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Abstract Exploring the relationship between land use cover/change (LUCC) and ecosystem service value (ESV) under different future scenarios can provide guidance for selecting future development patterns and for the scientific utilization of land resources in the region. In this study, LUCC under different scenarios in the North Slope of Tianshan Mountain (NSTM) was simulated using the PLUS model. The ESV coefficients were adjusted for regional differences and social development factors to better reflect the actual situation in the study area. The interactions between LUCC, landscape pattern (LSP), and ESV were systematically analyzed, while at the same time, ESV and the level of economic development were fitted to the Ecological Kuznets Curve, which was then used to determine its trend and inflection point. The following conclusions were drawn: (1) Cropland and unused land are the main types of land use change in the NSTM, both historically and in the future. Cropland shows an increase in the natural development scenario and a decrease in the ecological protection scenario. Unused land shows an increase in the different development scenarios, indicating that unused land has higher development potential in the NSTM. NSTM shows a continuous decrease in ESV in the natural development scenario and a continuous increase in ESV in the ecological protection scenario. (2) LSP in both historical and future NSTM have evolved to show fragmentation, heterogeneity, and complexity in patch forms. However, this trend is slower in the ecological protection scenario than in the natural development scenario. LUCC, LSP, and ESV form an integrated framework of interactions, where LUCC influences ESV through LSP, and changes in ESV feedback to LUCC through LSP, which acts as a bridging mediator. (3) The Ecological Kuznets Curve of NSTM exhibits an N-shape, showing a clear overall rightward trend across different development scenarios at the annual level. At the interannual level, the curves for the natural development scenario are situated in the middle of the declining phase of the N-shape, with no ecological inflection point occurring during the study period. In contrast, the curves for the ecological protection scenario display a declining-ascending trend, with the ecological inflection point occurring when per capita GDP reaches 2.5 × 10^6 CNY.

Abstract Melanin concentrating hormone (MCH) controls many brain functions, such as sleep/wake cycle and memory, and modulates the inflammation response. Previous studies have shown that influenza A virus (IAV) infection-induced neuroinflammation leads to central nervous damage. This study investigated the potential effects of MCH against neuroinflammation induced by IAV infection and its mechanism. MCH (1 and 2 mg/ml) was administrated for 5 consecutive days before IAV infection. Pentobarbital-induced sleep tests, an open-field test, and a Morris water maze were performed to measure sleep quality, spatial learning and memory ability. Neuronal loss and microglial activation were observed with Nissl staining and immunofluorescence assay. The levels of inflammatory cytokines and the expression of the JNK/ERK signaling pathway were examined by ELISA and western blot. IAV infection led to poor sleep quality, impaired the ability of spatial learning and memory, caused neuronal loss and microglial activation in mice’s hippocampus and cortex. Meanwhile the level of inflammatory cytokines increased, and the JNK/ERK signaling pathway was activated after IAV infection. MCH administration significantly alleviated IAV-induced neuroinflammation, cognitive impairment, and sleep disorder, decreased the levels of inflammatory cytokines, and inhibited neuronal loss and microglial activation in the hippocampus and cortex by regulating the JNK/ERK signaling pathway. Therefore, MCH alleviated the neuroinflammation, spatial learning and memory impairment, and sleep disorder in IAV-infected mice by regulating the JNK/ERK signaling pathway.

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 The effectiveness of Toll‐like 9 agonists (CpG) as an adjuvant for tumor immunotherapy is restricted due to their insufficient ability to activate anti‐tumor immunity. To address that, the common nutrient metal ions are explored (Mn2+, Cu2+, Ca2+, Mg2+, Zn2+, Fe3+, and Al3+), identifying Mn2+ as a key enhancer of CpG to mediate immune activation by augmenting the STING‐NF‐κB pathway. Mn2+ and CpG are then self‐assembled with epigallocatechin gallate (EGCG) into a nanoadjuvant MPN/CpG. Local delivery of MPN/CpG effectively inhibits tumor growth in a B16 melanoma‐bearing mouse model, reshaping the tumor microenvironment (TME) by repolarizing M2‐type tumor‐associated macrophages (TAMs) to an M1‐type and boosting intra‐tumoral infiltration of CD8+/CD4+ T lymphocytes and DCs. Furthermore, compared to free CpG, MPN/CpG exhibits heightened accumulation in lymph nodes, enhancing CpG uptake and DC activation, consequently inducing significant antigen‐specific cytotoxic CD8+ T cell immune response and humoral immunity. In a prophylactic tumor‐bearing mouse model, MPN/CpG vaccination with OVA antigen significantly delays B16‐OVA melanoma growth and extends mouse survival. These findings underscore the potential of MPN/CpG as a multifunctional adjuvant platform to drive powerful innate and adaptive immunity and regulate TME against tumors.

