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Abstract Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site’s pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.

To better protect the ecological environment, this study investigates the evolution of the landscape in Hubei Province and its impact on Net Primary Production (NPP). By utilizing landscape assessment tools and neighborhood proxy methods, it quantifies the changes in NPP resulting from landscape transformations over periods of 5 and 20 years. The results indicate significant NPP variations, with changes ranging from −406.199 to 507.181 g*C/m2. The geographical detector model identifies key drivers, particularly noting significant changes in landscape features at type boundaries, which affect NPP. Land cover, especially transitions between forests, shrublands, and croplands, is identified as a critical factor. This research highlights the complex relationship between landscape modifications and NPP variations, emphasizing the importance of ecological considerations in landscape management amid urbanization. It offers valuable insights for future landscape management and conservation strategies in Hubei, aiming for ecological preservation alongside development. Additionally, this work fills a theoretical gap by linking changes in landscape features to NPP, proposing new perspectives for landscape protection and ecological capacity enhancement, and providing practical guidance for improving NPP and coordinated landscape management in Hubei.

Abstract In the last ten years, stem cell (SC) therapy has been extensively used to treat a range of conditions such as degenerative illnesses, ischemia-related organ dysfunction, diabetes, and neurological disorders. However, the clinical application of these therapies is limited due to the poor survival and differentiation potential of stem cells (SCs). Extracorporeal shock wave therapy (ESWT), as a non-invasive therapy, has shown great application potential in enhancing the proliferation, differentiation, migration, and recruitment of stem cells, offering new possibilities for utilizing ESWT in conjunction with stem cells for the treatment of different systemic conditions. The review provides a detailed overview of the advances in using ESWT with SCs to treat musculoskeletal, cardiovascular, genitourinary, and nervous system conditions, suggesting that ESWT is a promising strategy for enhancing the efficacy of SC therapy for various diseases.

Objective: To investigate the effect and mechanism of Hippophae rhamnoides flavone (HRF) on polycystic ovarian syndrome (PCOS) in rats, and to provide new ideas for the prevention and treatment of PCOS. Methods: A PCOS rat model was duplicated by giving rats a high-fat diet combined with the intragastric administration of letrozole, and the model rats were randomly divided into model group, low-dose HRF group (200 mg/kg HRF), high-dose HRF group (400 mg/kg HRF) and metformin group (100 mg/kg). Changes in the related indicators were detected on the 21 d after the rats were administered with the different agents by gavage. Results: Compared with that in the model group, the ovarian index, the percentage of interestrus temporal image, the fasting blood glucose (FBG) level, the fasting insulin (FINS) content, the insulin resistance index (HOMA-IR), the activities of serum luteinizing hormone (LH) and testosterone (T), the levels of tumor necrosis factor α (TNF-α), interleukin 6 (IL-6) and C reactive protein (CRP) of rats in low- and high-dose HRF groups were respectively decreased significantly (P

Abstract Background Falls are common in the elderly and can lead to adverse consequences, like injuries, hospitalization, disability even mortality. Successful ageing emerged in sight to assess physical, psychological and social status of older adults. This study is conducted to explore the association between them in a large Indian community-dwelling population. Methods Data were based on the wave 1 survey of the Longitudinal Ageing Study in India (LASI). People aged 60 and above with complete information were included. The elderly met five standards including absence of chronic diseases, freedom from disability, high cognitive ability, free from depressive symptoms and active social engagement, were classified into successful agers. The assessment of falls, fall-related injuries and multiple falls depended on interview. Multivariate logistic regression was conducted to find the associations between falls, fall-injury, multiple falls and successful ageing after adjusting both socio-demographic and biological covariates. The log-likelihood ratio test was calculated interactions in subgroups. Results 31,345 participants in LASI were finally included in our study. Of them, 20.25% reported fall, and 25% were classified into successful agers. After full adjustment, successful ageing was negatively associated with falls (OR 0.70; 95%CI 0.65–0.76) and multiple falls (OR 0.70; 95%CI 0.63–0.78). And the association did not show the significance in older adults with fall-related injuries (OR 0.86; 95%CI 0.72–1.04). Conclusions Successful ageing was negatively associated with falls and multiple falls, but not fall-related injuries in older people in India. Future studies are demanded to explore the causal relationship and to reveal the underlying mechanism.

IntroductionScutellarein (Scu) is a natural occurring flavonoid found in multiple traditional Chinese medicines such as Oroxylum indicum (L.) Kurz and Scutellaria baicalensis, with various pharmacological activities including anti-inflammation, anti-oxidation and myocardial protection. Here, we investigated the therapeutic efficacy of Scu on ulcerative colitis (UC) and the underlying mechanism.MethodsEfficacy of Scu on UC was evaluated in dextran sulfate sodium (DSS) induced colitis mouse model. Inflammation in colonic tissues was assessed by myeloperoxidase activity assay and RT-qPCR. Barrier proteins expression was examined using immunostaining and Western blot. IL-1β-treated HT-29 cells was used for mechanical investigation.ResultsGavage of Scu significantly decreased the DAI score, improved colon shortening, ameliorated the pathological score in DSS-treated mice with better efficacy than the positive drug, 5-aminosalicylic acid. Scu also inhibited the expression levels of cytokines (Il-1β, Tnf-α, Il-1α, Il-6, and Cxcl1) as well as barrier proteins (E-cadherin, Occludin, and ZO-1) in colon tissues of DSS mice. In intestinal epithelial HT-29 cells, Scu attenuated the IL-1β-downregulated expression levels of E-cadherin, occludin, and ZO-1, while reduced IL-1β-upregulated IL-6 and IL-8 mRNA levels. Moreover, Scu inhibited the phosphorylation and nuclear translocation of NF-κB and suppression of NF-κB phosphorylation abolished IL-1β-disrupted epithelial barrier integrity and IL-1β-upregulated proinflammatory mediators expression in HT-29 cells.ConclusionThese data demonstrate that Scu is an efficacious therapeutic agent to treat UC. Inhibition of inflammatory responses and maintenance of epithelial barrier integrity through NF-κB signaling pathway underlines Scu therapeutic effect on UC.

