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Abstract Salmonella Enteritidis is a major foodborne pathogen throughout the world and the increase in antibiotic resistance of Salmonella poses a significant threat to public safety. Natural nanobodies exhibit high affinity, thermal stability, ease of production, and notably higher diversity, making them widely applicable for the treatment of viral and bacterial infections. Recombinant expression using Lactococcus lactis leverages both acid resistance and mucosal colonization properties of these bacteria, allowing the effective expression of exogenous proteins for therapeutic effects. In this study, nine specific nanobodies against the flagellar protein FliC were identified and expressed. In vitro experiments demonstrated that FliC-Nb-76 effectively inhibited the motility of S. Enteritidis and inhibited its adhesion to and invasion of HIEC-6, RAW264.7, and chicken intestinal epithelial cells. Additionally, a recombinant L. lactis strain secreting the nanobody, L. lactis-Nb76, was obtained. Animal experiments confirmed that it could significantly reduce the mortality rates of chickens infected with S. Enteritidis, together with alleviating the inflammatory response caused by the pathogen. These results provide a novel strategy for the treatment of antibiotic-resistant S. Enteritidis infection in the intestinal tract. Graphical Abstract

Abstract Rice blast is the primary disease affecting rice yield and quality, and its effective detection is essential to ensure rice yield and promote sustainable agricultural production. To address traditional disease detection methods’ time-consuming and inefficient nature, we proposed a method called Pyramid-YOLOv8 for rapid and accurate rice leaf blast disease detection in this study. The algorithm is built on the YOLOv8x network framework and features a multi-attention feature fusion network structure. This structure enhances the original feature pyramid structure and works with an additional detection head for improved performance. Additionally, this study designs a lightweight C2F-Pyramid module to enhance the model’s computational efficiency. In the comparison experiments, Pyramid-YOLOv8 shows excellent performance with a mean Average Precision (mAP) of 84.3%, which is an improvement of 9.9%, 4.3%, 7.4%, 6.1%, 1.5%, 3.7%, and 8.2% compared to the models Faster-RCNN, RT-DETR, YOLOv3-SPP, YOLOv5x, YOLOv9e, and YOLOv10x, respectively. Additionally, it reaches a detection speed of 62.5 FPS; the model comprises only 42.0 M parameters. Meanwhile, the model size and Floating Point Operations (FLOPs) are reduced by 41.7% and 23.8%, respectively. These results demonstrate the high efficiency of Pyramid-YOLOv8 in detecting rice leaf blast. In summary, the Pyramid-YOLOv8 algorithm developed in this study offers a robust theoretical foundation for rice disease detection and introduces a new perspective on disease management and prevention strategies in agricultural production.

Abstract Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Abstract Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

Abstract The presence of dysbiotic cervicovaginal microbiota has been observed to be linked to the persistent development of cervical carcinogenesis mediated by the human papillomavirus (HPV). Nevertheless, the characteristics of the cervical microbiome in individuals diagnosed with cervical cancer (CC) are still not well understood. Comprehensive analysis was conducted by re-analyzing the cervical 16S rRNA sequencing datasets of a total of 507 samples from six previously published studies. We observed significant alpha and beta diversity differences in between CC, cervical intraepithelial neoplasia (CIN) and normal controls (NC), but not between HPV and NC in the combined dataset. Meta-analysis revealed that opportunistic pernicious microbes Streptococcus, Fusobacterium, Pseudomonas and Anaerococcus were enriched in CC, while Lactobacillus was depleted compared to NC. Members of Gardnerella, Sneathia, Pseudomonas, and Fannyhessea have significantly increased relative abundance compared to other bacteria in the CIN group. Five newly identified bacterial genera were found to differentiate CC from NC, with an area under the curve (AUC) of 0.8947. Moreover, co-occurrence network analysis showed that the most commonly encountered Lactobacillus was strongly negatively correlated with Prevotella. Overall, our study identified a set of potential biomarkers for CC from samples across different geographic regions. Our meta-analysis provided significant insights into the characteristics of dysbiotic cervicovaginal microbiota undergoing CC, which may lead to the development of noninvasive CC diagnostic tools and therapeutic interventions.

Abstract Background Protein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed. Result In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method. Conclusion Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Abstract Coronavirus disease 2019 (COVID‐19) pandemic has caused millions of deaths. The risk of COVID‐19 spreading still exists after the deconfinement act, Omicron became the dominant variant. Although N6‐methyladenosine (m6A) regulators has been reported to affect the pathogenicity of COVID‐19, their mechanism in the progression of lung injury in COVID‐19 patients remain elusive. Here we show the landscape and specific mechanisms of m6A regulators in lung tissues through single‐nucleus RNA sequencing (snRNA‐Seq) data sets of 116,252 cells, and the external validation was performed using data from another snRNA‐Seq data. The m6A reader IGF2BP2 was specifically upregulated in alveolar type I (AT1) cells, resulting in impaired lung regeneration. ALKBH5 expression upregulation in macrophages, impairing immune responses. Moreover, WTAP markedly upregulated in fibroblasts, leading to pulmonary fibrosis. In addition, m6A regulators dysregulation induced aberrant cell–cell communication in pulmonary tissue and mediated ligand–receptor interactions across diverse cell types in lung tissues by activating the TGF‐β signaling pathway. Overall, these results indicated that the upregulation of m6A regulators in alveolar cells, myeloid cells, and fibroblasts may induce pulmonary injury in patients. The development of m6A‐regulator inhibitors could be as one potential antifibrotic drugs for COVID‐19.

Recent research has highlighted the beneficial effects of urban green spaces on physical and mental health. This study focused on the hospital population and innovatively subdivided the population into four groups: doctors, caregivers, patients and nurses. A total of 96 volunteers participated in this virtual reality experiment to assess the restoration of a linear canopy landscape and a landscape with different levels of hydrodynamics through interactive audiovisual immersion. We utilized pre-research method, brainwave monitoring technique, psychological scales, observation and interviews in this experiment. The research identified five key findings. First, both linear canopy landscapes and those with low to medium hydrodynamic forces significantly enhance physiological and psychological restoration for all groups, with the most substantial physiological benefits observed in doctors and patients, and the greatest psychological relief noted in caregivers. Second, landscapes with medium hydrodynamic forces yield higher restorative effects than those with low forces in hospital settings. Third, green landscapes with medium and low-density canopies prove more conducive to patient recovery compared to those with high-density canopies. Fourth, the inclusion of bird songs does not markedly affect physiological restoration across the hospital groups. Finally, landscapes that incorporate elements of water dynamics, open skies, and lightly foliated canopies draw significant interest from all groups involved. This study advocates for the integration of natural blue and green elements into hospital environments as complementary therapeutic interventions, aiming to alleviate stress and promote health recovery among hospital communities.