BackgroundExtensive-stage small-cell lung cancer (ES-SCLC) has a dismal prognosis owing to its high aggressiveness, rapid drug resistance, and early metastasis. ES-SCLC responds well to first-line chemotherapy, and chemotherapy coupled with immunotherapy can further improve overall survival. However, the long-term survival of patients remains unsatisfactory because of its high recurrence rate and the poor efficacy of second-line treatment. Although local radiotherapy is an important component of the overall treatment for ES-SCLC, its value in the age of immunotherapy remains controversial.Case descriptionA 54-year-old male with ES-SCLC achieved a complete response (CR), as determined using enhanced computed tomography (CT) after four cycles of immunochemotherapy (serplulimab, carboplatin, and etoposide). Whole-body positron emission tomography-CT was performed during maintenance treatment with serplulimab, which showed primary lung, liver, and bone metastatic lesions with CR. However, several mediastinal lymph nodes exhibited glucose metabolism uptake, and new lesions appeared on the head. The patient underwent palliative radiotherapy of the head and consolidative thoracic radiotherapy of the chest and continued maintenance treatment with serplulimab. Subsequent magnetic resonance imaging of the head suggested good control of metastatic lesions (CR). The patient received first-line immunotherapy for approximately 20 months.ConclusionsThis report presents a patient with ES-SCLC who underwent local radiotherapy in addition to serplulimab as maintenance therapy. Although the programmed death-ligand 1 (PD-L1) expression level was negative and a PD-1 inhibitor instead of a PD-L1 inhibitor was used, the patient did not experience significant pneumonia during treatment, and the efficacy of the current treatment was evident. This treatment model warrants further clinical investigation.

Alcoholic liver disease is one of the main causes of global liver disease-related deaths, which has emerged as a major health concern for people. In this study, a peptide was prepared from Pinctada Martensii meat (named HPM-3), and its protective effect against alcohol-induced liver damage in mice was investigated. The results indicated that intervention of HPM-3 significantly decreased the levels of transaminase and lactate dehydrogenase in serum, and improved the antioxidant enzyme activities in the liver. Besides, HPM-3 could alleviate alcohol-induced liver injury by mediating Keap1/Nrf2 signaling pathway. Moreover, HPM-3 could preserve the diversity of the gut microbiota, and restore the relative abundance of some microbial communities that were impacted by alcohol intervention. HPM-3 intervention also affected the secretion of L-glutamine and other associated metabolites and mediated the related metabolic pathways. The results would help to clarify the mechanisms of protective effects of marine peptides against alcohol-induced liver injury.

BackgroundPichia pastoris-secreted delta sleep inducing peptide and crossing the blood-brain barrier peptides (DSIP-CBBBP) fusion peptides holds significant promise for its potential sleep-enhancing and neurotransmitter balancing effects. This study investigates these properties using a p-chlorophenylalanine (PCPA) -induced insomnia model in mice, an approach akin to traditional methods evaluating sleep-promoting activities in fusion peptides.Aim of the studyThe research aims to elucidate the sleep-promoting mechanism of DSIP-CBBBP, exploring its impact on neurotransmitter levels and sleep regulation, and to analyze its composition and structure.Materials and methodsUsing a PCPA-induced insomnia mouse model, the study evaluates the sleep-promoting effects of DSIP-CBBBP. The peptide’s influence on neurotransmitters such as 5-HT, glutamate, dopamine, and melatonin is assessed. The functions of DSIP-CBBBP are characterized using biochemical and animal insomnia-induced behavior tests and compared without CBBBP.ResultsDSIP-CBBBP demonstrates a capacity to modulate neurotransmitter levels, indicated by changes in 5-HT, glutamate, DA, and melatonin. DSIP-CBBBP shows a better restorative effect than DSIP on neurotransmitter imbalance and the potential to enhance sleep.ConclusionThe study underscores DSIP-CBBBP potential in correcting neurotransmitter dysregulation and promoting sleep, hinting at its utility in sleep-related therapies.