Abstract Objective Mitochondrial DNA (mtDNA) reportedly has diagnostic and predictive value in critically ill patients. This study evaluated the diagnostic and predictive value of mtDNA in patients undergoing continuous venovenous haemofiltration (CVVH). Methods We consecutively enrolled 41 patients who were treated with CVVH from September 2018 to December 2019. Prefilter, postfilter, and ultrafiltrate samples were collected before the initiation of CVVH (T0) and 6 and 12 h after CVVH. The total mass removal rate (Mtr), total mass adsorption rate (Mad), plasma clearance (PC), and sieving coefficient (SC) were calculated based on the mass conservation principle. Results The plasma mtDNA concentration in patients at T0 prefilter was higher than that in healthy volunteers [13.77 (12.45–15.86) vs. 1.24 (1.15–1.34) ng/mL, P 0.05 for all). Conclusions Plasma mtDNA in critically ill patients was significantly affected by CVVH. Trial registration: Retrospectively registered on 20 August 2019 at ClinicalTrials.gov (NCT04083482).

Nanozyme cascade have garnered substantial interest in recent years due to their distinctive properties. However, the conventional stepwise cascade reaction undergoes tedious two-step operation process owing to the incompatibility of reaction conditions. Moreover, most of reported nanozymes exhibit favorable catalytic performance only in acidic medium, which greatly restricts their usage especially in biochemical analysis. To address above challenges, we developed gold nanoparticles/calcium hexacyanoferrate (Ⅲ)/nitrogen-doped graphitic alkyne (AuNPs/CaHF NPs/N-GDY) nanozyme with superior cascade catalytic activity at neutral pH comparable to that of acidic. Specifically, AuNPs/CaHF NPs/N-GDY simultaneously possessed glucose oxidase-like (GOx) and peroxidase-like (HRP) activities, which could induce one-step cascade reaction in the presence of glucose, resulting in 5-fold enhancement in catalytic efficiency compared with conventional two-step cascade reaction. Besides, tripedal DNA walker was equipped with sufficient walking legs to walk on directional and highly controllable stepped track, reducing the possibility of derailment and boosting walking efficiency. As a proof of concept, a novel electrochemical biosensor was constructed for miRNA-21 sensitive detection at physiological pH, and successfully applied in human serum samples as well as practical intracellular analysis, offering great potential in biomedical research and clinical diagnosis.

A supercritical water gasification strategy was employed to investigate the effects of different conditions on hydrogen-rich gas production from oily sludge, based on the Aspen Plus platform. Meanwhile, the entire process was assessed with a life cycle assessment (LCA) method. The results indicated that the gas yield was decreased by increasing the sludge concentration. The yield of hydrogen-rich gas (H2 and CH4) was improved with increasing temperature. Higher temperatures were more favorable to gas production. Excessive addition of oxidants could reduce the CH4 yield, thereby lowering the energy efficiency of the process. The LCA analysis found that, in comparison to the stages of raw material transportation, heat recovery, and wastewater treatment, the effect of more CO2 produced in cooling and separation stage on global warming potential (GWP) was more obvious. The corresponding process can be improved to mitigate the environmental effect of the whole gasification.

Graph neural networks (GNNs) offer a potent framework for learning and representing data with graph structures. In the context of hyperspectral images (HSIs) classification tasks, GNNs have been successfully employed. Among the diverse variations of GNNs, graph attention networks (GATs) stand out, demonstrating cutting-edge performance in node prediction. GATs excel by acquiring the capability to assign dense attention coefficients to each neighboring node during the process of feature aggregation. First, the intricate nature of land distribution, coupled with the high dimensionality of hyperspectral imaging (HSI) data, poses a hurdle for the direct application of GATs in effectively distinguishing between various coverage categories. Second, the insufficiency of spectral information further compounds the issue by adversely affecting classification accuracy, contributing to GATs’ susceptibility to overfitting during training. Additionally, prevailing classification approaches commonly derive features either at the pixel or superpixel levels. The utilization of single-scale feature extraction techniques proves inadequate in concurrently addressing both local and global features of land cover. This limitation contributes to less-than-optimal classification outcomes. To address this issue, this article proposes a double-branch multilevel skip sparse GAT for HSI classification. The network comprises two parallel branches of sparse GATs, extracting features from the perspectives of pixels and superpixels. Via the construction of a multilevel skip graph, these two branches adeptly gather both local and global context information. Subsequently, a fully connected network is deployed to classify the integrated features derived from both branches. The effectiveness of the proposed method is validated through experimental results on three benchmark HSI datasets, showcasing its superior performance.

Building extraction and change monitoring in remote sensing (RS) imagery play pivotal roles in various applications, including urban planning, disaster management, and infrastructure monitoring. While significant progress has been made in single and bitemporal RS images, effectively harnessing the rich temporal information of time series RS images remains a challenge. Time series RS images offer an extended temporal span for monitoring dynamic changes in building instances. However, they often exhibit noticeable appearance discrepancies and feature variations, presenting substantial obstacles to effective multitemporal information aggregation. To address these challenges, we introduce a Building Extraction and Change Monitoring Network (BuildMon), which jointly explores the segmentation masks, location tracking, and construction status of building instances. Our approach incorporates a spatial-temporal transformer to model relationships between images at different time spans. The windowed attention module within it can capture spatial-temporal context for a larger scope of feature aggregation. For enhancing the performance on both tasks, we adopted ground truth masks and semantic change information together as supervisory signals for BuildMon. This is complemented by the specially designed change-guided loss function, which specifically highlights regions of change and assigns targeted weights to building areas within the imagery. To validate the effectiveness of our method, we conduct comprehensive experiments on the SpaceNet 7 dataset. The results showcase the state-of-the-art performance of our approach, achieving mIoU and SpaceNet Change and Object Tracking metrics of 67.90 and 39.73, respectively.

Alternative oxidase (AOX) serves as a critical terminal oxidase within the plant respiratory pathway, playing a significant role in cellular responses to various stresses. Foxtail millet (Setaria italica), a crop extensively cultivated across Asia, is renowned for its remarkable tolerance to abiotic stresses and minimal requirement for fertilizer. In this study, we conducted a comprehensive genome-wide identification of AOX genes in foxtail millet genome, discovering a total of five SiAOX genes. Phylogenetic analysis categorized these SiAOX members into two subgroups. Prediction of cis-elements within the promoter regions, coupled with co-expression network analysis, intimated that SiAOX proteins are likely involved in the plant’s adaptive response to abiotic stresses. Employing RNA sequencing (RNA-seq) and real-time quantitative PCR (RT-qPCR), we scrutinized the expression patterns of the SiAOX genes across a variety of tissues and under multiple abiotic stress conditions. Specifically, our analysis uncovered that SiAOX1, SiAOX2, SiAOX4, and SiAOX5 display distinct tissue-specific expression profiles. Furthermore, SiAOX2, SiAOX3, SiAOX4, and SiAOX5 exhibit responsive expression patterns under abiotic stress conditions, with significant differences in expression levels observed between the shoot and root tissues of foxtail millet seedlings. Haplotype analysis of SiAOX4 and SiAOX5 revealed that these genes are in linkage disequilibrium, with Hap_2 being the superior haplotype for both, potentially conferring enhanced cold stress tolerance in the cultivar group. These findings suggest that both SiAOX4 and SiAOX5 may be targeted for selection in future breeding programs aimed at improving foxtail millet’s resilience to cold stress.