Abstract The radiative transfer model of vegetation leaves simulates the transmission mechanism of light inside the vegetation and simulates the reflectivity of blades according to the change law of different components in the process of plant growth. Based on the PIOSL model, this paper combines PIOSL with the structure of rice leaves to construct a radiation transfer model for rice leaves. The parameters of each layer of the RPIOSL model are determined by the Non-dominated Sorting Genetic Algorithm-III. (NSGA-III.) algorithm. The reflectance spectra of 218 rice leaf samples in different periods were simulated using the RPIOSL model. The results show that the mean (RMSE) between the simulated and measured spectra of the constructed RPIOSL model is 0.1074, which is 0.0191 lower than that of the PROSPECT model. Among them, the spectral simulation effect of RPIOSL model in yellow and red light band is the best, and the RMSE at tillering period, jointing period, heading period and grouting period are 0.0584, 0.0576, 0.0724 and 0.0820, respectively. Therefore, the establishment of the RPIOSL model can accurately describe the interaction mechanism between light, which is of great significance for the rapid acquisition of rice growth information and accurate crop management.

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation.

Expression of major histocompatibility complex I (MHC-I) on tumor cells is extremely important for the antitumor immune response for its essential role in activating various immune cells, including tumor-specific CD8+ T cells. Cancers of lower MHC-I expression commonly exhibit less immune cell infiltration and worse prognosis in clinic. In this study, we conducted bioinformatic-experimental screening to identify potential gene targets to enhance MHC-I expression in breast cancer (BRCA). Through a combination of MHC-I scoring, gene expression correlation analysis, survival prognostication, and Cibersort tumor-infiltrated lymphocytes (TILs) scoring, we identify 144 genes negatively correlated with both MHC-I expression and TILs in breast cancer. Furthermore, we verified partially according to KEGG functional enrichment or gene-dependency analysis and figured out multiple genes, including PIP5K1A, NCKAP1, CYFIP1, DIS3, TBP, and EXOC1, as effective gene targets for increasing MHC-I expression in breast cancer. Mechanistically, knockout of each of these genes activated the intrinsic interferon response in breast cancer cells, which not only promoted MHC-I expression but also caused immunogenic cell death of breast cancer. Finally, the scRNA-seq confirmed the negative correlation of PIP5K1A et al. with TILs in breast cancer patients. Collectively, we identified multiple gene targets for an increase in MHC-I expression in breast cancer in this study.

Oil content and the movability of shale oil are important indicators for the evaluation of continental shale oil. In recent years, the sandwiched shale oil in the fourth member of the Shahejie Formation in the Liaohe Western Depression area of the Bohai Bay Basin has shown great exploration potential, while the understanding of shale oil content and the movability of shale oil is weak. In this study, through a combination of core observations and experiments, we clarified the shale lithofacies types in the fourth member of the Shahejie Formation in the Liaohe Western Depression and explored the influencing factors of the characteristics in the oil-bearing and movability of shales in different lithofacies. The results of the study show that the organic matter content of the shale is high (TOC = 2.2–4.3%), but the maturity of thermal evolution is low (Ro = 0.38–0.55%), and the mineral component is dominated by clay minerals (30.3–72.7%), with quartz, feldspar, and other feldspar minerals developing secondarily. According to the content of organic matter, the mineral component, and the sedimentary structure, five types of lithofacies can be classified: organic-rich laminated clay-bearing felsic shale lithofacies (LS1), organic-rich laminated clay felsic mixed shale lithofacies (LS2), organic-rich layered clay felsic mixed shale lithofacies (LS3), organic-containing massive felsic-bearing clay shale lithofacies (LS4), and organic-containing massive clay felsic mixed shale lithofacies (LS5). The oil content of shale is mainly affected by the organic matter. The rate of increase in oil content of shale is fastest when the organic matter content is between 2 and 4%. The movability of shale oil is mainly controlled by the sedimentary structure, mineral component, and microscopic pore structure; the more the shale laminae is developed, the lower the clay content is, and the more the pore space is developed, the better the movability of shale oil is. Combined with the results of the shale oil content and mobility analysis in the study area, LS2 and LS3 are the dominant lithofacies in the fourth member of the Shahejie Formation in the study area, followed by LS1 >LS5 >LS4, so shale oil exploration should focus on the development of LS2 and LS3.

In order to explore the sediment deposition characteristic of ecological riverbanks associated with vegetation restoration in the deep waterway regulation scheme of Yangtze River, two kinds of typical ecological riverbanks and a traditional riprap riverbank (TRR) in engineering areas were investigated. The vegetation community characteristics, sediment aggregate compositions, nutrient contents, total organic carbon (TOC), sediment microbial biomass carbon (MBC), sediment microbial biomass nitrogen (MBN), and sediment microbial biomass phosphorus (MBP) were determined. The results indicated that the ecological restoration effect of the lattice gabion ballasted vegetation mat riverbank (LGBVR) was best, followed by the mesh grid riverbank (MGR), and that of the TRR was relatively poor. In different ecological riverbanks, the sediment aggregated compositions were not significantly varied. The sediment contents of NH4+-N, available phosphorus (AP), and TOC in ecological riverbank areas were relatively higher than those of the TRR. In the LGBVR, the sediment contents of MBC were relatively higher than those of the others. The sediment deposition characteristics and ecological restoration effects in the study area should be monitored for a long time.

Objective: This research aims to examine the involvement of lymphocyte subsets and inflammatory cytokines in the development and progression of COVID-19. Methods: 164 COVID-19 patients were admitted to hospital between December 2022 and January 2023. Based on lung CT scans and whether it is necessary for intensive care unit (ICU) admission, they were categorized into: severe groups (84) and mild disease groups (80). Peripheral blood were also collected from 101 healthy examinees and 164 patients. Flow cytometry (FCM) was used to measure the absolute and relative counts of lymphocyte subsets, while chemiluminescence was used to detect the level of inflammatory cytokines. Results: The COVID-19 patient group exhibited lower count of lymphocytes subsets than healthy control group. Moreover, COVID-19 patient case presented higher content of cytokines (IL-6, IL-4, IL-8, IL-10, and TNF-α) expression compared to healthy control case. Within the COVID-19 patient group, individuals with severe disease showed lower counts of lymphocytes subsets than the mild disease case. Furthermore, IL-6 levels in severe case were higher than the mild disease patients case. Multi-variate logistic regression analysis confirmed IL-6 (odds ratio: 0.985 [0.977–0.993]), CD3+ T cells (odds ratio:1.007 [1.004–1.010]), CD8+ T cells (odds ratio:1.016 [1.009–1.023]), and CD19+ B cells (odds ratio:1.011 [1.002–1.020]) independently predicted severe progression. ROC curve results indicated AUC for lymphocytes in patients with severe COVID-19 was 0.8686 (0.8112–0.9260), CD3+ T cells was 0.8762 (0.8237–0.9287), CD8+ T cells was 0.7963 (0.7287–0.8638), CD4+ T cells was 0.8600 (0.8036–0.9164), CD19+ B cells was 0.7217 (0.6434–0.8001), NK cells was 0.6492 (0.5627–0.7357), age was 0.6699 (0.5877–0.7521), diabetes was 0.5991 (0.5125–0.6857), and IL-6 was 0.7241 (0.6479–0.8003). Furthermore, the ROC curves for different factors (CD3+ T cells, age, IL-6) yielded an AUC of 0.9031 (0.8580–0.9483). Conclusions: The research indicated that COVID-19 patients experience a decrease in lymphocytes subset and an increase in the inflammatory factor IL-6, particularly in the severe case group. As a result, the count of lymphocyte subset (CD3+ T cells) and the content of inflammatory cytokine (IL-6) can serve as predictive markers for assessing the severity of COVID-19 and developing treatment plans efficacy.

