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Abstract Improper management of diabetic wound effusion and disruption of the endogenous electric field can lead to passive healing of damaged tissue, affecting the process of tissue cascade repair. This study developed an extracellular matrix sponge scaffold (K1P6@Mxene) by incorporating Mxene into an acellular dermal stroma-hydroxypropyl chitosan interpenetrating network structure. This scaffold is designed to couple with the endogenous electric field and promote precise tissue remodelling in diabetic wounds. The fibrous structure of the sponge closely resembles that of a natural extracellular matrix, providing a conducive microenvironment for cells to adhere grow, and exchange oxygen. Additionally, the inclusion of Mxene enhances antibacterial activity(98.89%) and electrical conductivity within the scaffold. Simultaneously, K1P6@Mxene exhibits excellent water absorption (39 times) and porosity (91%). It actively interacts with the endogenous electric field to guide cell migration and growth on the wound surface upon absorbing wound exudate. In in vivo experiments, the K1P6@Mxene sponge reduced the inflammatory response in diabetic wounds, increased collagen deposition and arrangement, promoted microvascular regeneration, Facilitate expedited re-epithelialization of wounds, minimize scar formation, and accelerate the healing process of diabetic wounds by 7 days. Therefore, this extracellular matrix sponge scaffold, combined with an endogenous electric field, presents an appealing approach for the comprehensive repair of diabetic wounds.

Abstract In recent years, deep learning methods were good solutions for object detection in synthetic aperture radar (SAR) images. However, the problems of complex scenarios, large object scale differences and imperfect fine‐grained classification in ship detection were intractable. In response, an improved model GDB‐YOLOv5s (Improved YOLOv5s model incorporating global attention mechanism (GAM), DCN‐v2 and BiFusion) is designed. This model introduces deformable convolution networks (DCN‐v2) into the Backbone to enhance the adaptability of the receptive field. It replaces the original Neck's PANet structure with a BiFusion structure to better fuse the extracted multiscale features. Additionally, it integrates GAM into the network to reduce information loss and improve global feature interaction. Experiments were conducted on single‐class dataset SSDD and multi‐class dataset SRSSD‐V1.0. The results show that the GDB‐YOLOv5s model improves mean average precision scores (mAP) significantly, outperforming the original YOLOv5s model and other traditional methods. GDB‐YOLOv5s also improves Precision‐score (P) and Recall‐score (R) for fine‐grained classification to some extent, thereby reducing false alarms and missed detections. It has been proved that the improved model is relatively effective.

Abstract The gut microbiota is one of the essential contributors of the pathogenesis and progress of inflammatory bowel disease (IBD). Compared with first-line drug therapy, probiotic supplementation has emerged as a viable and secure therapeutic approach for managing IBD through the regulation of both the immune system and gut microbiota. Nevertheless, the efficacy of oral probiotic supplements is hindered by their susceptibility to the gastrointestinal barrier, leading to diminished bioavailability and restricted intestinal colonization. Here, we developed a bacteria-microalgae symbiosis system (EcN-SP) for targeted intestinal delivery of probiotics and highly effective treatment of colitis. The utilization of mircroalge Spirulina platensis (SP) as a natural carrier for the probiotic Escherichia coli Nissle 1917 (EcN) demonstrated potential benefits in promoting EcN proliferation, facilitating effective intestinal delivery and colonization. The alterations in the binding affinity of EcN-SP within the gastrointestinal environment, coupled with the distinctive structural properties of the SP carrier, served to overcome gastrointestinal barriers, minimizing transgastric EcN loss and enabling sustained intestinal retention and colonization. The oral administration of EcN-SP could effectively treat IBD by reducing the expression of intestinal inflammatory factors, maintaining the intestinal barrier and regulating the balance of gut microbiota. This probiotic delivery approach is inspired by symbiotic interactions found in nature and offers advantages in terms of feasibility, safety, and efficacy, thus holding significant promise for the management of gastrointestinal disorders.

Abstract Bacteria-mediated cancer therapeutic strategies have attracted increasing interest due to their intrinsic tumor tropism. However, bacteria-based drugs face several challenges including the large size of bacteria and dense extracellular matrix, limiting their intratumoral delivery efficiency. In this study, we find that hyperbaric oxygen (HBO), a noninvasive therapeutic method, can effectively deplete the dense extracellular matrix and thus enhance the bacterial accumulation within tumors. Inspired by this finding, we modify Escherichia coli Nissle 1917 (EcN) with cypate molecules to yield EcN-cypate for photothermal therapy, which can subsequently induce immunogenic cell death (ICD). Importantly, HBO treatment significantly increases the intratumoral accumulation of EcN-cypate and facilitates the intratumoral infiltration of immune cells to realize desirable tumor eradication through photothermal therapy and ICD-induced immunotherapy. Our work provides a facile and noninvasive strategy to enhance the intratumoral delivery efficiency of natural/engineered bacteria, and may promote the clinical translation of bacteria-mediated synergistic cancer therapy.

Abstract Background The effects of household air pollution on urinary incontinence (UI) symptoms and stress urinary incontinence (SUI) symptoms have not been studied. This study seeks to investigate the correlation between household air pollution and UI/SUI symptoms among middle-aged and elderly adults in India. Methods We employed data derived from individuals aged 45 years and older who participated in the inaugural wave (2017–2018) of the Longitudinal Aging Study in India (LASI). The assessment of household air pollution exposure and the occurrence of UI/SUI symptoms relied on self-reported data. The analytical approach adopted was cross-sectional in nature and encompassed a cohort of 64,398 participants. To explore relationships, we utilized multivariate logistic regression analysis, incorporating subgroup analysis and interaction tests. Results 1,671 (2.59%) participants reported UI symptoms and 4,862 (7.55%) participants reported SUI symptoms. Also, the prevalence of UI/SUI symptoms is much higher among middle-aged and elderly adults who use solid polluting fuels (UI: 51.23% vs. 48.77%; SUI: 54.50% vs. 45.50%). The results revealed a noteworthy correlation between household air pollution and the probability of experiencing UI/SUI symptoms, persisting even after adjusting for all conceivable confounding variables (UI: OR = 1.552, 95% CI: 1.377–1.749, p

Backgroud: Iron overload is a prevalent condition in the elderly, often associated with various degenerative diseases, including intervertebral disc degeneration (IDD). Nevertheless, the mechanisms responsible for iron ion accumulation in tissues and the mechanism that regulate iron homeostasis remain unclear. Transferrin receptor-1 (TFR1) serves as the primary cellular iron gate, playing a pivotal role in controlling intracellular iron levels, however its involvement in IDD pathogenesis and the underlying mechanism remains obscure. Methods: Firstly, IDD mice model was established to determine the iron metabolism associated proteins changes during IDD progression. Then CEP chondrocytes were isolated and treated with TBHP or pro-inflammatory cytokines to mimic pathological environment, western blotting, immunofluorescence assay and tissue staining were employed to explore the underlying mechanisms. Lastly, TfR1 siRNA and Feristatin II were employed and the degeneration of IDD was examined using micro-CT and immunohistochemical analysis. Results: We found that the IDD pathological environment, characterized by oxidative stress and pro-inflammatory cytokines, could enhance iron influx by upregulating TFR1 expression in a HIF-2α dependent manner. Excessive iron accumulation not only induces chondrocytes ferroptosis and exacerbates oxidative stress, but also triggers the innate immune response mediated by c-GAS/STING, by promoting mitochondrial damage and the release of mtDNA. The inhibition of STING through siRNA or the reduction of mtDNA replication using ethidium bromide alleviated the degeneration of CEP chondrocytes induced by iron overload. Conclusion: Our study systemically explored the role of TFR1 mediated iron homeostasis in IDD and its underlying mechanisms, implying that targeting TFR1 to maintain balanced iron homeostasis could offer a promising therapeutic approach for IDD management. The translational potential of this article: Our study demonstrated the close link between iron metabolism dysfunction and IDD, indicated that targeting TfR1 may be a novel therapeutic strategy for IDD.