As a traditional Chinese herbal medicine, Corni Fructus (CF) possesses varieties of pharmacological functions including anti-diabetes, anti-oxidation, anti-inflammatory, and hepatoprotective properties. However, the most commonly used forms of CF are pills and decoctions in the type 2 diabetes (T2DM) medical treatment. Until now, it remains unclear whether and how nano-powder CF (NPCF) improves T2DM. Here, we prepared NPCF via ball milling nanotechnology. NPCF significantly improved the symptoms of T2DM mice through alleviating hepatic inflammation and oxidative stress, reducing hyperglycemic and hyperlipidemic, and restoring pancreatic and liver damages. Network pharmacology screening was utilized to obtain 293 NPCF active ingredient targets and 1235 T2DM targets, and 195 cross-targets were obtained by crossover. Mechanistically, NPCF may enhance glucose transport, promote glycogen synthesis, and improve insulin resistance by acting on insulin receptor (INSR)/phosphatidylinositol 3-kinase (PI3K/AKT)/glucose transporter 4 (GLUT4) signaling pathways. Therefore, NPCF may be a potential dosage form of CF for T2DM treatment.

Abstract The hemodynamic analysis of the blood stream inside the cerebral aneurysms reveals the risk of the aneurysm rupture. In addition, the high risk region prone to rupture would be determined by the hemodynamic analysis of the blood. In present article, computational fluid dynamic is used for the investigation of the hemodynamic effects on the aneurysm wall and risk of rupture. This study tries to find the connection between the risk of rupture with three geometrical features of aneurysm i.e., Ellipsoid Max semi-axis, Size ratio and Tortuosity. Statistical analysis is done over 30 different ruptured /unruptured ICA aneurysms to find meaningful relation between selected geometrical factors and rupture risk. The hemodynamic analysis is done over four distinct aneurysm models to attain more details on effects of chosen geometrical factors. The results of simulations indicate that the Ellipsoid Max semi-axis have meaningful impacts on the risk of rupture.

Abstract Background Hip fracture is a prevalent and hazardous injury among the elderly population that often results in intensive care unit (ICU) admission due to various complications, despite advanced medical science. One common complication experienced in the ICU by elderly hip fracture patients is heart failure, which significantly impacts short-term survival rates. Currently, there is a deficit of adequate predictive models to forecast the short-term risk of death following heart failure for elderly hip fracture patients in the ICU. This study aims to identify independent risk factors for all-cause mortality within 30 days for elderly patients with hip fractures and heart failure while in the ICU in order to develop a predictive model. Method A total of 641 elderly patients with hip fractures combined with heart failure were recruited from the Medical Information Mart for Intensive Care IV dataset and randomized to the training and validation sets. The primary outcome was all-cause mortality within 30 days. The least absolute shrinkage and selection operator regression was used to reduce data dimensionality and select features. Multivariate logistic regression was used to build predictive models. Consistency index (C-index), receiver operating characteristic curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram. Result Our results showed that these variables including MCH, MCV, INR, monocyte percentage, neutrophils percentage, creatinine, and combined sepsis were independent factors for death within 30 days in elderly patients with hip fracture combined with heart failure in the ICU. The C-index was 0.869 (95% CI 0.823–0.916) and 0.824 (95% CI 0.749–0.900) for the training and validation sets, respectively. The results of the area under the curve and decision curve analysis (DCA) confirmed that the nomogram performed well in predicting elderly patients with hip fractures combined with heart failure in the ICU. Conclusion We developed a new nomogram model for predicting 30-day all-cause mortality in elderly patients with hip fractures combined with heart failure in the ICU, which could be a valid and useful clinical tool for clinicians for targeted treatment and prognosis prediction.

Abstract Background Previous studies have reported that inflammatory responses can promote the onset of cardiovascular diseases; however, its association with cardiac conduction disorders remains unclear. The present community-based cohort study aimed to elucidate the effects of inflammatory responses on the risk of developing cardiac conduction disorders. Methods After the exclusion of participants failing to meet the inclusion criteria, 86,234 eligible participants (mean age: 50.57 ± 11.88 years) were included. The participants were divided into high-sensitivity C-reactive protein (hsCRP) ≤ 3 mg/L, and hsCRP > 3 mg/L groups based on hsCRP values. Multivariate Cox proportional hazard model was used to analyze the relationship between inflammatory responses and various cardiac conduction disorders. Results After adjusting for confounding factors, we observed that compared with the hsCRP ≤ 3 mg/L group, the hsCRP > 3 mg/L group exhibited increased risks of atrioventricular block (hazard ratio [HR]:1.64, 95%confidence interval [CI] 1.44–1.87) and left (HR:1.25, 95% CI 1.07–1.45) and right bundle branch block (HR:1.31, 95% CI 1.17–1.47). Moreover, the risk of various cardiac conduction disorders increased for every 1 standard deviation increase in log (hsCRP). The restricted cubic spline function confirmed a linear relationship between log (hsCRP) and the risk of developing cardiac conduction disorders (All nonlinearity P > 0.05). Conclusions High hsCRP levels are an independent risk factor for cardiac conduction disorders, and hsCRP levels are dose-dependently associated with the risk of conduction disorders. Our study results may provide new strategies for preventing cardiac conduction disorders.

Abstract GPR84 is a unique orphan G protein-coupled receptor (GPCR) that can be activated by endogenous medium-chain fatty acids (MCFAs). The signaling of GPR84 is largely pro-inflammatory, which can augment inflammatory response, and GPR84 also functions as a pro-phagocytic receptor to enhance phagocytic activities of macrophages. In this study, we show that the activation of GPR84 by the synthetic agonist 6-OAU can synergize with the blockade of CD47 on cancer cells to induce phagocytosis of cancer cells by macrophages. We also determine a high-resolution structure of the GPR84-Gi signaling complex with 6-OAU. This structure reveals an occluded binding pocket for 6-OAU, the molecular basis of receptor activation involving non-conserved structural motifs of GPR84, and an unusual Gi-coupling interface. Together with computational docking and simulations studies, this structure also suggests a mechanism for the high selectivity of GPR84 for MCFAs and a potential routes of ligand binding and dissociation. These results provide a framework for understanding GPR84 signaling and developing new drugs targeting GPR84.