IntroductionThe tumor immune environment and immune-related genes are instrumental in the development, progression, and prognosis of bladder cancer (BLCA). This study sought to pinpoint key immune-related genes influencing BLCA prognosis and decipher their mechanisms of action.Methods and resultsWe analyzed differentially expressed genes (DEGs) between high- and low- tumor mutational burden (TMB) groups. Subsequently, we constructed a reliable prognostic model based on immune-related gene pairs (IRGPs) and analyzed DEGs between high- and low-risk groups. A total of 22 shared DEGs were identified across differential TMB and IRGPs-derived risk groups in BLCA patients. Through univariate Cox and multivariate Cox analyses, we highlighted five genes - FLRT2, NTRK2, CYTL1, ZNF683, PRSS41 - significantly correlated with BLCA patient prognosis. Notably, the FLRT2 gene emerged as an independent prognostic factor for BLCA, impacting patient prognosis via modulation of macrophage infiltration in immune microenvironment. Further investigation spotlighted methylation sites - cg25120290, cg02305242, and cg01832662 - as key regulators of FLRT2 expression.DiscussionThese findings identified pivotal prognostic genes in BLCA and illuminated the intricate mechanisms dictating patient prognosis. This study not only presents a novel prognostic marker but also carves out potential avenues for immunotherapy and targeted therapeutic strategies in BLCA. By demystifying the profound impact of immune-related genes and the tumor immune environment, this study augments the comprehension and prognostic management of bladder cancer.

Abstract In this paper, we develop an exhaustive numerical simulator for the dynamic visualization and behavior prediction of the tether‐net system during the whole space debris capture phases, including spread, contact, and close. First of all, to perform its geometrically nonlinear deformation, discrete different geometry theory is applied to model the mechanical response of a flexible net. Based on the discretization of the whole structure into multiple vertexes and lines, the internal force and associated Hession are derived in a closed form to solve a series of nonlinear dynamic equations of motion. The spread and deployment of a packaged net can be realized using this well‐established net solver. Next, a multidimensional incremental potential formulation is selected to achieve the intersection‐free boundary nonlinear contact and collision between the deformable net and rigid debris. Finally, for the closing mechanism analysis, a log‐like barrier functional is derived to achieve the nondeviation condition between the ring–rod linkage system. The C2 continuous log barrier functionals constructed for both the contact model and the linkage system are smooth and differentiable, and, therefore, the nonlinear net capture dynamic system can be efficiently solved through a fully implicit time integrator. Overall, as a demonstration, the whole capture process of a defunct satellite using a hexagon net is simulated through our well‐established numerical framework. We believe that our comprehensive numerical methods could provide new insight into the optimal design of active debris removal systems and promote further development of the online control of tether tugging systems.

This study presents an innovative investigation into the wetting characteristic and flow behavior of sand particles, which have different shapes, sizes, and textures, when they bonded with silicate binder. The results revealed that the apparent contact angles of fine and angular particles with the binder are higher than those of coarse and circular particles. In addition, after modifying the four systems with anionic surfactants, the equilibrium contact angles decreased by 42.8%, 26.7%, 26.5%, and 7%. These findings suggest that coarse and circular particles demonstrate stronger wetting properties with the binder compared to fine and angled particles. Additionally, the cohesion and internal friction angle of the wet sand mixture can be measured through the ring shear test. Moreover, the unconfined yield strength and main consolidation stress can be calculated using the Mohr Coulomb model to obtain the yield locus and quantify the flowability of the wet sand mixture. The yield trajectory of sand mixtures at specific consolidation stresses suggests that the most important factor affecting the flowability of wet sand particles is particle size distribution. Wet mixtures of coarse and circular particles exhibit lower cohesive forces, indicating higher fluidity.

High-definition near-eye display technology has extremely close sight distance, placing a higher demand on the size, performance, and array of light-emitting pixel devices. Based on the excellent photoelectric performance of metal halide perovskite materials, perovskite light-emitting diodes (PeLEDs) have high photoelectric conversion efficiency, adjustable emission spectra, and excellent charge transfer characteristics, demonstrating great prospects as next-generation light sources. Despite their potential, the solubility of perovskite in photoresist presents a hurdle for conventional micro/nano processing techniques, resulting in device sizes typically exceeding 50 μm. This limitation impedes the further downsizing of perovskite-based components. Herein, we propose a plane-structured PeLED device that can achieve microscale light-emitting diodes with a single pixel device size < 2 μm and a luminescence lifetime of approximately 3 s. This is accomplished by fabricating a patterned substrate and regulating ion distribution in the perovskite through self-doping effects to form a PN junction. This breakthrough overcomes the technical challenge of perovskite–photoresist incompatibility, which has hindered the development of perovskite materials in micro/nano optoelectronic devices. The strides made in this study open up promising avenues for the advancement of PeLEDs within the realm of micro/nano optoelectronic devices.

Abstract Background and Aim Necroptosis plays an important role in hepatocellular carcinoma (HCC) development, recurrence, and immunotherapy tolerance. We aimed to build a new prognostic necroptosis-related gene signature that could be used for survival and immunotherapy prediction in HCC patients. Methods We found that necroptosis was associated with HCC progression and survival outcomes and was involved in the immune infiltration of HCC. Multiple bioinformatics methods including WGCNA, LASSO-Cox regression, stepwise Cox regression, and Random Forest and Boruta model analysis, were used to establish a prognostic profile related to necroptosis. The necroptosis-related gene signature was validated in ICGC and GSE14520 datasets. Results This five-gene signature showed excellent predictive performance and was an independent risk factor for patients’ overall survival outcome in the three cohorts. Moreover, this signature was an exact predictor using fewer genes than previous gene signatures. Finally, qRT-PCR and immunohistochemical staining investigations were performed in previously collected fresh frozen tumor tissues from HCC patients and their paracancerous normal tissues, and the results were consistent with the bioinformatics results. We found that LGALS3 not only affected the proliferation and migration ability of HepG2 cells but also affected necroptosis and the expression of inflammatory cytokines. Conclusion In summary, we established and validated an individualized prognostic profile related to necroptosis to forecast the therapeutic response to immune therapy, which might offer a potential non-apoptotic therapeutic target for HCC patients.

Abstract Adult stem cell niche is a special environment composed of a variety stromal cells and signals, which cooperatively regulate tissue development and homeostasis. It is of great interest to study the role of immune cells in niche. Here, we show that mammary resident macrophages regulate mammary epithelium cell division and mammary development through TNF-α-Cdk1/Cyclin B1 axis. In vivo, depletion of macrophages reduces the number of mammary basal cells and mammary stem cells (MaSCs), while increases mammary luminal cells. In vitro, we establish a three-dimensional culture system in which mammary basal cells are co-cultured with macrophages, and interestingly, macrophage co-culture promotes the formation of branched functional mammary organoids. Moreover, TNF-α produced by macrophages activates the intracellular PI3K/Cdk1/Cyclin B1 signaling in mammary cells, thereby maintaining the activity of MaSCs and the formation of mammary organoids. Together, these findings reveal the functional significance of macrophageal niche and intracellular PI3K/Cdk1/Cyclin B1 axis for maintaining MaSC activity and mammary homeostasis.