Abstract Microglial activation and polarization play a central role in poststroke inflammation and neuronal damage. Modulating microglial polarization from pro-inflammatory to anti-inflammatory phenotype is a promising therapeutic strategy for the treatment of cerebral ischemia. Polyphyllin I (PPI), a steroidal saponin, shows multiple bioactivities in various diseases, but the potential function of PPI in cerebral ischemia is not elucidated yet. In our study, the influence of PPI on cerebral ischemia–reperfusion injury was evaluated. Mouse middle cerebral artery occlusion (MCAO) model and oxygen–glucose deprivation and reoxygenation (OGD/R) model were constructed to mimic cerebral ischemia–reperfusion injury in vivo and in vitro. TTC staining, TUNEL staining, RT-qPCR, ELISA, flow cytometry, western blot, immunofluorescence, hanging wire test, rotarod test and foot-fault test, open-field test and Morris water maze test were performed in our study. We found that PPI alleviated cerebral ischemia–reperfusion injury and neuroinflammation, and improved functional recovery of mice after MCAO. PPI modulated microglial polarization towards anti-inflammatory M2 phenotype in MCAO mice in vivo and post OGD/R in vitro. Besides, PPI promoted autophagy via suppressing Akt/mTOR signaling in microglia, while inhibition of autophagy abrogated the effect of PPI on M2 microglial polarization after OGD/R. Furthermore, PPI facilitated autophagy-mediated ROS clearance to inhibit NLRP3 inflammasome activation in microglia, and NLRP3 inflammasome reactivation by nigericin abolished the effect of PPI on M2 microglia polarization. In conclusion, PPI alleviated post-stroke neuroinflammation and tissue damage via increasing autophagy-mediated M2 microglial polarization. Our data suggested that PPI had potential for ischemic stroke treatment.

IntroductionHyperarousal has been a significant pathophysiological theory related to insomnia disorder (ID), characterized by excessive cortical activation and abnormal electroencephalogram (EEG) power during daytime or sleep. However, there is currently insufficient attention to the EEG power during rapid eye movement (REM) sleep and different stages of non-rapid eye movement (NREM) sleep. Additionally, whether the abnormal sleep EEG power in ID patients can be restored by repetitive transcranial magnetic stimulation (rTMS) remains unclear.Methods> Data of 26 ID patients and 26 healthy controls (HCs) were included in the current observational study. The comparisons of relative power between patients and HCs at baseline in each band of each sleep stage and the changes in patients before and after rTMS treatment were performed. The correlations between relative power and behavioral measures of the patients were also investigated.ResultsAbnormalities in sleep EEG relative power in the delta, beta and gamma bands of the patients were observed in NREM2, NREM3 and REM sleep. Correlations were identified between relative power and behavioral measures in ID group, primarily encompassing sleep efficiency, sleep onset latency and depression scores. Post-treatment improvements in relative power of the delta and beta band were observed in NREM2 sleep.DiscussionThe relative power of sleep EEG exhibited a significant correlation with sleep measures in ID patients, and demonstrated notable differences from HCs across the delta, beta, and gamma frequency bands. Furthermore, our findings suggest that rTMS treatment may partially ameliorate relative power abnormalities in patients with ID.

Di-(2-ethylhexyl) phthalate (DEHP) might led to chronic and long-term effects on human organs due to its widespread use and bioaccumulation. Despite some cohorts reporting an association between DEHP exposure and BPH, its underlying mechanisms have not been investigated. Our findings indicate that exposure to DEHP or MEHP (main metabolites of DEHP in the human body) leads to increased prostate weights, elevated prostate index, and notable epithelial thickening in rats. It has been observed to promote BPH-1 cell proliferation with effects ranging from low to high concentrations. Transcriptome sequencing analysis of rat prostate tissues identified KIF11 as the key hub gene. KIF11 is highly expressed after DEHP/MEHP exposure, and knocking down of KIF11 inhibits the MEHP-induced promotion of cell proliferation. Exposure to MEHP has been observed to increase the expression of p-GSK-3β and elevate the levels of β-catenin, thereby activating the Wnt/β-catenin signaling pathway. Knocking down of KIF11 significantly inhibits these effects. Histone H3 at Lysine 27 acetylation (H3K27ac) is implicated in the upregulation of KIF11 expression, as evidenced by the addition of the acetylation inhibitor C646. In summary, our findings established that DEHP exposure could promote BPH through H3K27ac regulated KIF11/Wnt/β-catenin signaling pathway.

ObjectiveThe appropriate use of statins plays a vital role in reducing the risk of atherosclerotic cardiovascular disease (ASCVD). However, due to changes in diet and lifestyle, there has been a significant increase in the number of individuals with high cholesterol levels. Therefore, it is crucial to ensure the rational use of statins. Adverse reactions associated with statins, including liver enzyme abnormalities and statin-associated muscle symptoms (SAMS), have impacted their widespread utilization. In this study, we aimed to develop a predictive model for statin efficacy and safety based on real-world clinical data using machine learning techniques.MethodsWe employed various data preprocessing techniques, such as improved random forest imputation and Borderline SMOTE oversampling, to handle the dataset. Boruta method was utilized for feature selection, and the dataset was divided into training and testing sets in a 7:3 ratio. Five algorithms, including logistic regression, naive Bayes, decision tree, random forest, and gradient boosting decision tree, were used to construct the predictive models. Ten-fold cross-validation and bootstrapping sampling were performed for internal and external validation. Additionally, SHAP (SHapley Additive exPlanations) was employed for feature interpretability. Ultimately, an accessible web-based platform for predicting statin efficacy and safety was established based on the optimal predictive model.ResultsThe random forest algorithm exhibited the best performance among the five algorithms. The predictive models for LDL-C target attainment (AUC = 0.883, Accuracy = 0.868, Precision = 0.858, Recall = 0.863, F1 = 0.860, AUPRC = 0.906, MCC = 0.761), liver enzyme abnormalities (AUC = 0.964, Accuracy = 0.964, Precision = 0.967, Recall = 0.963, F1 = 0.965, AUPRC = 0.978, MCC = 0.938), and muscle pain/Creatine kinase (CK) abnormalities (AUC = 0.981, Accuracy = 0.980, Precision = 0.987, Recall = 0.975, F1 = 0.981, AUPRC = 0.987, MCC = 0.965) demonstrated favorable performance. The most important features of LDL-C target attainment prediction model was cerebral infarction, TG, PLT and HDL. The most important features of liver enzyme abnormalities model was CRP, CK and number of oral medications. Similarly, AST, ALT, PLT and number of oral medications were found to be important features for muscle pain/CK abnormalities. Based on the best-performing predictive model, a user-friendly web application was designed and implemented.ConclusionThis study presented a machine learning-based predictive model for statin efficacy and safety. The platform developed can assist in guiding statin therapy decisions and optimizing treatment strategies. Further research and application of the model are warranted to improve the utilization of statin therapy.

