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Abstract Background Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this. Methods Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation. Results We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively. Conclusion Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization. Graphical Abstract

Abstract As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency.

Abstract The recent development of nanobiomaterials has shed some light on the field of periodontal tissue regeneration. Laponite (LAP), an artificially synthesized two-dimensional (2D) disk-shaped nanosilicate, has garnered substantial attention in regenerative biomedical applications owing to its distinctive structure, exceptional biocompatibility and bioactivity. This study endeavors to comprehensively evaluate the influence of LAP on periodontal regeneration. The effects of LAP on periodontal ligament cells (PDLCs) on osteogenesis, cementogenesis and angiogenesis were systematically assessed, and the potential mechanism was explored through RNA sequencing. The results indicated that LAP improved osteogenic and cementogenic differentiation of PDLCs, the regulatory effects of LAP on PDLCs were closely correlated with activation of PI3K-AKT signaling pathway. Moreover, LAP enhanced angiogenesis indirectly via manipulating paracrine of PDLCs. Then, LAP was implanted into rat periodontal defect to confirm its regenerative potential. Both micro-CT and histological analysis indicated that LAP could facilitate periodontal tissue regeneration in vivo. These findings provide insights into the bioactivity and underlying mechanism of LAP on PDLCs, highlighting it might be a potential therapeutic option in periodontal therapy.

Abstract Urinary incontinence is a common complication in stroke survivors for whom new interventions are needed. This study investigated the therapeutic effect of low-frequency (LF) repeated transcranial magnetic stimulation (rTMS) on the contralesional primary motor cortex (M1) in patients with poststroke urinary incontinence (PSI). A total of 100 patients were randomly assigned to the rTMS group or sham-rTMS group on basis of the intervention they received. Both groups underwent five treatment sessions per week for 4 weeks. Data from the urodynamic examination were used as the primary outcome. The secondary outcome measures were questionnaires and pelvic floor surface electromyography. After 4 weeks of intervention, the maximum cystometric capacity (MCC), maximum detrusor pressure (Pdet.max), residual urine output, overactive bladder score (OABSS) (including frequency, urgency, and urgency urinary incontinence), and the ICIQ-UI SF improved significantly in the rTMS group compared with those in the sham-rTMS group (P 0.05). Our data confirmed that 4 weeks of LF-rTMS stimulation on the contralateral M1 positively affects poststroke urinary incontinence in several aspects, such as frequency, urgency urinary incontinence, MCC, end-filling Pdet, OABSS, and ICIQ-UI SF scores.

Abstract Object detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images often have complex backgrounds and small objects, which results in a highly unbalanced distribution of foreground and complex background information. In order to solve the above problems, this paper proposes a multi-head channel and spatial trans-attention (MCSTA) module, which performs remote pixel interaction from the channel and spatial dimensions respectively to complete the attention feature capture function. It is a plug-and-play module that can be easily embedded in any other natural image object detection convolutional neural network, making it quickly applicable to remote sensing images. First, in order to reduce computational complexity and improve feature richness, we use a special linear convolution to obtain three projection features instead of the simple matrix multiplication transformation in Transformer. Second, we obtain trans-attention maps in different dimensions in a manner similar to the self-attention mechanism to capture the interrelationships of features in channels and spaces. In this process, we use a multi-head mechanism to perform parallel operations to improve speed. Furthermore, in order to avoid large-scale matrix operations, we specially designed an attention blocking mode to reduce computer memory usage and increase operation speed. Finally, we embedded the trans-attention module into YOLOv8, added a new detection head and optimized the feature fusion method, thus designing a lightweight small object detection model named TA-YOLO for remote sensing images. It has fewer parameters than the benchmark model YOLOv8, and its mAP on the PASCAL VOC and VisDrone data sets increased by 1.3% and 6.2% respectively. The experimental results prove the powerful function of the trans-attention module and the excellent performance of TA-YOLO.