Objective The aim of this study was to identify the expression of miRNA and lymphocyte subsets in the blood of gastric cancer (GC) patients, elucidate their clinical significance in GC, and establish novel biomarkers for the early diagnosis and prognosis of GC. Methods The expression of miRNAs in the serum of GC patients was screened using second-generation sequencing and detected using qRT-PCR. The correlation between miRNA expression and clinicopathological characteristics of GC patients was analyzed, and molecular markers for predicting cancer were identified. Additionally, flow cytometry was used to detect the proportion of lymphocyte subsets in GC patients compared to healthy individuals. The correlations between differential lymphocyte subsets, clinicopathological features of GC patients, and their prognosis were analyzed statistically. Results The study revealed that hsa-miR-1306-5p, hsa-miR-3173-5p, and hsa-miR-296-5p were expressed at lower levels in the blood of GC patients, which is consistent with miRNA-seq findings. The AUC values of hsa-miR-1306-5p, hsa-miR-3173-5p, and hsa-miR-296-5p were found to be effective predictors of GC occurrence. Additionally, hsa-miR-296-5p was found to be negatively correlated with CA724. Furthermore, hsa-miR-1306-5p, hsa-miR-3173-5p, and hsa-miR-296-5p were found to be associated with the stage of the disease and were closely linked to the clinical pathology of GC. The lower the levels of these miRNAs, the greater the clinical stage of the tumor and the worse the prognosis of gastric cancer patients. Finally, the study found that patients with GC had lower absolute numbers of CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ B cells, and lymphocytes compared to healthy individuals. The quantity of CD4+ T lymphocytes and the level of the tumor marker CEA were shown to be negatively correlated. The ROC curve and multivariate logistic regression analysis demonstrated that lymphocyte subsets can effectively predict gastric carcinogenesis and prognosis. Conclusion These miRNAs such as hsa-miR-1306-5p, hsa-miR-3173-5p, hsa-miR-296-5p and lymphocyte subsets such as the absolute numbers of CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ B cells, lymphocytes are down-regulated in GC and are closely related to the clinicopathological characteristics and prognosis of GC patients. They may serve as new molecular markers for predicting the early diagnosis and prognosis of GC patients.

Objective To explore the correlation between neutrophil-to-lymphocyte ratio (NLR) and contrast-induced acute kidney injury (CI-AKI). To develop machine-learning (ML) methods based on NLR and other relevant high-risk factors to establish new and effective predictive models of CI-AKI. Methods: The data of 2230 patients, who underwent elective vascular intervention, coronary angiography and percutaneous coronary intervention were retrospectively collected. The patients were divided into a CI-AKI group and a non-CI-AKI group. Logistic regression was used to analyze the correlation of NLR with CI-AKI and high-risk factors for CI-AKI, and logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and naïve Bayes (NB) models based on NLR and the high-risk factors were established.Results A high NLR(>2.844) was an independent risk factor for CI-AKI (odds ratio = 2.304, p

Access to rural primary healthcare services has been broadly studied in the past few decades. However, most earlier studies that focused on examining access to rural healthcare services have conventionally treated spatial and non-spatial access as separate factors. This research aims to measure access to primary healthcare services in rural areas with the consideration of both spatial and non-spatial dimensions. The methodology of study is threefold. First, the Gaussian two-step floating catchment area (G-2SFCA) method was adopted to measure spatial access to primary healthcare services. Then, a questionnaire survey was conducted to investigate non-spatial access factors, including demographic condition, patient’s household income, healthcare insurance, education level, and patient satisfaction level with the services. After that, a comprehensive evaluation index system was employed to integrate both spatial and non-spatial access. The empirical study showed a remarkable disparity in spatial access to primary healthcare services. In total, 78 villages with 185,137 local people had a “low” or “very low” level of spatial access to both clinics and hospitals. For the non-spatial dimension, the results depicted that Songzi had significant inequalities in socioeconomic status (e.g., income, education) and patient satisfaction level for medical service. When integrating both spatial and non-spatial factors, the disadvantaged areas were mainly located in the eastern and middle parts. In addition, this study found that comprehensively considering the spatial and non-spatial access had a significant impact on results in healthcare access. In conclusion, this study calls for policymakers to pay more attention to primary healthcare inequalities within rural areas. The spatial and non-spatial access should be considered comprehensively when the long-term rural medical support policy is designated.

Streptococcus suis (S. suis) is a zoonotic pathogen with a global distribution, which causes serious diseases in both humans and animals and economic losses in the swine industry. As antibiotic resistance increases, there is an urgent imperative to explore novel antibacterial alternatives. In the present study, we selected the anticancer drug 5-fluorouracil (5-FU) approved by the Food and Drug Administration (FDA) as a candidate drug to treat S. suis infections. The results showed that various pathogens, especially S. suis, are more sensitive to 5-FU. Moreover, the cytotoxicity of 5-FU is relatively low. Extensive in vitro assays demonstrated the pronounced bacteriostatic and bactericidal efficacy of 5-FU against susceptible and multidrug-resistant S. suis strains. Its mechanisms of action include damage to the bacterial cell walls and membranes, resulting in the leakage of intracellular components, and the inhibition of thymidylate synthase (TS), leading to a depletion of deoxythymidine triphosphate (dTTP) pools, ultimately causing thymine-less death and lethal DNA damage in bacteria. Gene-knockout experiments further showed that 5-FU played a role by inhibiting the thyA gene-encoding thymidine synthase. Finally, we determined that S. suis infections can be alleviated by 5-FU in the mouse infection model. This study emphasizes the antibacterial potential of 5-FU against S. suis and provides evidence for its targeting of bacterial membrane damage and DNA damage. In summary, 5-FU can control S. suis infection and is expected to become a new alternative to antibiotics.

Given the widespread prevalence of viruses, there is an escalating demand for antimicrobial composites. Although the composite of dialdehyde cellulose and silver nanoparticles (DAC@Ag1) exhibits excellent antibacterial properties, its weak mechanical characteristics hinder its practical applicability. To address this limitation, cellulose nanofibers (CNFs) were initially ammoniated to yield N-CNF, which was subsequently incorporated into DAC@Ag1 as an enhancer, forming DAC@Ag1/N-CNF. We systematically investigated the optimal amount of N-CNF and characterized the DAC@Ag1/N-CNF using FT-IR, XPS, and XRD analyses to evaluate its additional properties. Notably, the optimal mass ratio of N-CNF to DAC@Ag1 was found to be 5:5, resulting in a substantial enhancement in mechanical properties, with a 139.8% increase in tensile elongation and a 33.1% increase in strength, reaching 10% and 125.24 MPa, respectively, compared to DAC@Ag1 alone. Furthermore, the inhibition zones against Escherichia coli and Staphylococcus aureus were significantly expanded to 7.9 mm and 15.9 mm, respectively, surpassing those of DAC@Ag1 alone by 154.8% and 467.9%, indicating remarkable improvements in antimicrobial efficacy. Mechanism analysis highlighted synergistic effects from chemical covalent bonding and hydrogen bonding in the DAC@Ag1/N-CNF, enhancing the mechanical and antimicrobial properties significantly. The addition of N-CNF markedly augmented the properties of the composite film, thereby facilitating its broader application in the antimicrobial field.