Abstract PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle physics problems. Compared to many approaches that use non-specialized architectures that neglect underlying physics principles and require very large numbers of parameters, PELICAN employs a fundamentally symmetry group-based architecture that demonstrates benefits in terms of reduced complexity, increased interpretability, and raw performance. We present a comprehensive study of the PELICAN algorithm architecture in the context of both tagging (classification) and reconstructing (regression) Lorentz-boosted top quarks, including the difficult task of specifically identifying and measuring the W-boson inside the dense environment of the Lorentz-boosted top-quark hadronic final state. We also extend the application of PELICAN to the tasks of identifying quark-initiated vs. gluon-initiated jets, and a multi-class identification across five separate target categories of jets. When tested on the standard task of Lorentz-boosted top-quark tagging, PELICAN outperforms existing competitors with much lower model complexity and high sample efficiency. On the less common and more complex task of 4-momentum regression, PELICAN also outperforms hand-crafted, non-machine learning algorithms. We discuss the implications of symmetry-restricted architectures for the wider field of machine learning for physics.

Abstract According to previous observational researches and clinical trials, the gut microbiota is related to prostate diseases. However, the potential association between gut microbiota and prostate disorders is still uncertain. We first identified groups of gut microbiota based on the phylum, class, order, family, and genus levels from consortium MiBioGen. And we acquired prostate diseases statistics from the FINNGEN study and PRACTICAL consortium. Next, two-sample Mendelian randomization was used to investigate the potential associations between three prevalent prostate disease and gut microbiota. In addition, we performed a reverse MR analysis and Benjamini-Hochberg (BH) test for further research. We investigated the connection between 196 gut microbiota and three prevalent prostate diseases. We identified 42 nominally significant associations and 2 robust causative links. Upon correction for multiple comparisons using the Benjamini–Hochberg procedure, our analysis revealed a positive correlation between the risk of prostatitis and the presence of the taxonomic order Gastranaerophilales. Conversely, the risk of prostate cancer exhibited an inverse correlation with the presence of the taxonomic class Alphaproteobacteria. Our study revealed the potential association between gut microbiota and prostate diseases. The results may be useful in providing new insights for further mechanistic and clinical studies of prostate diseases.

Abstract Cancer is a major socioeconomic burden that seriously affects the life and spirit of patients. However, little is known about the role of environmental toxicant exposure in diseases, especially ubiquitous di-(2-ethylhexyl) phthalate (DEHP) which is one of the most widely used plasticizers. Hence, the objective of this study was to assess the potential association between cancer and DEHP. The data were collected using the 2011–2018 National Health and Nutrition Examination Survey (NHANES) data (n = 6147), and multiple logistic regression was conducted to evaluate the association. The concentrations of DEHP were calculated by each metabolite and split into quartiles for analysis. After adjusting for confounding factors, DEHP was significantly associated with an increased risk of cancer prevalence, and the metabolites of DEHP showed similar results (OR > 1.0, p 1.0, p

Abstract SARS-CoV-2 and filovirus enter cells via the cell surface angiotensin-converting enzyme 2 (ACE2) or the late-endosome Niemann-Pick C1 (NPC1) as a receptor. Here, we screened 974 natural compounds and identified Tubeimosides I, II, and III as pan-coronavirus and filovirus entry inhibitors that target NPC1. Using in-silico, biochemical, and genomic approaches, we provide evidence that NPC1 also binds SARS-CoV-2 spike (S) protein on the receptor-binding domain (RBD), which is blocked by Tubeimosides. Importantly, NPC1 strongly promotes productive SARS-CoV-2 entry, which we propose is due to its influence on fusion in late endosomes. The Tubeimosides’ antiviral activity and NPC1 function are further confirmed by infection with SARS-CoV-2 variants of concern (VOC), SARS-CoV, and MERS-CoV. Thus, NPC1 is a critical entry co-factor for highly pathogenic human coronaviruses (HCoVs) in the late endosomes, and Tubeimosides hold promise as a new countermeasure for these HCoVs and filoviruses.

Objective The Gouli area is one of the important gold and polymetallic belt in the East Kunlun Orogen. There is no research on lineament identification and mineral exploration based on multisource remote sensing data in the area. Methods In this paper, various enhancement techniques were employed on Landsat 8 OLI, GF-2 and DEM data to delineate structural lineaments. Meanwhile, information on geological, mineral and geochemical anomalies was combined to define prospecting targets in the Gouli area. Results The results show that (1) The tectonic framework of the study area can be divided into two regions by the boundary of Kaketeer-Seride. The lineament in the northern region is most developed in the NNE direction, followed by the near-EW direction, and less distributed in the NW and NE directions. The lineament in the southern region is mainly developed in the near-EW direction, and the NW to NE directions are less distributed. (2) The gold ore is controlled by lineaments. (3) The areas of densely distributed and intersected lineaments, as well as the areas connected by different colors on remote sensing images, are favorable locations for gold mineralization. Finally, based on the comprehensive analysis of multiple geological information and remote sensing interpreted tectonic information, four prospecting targets were identified in the study area and verified in the field. Conclusion This study proves that multisource remote sensing data can better identify the spatial structure characteristics of surface tectonics, which can provide a reference and basis for prospecting new deposits in the Gouli area.

Antarctic krill oil (KO) is known for its poor oxidative stability, especially in emulsion systems. In this experiment, a complex of scallop water-soluble protein-resveratrol (SWPs-RES) was mixed with KO to create high internal phase emulsions (HIPEs) with varying RES ratios. The addition of RES led to noticeable conformational changes in SWPs, including fluorescence bursts, alterations in secondary structure, and modifications in binding motifs. The SWPs-RES complex (1:0.2) demonstrated the most effective free radical scavenging activities (HO: 38.61%, DPPH: 72.49%, ABTS: 85.66%), while the SWPs-RES complex (1:0.025) exhibited the highest emulsifying capacity. Furthermore, HIPEs containing the SWPs-RES complex (1:0.2) displayed improved rheological properties, physical stability, and enhanced oxidative stability against lipid oxidation during storage and simulated in vitro digestion. This study lays a scientific foundation for the utilization of scallop protein and Antarctic krill oil in the food industry.