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.

PurposeThis study investigated the satisfaction of current community-based home care services and its factors in adults aged ≥60 years.MethodsUsing stratified cluster random sampling, we surveyed 1,494 older adults in Jinan and Qingdao, Shandong province, between 2021 and 2023. The baseline and satisfaction surveys were designed by our research team, and the questionnaires were conducted in the form of structured interviews. Kruskal-Wallis H-test and Logistic regression analysis were used to explore the influencing factors of satisfaction.ResultsThe satisfaction was mainly affected by age (p = 0.007), marital status (p

This study was conducted to investigate how high temperatures and the addition of basalt fiber influence the mechanical behavior and failure characteristics of concrete. Two types of concrete were prepared: basalt fiber concrete (BFC), containing 1.0% basalt fiber by mass, and ordinary concrete (OC), without any fiber addition. The high-temperature effects of 200, 400, 600, and 800°C were carried out, and the compressive strength test, variable angle shear test, and cyclic compression test were carried out. The results show that basalt fiber will not change the mechanical characteristics of concrete and the change trend of stress–strain curve with temperature. The mass loss rate of BFC is always lower than that of OC. With the increase in temperature, the compressive strength, elastic modulus, and deformation modulus of the two types of concrete gradually decrease, and the plastic strain and dissipation energy gradually increase. Basalt fiber delays the loss of compressive strength in concrete and significantly increases the elastic modulus and the ability to resist cyclic compression and shear. Basalt fiber enhances the cohesion and friction resistance between concrete particles, thereby improving the structural stability and shear resistance of concrete. The severe damage temperature range of OC is 200–400°C, and the severe damage temperature range of BFC is 400–600°C. Based on the Holmquist–Johnson–Cook model, the meso-deformation and failure processes of OC and BFC were analyzed by LS-DYNA software. It was found that OC showed obvious brittle failure characteristics. With the increase in temperature, basalt fiber began to oxidize and decompose, and the meso-failure form of BFC changed from pullout failure to fracture failure.

Weichang'an pill (WCAP) is a traditional Chinese patent medicine, which is clinically used for the treatment of bowel syndrome and functional dyspepsia such as diarrhea, abdominal distension, and enteritis. So far, quality control studies of WCAP have mainly focused on the determination of chemical composition content, which has little relevance to biological activity and clinical effects. With the aim of identifying the multi-index ingredients with NF-κB inhibitory activities related to WCAP clinical effect, this present work described the chemical profile of WCAP by UHPLC-QE-MS, established the correlated relationship between chromatographic fingerprints and the NF-κB inhibitory activities based on multivariate statistical analysis, including hierarchical clustering analysis (HCA), Pearson correlation analysis, and Partial least squares regression analysis (PLSR). The spectrum-effect relationship analysis indicated 10 compounds, which were ferulic acid, naringin, narirutin, hesperidin, neohesperidin, aloe emodin, emodin, honokiol, magnolol, and physcion, might be the potential NF-κB inhibitory constituents in the pill. The NF-κB inhibitory effects of the ten compounds were verified by in vitro dual luciferase reporting detection system. Considering that the detection index should be representative of more medicinal materials, a rapid and efficient UPLC-DAD method was eventually developed to determine the content of the 13 components. Our findings will provide data support for WCAP quality control and advance the understanding of the quality assessment of traditional Chinese patent medicines.

Lysophosphatidylserine (LysoPS) is a naturally occurring lipid mediator involved in various physiological and pathological processes especially those related to the immune system. GPR34, GPR174, and P2Y10 have been identified as the receptors for LysoPS, and its analogues have been developed as agonists or antagonists for these receptors. However, the lack of structural information hinders the drug development with novel characteristics, such as nonlipid ligands and allosteric modulators. Here, we determined the structures of human GPR34 and GPR174 in complex with LysoPS and G protein by cryo-EM. Combined with structural analysis and functional studies, we elucidated the lipid-binding modes of these receptors. By structural comparison, we identified the structural features of GPR34 and GPR174 in active state. Taken together, our findings provide insights into ligand recognition and signaling of LysoPS receptors and will facilitate the development of novel therapeutics for related inflammatory diseases and autoimmune diseases.

The main varieties of Echinopsis Radix recorded in the Chinese Pharmacopoeia are the roots of Echinops latifolius Tausch or Echinops grijsii Hance. However, the chemical constituents and biological activities of this herb have not been reviewed. In order to clarify the chemical constituents of the main varieties of this herb and improve the quality of Chinese medicinal material resources, this paper systematically reviewed their chemical constituents and related biological activities. Phytochemical investigations reveal eighty-five compounds including fort y-nine thiophenes (1–49), eight flavonoids (50–57), seven caffeic acids and its derivatives (58–64), eight sesquiterpenoids (65–72), and thirteen triterpenoids and other compounds (73–85) were reported from Echinopsis Radix. The review of biological activities suggests that thiophenes are the main secondary metabolites of the medicinal material which exert antitumor, insecticidal and antifungal activities. In addition, caffeic acid and its derivatives and sesquiterpenes are potential active ingredients worthy of further study. This review provides an important scientific basis for the development of active ingredients and resource quality evaluation of Echinopsis Radix.

Aerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies predominantly function within a closed-set framework, thereby encountering challenges when confronted with the identification of newly emerging scenes. To address this, this paper explores an aerial image recognition scenario in which a dataset comprises both labeled and unlabeled aerial images, intending to classify all images within the unlabeled subset, termed Generalized Category Discovery (GCD). It is noteworthy that the unlabeled images may pertain to labeled classes or represent novel classes. Specifically, we first develop a contrastive learning framework drawing upon the cutting-edge algorithms in GCD. Based on the multi-object characteristics of aerial images, we then propose a slot attention-based GCD training process (Slot-GCD) that contrasts learning at both the object and image levels. It decouples multiple local object features from feature maps using slots and then reconstructs the overall semantic feature of the image based on slot confidence scores and the feature map. Finally, these object-level and image-level features are input into the contrastive learning module to enable the model to learn more precise image semantic features. Comprehensive evaluations across three public aerial image datasets highlight the superiority of our approach over state-of-the-art methods. Particularly, Slot-GCD achieves a recognition accuracy of 91.5% for known old classes and 81.9% for unknown novel class data on the AID dataset.