Leaf blast is recognized as one of the most devastating diseases affecting rice production in the world, seriously threatening rice yield. Therefore, early detection of leaf blast is extremely important to limit the spread and propagation of the disease. In this study, a leaf blast-specific spectral vegetation index RBVI = 9.78R816−R724 − 2.08(ρ736/R724) was designed to qualitatively detect the level of leaf blast disease in the canopy of a field and to improve the accuracy of early detection of leaf blast by remote sensing by unmanned aerial vehicle. Stacking integrated learning, AdaBoost, and SVM were used to compare and analyze the performance of the RBVI and traditional vegetation index for early detection of leaf blast. The results showed that the stacking model constructed based on the RBVI spectral index had the highest detection accuracy (OA: 95.9%, Kappa: 93.8%). Compared to stacking, the detection accuracy of the SVM and AdaBoost models constructed based on the RBVI is slightly degraded. Compared with conventional SVIs, the RBVI had higher accuracy in its ability to qualitatively detect leaf blast in the field. The leaf blast-specific spectral index RBVI proposed in this study can more effectively improve the accuracy of UAV remote sensing for early detection of rice leaf blast in the field and make up for the shortcomings of UAV hyperspectral detection, which is susceptible to interference by environmental factors. The results of this study can provide a simple and effective method for field management and timely control of the disease.

In this study, an upper sheet of an A6061 aluminum alloy and a lower sheet of Q235 steel were welded by resistance element welding with a steel rivet. The temperature field during welding was calculated using ABAQUS numerical simulation software, and the interfacial microstructure was observed. A nugget was formed between the rivet shank and the lower sheet. With increases in welding current and welding time, the tensile shear load of the joint increased first and then decreased slightly. When the welding current was 14 kA and the welding time was 300 ms, the tensile shear load of the joint reached a maximum of 7.93 kN. The smaller the distance from the position to the lower sheet along the interface between the rivet shank and upper sheet, the longer the high-temperature duration and the higher the peak temperature during welding. At the junction of the rivet shank, upper sheet, and lower sheet in the joint, the high-temperature duration was the longest, at about 392 ms, and the peak temperature was the highest, at about 1237 °C. The results show that the smaller the distance from the position to the lower sheet along the interface between the rivet shank and the upper sheet in the joint, the thicker the reaction layer generated there, and that the thickness of the reaction layer was about 2.0 μm at the junction of the rivet shank, upper sheet, and lower sheet in the joint.

Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes.

ObjectiveIncreased inflammation and cytokine levels are considered risk factors and promoters of preterm birth (PTB). However, the regulatory mechanism of pregnancy-related inflammation remains unclear. Toll-like receptor 4 (TLR4) plays a critical role in inflammatory responses in various diseases. Therefore, our study aimed to investigate whether TLR4 is involved in the inflammatory responses during uterine activation for labor, with the goal of identifying potential biomarkers for uterine activation at term.Materials and methodsWe used flow cytometry to detect TLR4 expression on CD14+ maternal blood monocytes in the first, second, and third trimesters. ELISA was employed to measure TLR4 and cytokines levels in the maternal serum of term non-labor (TNL), term labor (TL) women and LPS induced preterm labor and PBS injected controls. TLR4siRNA was transfected into the human myometrial smooth muscle cells (HMSMCs), which were subsequently treated with IL-1β. The mRNA and protein levels of TLR4, uterine contraction-related protein connexin 43 (CX43), oxytocin receptor (OTR), MAPK/NF-κB signaling pathway, and cytokines were analyzed using qRT-PCR, western blotting, and immunohistochemistry.ResultsThe study revealed TLR4 expression on CD14+ maternal blood monocytes was higher in the third trimester group compared to the first and second trimester groups (p

Introduction: The immune-related lncRNAs (IRLs) are critical for the development of cervical cancer (CC), but it is still unclear how exactly ILRs contribute to CC. In this study, we aimed to examine the relationship between IRL and CC in detail.Methods: First, the RNAseq data and clinical data of CC patients were collected from The Cancer Genome Atlas (TCGA) database, along with the immune genes from the Import database. We used univariate cox and least absolute shrinkage and selection operator (lasso) to obtain IRLs for prediction after screening the variables. According to the expression levels and risk coefficients of IRLs, the riskscore were calculated. We analyzed the relationship between the model and oxidative stress. We stratified the risk model into two as the high and low-risk groups. We also evaluated the survival differences, immune cell differences, immunotherapeutic response differences, and drug sensitivity differences between the risk groups. Finally, the genes in the model were experimentally validated.Results: Based on the above analyses, we further selected four IRLs (TFAP2A.AS1, AP000911.1, AL133215.2, and LINC02078) to construct the risk model. The model was associated with oxidative-stress-related genes, especially SOD2 and OGG1. Patients in the high-risk group had a lower overall survival than those in the low-risk group. Riskscore was positively correlated with resting mast cells, neutrophils, and CD8+ T-cells. Patients in the low-risk group showed a greater sensitivity to immunosuppression therapy. In addition, we found that patients with the PIK3CA mutation were more sensitive to chemotherapeutic agents such as dasatinib, afatinib, dinaciclib and pelitinib. The function of AL133215.2 was verified, which was consistent with previous findings, and AL133215.2 exerted a pro-tumorigenic effect. We also found that AL133215.2 was closely associated with oxidative-stress-related pathways.Discussion: The results suggested that risk modeling might be useful for prognosticating patients with CC and opening up new routes for immunotherapy.

In order to effectively improve the path‐finding capability of a multirobot system in a decentralized control approach, a dynamic motion planning model based on graph neural networks and historical information (GNNHIM) is proposed. Due to the limited sensing range of the robot's onboard sensors, GNNHIM uses convolutional neural networks to extract features from the heterogeneous environmental information collected and analyzes the motion trends of other robots in conjunction with the historical path information stored locally. After finishing feature fusion, each robot exchanges its feature vectors with other robots still within sensing range to obtain the next action by the graph neural network model processing. Herein, imitation learning is used to train robots to choose behavioral strategies to maximize team benefit. The experimental results show that GNNHIM can make path planning for multirobot systems more efficient and reliable, enabling all robots to reach their own goal within a limited time in 95.1% of the experimental dataset. Moreover, GNNHIM still has great generalization when applied to unseen scenarios and larger scale robot systems. GNNHIM in unseen scenarios has improved the success rate by 6%‐34% and reduced the detour percentage by 3.8% on average compared to the other scheduling model.

N6-methyladenosine (m6A) methylation of RNA by the methyltransferase complex (MTC), with core components including METTL3–METTL14 heterodimers and Wilms’ tumor 1-associated protein (WTAP), contributes to breast tumorigenesis, but the underlying regulatory mechanisms remain elusive. Here, we identify a novel cleaved form METTL3a (residues 239–580 of METTL3). We find that METTL3a is required for the METTL3–WTAP interaction, RNA m6A deposition, as well as cancer cell proliferation. Mechanistically, we find that METTL3a is essential for the METTL3–METTL3 interaction, which is a prerequisite step for recruitment of WTAP in MTC. Analysis of m6A sequencing data shows that depletion of METTL3a globally disrupts m6A deposition, and METTL3a mediates mammalian target of rapamycin (mTOR) activation via m6A-mediated suppression of TMEM127 expression. Moreover, we find that METTL3 cleavage is mediated by proteasome in an mTOR-dependent manner, revealing positive regulatory feedback between METTL3a and mTOR signaling. Our findings reveal METTL3a as an important component of MTC, and suggest the METTL3a–mTOR axis as a potential therapeutic target for breast cancer.