Precooked treatments are essential in food processing, extending beyond mere sterilization to include the enhancement of nutritional value, flavor profile, and digestibility. This research scrutinizes the effects of water boiling and microwaving on red swamp crayfish, two distinct precooked methodologies. A comparative analytical framework has been employed to assess the efficacy of two precooked methods across a spectrum of quality indicators, including aerobic plate counts, texture, nutrient composition, volatile compound characterization, protein oxidation, and digestive properties. The findings revealed that both water boiling and microwaving effectively reduced bacterial counts to a safe level of 500 CFU/g. Microwave precooking facilitated a moderate oxidation of lipids in crayfish, preferentially liberating flavor compounds, thereby enhancing their sensory attributes. The boiling process imparted a pronounced denaturation to proteins, consequently augmenting the hardness of the crayfish. Notably, the enhanced digestibility of boiled crayfish proteins results from the denaturing action of boiling, promoting efficient protein digestion.

As traditional recommendation systems ignore the hidden information among different user behaviors (such as clicks, add-to-favorites, add-to-cart, and purchases), this often leads to low accuracy in recommendation results. We propose a meta-graph network recommendation system via multi-behavior encoding (MBGR). Firstly, the graph convolutional neural network is used to extract features from various interactive behavior heterogeneous graphs of user-items for behavior heterogeneous modeling. Secondly, matrix decomposition algorithm and meta-knowledge learner are used respectively to process the semantic information of user behavior, and then attention mechanism is used to learn and distinguish the importance of different types of user item interaction behaviors. Finally, meta-knowledge transfer network is used to combine meta-learning paradigm and neural network framework to establish user target behavior recommendation. We conducted comparative experiments comparing MBGR with 7 different baseline models such as NCF and DMF. Extensive experiments on three real datasets (Tmall, Yelp, ML10M) demonstrate that the proposed MBGR method outperforms the baselines. The performance of MBGR is improved by 10.97 % on average with the metric of HR@10 and 10.96 % with the metric of NDCG@10. Under different top-N value evaluation conditions (HR@10, HR@7, NDCG@10, NDCG@7, etc.), the proposed model's performance can also be improved by more than 10 %, which proves the rationality and effectiveness of the proposed MBGR method.

The increasingly improved transportation infrastructure network is the prerequisite and foundation for driving regional development. Scientifically investigating the mechanisms by which the transport superiority degree (TSD) in arid regions affects the health of ecosystems holds significant practical value and importance for ecosystem protection and regional sustainable development. This study investigates the impact of the TSD on the Ecosystem Health Levels (EHL) in Southern Xinjiang, China, an area characterized by its arid climate and ecological vulnerability. Arid regions, such as Southern Xinjiang, often face unique ecological challenges, including water scarcity, soil erosion, and biodiversity loss, which can be exacerbated by human activities including transportation infrastructure development. By leveraging multi-source remote sensing data, this research aims to uncover the intricate dynamics between TSD and EHL in such a fragile ecological context. The findings of this study are critical for understanding the balance between infrastructural development and environmental conservation in arid regions and provide valuable insights for sustainable planning and policy-making in similar ecological settings. The research results show the following: (1) Over the study period, county-scale TSD in southern Xinjiang generally increased, while the conditions of EHL deteriorated, with higher EHL in the eastern counties than in the western counties. (2) Bivariate spatial autocorrelation analysis reveals a significant spatial dependency between county-scale TSD and EHL, with primary relationship types being LH (Low TSD − High EHL) and HL (High TSD − Low EHL). (3) Spatial regression model results indicate that an increase in TSD leads to the deterioration of county-scale EHL, and during the study period, it was observed that the interference of TSD on EHL gradually weakened.

Abstract The tumour‐targeting efficiency of systemically delivered chemodrugs largely dictates the therapeutic outcome of anticancer treatment. Major challenges lie in the complexity of diverse biological barriers that drug delivery systems must hierarchically overcome to reach their cellular/subcellular targets. Herein, an “all‐in‐one” red blood cell (RBC)‐derived microrobot that can hierarchically adapt to five critical stages during systemic drug delivery, that is, circulation, accumulation, release, extravasation, and penetration, is developed. The microrobots behave like natural RBCs in blood circulation, due to their almost identical surface properties, but can be magnetically manipulated to accumulate at regions of interest such as tumours. Next, the microrobots are “immolated” under laser irradiation to release their therapeutic cargoes and, by generating heat, to enhance drug extravasation through vascular barriers. As a coloaded agent, pirfenidone (PFD) can inhibit the formation of extracellular matrix and increase the penetration depth of chemodrugs in the solid tumour. It is demonstrated that this system effectively suppresses both primary and metastatic tumours in mouse models without evident side effects, and may represent a new class of intelligent biomimicking robots for biomedical applications.

Abstract Reactive oxygen species (ROS) constitute a spectrum of oxygenic metabolites crucial in modulating pathological organism functions. Disruptions in ROS equilibrium span various diseases, and current insights suggest a dual role for ROS in tumorigenesis and the immune response within cancer. This review rigorously examines ROS production and its role in normal cells, elucidating the subsequent regulatory network in inflammation and cancer. Comprehensive synthesis details the documented impacts of ROS on diverse immune cells. Exploring the intricate relationship between ROS and cancer immunity, we highlight its influence on existing immunotherapies, including immune checkpoint blockade, chimeric antigen receptors, and cancer vaccines. Additionally, we underscore the promising prospects of utilizing ROS and targeting ROS modulators as novel immunotherapeutic interventions for cancer. This review discusses the complex interplay between ROS, inflammation, and tumorigenesis, emphasizing the multifaceted functions of ROS in both physiological and pathological conditions. It also underscores the potential implications of ROS in cancer immunotherapy and suggests future research directions, including the development of targeted therapies and precision oncology approaches. In summary, this review emphasizes the significance of understanding ROS‐mediated mechanisms for advancing cancer therapy and developing personalized treatments.

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02–15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Abstract Background There have been few investigations on the association between depression and benign prostatic hyperplasia (BPH). This study aims to explore the correlation between depression and BPH among middle-aged and older men in India. Methods We utilized data from male individuals aged 45 years and older who participated in the initial wave (2017–2018) of the Longitudinal Aging Study in India (LASI). The presence of BPH symptoms was based on self-reported information, while depressive symptoms were evaluated using CESD-10. The analysis was a cross-sectional study conducted on a final sample size of 30,108 male participants. To examine associations, we employed multivariate logistic regression analysis along with subgroup analysis and interaction tests. Results A total of 439 (1.46%) men reported BPH and had a higher depression score (10.18 ± 4.22 vs. 9.28 ± 4.00). The findings indicated a significant association between the depression score and the likelihood of developing BPH, even after accounting for all potential confounding variables (OR = 1.054, 95% CI: 1.030–1.078, p