Wireless Sensor Network (WSN) is a distributed sensor network composed a large number of nodes with low cost, low performance and self-management. The special structure of WSN brings both convenience and vulnerability. For example, a malicious participant can launch attacks by capturing a physical device. Therefore, node authentication that can resist malicious attacks is very important to network security. Recently, blockchain technology has shown the potential to enhance the security of the Internet of Things (IoT). In this paper, we propose a Blockchain-empowered Authentication Scheme (BAS) for WSN. In our scheme, all nodes are managed by utilizing the identity information stored on the blockchain. Besides, the simulation experiment about worm detection is executed on BAS, and the security is evaluated from detection and infection rate. The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Abstract Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation, collusion risk, credit evaluation and unreasonable resource allocation, making it difficult to ensure the security of cross-chain asset transactions. So this paper proposes a cross-chain asset trading scheme based on edge cloud storage to improve the reliability of notaries and the security of cross-chain value exchange. Firstly, introduce the entropy weight method to determine indicators and adopt multi indicator evaluation to reduce the risk of collusion between notaries and users; Secondly, design a multi-indicator credit evaluation method to improve the accuracy of the evaluation; Finally, design a new and old notary node share allocation method to improve the rationality of resource allocation.The experiment shows that the scheme designed in this paper can reduce the risk of collusion, more accurately screen out high credit nodes to act as notaries, and make resource allocation more reasonable.

Artificial materials for the potential treatment of bone defects are very trendy in such research fields. To investigate a hydrogel scaffold with better mechanical strength and osteoinductivity is always a challenge since brittle and soft properties are always the problems in traditional hydrogels. In our study, a novel gelatin/chitosan hydrogel crosslinked by a double network filled with lignin and calcium silicate is fabricated. Enhancements of mechanical strength, low swelling ratio, and improvement in osteoinductivity were achieved in this composite double network hydrogels. The results revealed that lignin was a key factor in raising the compressive strength and stability of the hydrogel because more hydrogen bonds were formed between lignin and the crosslinking network. Even after swelling equilibrium, the compressive strength remained almost unchanged. In addition, the hydrogel groups with a higher ratio of lignin also had lower cytotoxicity and improved osteoinductivity of pre-osteoblasts while the increasing concentration of calcium silicate progressively weakened these performances. Therefore, the gelatin/chitosan double network hydrogel reinforced by lignin would be a potential bone tissue-engineered artificial material.

Abstract Aiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods.

Abstract Oncolytic virotherapy holds promise for cancer treatment, but the factors determining its oncolytic activity remain unclear. Neutrophil extracellular traps (NETs) are associated with cancer progression, yet their formation mechanism and role in oncolytic virotherapy remain elusive. In this study, we demonstrate that, in glioma, upregulation of IGF2BP3 enhances the expression of E3 ubiquitin protein ligase MIB1, promoting FTO degradation via the ubiquitin-proteasome pathway. This results in increased m6A-mediated CSF3 release and NET formation. Oncolytic herpes simplex virus (oHSV) stimulates IGF2BP3-induced NET formation in malignant glioma. In glioma models in female mice, a BET inhibitor enhances the oncolytic activity of oHSV by impeding IGF2BP3-induced NETosis, reinforcing virus replication through BRD4 recruitment with the CDK9/RPB-1 complex to HSV gene promoters. Our findings unveil the regulation of m6A-mediated NET formation, highlight oncolytic virus-induced NETosis as a critical checkpoint hindering oncolytic potential, and propose targeting NETosis as a strategy to overcome resistance in oncolytic virotherapy.

Abstract Artificial intelligence and blockchain technology have become indispensable in the era of the digital economy, particularly in the field of financial financing. However, when it comes to supply chain finance (SCF), existing models primarily focus on risk identification and credit evaluation, neglecting the critical aspects of trust transfer continuity and reliability within the chain. To address this issue, this paper proposes a blockchain-based transitive trust model for SCF, which ensures seamless trust transfer from core enterprises to bottom suppliers during financing enterprise credit evaluation. The model utilizes multi-layer metrics to calculate the comprehensive trust value of underlying suppliers, serving as the basis for credit delivery. Additionally, the model stores transitive signature receivable warrants on the blockchain and utilizes splittable delivery of warrants to underlying suppliers. The model’s rationality and correctness are verified through experimental analysis, with results demonstrating that the transitive trust model enhances Small and Medium-Sized Enterprises’ (SMEs) trust at the bottom of the supply chain, thus alleviating financing difficulties for SMEs.