In order to address the problem that the dynamic decay of shear strength of expansive soil in the process of rainfall infiltration cannot be considered in the traditional slope stability analysis of expansive soil, indoor direct shear test was carried out to systematically study the influence of dry density and water content on the indicators of shear strength of expansive soil, viz., cohesion and internal friction angle. At the same time, the stability analysis of the expansive soil slope based on the strength reduction method was carried out with the shear strength testing results, and the change law of slope stability with the dynamic decay of shear strength was obtained. The results show that the increase of water content and the decrease of dry density will cause the decay of cohesion and internal friction angle of expansive soil, and the decay of cohesion is significant, while the decay of internal friction angle is less; the stability of expansive soil slope is mainly controlled by the water content of weathering layer, and with the increase of water content of expansive soil, the slope gradually evolves from stable state to unstable state, while the influence of dry density on the stability of expansive soil slope is relatively small.

ObjectiveThis study was aimed to evaluate the efficacy and safety of transarterial chemoembolization combined with tyrosine kinase inhibitors and camrelizumab in the treatment of unresectable hepatocellular carcinoma and to explore a new therapeutic strategy for the treatment of advanced HCC.Patients and methodsA total of 87 patients aged 18-75 years with at least one measurable lesion per Response Evaluation Criteria in Solid Tumors (version 1.1) were included in the study. TACE was administered as needed, and camrelizumab and TKI medication were initiated within two weeks and one week after TACE, respectively. The primary endpoints were progression-free survival and objective response rate.ResultsThe 87 patients in this trial were last evaluated on September 28, 2022, and 35.8% were still receiving treatment at the data cutoff. A total of 34 patients (39.1%) died, and the median OS was not reached. The median PFS was 10.5 months (95% CI: 7.8-13.1). The ORR rate was 71.3% (62/87), and the DCR rate was 89.7% (78/87) per mRECIST. According to RECIST version 1.1, the ORR rate was 35.6% (31/87), and the DCR rate was 87.4% (76/87). Ten patients (11.5%) successfully underwent conversion therapy and all achieved R0 resection. Two patients achieved a complete pathological response, four achieved a major pathological response, and four had a partial response. All treatment-related adverse events were tolerated. No serious adverse events were observed, and no treatment-related deaths occurred.ConclusionsTACE combined with TKI and camrelizumab was safe and effective in treating advanced HCC. Triple therapy may benefit patients with large tumor burden and portal vein cancer thrombus and is expected to provide a new treatment strategy for advanced HCC.Clinical Trial RegistrationClinicalTrials.gov, identifier ChiCTR2000039508

Most polyp segmentation methods use convolutional neural networks (CNNs) as their backbone, leading to two key issues when exchanging information between the encoder and decoder: (1) taking into account the differences in contribution between different-level features, and (2) designing an effective mechanism for fusing these features. Unlike existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations. In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three standard modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), and a similarity aggregation module (SAM). Among these, the CFM is used to collect the semantic and location information of polyps from high-level features; the CIM is applied to capture polyp information disguised in low-level features, and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features. The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities. Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects, and rotation) than existing representative methods. The proposed model is available at https://github.com/DengPingFan/Polyp-PVT.

Abstract Background Cyclin-dependent kinase 16 (CDK16) is an atypical PCTAIRE kinase, and its activity is dependent on the Cyclin Y (CCNY) family. Ccnys have been reported to regulate mammary stem cell activity and mammary gland development, and CCNY has been recognized as an oncoprotein in various cancers, including breast cancer. However, it remains unclear whether CDK16 has a role in breast cancer and whether it can be used as a therapeutic target for breast cancer. Methods Publicly available breast cancer datasets analyses and Kaplan-Meier survival analyses were performed to reveal the expression and clinical relevance of atypical CDKs in breast cancer. CDK16 protein expression was further examined by immunohistochemical and immunoblot analyses of clinical samples. Cell proliferation was measured by colony formation and MTT analyses. Cell cycle and apoptosis were examined by fluorescence-activated cell sorting (FACS) analysis. Wound-healing and trans-well invasion assays were conducted to test cell migration ability. The functions of CDK16 on tumorigenesis and metastasis were evaluated by cell line-derived xenograft, patient-derived organoid/xenograft, lung metastasis and systemic metastasis mouse models. Transcriptomic analysis was performed to reveal the potential molecular mechanisms involved in the function of CDK16. Pharmacological inhibition of CDK16 was achieved by the small molecular inhibitor rebastinib to further assess the anti-tumor utility of targeting CDK16. Results CDK16 is highly expressed in breast cancer, particularly in triple-negative breast cancer (TNBC). The elevated CDK16 expression is correlated with poor outcomes in breast cancer patients. CDK16 can improve the proliferation and migration ability of TNBC cells in vitro, and promote tumor growth and metastasis of TNBC in vivo. Both genetic knockdown and pharmacological inhibition of CDK16 significantly suppress the tumor progression of TNBC. Mechanistically, CDK16 exerts its function by phosphorylating protein regulator of cytokinesis 1 (PRC1) to regulate spindle formation during mitosis. Conclusion CDK16 plays a critical role in TNBC and is a novel promising therapeutic target for TNBC.

Increased grain yield (GY) and grain protein concentration (GPC) are the two main targets of efforts to improve wheat (Triticum aestivum L.) production in the North China Plain (NCP). We conducted a three-year field experiment in the 2014–2017 winter wheat growing seasons to compare the effects of conventional irrigation practice (CI) and micro-sprinkling irrigation combined with nitrogen (N) fertilizer (MSI) on GY, GPC, and protein yield (PY). Across the three years, GY, GPC, and PY increased by 10.5%–16.7%, 5.4%–8.0%, and 18.8%–24.6%, respectively, under MSI relative to CI. The higher GY under MSI was due primarily to increased thousand-kernel weight (TKW). The chlorophyll content of leaves was higher under MSI during the mid–late grain filling period, increasing the contribution of post-anthesis dry matter accumulation to GY, with consequent increases in total dry matter accumulation and harvest index compared to CI. During the mid–late grain filling period, the canopy temperature was markedly lower and the relative humidity was higher under MSI than under CI. The duration and rate of filling during the mid–late grain filling period were also higher under MSI than CI, resulting in higher TKW. MSI increased the contribution of post-anthesis N accumulation to grain N but reduced the pre-anthesis remobilization of N in leaves, the primary site of photosynthetic activity, possibly helping maintain photosynthate production in leaves during grain filling. Total N at maturity was higher under MSI than CI, although there was little difference in N harvest index. The higher GPC under MSI than under CI was due to a larger increase in grain N accumulation than in GY. Overall, MSI simultaneously increased both GY and GPC in winter wheat grown in the NCP.