Plant–microbe interactions are key to nutrient-use efficiency. Root microbes are influenced by rhizosphere soil and plant cultivars. The impact of cultivar-by-nitrogen (N) interactions on the maize-root microbiome remains unclear, yet it is crucial for understanding N use efficiency in maize. This study evaluated the effects of maize cultivars and N forms, along with their interactions, on the diversity and composition of root bacteria and fungi. Additionally, we examined correlations between soil microbes and root metabolites. The maize cultivar Zhengdan 958 (ZD958) showed a positive response to the mixture of nitrate and ammonium N, resulting in increased in biomass, grain yield, shoot N content, grain N content, and root area. In contrast, the cultivar Denghai605 (DH605) did not exhibit a similar response. The diversity and composition of root bacteria and fungi differed between ZD958 and DH605. The N form primarily affected the community structure of rhizospheric fungi in ZD958 and rhizospheric bacteria in DH605, rather than endophytic microbes. A mixed N supply increased the relative abundance of Basidiomycota, which was positively correlated with ZD958 yield. For DH605, a mixed N treatment enhanced nitrification functions involving Bacteroidetes and Proteobacteria, while it reduced the effects of ammonium N supply. The dominant rhizospheric microbes in DH605 showed a stronger response to changes in root metabolites compared to those in ZD958. A mixed N supply increased the content of palmitoleic acid in ZD958 root exudates, facilitating the recruitment of beneficial rhizospheric microbes, which promotes maize growth. In DH605, a mixed N supply decreased the concentration of sphinganine, which is significantly correlated with Acidobacteria (negatively), Proteobacteria (negatively), Bacteroidetes (positively), and TM7 (positively). Our findings suggest that different maize cultivars respond differently to N forms, causing distinct rhizospheric microbial effects, and that root metabolites send metabolic signals to regulate and recruit key bacterial and fungal communities.

Community detection in complex networks often suffers from insufficient data and limited utilization of prior knowledge. In this paper we propose “Semi-supervised Generative Adversarial Network” (GANSE), a novel algorithm that integrates Generative Adversarial Networks (GANs) and semi-supervised learning to address these challenges. This method addresses the issues above through a multi-step process. Initially, the network is rewired using vertex similarity metrics, thereby enhancing its structural integrity. Subsequently, a novel generative adversarial network model is designed, and our model facilitates the reconstruction of the network, thereby yielding partitions. Which form the basis for identifying core communities. Additionally, the local clustering coefficient is incorporated as a reward signal and injected into the node selection process. Moreover, isolated nodes are reallocated, ultimately culminating in the derivation of the final community structure. Experimental results on four large real-life datasets demonstrate the clear superiority of the proposed algorithm in terms of F1 and Jaccard metrics when compared to existing algorithms. Notably, our GANSE method outperforms the traditional algorithms in networks with “missing data”. Thus showing its robustness and effectiveness in real-world incomplete datasets. Our findings highlight the potential of GANs and semi-supervised learning for enhancing community detection accuracy in complex networks.

BackgroundScreening and treatment of dysglycemia (prediabetes and diabetes) represent significant challenges in advancing the Healthy China initiative. Identifying the crucial factors contributing to dysglycemia in urban-rural areas is essential for the implementation of targeted, precise interventions.MethodsData for 26,157 adults in Fujian Province, China, were collected using the Social Factors Special Survey Form through a multi-stage random sampling method, wherein 18 variables contributing to dysglycemia were analyzed with logistic regression and the random forest model.ObjectiveInvestigating urban-rural differences and critical factors in dysglycemia prevalence in Fujian, China, with the simultaneous development of separate predictive models for urban and rural areas.ResultThe detection rate of dysglycemia among adults was 35.26%, with rates of 34.1% in urban areas and 35.8% in rural areas. Common factors influencing dysglycemia included education, age, BMI, hypertension, and dyslipidemia. For rural residents, higher income (OR = 0.80, 95% CI [0.74, 0.87]), average sleep quality (OR = 0.89, 95% CI [0.80, 0.99]), good sleep quality (OR = 0.89, 95% CI [0.80, 1.00]), and high physical activity (PA) (OR = 0.87, 95% CI [0.79, 0.96]) emerged as protective factors. Conversely, a daily sleep duration over 8 hours (OR = 1.46, 95% CI [1.03, 1.28]) and middle income (OR = 1.12, 95% CI [1.03, 1.22]) were specific risk factors. In urban areas, being male (OR = 1.14, 95% CI [1.02, 1.26]), cohabitation (OR = 1.18, 95% CI [1.02, 1.37]), and central obesity (OR = 1.35, 95% CI [1.19, 1.53]) were identified as unique risk factors. Using logistic regression outcomes, a random forest model was developed to predict dysglycemia, achieving accuracies of 75.35% (rural) and 76.95% (urban) with ROC areas of 0.77 (rural) and 0.75 (urban).ConclusionThis study identifies key factors affecting dysglycemia in urban and rural Fujian residents, including common factors such as education, age, BMI, hypertension, and dyslipidemia. Notably, rural-specific protective factors are higher income and good sleep quality, while urban-specific risk factors include being male and central obesity. These findings support the development of targeted prevention and intervention strategies for dysglycemia, tailored to the unique characteristics of urban and rural populations.

ObjectivesMeta-analysis was conducted to examine the effect of supplemental curcumin intake on skeletal muscle injury status and to propose an optimal intervention program.MethodsIn accordance with the procedures specified in the PRISMA statement for systematic reviews and meta-analyses of randomized controlled trials, the Review Manager 5.3 was used to analyze the results of creatine kinase (CK), muscle soreness, interleukin-6 (IL-6), and range of motion (ROM) as outcome indicators in the 349 subjects included in the 14 articles.ResultsThe effect size of curcumin supplementation on muscle soreness, mean difference (MD) = -0.61; the relationship between curcumin supplementation and muscle soreness for time of measurement (I2 = 83.6%)、the relationship between curcumin supplementation and muscle soreness for period of intervention (I2 = 26.2%)、the relationship between whether one had been trained (I2 = 0%) and supplementation dose (I2 = 0%) were not heterogeneous for the relationship between curcumin supplementation and muscle soreness; The effect size on CK, MD = -137.32; the relationship between curcumin supplementation and CK (I2 = 79.7%)、intervention period (I2 = 91.9%)、whether or not trained (I2 = 90.7%)、and no heterogeneity in the relationship between curcumin supplementation and CK for the time of measurement (I2 = 0%); The effect size MD = 4.10 for the effect on ROM; The effect size for IL-6 was MD = -0.33.ConclusionsThis meta-analysis highlights that curcumin supplementation significantly mitigates skeletal muscle damage, with notable improvements in CK levels, muscle soreness, IL-6 levels, and ROM. The results highlight the importance of curcumin dosage and timing, revealing that prolonged supplementation yields the best results, especially for untrained individuals or those less exposed to muscle-damaging exercise. For muscle soreness and ROM enhancement, a pre-emptive, low-dose regimen is beneficial, while immediate post-exercise supplementation is most effective at reducing CK and IL-6 levels.