To at the low robustness of the existing model for occluded object detection, an occluded object detection algorithm based on fuzzy sample anchor box IoU Matching degree Deviation Aware(IoU_MDA)is proposed. Firstly, fuzzy samples are defined based on Anchor-based, which reflects the degree of object occlusion. Secondly, IoU_MDA is proposed to quantify the degree of interference experienced by fuzzy samples. Then, IoU_MDA_Loss is constructed based on IoU_MDA, combined with IoU and the balance parameter $\Phi$ . To address class imbalance issues and enhance model generality, intra-class and inter-class fuzzy weights, and fuzzy sample focusing parameters are designed on the basis of the initial IoU_MDA_Loss. An occluded object training scheme is designed based on IoU perception, and non-fuzzy sample weight balancing parameters are constructed. Finally, IoU_MDA_Loss is merged with Focal Loss to obtain IoU_MDA_Focal Loss, simultaneously enhancing the detection performance of fuzzy samples and difficult-to-distinguish samples. The experimental results on the WiderPerson and VOC2007 datasets show that the mAP of IoU_MDA_Loss has increased by 2.04%, 2.36%, and IoU_MDA_Focal Loss has increased by 1.82%, 2.65%, respectively, compared to the baseline model. The detection performance surpasses current mainstream algorithms.

Retinal diseases impair the normal function of the visual system, making accurate segmentation of retinal vessels crucial. This paper proposes an improved U-Net network, namely Mitigating Information Loss U-Net (MILU-Net), for retinal vessel segmentation. The network introduces the Multi-Scale Adaptive Detail Feature Fusion (MSADFF) module, ensuring effective fusion of features at different scales before skip connections to reduce information loss. Simultaneously, the Dual Path Upsampling (DPUS) module is employed to enhance image resolution and compensate for spatial and channel information. Experiments on the DRIVE/STARE datasets demonstrate that MILU-Net outperforms in accuracy, sensitivity, specificity, AUC, and F1-Score metrics. Compared to the original U-Net, MILU-Net shows improvements of 1.44% and 1.84% in AUC, as well as 7.15% and 6.35% in sensitivity. Compared to the advanced Attention U-Net, MILU-Net achieves increases of 1.20% and 0.45% in ACC, as well as 2.63% and 2.71% in F1-Score, respectively. These results indicate the significant advantages of MILU-Net in retinal vessel segmentation tasks.

Abstract Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide a higher level of data protection while maintaining the benefits of cloud computing. In this case, data can be stored in the cloud and computed through SMPC protocols, thus protecting the privacy and security of the data. However, multiple rounds of information interaction are often required, increasing the communication overhead, and the security strength is limited by the hardness assumption. In this paper, we work to achieve an optimal setting of the number of rounds in secure multi-party computation on the cloud to achieve a sublinear communication overhead and improve the security concept. A 2-round SMPC protocol is constructed in the framework of Universally Composable (UC). A 2-round SMPC protocol is constructed that uses multilinear maps based on the Learning from Errors (LWE) assumption. The participant encodes the input and sends it via broadcast to reduce the interaction, homomorphic computational encoding information for secure access to computational data and secure the SMPC protocol through UC security. This paper extends the participants to multiple parties, reduces the communication rounds to 2, the protocol achieves sublinear communication overhead in poly polynomial time, smaller setup size to poly(k), and static security is achieved.

BackgroundThe human gut microbiota has been identified as a potentially important factor influencing the development of COVID-19. It is believed that the disease primarily affects the organism through inflammatory pathways. With the aim of improving early diagnosis and targeted therapy, it is crucial to identify the specific gut microbiota associated with COVID-19 and to gain a deeper understanding of the underlying processes. The present study sought to investigate the potential causal relationship between the gut microbiota and COVID-19, and to determine the extent to which inflammatory proteins act as mediators in this relationship.MethodsBidirectional mendelian randomization (MR) and Two-step mediated MR analyses were applied to examine causative associations among 196 gut microbiota, 91 inflammatory proteins and COVID-19. The main analytical method used in the MR was the random effects inverse variance weighted (IVW) method. This was complemented by the Bayesian weighted Mendelian randomization (BWMR) method, which was utilized to test the hypothesis of MR. In order for the results to be deemed reliable, statistical significance was required for both methods. Validation was then carried out using an external dataset, and further meta-analyses were conducted to authenticate that the association was reliable.ResultsResults of our research indicated that seven gut microbiota were actively associated to the COVID-19 risk. Five inflammatory proteins were associated with COVID-19 risk, of which three were positively and two were negatively identified with COVID-19. Further validation was carried out using sensitivity analyses. Mediated MR results revealed that CCL2 was a possible mediator of causality of family Bifidobacteriaceae and order Bifidobacteriales with COVID-19, mediating at a ratio of 12.73%.ConclusionSuggesting a genetic causation between specific gut microbiota and COVID-19, our present research emphasizes the underlying mediating role of CCL2, an inflammatory factor, and contributes to a deeper understanding of the mechanism of action underlying COVID-19.