Streptococcus suis (S. suis), more specifically serotype 2, is a bacterial pathogen that threatens the lives of pigs and humans. Like many other pathogens, S. suis exhibits quorum sensing (QS) system-controlled virulence factors, such as biofilm formation that complicates treatment. Therefore, impairing the QS involving LuxS/AI-2 cycle in S. suis, may be a promising alternative strategy for overcoming S. suis infections. In this study, we investigated paeoniflorin (PF), a monoterpenoid glycoside compound extracted from peony, as an inhibitor of S. suis LuxS/AI-2 system. At a sub-minimal inhibitory concentration (MIC) (1/16 MIC; 25 μg/ml), PF significantly reduced biofilm formation by S. suis through inhibition of extracellular polysaccharide (EPS) production, without affecting bacterial growth. Moreover, evidence was brought that PF reduces AI-2 activity in S. suis biofilm. Molecular docking indicated that LuxS may be the target of PF. Monitoring LuxS enzymatic activity confirmed that PF had a partial inhibitory effect. Finally, we showed that the use of PF in a mouse model can relieve S. suis infections. This study highlighted the anti-biofilm potential of PF against S. suis, and brought evidence that it may as an inhibitor of the LuxS/AI-2 system to prevent S. suis biofilm-related infections. PF can thus be used as a new type of natural biofilm inhibitor for clinical application.

Common wheat (Triticum aestivum, BBAADD) is an allohexaploid species combines the D genome from Ae. tauschii and with the AB genomes from tetraploid wheat (Triticum turgidum). Compared with tetraploid wheat, hexaploid wheat has wide-ranging adaptability to environmental adversity such as salt stress. However, little is known about the molecular basis underlying this trait. The plasma membrane Na+/H+ transporter Salt Overly Sensitive 1 (SOS1) is a key determinant of salt tolerance in plants. Here we show that the upregulation of TaSOS1 expression is positively correlated with salt tolerance variation in polyploid wheat. Furthermore, both transcriptional analysis and GUS staining on transgenic plants indicated TaSOS1-A and TaSOS1-B exhibited higher basal expression in roots and leaves in normal conditions and further up-regulated under salt stress; while TaSOS1-D showed markedly lower expression in roots and leaves under normal conditions, but significant up-regulated in roots but not leaves under salt stress. Moreover, transgenic studies in Arabidopsis demonstrate that three TaSOS1 homoeologs display different contribution to salt tolerance and TaSOS1-D plays the prominent role in salt stress. Our findings provide insights into the subgenomic homoeologs variation potential to broad adaptability of natural polyploidy wheat, which might effective for genetic improvement of salinity tolerance in wheat and other crops.

Faced with the integrated system composed of the train power system, the photovoltaic (PV) power system, and the energy storage system (ESS), this research studies the energy-efficient operation and energy management strategy from the perspective of both train optimal control and timetable optimization, aiming at achieving a long-term energy consumption reduction. A two-step approach for collaboratively optimizing the train timetable, speed trajectory, and energy management strategy considering the stochastic characteristics of PV power generation is proposed to solve this large-scale complex problem. Before the two-step approach, a mixed-integer linear programming (MILP) model is established to optimize the energy consumption of the inter-station operation. On this basis, explicit energy consumption expressions for all inter-stations of the entire line are obtained by the proposed data fitting method. The historical PV power data is clustered to generate scenarios with different probabilities to characterize the stochastic PV power. The first step of this two-step approach is to minimize the total energy consumption expectations of all inter-stations determined by the obtained explicit energy consumption expressions to optimize the timetable while ensuring the total time and time window constraints are met. The second step is to minimize the weighted sum of energy consumption under all possible scenarios to obtain the optimal speed trajectory and energy management strategy based on the optimized timetable obtained in the first step. The validity of the model is verified by case studies using the real data of Qingdao Metro Line 11 under both scenarios with and without PV power. This study provides a novel method for energy-efficient operation and energy management of the integrated system and demonstrates the prospect of the proposed two-step stochastic optimization in reducing the net grid-supplied energy for the long-term operation of urban rail transportation systems.

Large-aperture space optical systems are important tools for observing our planet and conducting deep-space scientific research. More requirements have been put forward for large-aperture optical systems as the depth and breadth of related applications continue to increase. However, achieving the desired surface accuracy on lightweight materials for large-aperture mirror brings higher demands on relevant processing technologies, which increases the processing difficulty, cycle, and cost of large-aperture optical systems. Therefore, it is necessary to develop a new optical system technique with high tolerance for primary mirror machining errors to significantly reduce its machining accuracy requirements. This paper proposes a new optical system technique which introduces a small-aperture free-form surface into the large-aperture optical system’s post optical path. By combining the freeform correction and its misalignment on the system’s aberrations, the system’s wavefront can be adjusted to correct the wavefront distortion caused by the primary mirror’s machining errors. This reduced the machining accuracy requirements of the large-aperture primary mirror by about one order of magnitude, and high-quality imaging with a low-precision primary mirror is achieved.

To enhance the thermal performance of the wet cooling tower under crosswind environment, a 3D numerical model of a wet cooling tower is proposed based on the method of utilizing the water-dropping potential energy to achieve the purpose of forced ventilation of the wet cooling tower. Under the condition of an annual average crosswind speed of 5 m/s, the effects of the number, power, and installation layouts of the fan groups on the thermal performance of the tower are investigated. The results show that the forced ventilation method can effectively alleviate the unfavorable impacts of the crosswind, and the vortices area on the leeward is reduced. Within the scope of this paper, the number of fans has a slight effect on the performance when the power of the fan is less than 200 kW. When the power of the fans exceeds 200 kW, the thermal performance of the forced ventilation tower with the fan groups is obviously better than that with an axial fan. The research indicates that the cooling tower equipped with four fans has better performance. Under the power of 200 kW and 1000 kW, the outlet water temperature is about 0.12 °C and 0.34 °C lower than that of the conventional tower.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

Abstract Background Streptococcus suis type 2 (SS2) is an important zoonotic pathogen. We have previously reported the structure of LuxS protein and found that the luxS gene is closely related to biofilm, virulence gene expression and drug resistance of SS2. However, the mechanism of luxS mediated SS2 stress response is unclear. Therefore, this experiment performed stress response to luxS mutant (ΔluxS) and complement strain (CΔluxS), overexpression strain (luxS+) and wild-type SS2 strain HA9801, and analyzed the differential phenotypes in combination with transcriptome data. Results The results indicate that the luxS gene deletion causes a wide range of phenotypic changes, including chain length. RNA sequencing identified 278 lx-regulated genes, of which 179 were up-regulated and 99 were down-regulated. Differential genes focus on bacterial growth, stress response, metabolic mechanisms and drug tolerance. Multiple mitotic genes were down-regulated; while the ABC transporter system genes, cobalamin /Fe3+-iron carrier ABC transporter ATPase and oxidative stress regulators were up-regulated. The inactivation of the luxS gene caused a significant reduction in the growth and survival in the acid (pH = 3.0, 4.0, 5.0) and iron (100 mM iron chelator 2,2′-dipyridyl) stress environments. However, the mutant strain ΔluxS showed increased antioxidant activity to H2O2 (58.8 mmol/L). Conclusions The luxS gene in SS2 appears to play roles in iron metabolism and protective responses to acidic and oxidative environmental conditions.