Summary: This study found that the level of neuroepithelial cell-transforming gene 1 protein (NET1) was significantly increased in a mouse cardiac fibrosis model. Moreover, the expression level of NET1 was increased in cardiac fibrosis induced by TGF-β1, suggesting that NET1 was involved in the pathological process of cardiac fibrosis. Overexpression of NET1 promoted β-catenin expression in the nucleus and significantly increased the proliferation and migration of cardiac fibroblasts. NET1 may form a complex with β-catenin through GSK3β. Knockdown of β-catenin alleviated the effects of NET1 overexpression on collagen production and cell migration. In the heart of NET1 knockout mice, NET1 knockout can reduce the expression of β-catenin, α-SMA, and collagen content induced by MI. In conclusion, NET1 may regulate the activation of Wnt/β-catenin and TGF/Smads signaling pathway, promote collagen synthesis in fibroblasts, and participate in cardiac fibrosis. Thus, NET1 may be a potential therapeutic target in cardiac fibrosis.

Perinatal exposure to bifenthrin (BF) alters neurodevelopment. However, the most susceptible time period to BF exposure and the possible mechanisms are not clear. In the current study, pregnant female mice were treated with BF (0.5 mg/kg/d) at three different stages [gestational day (GD) 0–5, 6–15 and 16-birth (B)] and neurologic deficits were evaluated in offspring mice. BF exposure at GD 16-B significantly altered the locomotor activity and caused learning and memory impairments in 6-week-old offspring. Gestational BF exposure also caused neuronal loss in the region of cornu ammonis of hippocampi of 6-week-old offspring. Interestingly, neurobehavioral impairments and neuronal loss were not observed in offspring at 10-week-old. BF exposure at GD 16-B also decreased protein levels of VGluT1, NR1 and NR2A while increased the protein levels of NR2B and VGAT1, as well as the gene levels of Il-1β, Il-6 and Tnf-α in hippocampi of 6-week-old offspring. Collectively, these data demonstrate that gestational exposure to a low dose BF causes neurodevelopmental deficits that remit with the age and the late-stage of pregnancy is the most susceptible time window to BF exposure. Imbalance in excitatory/inhibitory neuronal transmission, altered expression levels of NMDA receptors and increased neural inflammation may be associated with BF prenatal exposure-triggered neurobehavioral impairments.

Abstract Immune non-responder after highly active antiretroviral therapy (HAART) is the main cause of opportunistic infections and high mortality in AIDS patients, but the mechanism underlying immune reconstitution failure is poorly understood. Here, we performed scRNA-seq, and scATAC-seq analysis of peripheral blood mononuclear cells (PBMCs) derived from immune non-responder (INR) and responder (IR) HIV-1-infected subjects. We found low expression of mucosal-associated invariant T (MAIT) cells in INRs, which exhibited transcriptional profiles associated with impaired mitochondrial function and apoptosis signaling. Single-cell assays for transposase-accessible chromatin (scATAC-seq) and flow cytometry revealed diminished mitochondrial fitness in MAIT cells from INRs, and MAIT had low expression of transcription factor A for mitochondria (TFAM) and peroxisomal proliferator-activated receptor alpha (PPARA). These findings demonstrate that restoring mitochondrial function could modulate the immune dysfunction characteristic of MAIT against bacterial co-infections in INRs subjects.

As a special branch of global trade, the trade of agricultural products has an important impact on food security and the environment. In this paper, we studied international trade network of agricultural products from 2000 to 2016 as a whole and in part. We explored the overall characteristics of the network, analyzed the evolution of communities and identified core countries of the communities. The results show that the structure of the trade network became increasingly complex and the trade relations became closer over time. There were four major communities in the network, whose primary core countries were Germany, the United States, Brazil, and China. Since 2007, the community represented by China has disappeared, and the community pattern of the network has been in a three-pillar state and basically stable. We discuss the actual roles of certain trading countries, the formation of communities and the impact of economic events on agricultural products trade. This paper reveals the underlying patterns of the agricultural products trade and provides a way to track its evolution over time.

As a common endocrine disease, type 2 diabetes mellitus (T2DM) has a rising incidence year by year, which is producing various complications seriously threatening human health. Therefore, great attention has been paid to T2DM treatment, in which medicinal and food homologous plants play key roles because of their natural, healthy and widely used characteristics. Corni Fructus (CF) has been used centuries for the hypoglycemics and hypolipidemics. However, the detailed mechanism of CF on T2DM is still far from elucidated. This study aimed to investigate the effect of different extracts of CF on T2DM hepatic lipid metabolism and the underlying mechanisms. The results showed that CF can inhibit the body weight loss, decrease blood glucose and regulate serum lipid contents in T2DM mice. It also restored the islet function of the mice and reversed their abnormal lipid metabolism. Additionally, the mechanism of CF extracts to improve T2DM was explored, CF enhanced the activation of AMP-activating protein kinase and upregulated lipid oxidation related proteins, such as ACC and CPT-1, these data suggested that CF significantly improved T2DM mice at least partially through AMPK/ACC/CPT-1 signaling pathway. Taken together, our data demonstrated that CF may be a novel potential agent for T2DM treatment.

Introduction Helicobacter pylori is the most well-known risk factor for gastric cancer. Antibiotic resistance is the main reason for the failure of H. pylori eradication, and understanding the antibiotic resistance before treatment may be the main determinant of successful eradication of H. pylori. This study aims to evaluate the efficacy and safety of quadruple therapy based on faecal molecular antimicrobial susceptibility tests for the first-line eradication of H. pylori infection.Methods and analysis This is a single-centre, single-blind, randomised controlled trial, enrolling 855 patients with H. pylori infection. Patients are randomised to three groups for a 14-day treatment: group A: amoxicillin- and clarithromycin-based bismuth-containing quadruple therapy (BQT) (rabeprazole 10 mg, amoxicillin 1 g, clarithromycin 500 mg and colloidal bismuth 200 mg two times per day); group B: clarithromycin medication history-based BQT (rabeprazole 10 mg, amoxicillin 1 g, furazolidone 100 mg (with clarithromycin medication history)/clarithromycin 500 mg (without clarithromycin medication history) and colloidal bismuth 200 mg two times per day); group C: antimicrobial susceptibility test-based BQT (rabeprazole 10 mg, amoxicillin 1 g, clarithromycin 500 mg (clarithromycin-sensitive)/furazolidone 100 mg (clarithromycin resistant) and colloidal bismuth 200 mg two times per day). The primary end point is the eradication rate. The secondary end points are the incidence of adverse events and compliance.Ethics and dissemination This study was approved by the Ethics Committee of Second Affiliated Hospital, School of Medicine, Zhejiang University (Number 20230103). The results will be published in the appropriate peer-reviewed journal.Trial registration number NCT05718609.