Urban agglomerations are important spatial carriers for regional economic development, but sustained urban expansion has triggered a series of issues such as conflicts between “people and land” and “built-up areas and green spaces”. However, many studies have rarely incorporated spatiotemporal patterns and driving mechanism into scenario simulation of urban growth, and has also overlooked subsequent discussions on the specific integration of simulation results with spatial planning. Considering the case of Min Delta Region, this study analyzes the spatiotemporal evolution of urban expansion based on multi-source data and explore the influential driving mechanism using Random forest algorithm. A combination of Markov and FLUS models was used to dynamically simulate future urban growth patterns. The results showed that 1) From 1995 to 2015, the urban expansion increased by 2.26 times, showing a trend of first accelerating and then slowing down. It was also discovered that there is an anisotropy toward expansion in the trajectory of urban growth. 2) With marginal increase and enclave increase, general urban expansion was observed to be changing from diffusion to agglomeration, and the urban expansion hotspots mainly located in the southeast coastal area. 3) Humanistic links, geographic position, social and economic considerations all played a role in the development of the Min Delta region, and can be summarized as the “four forces” driving mechanism model. 4) By 2035, the trend of continuous development with the Min Delta built-up area as the main body will be basically formed, and incorporating ecological restrictions can effectively restrain urban growth's encroachment into ecological spaces. Based on the simulation results, Min Delta region gradually showing a spatial structure evolution trend from the single core to the dual core mode, and then to the multi-center and networked mode. This study would serve as a multi-angle decision-making reference for regional spatial and ecological protection planning and urban growth management, and as a scientific foundation for high-quality development planning in the region.

Social media platforms have rich text data, which can be used in data mining and analysis. However, given the fact, the evolution speed of natural languages is rapid in social media, and data on social media is very noisy. This is a great challenge to the accuracy of data analysis. To overcome this problem, we propose a topic-relevant content extraction (TRCE) based on deep multiple instance classification, leveraging existing information and hierarchical relationships among texts under a thread on social media as weak supervision to extract topic-strong-relevant data and filter out noise accurately without manually labeling data. The proposed method introduces latent variables, Bernoulli distribution, and variational inference into multiple-instance learning (MIL) to generate pseudo labels. Then we employ a dual-stream neural network with a 3-stage training process to achieve training MIL end-to-end. Experimental results show TRCE has a significant improvement compared with other MIL methods. Meanwhile, it only has a little decrease compared with supervised text classification on accuracy and F1 score. Given the fact TRCE does not need manually labeled data at all, while supervised classification relies heavily on labeled data, TRCE is a competitive method to extracting topic-relevant data and filtering out noise on social media.

Abstract Drugs causing ferroptosis, iron-mediated cell death, represent promising tools for cancer treatment. While exploring the effect of these drugs on breast cancer (BC), we found that a ferroptosis-inducing drug erastin dramatically inhibits tumorigenicity of human BC cells in mice but when used at a concentration known to effectively kill other cell types only modestly reduces such growth in 2D monolayer culture. BCs grow in vivo as 3D masses, and we found that ferroptosis inducers erastin and sulfasalazine inhibit growth of multiple human BC cell lines in 3D culture significantly stronger than in 2D culture. To understand the mechanism of this differential effect, we found that ferroptosis inducers upregulate mRNAs encoding multiple direct and indirect autophagy stimulators, such as ATG16L2, ATG9A, ATG4D, GABARAP, SQSTM/p62, SEC23A and BAX, in tumor cells growing in 2D but not in 3D culture. Furthermore, these drugs promoted autophagy of tumor cells growing in a 2D but not in a 3D manner. We observed that pharmacological inhibition of autophagy-stimulating protein kinase ULK1 or RNA interference-mediated knockdown of autophagy mediator ATG12 significantly sensitized tumor cells to erastin treatment in 2D culture. We also found that ferroptosis-promoting treatments upregulate heme oxygenase-1 (HO-1) in BC cells. HO-1 increases cellular free iron pool and can potentially promote ferroptosis. Indeed, we observed that HO-1 knockdown by RNA interference reversed the effect of ferroptosis inducers on BC cell 3D growth. Hence, the effect of these drugs on such growth is mediated by HO-1. In summary, autophagy triggered by ferroptosis-promoting drugs reduces their ability to kill BC growing in a 2D manner. This protection mechanism is inhibited in BC cells growing as a 3D mass, and ferroptosis-promoting drugs kill such cells more effectively. Moreover, this death is mediated by HO-1. Thus, ferroptosis induction represents a promising strategy for blocking 3D BC growth.

Although the use of heterosis in maize breeding has increased crop productivity, the genetic causes underlying heterosis for nitrogen (N) use efficiency (NUE) have been insufficiently investigated. In this study, five N-response traits and five low-N-tolerance traits were investigated using two inbred line populations (ILs) consisting of recombinant inbred lines (RIL) and advanced backcross (ABL) populations, derived from crossing Ye478 with Wu312. Both populations were crossed with P178 to construct two testcross populations. IL populations, their testcross populations, and the midparent heterosis (MPH) for NUE were investigated. Kernel weight, kernel number, and kernel number per row were sensitive to N level and ILs showed higher N response than did the testcross populations. Based on a high-density linkage map, 138 quantitative trait loci (QTL) were mapped, each explaining 5.6%–38.8% of genetic variation. There were 52, 34 and 52 QTL for IL populations, MPH, and testcross populations, respectively. The finding that 7.6% of QTL were common to the ILs and their testcross populations and that 11.7% were common to the MPH and testcross population indicated that heterosis for NUE traits was regulated by non-additive and non-dominant loci. A QTL on chromosome 5 explained 27% of genetic variation in all of the traits and Gln1-3 was identified as a candidate gene for this QTL. Genome-wide prediction of NUE traits in the testcross populations showed 14%–51% accuracy. Our results may be useful for clarifying the genetic basis of heterosis for NUE traits and the candidate gene may be used for genetic improvement of maize NUE.

Abstract Azide-alkyne cycloaddition reaction is a very common organic reaction to synthesize nitrogen-containing heterocycles. Once catalyzed by Cu(I) or Ru(II), it turns out to be a click reaction and thus is widely applied in chemical biology for labeling. However, besides their poor regioselectivity towards this reaction, these metal ions are not biologically friendly. Hence, it is an urgent need to develop a metal-free azide–alkyne cycloaddition reaction for biomedical applications. In this work, we found that, in the absence of metal ions, supramolecular self-assembly in an aqueous solution could realize this reaction with excellent regioselectivity. Nap-Phe-Phe-Lys(azido)-OH firstly self-assembled into nanofibers. Then, Nap-Phe-Phe-Gly(alkynyl)-OH at equivalent concentration approached to react with the assembly to yield the cycloaddition product Nap-Phe-Phe-Lys(triazole)-Gly-Phe-Phe-Nap to form nanoribbons. Due to space confinement effect, the product was obtained with excellent regioselectivity. Employing the excellent properties of supramolecular self-assembly, we are applying this strategy to realize more reactions without metal ion catalysis.

