different xenograft mouse models and prolonged the survival of these mice compared with GFP-transduced T cells in the intraperitoneal and pulmonary metastatic GC models. Importantly, peritumoral delivery strategy can lead to improved CAR-T cells infiltration into tumor tissue and significantly suppress the growth of GC in a subcutaneous GC model. Conclusion These results demonstrate that M28z10 T cells possess strong antitumor activity and represent a promising therapeutic approach to GC.

Lilies are popular ornamental and medicinal plants with gigantic genomes. Due to the challenge of assembling complex giga-genomes, our understanding of the genetic architecture, epigenetic regulation and evolution of large-genome plants such as lily remains limited. Here, we report a high-quality chromosome-level 35.6 Gb reference genome of royal lily ( Lilium regale ), a parent of many modern lily cultivars, using PacBio HiFi and Hi-C sequencing data. We show that genome expansion of L. regale is mainly caused by extensive proliferation of transposable elements resulting in long intergenic and intronic regions, along with whole-genome duplications and tandem repeats. L. regale genome is repeat-rich (80.06%) encoding abundant large genes (>10 Kb) with long introns that account for ~90% length of 67,862 genes encoded. Phylogenomics reveals significant gene family expansion related to defense response and biosynthesis of terpenoids, reflecting its adaptation strategy. Through multiomic analysis, we reveal how transposable element activity and epigenetic regulations may impact transcription, alternative splicing, and three-dimensional organization, which contribute to its adaptive evolution. Collectively, this significantly improved lily genome assembly and annotation will serve as an essential resource for research on lily genetics, breeding, conservation biology, and angiosperm genome evolution. Lilies are popular ornamental and medicinal plants with gigantic genomes. This study reports a high-quality genome assembly of royal lily ( Lilium regale ), revealing its transposon-driven genome expansion and epigenetic mechanisms for adaptation, offering a resource for lily research and breeding.

Background Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community’s exposures , pre-existing sensitivities, and adaptive capacities in response to a hazard’s impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. Methods A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. Results Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16–49%) across all hazards. Distinct spatial patterns were observed – for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. Conclusions The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Fasciclin-like arabinogalactan proteins (FLAs) are critical components of the plant cell wall, playing vital roles in development and abiotic stress responses. However, a systematic genome-wide analysis of the FLA family in soybean ( L.), a major legume crop susceptible to drought, is lacking. This study aimed to comprehensively identify members and investigate their potential functions in drought tolerance. We identified genes via BLASTP (v2.16.0) and Hidden Markov Model (HMM) searches against the soybean genome. Subsequent analyses encompassed their physicochemical properties, chromosomal distribution, gene structure, phylogenetic relationships, conserved domains, cis-acting elements, and subcellular localization. Drought-responsive candidates were screened using a Gene Expression Omnibus (GEO) dataset, and their expression profiles were validated under drought stress using quantitative real-time PCR (qRT-PCR). A total of 55 genes were identified and unevenly distributed across 14 chromosomes. Most genes featured a single-exon structure and contained a conserved Fasciclin domain, with predicted localization primarily to the chloroplast. Phylogenetic analysis grouped them into three distinct subclasses with Arabidopsis homologs, suggesting lineage-specific expansion. Promoter analysis revealed an abundance of stress- and hormone-related cis-elements. Expression analysis identified five candidate genes ( 5, 15, 40, 47, and 54) that showed tissue-specific expression changes under drought treatment. This study provides the first comprehensive genomic characterization of the gene family and identifies candidate s with drought-responsive expression patterns. Our findings establish a foundation for future functional research to investigate their potential roles in soybean drought response. Furthermore, these candidates serve as potential targets for further investigation in strategies aimed at improving soybean drought tolerance.

Micropeptides, these small proteins derived from non-coding RNA, typically consist of no more than 100 amino acids in length. Despite the challenges in analysis and identification, their various critical functions within organisms cannot be overlooked. They play a significant role in maintaining energy metabolism balance, regulating the immune system, and influencing the development of tumors, which also gives them a decisive impact on the occurrence and development of various diseases. This review aims to outline the role and potential value of micropeptides, introducing their tissue classification and distribution, biological functions, and mechanisms, with a focus on their potential as diagnostic markers and therapeutic drugs.