Abstract Aims Many studies have explored the clinical characteristics of patients with coronavirus disease (COVID‐19), especially patients with cardiovascular disease. However, associated mechanisms and markers remain to be further investigated. This study aimed to investigate the effect of α‐hydroxybutyrate dehydrogenase (α‐HBDH) levels on disease progression and prognosis of patients with COVID‐19. Methods and results One thousand seven hundred and fifty‐one patients from the Leishenshan hospital in Wuhan were divided into elevated and normal groups by α‐HBDH level, and the clinical information between the two groups was compared retrospectively. The main outcome evaluation criteria included in‐hospital death and disease severity. Univariate and multivariate regression analyses, survival curves, logistic regression, and receiver operating characteristic curve models were performed to explore the relationship between elevated α‐HBDH and the two outcomes. Besides, curve fitting analyses were conducted to analyse the relationship between computed tomography score and survival. Among 1751 patients with confirmed COVID‐19, 15 patients (0.87%) died. The mean (SD) age of patients was 58 years in normal α‐HBDH group and 66 years in elevated α‐HBDH group (P

As one of the most important wheat-producing areas in China, wheat is prone to heat stress during the grain filling period in the Huang-Huai-Hai Plain (3HP), which lowers yields and degrades the grain quality of wheat. To assess the effects of spraying potassium dihydrogen phosphate (KH2PO4) on the physiological traits in flag leaves and grain yield (GY) and quality under heat stress during the filling period, we conducted a two-year field experiment in the winter wheat growing seasons of 2020–2022. In this study, spraying water combined with heat stress (HT), 0.3% KH2PO4 (KDP), and 0.3% KH2PO4 combined with heat stress (PHT) were designed, and spraying water alone was used as a control (CK). The dates for the spraying were the third and eleventh day after anthesis, and a plastic film shed was used to impose heat stress on the wheat plants during the grain filling period. The results showed that spraying KH2PO4 significantly improved the chlorophyll content and net photosynthesis rate (Pn) in flag leaves compared with the non-sprayed treatments. Compared with CK, the Pn in HT decreased by 8.97% after heat stress, while Pn in PHT decreased by 7.44% compared to that of KDP. The activities of superoxide dismutase, catalase, and peroxidase in flag leaves were significantly reduced when the wheat was subjected to heat stress, while malonaldehyde content increased, and the enzyme activities were significantly enhanced when KH2PO4 was sprayed. Heat stress significantly decreased the contribution rate of dry matter accumulation (DM) after anthesis of wheat to grain (CRAA), whereas spraying KH2PO4 significantly increased the CRAA and harvest index. At maturity, the DM in CK was significantly higher than that in HT, KDP was significantly higher than PHT, and KDP had the highest DM. Compared with CK, the GY in KDP significantly increased by 9.85% over the two years, while the GY in HT decreased by 11.44% compared with that of CK, and the GY in PHT decreased by 6.31% compared to that of KDP. Spraying KH2PO4 after anthesis primarily helped GY by maintaining a high thousand grain weight to lessen the negative effects of heat stress on wheat. Moreover, heat stress significantly reduced protein concentration, wet gluten content, dough development time, and hardness index in grains of mature, while spraying KH2PO4 maintained a sufficient grain quality under the conditions of achieving higher yields. Overall, spraying KH2PO4 after anthesis could enhance the heat stress resistance of wheat and maintain the photosynthetic capacity of flag leaves, ensuring the dry matter production and reducing the negative effects on grain yield and quality in the 3HP.

The management of stand structure and the protection of species diversity are crucial to forest ecosystem service functions. Changbai Mountain is one of the three major forest regions in Northeast China, and understanding the characteristics of stand structure and the allometric relationship between stand structure variables and the correlation between stand factors and species diversity is the basis for achieving effective forest management and ecological function improvement. In this study, the typical deciduous broad-leaved mixed forest and coniferous and broad-leaved mixed forest of the Longwan Nature Reserve in the Changbai Mountain were taken as the research objects, and the stand structure and diversity were investigated in detail. Allometric growth equations were established using the breast height diameter (DBH), tree height, and crown width of the main dominant species. Pearson correlation analysis was conducted on the stand structure and diversity of the forest community. The experimental results showed that young forests with small diameters and small individuals were the main part of the stands. Optimal allometric growth models of the main tree species (Acer mandshuricum, Ulmus davidiana var. japonica, Juglans mandshurica, Acer mono, and Tilia amurensis) were 0.807D0.646, 0608D0.381H0.390, 0.502D0.533H0.295, 0.795D0.540H0.157, and 0.541D0.484H0.301, respectively, with R2 values ranging between 0.6 and 0.8, indicating a good fit for the models. Furthermore, tree density, crown width, and DBH were the main factors affecting tree diversity. The research results will provide theoretical support for the efficient management of forest stand structure and diversity conservation in the Longwan Reserve.

The demand for antimicrobial materials is gradually increasing due to the threat of infections and diseases caused by microorganisms. Silver nanoparticles (AgNPs) are widely used because of their broad-spectrum antimicrobial properties, but their synthesis methods are often environmentally harmful and AgNPs difficult to isolate, which limits their application in several fields. In this study, an aqueous solution of dialdehyde cellulose (DAC) was prepared and used as a reducing agent to synthesize AgNPs in an efficient and environmentally friendly process. The synthesized AgNPs can be easily separated from the reducing agent to expand their applications. In addition, the AgNPs were immobilized in situ on dialdehyde cellulose to form antibacterial composite films. The results showed that the prepared silver nanoparticles were mainly spherical and uniformly dispersed, with an average size of about 25 nm under optimal conditions. Moreover, the dialdehyde cellulose–nanosilver (DAC@Ag) composite films had excellent mechanical properties, positive transparency, ultraviolet-blocking properties, and effective antibacterial activity against E. coli and S. aureus. Notably, the composite films exhibited excellent oxygen and water vapor barrier properties, with WVT and ORT of 136.41 g/m2·24 h (30 °C, 75% RH) and 3/m2·24 h·0.1 MPa (30 °C, 75% RH), respectively, better than commercial PE films. Hence, this study not only provides an environmentally friendly method for the preparation of silver nanoparticles, but also offers a simple and novel strategy for the in situ synthesis of silver-loaded antibacterial composite films.

The charge plays an important role in cellulose nanocrystal (CNC) self-assembly to form liquid crystal structures, which has rarely been systematically explored. In this work, a novel technique combining atomic force microscopy force and atomistic molecular dynamics simulations was addressed for the first time to systematically investigate the differences in the CNC self-assembly caused by external positive and negative charges at the microscopic level, wherein sodium polyacrylate (PAAS) and chitosan oligosaccharides (COS) were used as external positive and negative charge additives, respectively. The results show that although the two additives both make the color of CNC films shift blue and eventually disappear, their regulatory mechanisms are, respectively, related to the extrusion of CNC particles by PAAS and the reduction in CNC surface charge by COS. The two effects both decreased the spacing between CNC particles and further increased the cross angle of CNC stacking arrangement, which finally led to the color variations. Moreover, the disappearance of color was proved to be due to the kinetic arrest of CNC suspensions before forming chiral nematic structure with the addition of PAAS and COS. This work provides an updated theoretical basis for the detailed disclosure of the CNC self-assembly mechanism.