Carbon emission from the coal-burning power plant is the main source of CO2 emitted in the China’s energy industry. Biomass power generation is deemed to be a carbon-neutral or even carbon-negative mode. Therefore, the development of coal coupled biomass gasification technology for power generation can help to achieve a coal substitution and carbon emission reduction and is a realistic and feasible path for maintaining a low-carbon coal power generation. In this context, an overview of three coal coupled biomass power generation technologies (the coal/biomass direct combustion of coal and biomass, the coal/biomass direct combustion coupled in parallel mode, and the coal direct combustion coupled with biomass gasification) has been conducted in this study. The characteristics of the above three technologies have been analyzed. Future development trends in the coal-burning coupled biomass gasification power generation technology have been put forward. The physicochemical adaptability of the feedstock and the influence of the feedstock on the gasifier are also studied based on the coupled gasification power generation technology. This work has emphasized the technological process of coal coupled biomass gasification power generation technology, including biomass transportation system, gasifiers (the technical characteristics, technical challenges and development direction of circulating fluidized bed gasifier (CFB), double fluidized bed gasifier (DFB), downdraft fixed bed gasifier, updraft fixed bed gasifier, crossdraft fixed bed gasifier and chain grate have been analyzed), gas cooling system, gas-pressurized transportation system, combustion system and gas purification system. The energy utilization efficiency analysis, economic analysis and environmental benefit analysis of the technology have been carried out. The analysis obtained would provide a theoretical reference and technical support for the research and application of coal-coupled biomass power generation technology. In addition, a series of national policies to promote coal coupled biomass gasification power generation technology and the current successful cases of coal coupled biomass gasification power stations are also presented. Finally, the challenges and future development of coal coupled biomass power generation technology have been prospected.

Background Insomnia disorder (ID) is one of the most common mental disorders. Research on ID focuses on exploring its mechanism of disease, novel treatments and treatment outcome prediction. An emerging technique in this field is the use of electroencephalography (EEG) microstates, which offer a new method of EEG feature extraction that incorporates information from both temporal and spatial dimensions.Aims To explore the electrophysiological mechanisms of repetitive transcranial magnetic stimulation (rTMS) for ID treatment and use baseline microstate metrics for the prediction of its efficacy.Methods This study included 60 patients with ID and 40 age-matched and gender-matched good sleep controls (GSC). Their resting-state EEG microstates were analysed, and the Pittsburgh Sleep Quality Index (PSQI) and polysomnography (PSG) were collected to assess sleep quality. The 60 patients with ID were equally divided into active and sham groups to receive rTMS for 20 days to test whether rTMS had a moderating effect on abnormal microstates in patients with ID. Furthermore, in an independent group of 90 patients with ID who received rTMS treatment, patients were divided into optimal and suboptimal groups based on their median PSQI reduction rate. Baseline EEG microstates were used to build a machine-learning predictive model for the effects of rTMS treatment.Results The class D microstate was less frequent and contribute in patients with ID, and these abnormalities were associated with sleep onset latency as measured by PSG. Additionally, the abnormalities were partially reversed to the levels observed in the GSC group following rTMS treatment. The baseline microstate characteristics could predict the therapeutic effect of ID after 20 days of rTMS, with an accuracy of 80.13%.Conclusions Our study highlights the value of EEG microstates as functional biomarkers of ID and provides a new perspective for studying the neurophysiological mechanisms of ID. In addition, we predicted the therapeutic effect of rTMS on ID based on the baseline microstates of patients with ID. This finding carries great practical significance for the selection of therapeutic options for patients with ID.

Information cascade prediction is a crucial task in social network analysis. However, previous research has only focused on the impact of social relationships on cascade information diffusion, while ignoring the differences caused by the characteristics of cascade information itself, which limits the performance of prediction results. We propose a novel cascade information diffusion prediction model (Topic-HGAT). Firstly, we extract features from different topic features to enhance the learned cascade information representation. To better implement this method, we use hypergraphs to better characterize cascade information and dynamically learn multiple diffusion sub-hypergraphs according to the time process; secondly, we introduce cross-attention mechanisms to learn each other's feature representations from the perspectives of both user representation and cascade representation, thereby achieving deep fusion of the two features. This solves the problem of poor feature fusion effect caused by simply calculating self-attention on learned user representation and cascade representation in previous studies; finally, we conduct comparative experiments on four real datasets, including Twitter and Douban. Experimental results show that the proposed Topic-HGAT model achieves the highest improvements of 2.91% and 1.59% on Hits@100 and MAP@100 indicators, respectively, compared to other 8 baseline models, verifying the rationality and effectiveness of the proposed Topic-HGAT model.

Osteoimmunology is a concept involving molecular and cellular crosstalk between the skeletal and immune systems. Toll-like receptors (TLRs) are widely expressed both on mesenchymal stromal cells (MSCs), the hematopoietic cells, and immune cells in the osteogenic microenvironment for bone development or repair. TLRs can sense both exogenous pathogen-associated molecular patterns (PAMPs) derived from microorganisms, and damage-associated molecular patterns (DAMPs) derived from normal cells subjected to injury, inflammation, or cell apoptosis under physiological or pathological conditions. Emerging studies reported that TLR signaling plays an important role in bone remodeling by directly impacting MSC osteogenic differentiation or osteoimmunology. However, how to regulate TLR signaling is critical and remains to be elucidated to promote the osteogenic differentiation of MSCs and new bone formation for bone tissue repair. This review outlines distinct TLR variants on MSCs from various tissues, detailing the impact of TLR pathway activation or inhibition on MSC osteogenic differentiation. It also elucidates TLR pathways’ interplay with osteoclasts, immune cells, and extracellular vesicles (EVs) derived from MSCs. Furthermore, we explore biomaterial-based activation to guide MSCs’ osteogenic differentiation. Therefore, understanding TLRs’ role in this context has significant implications for advancing bone regeneration and repair strategies.

Ecological security pattern (ESP) is an effective way to integrate landscape patterns and ecological processes from the perspective of ecological connectivity. Its scientific construction can promote the coordinated development of social system and ecosystem. However, the construction methods of ESP still need to be explored, most studies assume that different ecological processes do not interfere with each other, and then spatial overlay different types of ecosystem services to determine the ecological source. Metropolitan area is an important space carrier for the high-quality development of Chinese urban agglomeration. Taking Shenzhen metropolitan area as the target area, we identified ecological sources according to the ecosystem services trade-offs and ecological sensitivity assessment system and OWA algorithm, revised the basic resistance surface by using the night light data, and simulated and extracted ecological corridors by combining the minimum cumulative resistance (MCR) model and potential model. We identified 13 important ecological sources with a total area of 9938.23.23 km2. The spatial distribution of them was concentrated in Huizhou and Shanwei, as well as the junctions of mountain woodlands in Huizhou, Heyuan, and Shanwei. Notably, 66 ecological corridors were simulated, including 26 important and 40 general corridors. The ecological corridors generally exhibited a spider web radial distribution with substantial spatial heterogeneity and were densely distributed in the suburbs and northern hilly regions far from urban areas. Aggregated ecological elements were used to build a comprehensive ESP for the Shenzhen metropolitan area, which comprises 13 ecological sources, 26 important ecological corridors, 40 general ecological corridors, 4 security levels of resistance surface, and 24 ecological strategic nodes. This study comprehensively considered the ecosystem services interactions and the environmental sensitivity of ecological processes, provided an effective quantitative framework for constructing the ESP, and the results can provide decision-making reference for territorial ecological protection and restoration planning of Shenzhen metropolitan area.