Parkinson disease (PD) is a multifactorial disease without aging, which is considered to be the main cause of PD apart from genetic, life-style, and environmental patterns. Phytochemicals play an immense role in treating various neurological diseases, and they possess immense antioxidant and anti-inflammatory properties. In the current work, the neuroprotective effect of ruscogenin in an in vitro and in vivo mouse model was examined. The mouse microglial BV-2 cell line was stimulated with lipopolysaccharides (LPS) and used to assess the cytotoxicity, antioxidant, and anti-inflammatory activities of ruscogenin. In the in vivo model, the PD was induced with MPTP and treated with 10 and 20 mg/kg of ruscogenin. The mice were then subjected to motor function analysis with the foot print test, grip strength test, and rota rod test. Antioxidant, anti-inflammatory, and neuroprotective activities of ruscogenin on PD-induced mice were assessed with biochemical analysis. To confirm the neuroprotective activity of ruscogenin, histopathological analysis of substantia nigra tissue was done. Our in vitro results confirm that ruscogenin did not disturb the viability of BV-2 cells. Ruscogenin decreased the oxidative stress and inflammatory marker levels in BV-2 cells. Ruscogenin treatment persuasively improved motor activities in PD-induced mice. The levels of inflammatory cytokines were considerably reduced by the ruscogenin treatment. Histopathological analysis revealed that ruscogenin has neuroprotective properties. In summary, ruscogenin is a potent neuroprotective agent that can be utilized to treat PD in the future. Furthermore, our study lacks the in-depth molecular analysis needed to understand the mode of action of ruscogenin on the PD, and hence we strongly recommend further molecular studies in the future.

Background & Aims: Sepsis-induced acute liver dysfunction often occurs early in sepsis and can exacerbate the pathology by triggering multiple organ dysfunction and increasing lethality. Nevertheless, our understanding of the cellular heterogeneity and dynamic regulation of major nonparenchymal cell lineages remains unclear. Methods: Here, single-cell RNA sequencing was used to profile multiple nonparenchymal cell subsets and dissect their crosstalk during sepsis-induced acute liver dysfunction in a clinically relevant polymicrobial sepsis model. The transcriptomes of major liver nonparenchymal cells from control and sepsis mice were analysed. The alterations in the endothelial cell and neutrophil subsets that were closely associated with acute liver dysfunction were validated using multiplex immunofluorescence staining. In addition, the therapeutic efficacy of inhibiting activating transcription factor 4 (ATF4) in sepsis and sepsis-induced acute liver dysfunction was explored. Results: Our results present the dynamic transcriptomic landscape of major nonparenchymal cells at single-cell resolution. We observed significant alterations and heterogeneity in major hepatic nonparenchymal cell subsets during sepsis. Importantly, we identified endothelial cell (CD31+Sele+Glut1+) and neutrophil (Ly6G+Lta4h+Sort1+) subsets that were closely associated with acute liver dysfunction during sepsis progression. Furthermore, we found that ATF4 inhibition alleviated sepsis-induced acute liver dysfunction, prolonging the survival of septic mice. Conclusions: These results elucidate the potential mechanisms and subsequent therapeutic targets for the prevention and treatment of sepsis-induced acute liver dysfunction and other liver-related diseases. Impact and Implications: Sepsis-induced acute liver dysfunction often occurs early in sepsis and can lead to the death of the patient. Nevertheless, the pathogenesis of sepsis-induced acute liver dysfunction is not yet clear. We identified the major cell types associated with acute liver dysfunction and explored their interactions during sepsis. In addition, we also found that ATF-4 inhibition could be invoked as a potential therapeutic for sepsis-induced acute liver dysfunction.

We theoretically propose a single-photon source design based on a circular Bragg grating microcavity incorporating two air triangles in a bowtie configuration. Our proposed design allows for extreme light confinement with a Purcell factor of approximately 166 with a bandwidth of ~9 nm for a triangle separation of 5 nm combined with a collection efficiency above 80 $\%$ . The Purcell factor is enhanced by a factor of 8 relative to the reference cavity thanks to the reduction of the mode volume. Furthermore, we demonstrate the critical role of the dipole emitter polarization orientation and spatial position in achieving optimal Purcell enhancement.

This study investigates nanobeam cavities on a GaAs-on-insulator (GaAsOI) chip with InAs quantum dots (QDs), including design, fabrication, and experimental characterization. The nanobeam cavities are optimized for high photon coupling efficiency and pronounced light-matter interaction. Numerical studies yield Q factors up to about 1400, a coupling efficiency of nearly 70 $\%$ and a maximum Purcell factor of approximately 100. Experimentally, these devices have a Q factor of about 1300, and comparing the lifetime of QDs in on-resonance and off-resonance conditions, a Purcell factor of $10.46\pm0.14$ is obtained. Moreover, in the single-emitter regime, we observe strong multiphoton suppression with $g^{(2)}(0) = 0.297$ . Our results demonstrate the high potential of nanobeam cavities on a GaAsOI platform for quantum photonic applications.

In the face of complex scenarios, the information insufficiency of classification tasks dominated by a single modality has led to a bottleneck in classification performance. The joint application of multimodal remote sensing data for surface observation tasks has garnered widespread attention. However, issues such as sample differences between modalities and the lack of correlation in physical features have limited the performance of classification tasks. Establishing effective interaction between multimodal data has become another significant challenge. To fully integrate heterogeneous information from multiple modalities and enhance classification performance, this paper proposes a dual-branch cross-Transformer feature fusion network aimed at joint land cover classification of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. The core idea is to leverage the potential of convolutional operators to represent spatial features, combined with the advantages of the Transformer architecture in learning remote dependencies. The framework employs an improved self-attention mechanism to aggregate features within each modality, highlighting the spectral information of HSI and the spatial (elevation) information of LiDAR. The feature fusion module based on cross-attention integrates deep features from two modalities, achieving complementary information through cross-modal attention. The classification task is performed using jointly obtained spectral and spatial features. Experiments were conducted on three multi-source remote sensing classification datasets, demonstrating the effectiveness of the proposed model compared to existing methods.

To enhance the continuity of character in the preservation of architectural heritage, this approach focuses on the horizontal self-similarity characteristics of architectural texture. A method using K-means and the Bhattacharyya approach for color selection in architectural repairs is proposed. It quantifies the visual coherence between the repaired structure and the original structure. Analyzing 12 images (A–L), with the original façade (image 0) as a reference, demonstrates that repairs using color-matched materials significantly improve visual coherence. Image A, created using the Visual Enhancement Through Adaptive Repair (VETAR) method, achieves the highest visual alignment with the original image. VETAR, grounded in Gestalt psychology, moves away from traditional materials to concentrate on visual consistency. Its successful implementation in the restoration of Mengzhong Fort illustrates a complex approach to material use in heritage conservation. After comparison, this method is deemed superior to traditional techniques, integrating modern interventions with historical aesthetics. The study suggests that VETAR may offer a referential method for architectural conservation, potentially facilitating a balanced integration of historical and contemporary elements in architectural renovation.