Abiotic stresses can increase the total fatty acid (TFA) and astaxanthin accumulation in microalgae. However, it remains unknown whether a unified signal transduction mechanism exists under different stresses. This study explored the link between nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-derived reactive oxygen species (ROS) and the accumulation of fatty acids and astaxanthin in Chromochloris zofingiensis under three abiotic stresses. Results showed significant increases in fatty acid, astaxanthin, and ROS levels under nitrogen deficiency, phosphorus deficiency, and high-salinity stress. The introduction of the NADPH oxidase inhibitor diphenyleneiodonium (DPI) decreased the content of these components. This underscores the pivotal role of NADPH oxidase-derived ROS in the accumulation of fatty acid and astaxanthin under abiotic stress. Analysis of transcriptomes across three conditions following DPI addition revealed 1,445 shared differentially expressed genes (DEGs). Enrichment analysis revealed that biotin, betalain, thiamine, and glucosinolate may be important in stress responses. The heatmap demonstrated that DPI notably suppressed gene expression in the fatty acid and carotenoid biosynthesis pathways. Our findings underscore the pivotal role of NADPH oxidase-derived ROS in the accumulation of fatty acid and astaxanthin under abiotic stresses.

Background Asian people in the United States have different sociodemographic and health‐related characteristics that might affect cardiovascular disease (CVD) risk by ethnicity and birthplace. However, they are often studied as a monolithic group in health care research. This study aimed to examine heterogeneity in CVD risk factors on the basis of birthplace among the 3 largest Asian subgroups (Chinese, Asian Indian, and Filipino) compared with US‐born non‐Hispanic White (NHW) adults. Methods and Results A cross‐sectional analysis was conducted using the 2010 to 2018 National Health Interview Survey data from 125 008 US‐born and foreign‐born Chinese, Asian Indian, Filipino, and US‐born NHW adults. Generalized linear models with Poisson distribution were used to examine the prevalence and prevalence ratios of self‐reported hypertension, diabetes, high cholesterol, physical inactivity, smoking, and overweight/obesity among Asian subgroups compared with US‐born NHW adults. The study included 118 979 US‐born NHW and 6029 Asian adults who self‐identified as Chinese (29%), Asian Indian (33%), and Filipino (38%). Participants' mean (±SD) age was 49±0.1 years, and 53% were females. In an adjusted analysis, foreign‐born Asian Indians had significantly higher prevalence of diabetes, physical inactivity, and overweight/obesity; foreign‐born Chinese had higher prevalence of physical inactivity, and foreign‐born Filipinos had higher prevalence of all 5 CVD risk factors except smoking compared with NHW adults. Conclusions This study revealed significant heterogeneity in the prevalence of CVD risk factors among Asian subgroups by ethnicity and birthplace, stressing the necessity of disaggregating Asian subgroup data. Providers should consider this heterogeneity in CVD risk factors and establish tailored CVD prevention plans for Asian subgroups.

Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC.

Carbon sequestration management of plantation forests has become an important topic in the current context of vigorously promoting carbon peaking and carbon neutrality goals and will be the goal and task of the forest industry for a long time. The objective of this study was to explore the applications of genetic algorithm (GA) in both near-optimal thinning regimes at stand level and near-optimal forest management planning at the regional level under the forest management objectives of carbon sequestration. This research integrates a carbon assessment technique with GA optimization to effectively enhance the management of carbon sequestration within plantation forests. Results indicate that the density effect model was an accurate and reliable carbon assessment method (R2 = 0.8701, RMSE = 7.548). The GA optimization approach is efficient in the near-optimal thinning regime and the appropriate forest management planning schedule under the forest management objectives of carbon sequestration. In the research area, the near-optimal carbon sequestration is 38,045.71 t, and in the 15 years from 2016 to 2030, the carbon sequestration of 20 Chinese fir stands should meet the annual thinning constraint condition of not less than 50 t. A near-optimal decision of the carbon sequestration management of plantation forests based on GA provides a theoretical basis and technical support for the compilation of a forest management plan at the stand and regional scales in the plantation operation management of carbon sequestration.