The effective integration of ecosystem services (ESs) and urban growth simulation is crucial for spatial planning decisions that support sustainable and high-quality development. Interactions (i.e., trade-offs, synergies, and irrelevance) generally exist among ESs, however, few studies have been done on the methodology for simulating urban growth that optimizes the synergistic effect of ESs. In this study, the Wuhan metropolitan area was taken as the research area. Four key ESs in 2000, 2010, and 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil retention (SR) were evaluated by using the InVEST model. Then the ordered weighted averaging (OWA) method was applied to optimally allocate ESs trade-offs and identify ecological priority protection areas (EPPAs). Finally, the PLUS model was used to simulate urban growth scenarios under different levels of ESs protection constraints and propose spatial planning strategies. The results revealed the following. (1) From 2000 to 2020, the three ESs (i.e., WY, CS, and HQ) exhibited the spatial distribution pattern with high in the east and low in the west, and higher in the woodland than in built-up areas, except SR, which remained at a weak level throughout the region. (2) During the 20 years, SR and the other three ESs, HQ and CS showed a synergistic relationship; WY exhibited a very weak trade-off with HQ; and WY and CS changed from a very weak trade-off to a weak synergy. (3) Seven ecosystem service patterns (ESPs) were generated, and ESP 5 was compared as the ideal option. The weights of SR, WY, CS, and HQ in this pattern were 0.063, 0.187, 0.313, and 0.437, respectively. (4) By 2030, the built-up areas will exhibit edge expansion and interconnection development trends, with the high-constraint ecological protection scenario as the most compact mode, and the spatial structure of “two districts, one axis, four cities as one” is consistent with the development tendency. Our findings can help to enrich the method of urban growth management oriented by ecological protection, and provide a scientific basis for ecological environment restoration and land use spatial planning practices.

Current knowledge graph-based recommendation methods heavily rely on high-quality knowledge graphs, often falling short in effectively addressing issues such as the cold start problem and heterogeneous noise in user interactions. This leads to biases in user interest and popularity. To overcome these challenges, this paper introduces a novel recommendation approach termed Knowledge-enhanced Perceptive Graph Attention with Graph Contrastive Learning (KPA-GCL), which leverages relational graph convolutional neural networks. The proposed method optimizes the triplet embedding representation of entity-item interactions based on relationships between adjacent entities in a heterogeneous graph. Subsequently, a graph convolutional neural network is employed for enhanced aggregation. Similarity scores from a contrastive view serve as the selection criterion for high-quality embedded representations, facilitating the extraction of refined knowledge subgraphs. Multiple adaptive contrast-loss optimization functions are introduced by combining Bayesian Personalized Ranking (BPR) and hard negative sampling techniques. Comparative experiments are conducted with ten popular existing methods using real public datasets. Results indicate that the KPA-GCL method outperforms compared methods in all datasets based on Recall, NDCG, Precision, and Hit-ratio measures. Furthermore, in terms of mitigating cold start and noise, the KPA-GCL method surpasses other ten methods. This validates the reasonability and effectiveness of KPA-GCL in real-world datasets.

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.

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.

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.

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.

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.

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.

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

The field of animated portrait generation, driven by audio cues, has seen remarkable advancements in creating lifelike visuals. Despite these strides, current methodologies struggle with the challenge of balancing stability and naturalism. Audio-driven techniques often encounter difficulties due to the subtlety of audio signals, leading to inconsistencies. In contrast, methods that rely solely on facial landmarks, while more stable, can produce artificial-looking results due to excessive manipulation of key point data. This paper introduces RealMock, an innovative approach that harmonizes audio inputs with facial landmarks during the training phase. RealMock pioneers a dual-driven training regimen, enabling the generation of animated portraits from audio, facial landmarks, or a combination of both. This novel methodology results in a more stable and authentic animation process. Extensive testing against existing algorithms across various public datasets, as well as our proprietary dataset, has highlighted RealMock’s superiority in both quantitative metrics and qualitative assessments. The RealMock framework has broad practical implications, including revolutionizing media production with realistic animated characters and improving online education through engaging avatar-based learning experiences.