Abstract Background The global coronavirus disease 2019 (COVID-19) has caused many negative effects on physical and mental health of patients who have survived COVID-19. Apart from some long-lasting physical sequelae, those COVID-19 survivors are also suffering stigma and discrimination at different levels around the world. The current study aims to assess the role resilience played in stigma and mental disorders among COVID-19 survivors. Methods The cross-sectional study was carried out among former COVID-19 patients in Jianghan District (Wuhan, China) from June 10 to July 25, 2021. The demographic questions, the Impact of Events Scale-Revised, the Generalized Anxiety Disorder Questionnaire, the Patient Health Questionnaire, the Resilience Style Questionnaire and the Short Version of COVID-19 Stigma Scale of 12 items were used to collect relevant information of the participants. Descriptive analyses, Pearson correlation analysis and Structural Equation Modeling were used to make data description and analysis. Results A total of 1541 out of 1601 COVID-19 survivors (887 females and 654 males) were included in the analysis. Perceived stigma of those COVID-19 survivors correlates significantly with anxiety (r = 0.335, P

The traditional 2D laser slam back-end graph optimization (LSBGO) is not efficient in some special situations, such as fast scene switching, limited computing time, and limited hardware facilities. This paper presents a partitioned pose vector method (PPVM) to optimize a pose vector by dividing it into two parts: a position term and an angle term. Based on this partition, the traditional graph optimization problem has been transformed into two linear equations. The least square solutions of the two equations help to find the pose vector. Furthermore, the paper applies a Cholesky decomposition (CD) to improve the speed of solving the two linear equations. Cholesky decomposition has great advantages in solving linear equations with symmetric positive definite coefficient matrix. The effectiveness of PPVM-CD is numerically verified by MATLAB simulation. Compared with the traditional LSBGO method, PPVM-CD improves the optimization speed by 26% and the optimization accuracy by 11.1%.

Bone regeneration scaffolds loaded with osteoblast-related cells or cytokines exhibit outstanding therapeutic potential during large-scale bone defect repair. However, limited sources of cells, opportune choosing of growth factors and their concentration, as well as immunological rejection, seriously hinder its clinical application. Developing a scaffold that can effectively recruit MSCs in situ and achieve endogenous bone regeneration is a viable strategy. Herein, we report a chitosan-calcium carbonate scaffold with high mineral content and centripetal pore arrangement using a simple in situ mineralization method. In vivo results first time demonstrate that the scaffold with high calcium carbonate content can effectively recruit MSCs near the defect area, induce their osteogenic differentiation, and ultimately accelerate the process of bone regeneration. Considering the accessible preparation and excellent osteogenicity, the chitosan-calcium carbonate scaffold possesses high potential for the therapeutics of massive bone defects.

Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [...]

Multi-objective reservoir operation of reservoir flood control involves numerous factors and complex model solving, and exploring effective methods for solving the operation models has always been a hot topic in reservoir optimization operation research. The Multi-Objective Ant Lion Algorithm (MOALO) is an emerging heuristic intelligent optimization algorithm, but it has not yet been applied in reservoir optimization operation. Testing the effectiveness of this method on multi-objective reservoir scheduling and further improving the optimization performance of this method is of great significance for enhancing the overall benefits of reservoir operation. In this study, MOALO is applied to the optimal scheduling of reservoir flood control. To increase the search efficiency of MOLAO, the advanced MOALO method (AMOLAO) is proposed by reconstructing the search distribution in MOALO using a power function. Taking the Songshu Reservoir and Dongfeng Reservoir in the Fuzhou River Basin in Dalian City as an example, MOALO, AMOLAO, and other two traditional methods are applied for solving the multi-objective reservoir operation problem. Results show that the AMOALO method has high search efficiency, strong optimization ability, and good stability. AMOALO performs better than MOALO and the two traditional methods. The study provides an efficient method for solving the problems in multi-objective reservoir operation.

Transportation significantly influences economically underdeveloped arid regions, impacting economic growth and social progress. Analyzing Transport Superiority Degree (TSD) and its implications in such regions is crucial. A new arid region-specific evaluation framework addresses traditional limitations by considering indicators like route connectivity and desert interference. This article conducts an empirical study using Southern Xinjiang as a research case. It combines comprehensive evaluation methods, spatial autocorrelation methods, spatial Durbin models, and coupling coordination models to depict Transport Support Capability (TSC), Transport Access Capacity (TAC), and Transport Guarantee Capacity (TGC) at different scales in Southern Xinjiang from 2000 to 2020. The study reveals spatial patterns, evolutionary characteristics, economic impacts, and social effects of TSD at various scales. Key findings include: (1) Rapid expansion of transportation infrastructure in Southern Xinjiang. The levels of TSD at different scales have gradually increased, and spatial and temporal pattern differences are evident. At the county level, TSD forms a “core-periphery” spatial pattern centered around the Southern Xinjiang Railway, with “high-high” agglomeration areas centered around Kashgar city and “low-low” agglomeration areas centered around Qiemo County. (2) Prefectural-level TSD improvements have limited impacts on regional development, while county-level TSC, TAC, TGC, and TSD positively affect economic and social growth but also exhibit competitive effects. (3) TSD is transitioning from non-coordinated to coordinated development with economic and social progress at different scales. This research informs transportation facility evaluation in arid regions.

Highly sensitive surface acoustic wave (SAW) sensors have recently been recognized as a promising tool for various industrial and medical applications. However, existing SAW sensors generally suffer from a complex design, large size, and poor robustness. In this paper, we develop a simple and stable delay line ultra-high frequency (UHF) SAW sensor for highly sensitive detection of temperature. A Z-shaped delay line is specially designed on the piezoelectric substrate to improve the sensitivity and reduce the substrate size. Herein, the optimum design parameters of extremely short-pitch interdigital transducers (IDTs) are given by numerical simulations. The extremely short pitch gives the SAW sensor ultra-high operating frequency and consequently ultra-high sensitivity. Several experiments are conducted to demonstrate that the sensitivity of the Z-shaped SAW delay line sensor can reach up to 116.685°/°C for temperature detection. The results show that the sensor is an attractive alternative to current SAW sensing platforms in many applications.

Text classification is an emerging topic in the field of text data mining, but the current methods of deducing sentence polarity have two major shortcomings: on the one hand, there is currently a lack of a large and well-curated corpus; on the other hand, current solutions based on deep learning are particularly vulnerable to attacks from adversarial samples. To overcome the limitations above, we propose an adversarial training method HNN-GRAT (Hierarchical Neural Network and Gradient Reversal) for text classification. Firstly, A Robustly Optimized BERT Pretraining Approach (RoBERTa) pretraining model is used to extract text features and feature gradient information; secondly, the original gradient information is passed through the gradient reversal layer designed to obtain the inverted gradient information; finally, the original gradient information and the inverted gradient information are fused to obtain the new gradient of the model. HNN-GRAT method are tested on three real datasets and five attack methods, compared with RoBERTa pretraining model, HNN-GRAT improves the robustness accuracy and reduces the probability of the model being attacked. In addition, using six text defense methods, HNN-GRAT achieves the best Boa and Succ (Such as, DeepWordBug attack, for AGNEWS, IMDB, SST-2 datasets, with an improvement of Boa up to 41.50%, 67.50%, 28.15% and Succ drop to 55.90%, 27.45%, 69.89%, respectively).