With the increase in port throughput and the development of the trend of large-scale ships, selecting applicable anchor positions for ships and ensuring the rational and comprehensive utilization of anchorage areas have become a key issue in utilizing the rate of anchorage resources, ensuring the safety of ships anchoring operations and promoting the development of the shipping industry. Existing anchor position selection and detection algorithm studies are limited to a two-dimensional plane for ship anchor position selection, with few studies considering intelligent detection algorithms for safe ship anchoring water depths based on three-dimensional space, considering conditions such as wind and waves. By considering water depth conditions and the objectives of anchorage safety issues, this study proposes an intelligent detection method for ship anchor detection to find the ship’s ideal anchor location in the anchorages by applying the Monte Carlo algorithm. In the processing step, in combination with the Monte Carlo random plane anchor position detection algorithm and Monte Carlo random sampling water depth detection method, the anchor position circle radius model, safe spacing model, anchoring area detection model and safe water depth model are used for examining the anchorage area for awaiting the ship in three-dimensions. To verify the accuracy of the proposed model, based on the scale of common ship types and considering the most conservative parameters, a series of simulation experiments are conducted to check whether the water depth meets the requirements and fully ensure the safety of the experimental results. The research results indicate that the detection almost cover the whole anchorage area and obtain safe water depth restrictions. This method helps to improve the efficiency of ship anchoring and makes actual anchoring operations safer. Under the premise of ensuring sufficient safe spacing between ships, the anchorage ground can accommodate more ships and simulate multi-type ship anchor position detection operations concerning various ship-type parameters to further ensure the safety of ship anchoring.

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Steganography, the use of algorithms to embed secret information in a carrier image, is widely used in the field of information transmission, but steganalysis tools built using traditional steganographic algorithms can easily identify them. Steganography without embedding (SWE) can effectively resist detection by steganography analysis tools by mapping noise onto secret information and generating secret images from secret noise. However, most SWE still have problems with the small capacity of steganographic data and the difficulty of extracting the data. Based on the above problems, this paper proposes image steganography without embedding carrier secret information. The objective of this approach is to enhance the capacity of secret information and the accuracy of secret information extraction for the purpose of improving the performance of security network communication. The proposed technique exploits the carrier characteristics to generate the carrier secret tensor, which improves the accuracy of information extraction while ensuring the accuracy of secret information extraction. Furthermore, the Wasserstein distance is employed as a constraint for the discriminator, and weight clipping is introduced to enhance the secret information capacity and extraction accuracy. Experimental results show that the proposed method can improve the data extraction accuracy by 10.03% at the capacity of 2304 bits, which verifies the effectiveness and universality of the method. The research presented here introduces a new intelligent information steganography secure communication model for secure communication in networks, which can improve the information capacity and extraction accuracy of image steganography without embedding.

BackgroundNumerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown. ObjectiveWe aim to assess the associations between the composite CVD risk and each component of mHealth use among US adults regardless of whether they have a history of CVD or not. MethodsThis study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey. The exposure was CVD risk (diabetes, hypertension, smoking, physical inactivity, and overweight or obesity). We defined low, moderate, and high CVD risk as having 0-1, 2-3, and 4-5 CVD risk factors, respectively. The outcome variables of interest were each component of mHealth use, including using mHealth to make health decisions, track health progress, share health information, and discuss health decisions with health providers. We used multivariable logistic regression models to examine the association between CVD risk and mHealth use adjusted for demographic factors. ResultsWe included 10,531 adults, with a mean age of 54 (SD 16.2) years. Among the included participants, 50.2% were men, 65.4% were non-Hispanic White, 41.9% used mHealth to make health decisions, 50.8% used mHealth to track health progress toward a health-related goal, 18.3% used mHealth to share health information with health providers, and 37.7% used mHealth to discuss health decisions with health providers (all are weighted percentages). Adults with moderate CVD risk were more likely to use mHealth to share health information with health providers (adjusted odds ratio 1.49, 95% CI 1.24-1.80) and discuss health decisions with health providers (1.22, 95% CI 1.04-1.44) compared to those with low CVD risk. Similarly, having high CVD risk was associated with higher odds of using mHealth to share health information with health providers (2.61, 95% CI 1.93-3.54) and discuss health decisions with health providers (1.56, 95% CI 1.17-2.10) compared to those with low CVD risk. Upon stratifying by age and gender, we observed age and gender disparities in the relationship between CVD risk and the usage of mHealth to discuss health decisions with health providers. ConclusionsAdults with a greater number of CVD risk factors were more likely to use mHealth to share health information with health providers and discuss health decisions with health providers. These findings suggest a promising avenue for enhancing health care communication and advancing both primary and secondary prevention efforts related to managing CVD risk factors through the effective usage of mHealth technology.