ObjectiveTo compare the differences in gut microbiota and short-chain fatty acids (SCFAs; metabolites of gut microbiota) in the serum of patients with first-episode depression and the healthy population and to analyze the relationship between gut microbiota and metabolite SCFAs and the clinical symptoms of major depressive disorder (MDD).MethodsA total of 45 patients with first-episode depression and 22 healthy volunteers were chosen to complete relevant scale evaluations, and feces samples and venous blood samples were collected. The 16S RNA method was used to analyze the intestinal microflora and the characteristics of serum SCFAs detection by ELISA kit, as well as the intestinal flora, SCFAs content and their correlation with MDD clinical indicators.ResultsThe abundance of Akkermansia, Megamonas, Prevotellaceae NK3B31 group, and butyrate-producing bacteria, Lachnospira, Subdoligranulum, Blautia, and Dialister, and acetate-producing bacteria, Streptococcus, in the gut microbiota of the MDD group was lower than that in the control (C) group. The abundance of Parasutterella in the MDD group was higher than that in the C group. Dialister negatively correlated with all measured clinical symptoms (r < 0, P < 0.05). The serum SCFA content in the MDD group was higher than that in the C group, and the content positively correlated with the Hamilton anxiety scale scores (r = 0.584, P < 0.05).ConclusionThe results demonstrated that the MDD group differed from the C group in terms of gut microbiota and SCFAs in the serum and that the change in certain intestinal bacteria might participate in the pathogenic mechanism of MDD.

China holds large-scale lead–zinc mineral resources; however, mining activities often cause severe contamination by heavy metals. This study systemically assessed contamination by eight heavy metals (Cu, Zn, Cd, Pb, Cr, Hg, Ni, and As) in mine tailings, soil, and groundwater from 27 contaminated sites across China. Regarding mine tailings, 1% of the mine tailing samples were hazardous waste and 20% were class II non-hazardous waste. Regarding soil, Zn and Pb showed the highest mean concentrations, at 5574.67 mg/kg and 2034.88 mg/kg, respectively. The indexes of geo-accumulation (Igeo) of eight heavy metals ranged from −3.62 to 7.67, while Zn, Pb, and Cd showed the highest environmental risk levels as the priority pollutants. The contamination levels of these heavy metals in groundwater were generally in the order of Zn>As>Pb>Ni>Cd>Cu>Hg>Cr. In this study, 20% of the soil and 10% of the groundwater samples exceeded the corresponding quality limits. The content of heavy metals in soil, groundwater, and mine tailing were positively correlated, demonstrating the main pollution source and transport paths. The pollution levels of heavy metals in soil and groundwater were listed in the foremost and moderate positions compared with similar sites from other countries, respectively. These results may help determine the pollution levels of lead–zinc mining regions and direct the remediation activities of target sites to support the environmental management of abandoned mining and tailing waste in China.

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

【Objective】The study was conducted to explore the remediation effects of biochars derived from mulberry and cinnamon branches on cadmium(Cd) pollution in water and soil environments, with a view to providing a theoretical basis for the resource utilization of woody agroforestry wastes and the remediation of Cd-contaminated environment.【Method】Both mulberry and cinnamon branches were slowly pyrolyzed under 400℃ for 2 h. The obtained biochars were tested for their isothermal adsorption of Cd, and then added into the Cd contaminated soil grown with Brassica chinensis L. in a pot experiment.【Result】Adsorption of Cd b y either mulberry biochar (MB400) or cinnamon biochar (CB400) could be fitted by Langmuir isothermal equation, and the maximum adsorption capacities (Q0) were 4 305.61 mg/kg and 442.77 mg/kg, respectively. It was obvious that MB400 had much higher capacity for adsorption of Cd than that of CB400 treatment, which may be attributed to its higher values in pH, ash and CEC and available phosphorus(AP). Furthermore, MB400 addition significantly reduced the content of available cadmium(AvCd) while it showed little effect under CB400 treatment. The dry matter of B. chinensis L. was remarkably enhanced by adding both biochars. The bioconcentration factor(BCF) of Cd decreased from 5.95 in the control to 4.29 (MB400) and 4.85 (CB400).【Conclusion】Both biochars had high adsorption capacity for Cd, and could inactivate Cd availability and thereafter inhibited Cd accumulation in cabbage tissue. MB400 showed better effect than CB400.

Abstract Background To explore the masticatory performance in patients undergoing an osteo(cutaneous) free fibula (OFF) flap for mandible reconstruction by a prospective design. Methods A total of 56 patients who had undergone OFF flap reconstructions for mandibular reconstruction secondary to malignant (squamous cell carcinoma) or benign (ameloblastoma) tumor resection were prospectively enrolled. They were asked to complete the masticatory performance test by the weigh method and the chew domain of the University of Washington quality of life questionnaire (version 4) preoperatively and at 3, 6, and 12 months postoperatively. The pair nonparametric test was used to analyze the dynamic change of masticatory performance and subjective chew function. Results Fifty-one patients were included for analysis finally. The mean masticatory performance for patients with malignant tumors were 53.4% ± 10.3%, 36.4% ± 10.3%, 42.6% ± 9.6%, 52.8% ± 10.9%, and 53.1% ± 11.8% preoperatively, at 2 weeks, 3 months, 6 months, and 12 months postoperatively, respectively. Compared with the preoperative level, the masticatory performance had a significant reduction immediately after surgery (p

To assess the freeze–thaw (F-T) durability of coal gangue pervious concrete (CGPC) in different F-T cycle media (water, 3.5 wt% NaCl solution), experimental studies on 36 groups of cube specimens and 6 groups of prismatic specimens were carried out, with designed porosity, F-T cycling media, and F-T failure times as variables. The changes in apparent morphology, mass, compressive behavior, relative dynamic elastic modulus, and permeability coefficient have been analyzed in detail. To predict the compressive strength after F-T cycles, a GM (1,1) model based on the grey system theory was developed and further improved into a more accurate grey residual–Markov model. The results reported that the cement slurry and coal gangue aggregates (CGAs) on the specimen surface continued to fall off as F-T cycles increased, and, finally, the weak point was fractured. Meanwhile, the decrease in compressive behavior and relative dynamic elastic modulus was gentle in the early phase of F-T cycles, and they gradually became faster in the later stage, showing a parabolic downward trend. The permeability coefficient increased gradually. When F-T failure occurred, specimen mass dropped precipitously. The F-T failure of CGPC was more likely to occur in 3.5 wt% NaCl solution, and the F-T failure times of samples were 25 times earlier than that of water. This study lays the foundation for an engineering application and provides a basis for the large-scale utilization of CGPC.

Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.

Satellite pose estimation plays a crucial role within the aerospace field, impacting satellite positioning, navigation, control, orbit design, on-orbit maintenance (OOM), and collision avoidance. However, the accuracy of vision-based pose estimation is severely constrained by the complex spatial environment, including variable solar illumination and the diffuse reflection of the Earth’s background. To overcome these problems, we introduce a novel satellite pose estimation network, FilterformerPose, which uses a convolutional neural network (CNN) backbone for feature learning and extracts feature maps at various CNN layers. Subsequently, these maps are fed into distinct translation and orientation regression networks, effectively decoupling object translation and orientation information. Within the pose regression network, we have devised a filter-based transformer encoder model, named filterformer, and constructed a hypernetwork-like design based on the filter self-attention mechanism to effectively remove noise and generate adaptive weight information. The related experiments were conducted using the Unreal Rendered Spacecraft On-Orbit (URSO) dataset, yielding superior results compared to alternative methods. We also achieved better results in the camera pose localization task, indicating that FilterformerPose can be adapted to other computer vision downstream tasks.

Graph neural network has an excellent performance in obtaining the similarity relationship of samples, so it has been widely used in computer vision. But the hyperspectral remote sensing image (HSI) has some problems, such as data redundancy, noise, lack of labeled samples, and insufficient utilization of spatial information. These problems affect the accuracy of HSI classification using graph neural networks. To solve the aforementioned problems, this article proposes graph-based semisupervised learning with weighted features for HSI classification. The method proposed in this article first uses the stacked autoencoder network to extract features, which is used to remove the redundancy of HSI data. Then, the similarity attenuation coefficient is introduced to improve the original feature weighting scheme. In this way, the contribution difference of adjacent pixels to the center pixel is reflected. Finally, to obtain more generalized spectral features, a shallow feature extraction mechanism is added to the stacked autoencoder network. And features that have good generalization can solve the problem of the lack of labeled samples. The experiment on three different types of datasets demonstrates that the proposed method in this article can get better classification performance in the case of the scarcity of labeled samples than other classification methods.

Introduction: High altitude-related hypoxia-induced organ damage significantly impacts people who are exposed to acute high-altitude environment. At present, kidney injury still lacks effective treatment strategies. Iridium nanozymes (Ir-NPs) are a nanomaterial with various enzymatic activities and are expected to be used in kidney injury treatment.Methods: In this study, we simulated a high-altitude environment (6000 m) to induce a kidney injury model, and explored the therapeutic effect of Ir-NPs in mice with kidney injury in this environment. Changes in the microbial community and metabolites were analyzed to explore the possible mechanism underlying the improvement of kidney injury during acute altitude hypoxia in mice treated with Ir-NPs.Results: It was discovered that plasma lactate dehydrogenase and urea nitrogen levels were considerably increased in mice exposed to acute altitude hypoxia compared to mice in a normal oxygen environment. Furthermore, there was a substantial increase in IL-6 expression levels in hypoxic mice; contrastingly, Ir-NPs decreased IL-6 expression levels, reduced the levels of succinic acid and indoxyl sulfate in the plasma and kidney pathological changes caused by acute altitude hypoxia. Microbiome analysis showed that bacteria, such as Lachnospiraceae_UCG_006 predominated in mice treated with Ir-NPs.Conclusion: Correlation analysis of the physiological, biochemical, metabolic, and microbiome-related parameters showed that Ir-NPs could reduce the inflammatory response and protect kidney function under acute altitude hypoxia, which may be related to intestinal flora distribution regulation and plasma metabolism in mice. Therefore, this study provides a novel therapeutic strategy for hypoxia-related kidney injury, which could be applied to other hypoxia-related diseases.

Objective: functional gastroduodenal disease is the main type of functional gastrointestinal disease in the clinical department of Gastroenterology and psychosomatic medicine at present, which accounts for a large proportion of outpatients in gastroenterology. The main manifestations are epigastric pain, dyspepsia, belching, chronic nausea, and vomiting. The purpose of this study is to explore the changes in brain function in patients with functional gastroduodenal diseases through experiments to reveal the possible central etiology and development process. Methods: the functional changes of the prefrontal lobe in patients with functional gastroduodenal diseases and normal controls were detected and analyzed by near-infrared brain imaging. At the same time, SCL-90 was used to evaluate the mental health status of patients with functional gastroduodenal diseases and normal controls. The changes in the autonomic nerve system in patients and normal controls were detected and compared by heart rate variability trend chart. Results: the activity of left prefrontal lobe areas s8-d8, s10-d4, s10-d10 and s10-d15 in patients with functional gastroduodenal disease was significantly lower than normal controls (p 0.05). Conclusion: the function of the left frontal lobe is decreased in patients with functional gastroduodenal disease. The autonomic nervous system may be related to the connection system between the brain center and internal organs.

Parkinson’s disease (PD) is a brain-related disease condition, globally it is the second most common neurodegenerative disease that mostly disturbs the brain motor control and action of the aged populations. It involves a tiny, dark portion of the brain denoted as substantia nigra. Dopamine is produced in the substantia nigra for the usage of the brain. This dopamine communicates messages among nerves and controls muscle movements. Coronarin D is a diterpenoid with exclusive pharmacological possessions and its effectiveness over a few neurogenerative diseases exposed to some intense properties. The present study was intended to exhibit the neuroprotective efficacy of Coronarin D over MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) induced animals. Oral management of Coronarin D (10 and 20 mg/kg body wt.) protects the MPTP-induced nigrostriatal dopamine (DA) depletion and its intermediate substances. Behavioral studies like narrow beam walk test, open field test and hang test were conducted to study the movement and actions of Coronarin D treated MPTP induced experimental animals, in which Coronarin D treated group III & IV animals showed better behavioral alterations. Our compound as well attenuated MPTP persuaded oxidative pressure in experimental animals. Apoptotic marker studies displayed that management with Coronarin D upturned MPTP induced programmed cell death, which might be its anti-apoptotic properties. Ultimately to conclude, Coronarin D recovered oxidative pressure, neurochemical alterations, apoptosis, and functional irregularities in investigational mice and propose a potential approach to disease management of neurodegenerative and its related disease.