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.

The lack of reliable negative samples is an important factor limiting the quality of machine learning-based debris flow susceptibility mapping (DFSM). The purpose of this paper is to propose multiple negative-sample acquisition strategies for DFSM considering different sample representation forms. The sample representation forms mainly include a single grid, multi-grid, and watershed unit, and the negative-sample acquisition strategies are based on support vector machine (SVM), spy technique, and isolation forest (IF) methods, respectively. These three strategies can assign a value to all the samples based on different assumptions, and reliable, negative samples can be generated from samples with values below a predefined threshold. Combining different sample representation forms with negative sample acquisition strategies, nine datasets were then involved in random forest (RF) modeling. The receiver operating characteristic (ROC) curves and related statistical results were used to evaluate the models. The results show that the strategy based on the spy technique is suitable for multiple datasets, while the IF-based strategy is well-adapted to the watershed unit datasets. This study can provide more options for improving the quality of datasets in DFSM, which can further improve the performance of machine learning models.

The study of the surface energy balance characteristics of different ecosystems in the Qinghai Plateau is of great significance for a deeper understanding of land surface processes, the water cycle, and global climate change. This study aims to compare the seasonal variations in energy balance and partitioning of four typical ecosystems on the Qinghai Plateau—swamp meadows, subalpine mountain meadows, alpine shrublands, and alpine deserts. Mantel analysis and path analysis were used to explore the regulatory mechanisms of meteorological elements on energy fluxes and the Bowen ratio (β). The results showed the following: (1) Net radiation (Rn), sensible heat flux (H), and latent heat flux (LE) all exhibited a single-peak pattern of change, and the energy partitioning was closely related to the hydrothermal conditions. Swamp meadows and subalpine mountain meadows were dominated by LE throughout the year and the growing season, while H dominated in the non-growing season. Meanwhile, alpine shrublands and alpine deserts were dominated by H throughout the year. (2) β reflected the characteristics of turbulent fluxes variations and the moisture level of the underlying surface. Swamp meadows and subalpine mountain meadows were relatively moist, with the value of β all being less than 1. Alpine shrublands and deserts were comparatively arid, with the values of β all exceeding 1. The energy closure rate ranged from 48% to 90%, with better energy closure conditions observed during the growing season compared to the non-growing season. (3) Meteorological factors collectively regulated the variations in energy fluxes and its partitioning, with H and LE being primarily influenced by Rn, relative humidity (RH), and soil moisture (Ms). β was significantly affected by RH, Ms, and the saturated vapor pressure deficit (VPD). The sensitivity of the ecosystems to changes in fluxes increased with decreasing moisture, especially in alpine deserts, with Ms, VPD and RH being the most affected. Swamp meadows were significantly associated with air temperature (Ta), soil temperature (Ts), and wind speed; subalpine mountain meadows with Ta and Ts; and alpine shrublands with Ta. These results provided a basis for further analyses of the energy balance characteristics and partitioning differences of different ecosystems on the Qinghai Plateau.

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates.

Biomass can serve as an important indicator for measuring the effectiveness of slope ecological restoration, and unmanned aerial vehicle (UAV) remote sensing provides technical support for the rapid and accurate measurement of vegetation biomass on slopes. Considering a highway slope as the experimental area, in this study, we integrate UAV data and Sentinel-2A images; apply a deep learning method to integrate remote sensing data; extract slope vegetation features from vegetation probability, vegetation indices, and vegetation texture features; and construct a slope vegetation biomass inversion model. The R2 of the slope vegetation biomass inversion model is 0.795, and the p-value in the F-test is less than 0.01, which indicates that the model has excellent regression performance and statistical significance. Based on laboratory biomass measurements, the regression model error is small and reasonable, with RMSE = 0.073, MAE = 0.064, and SE = 0.03. The slope vegetation biomass can be accurately estimated using remote-sensing images with a high precision and good applicability. This study will provide a methodological reference and demonstrate its application in estimating vegetation biomass and carbon stock on highway slopes, thus providing data and methodological support for the simulation of the carbon balance process in slope restoration ecosystems.

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.

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.

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

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.

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.

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.

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.

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.