Background: Mounting evidence indicates that complement components play a crucial role in cancer progression. Recent findings indicate that certain complement components display a significant rise in expression within esophageal squamous cell carcinoma (ESCC). However, the specific tumorigenic functions of these components remain unclear. This study focuses on investigating the expression pattern of C1r, elucidating a role for C1r in ESCC, as well as exploring underlying mechanisms controlled by C1r. Methods: The expression of C1r in ESCC tissues, malignant epithelial cells, and its relationship with survival were analyzed using the Gene Expression Omnibus (GEO) database and tissue microarrays. Single-cell RNA sequencing (scRNA-seq) was used to study the expression of C1r in malignant epithelial cells. C1r knockdown or C1r overexpression in cultured ESCC cells were used to assess the effects of C1r on proliferation, migration, invasion, cell-matrix adhesion, apoptosis, and growth of xenografted tumors in immunocompromised (nude) mice. Western blotting was used to detect the expression of MMP-1 and MMP-10 in C1r knockdown or C1r overexpressing ESCC cells. Results: C1r was highly expressed in ESCC tissues, malignant epithelial cells, and cultured ESCC cell lines. High C1r expression indicated a poor prognosis. Knockdown of C1r significantly suppressed the proliferation, migration, invasion, cell-matrix adhesion, and promoted apoptosis in cultured ESCC cells. Additionally, knockdown of C1r markedly inhibited tumor growth in nude mice. Overexpression of C1r had the opposite effects. C1r induced the expression of MMP-1 and MMP-10. Conclusions: C1r is highly expressed in ESCC and promotes the progression of this tumor type. Our findings suggest that C1r may serve as a novel prognostic biomarker and therapeutic target in ESCC.

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data.

MicroRNAs (miRNAs) in oral squamous cell carcinoma (OSCC)-derived small extracellular vesicles (sEVs) play a pivotal role in modulating intercellular communications between tumor cells and other cells in the microenvironment, thereby influencing tumor progression and the efficacy of therapeutic interventions. However, a comprehensive inventory of these secretory miRNAs in sEVs and their biological and clinical implications remains elusive. This study aims to profile the miRNA content of OSCC cell line sEVs and computationally elucidate their biological and clinical relevance. We conducted miRNA sequencing to compare the miRNA profiles of OSCC cells and their corresponding sEVs. Our motif enrichment analysis identified specific sorting motifs that are implicated in either cellular retention or preferential sEV secretion. Target cell analysis suggested that the sEV miRNAs potentially interact with various immune cell types, including natural killer cells and dendritic cells. Additionally, we explored the clinical relevance of these miRNAs by correlating their expression levels with TNM stages and patient survival outcomes. Intriguingly, our findings revealed that a distinct sEV miRNA signature is associated with lymph node metastasis and poorer survival in patients in TCGA-HNSC dataset. Collectively, this research furthers our understanding of the miRNA sorting mechanisms in OSCC and underscores their clinical implications.

The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users' privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data, while guaranteeing improved data usability. Specifically, we construct a new information loss function based on the information quantity theory. Considering that different quasi-identification attributes have different impacts on sensitive attributes, we set weights for each quasi-identification attribute when designing the information loss function. In addition, to reduce information loss, we improve K-anonymity in two ways. First, we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms, i.e., greedy algorithm and 2-means clustering algorithm. In addition, we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass. Meanwhile, we design the K-anonymity algorithm of this scheme based on the constructed information loss function, the improved 2-means clustering algorithm, and the greedy algorithm, which reduces the information loss. Finally, we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss. [ABSTRACT FROM AUTHOR]