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Traditional medicine encompasses a rich trove of knowledge and practices for disease prevention, diagnosis, and treatment. However, it faces challenges such as poorly defined compositions of preparations and limited high-quality efficacy data. The development of artificial intelligence presents new opportunities for traditional medicine research and applications, especially in predicting MDAs (MDAs), which is of great significance for understanding disease mechanisms and developing new treatments. This study proposes a MDAs prediction method based on double variational autoencoders (DVAMDA). This method innovatively integrates double variational autoencoders and multi-information fusion techniques. Firstly, the graph SAGE encoder is utilized to preliminarily extract the local and global structural information of nodes. Subsequently, the double variational autoencoders are employed to separately extract the latent probability distribution information of the initial input data and the graph-specific property information from the output of the graph SAGE encoder. Then, these different sources of information are fused to provide rich and powerful feature support for subsequent prediction tasks. Finally, the Hadamard product operation and a deep neural network are used to predict MDAs. Experimental results on the HMDAD and Disbiome datasets show that the DVAMDA model performs outstandingly in multiple evaluation metrics. The findings of this research contribute to a deeper understanding of microbe-disease relationships and provide strong support for drug development in traditional medicine based on MDAs. The relevant data and code are publicly accessible at: https://github.com/yxsun25/DVAMDA .

The Sanjiangyuan Region is among China’s most critical ecological function zones and serves as an important habitat for rare wildlife species such as brown bears and snow leopards. Driven by factors including climate change and intensified human activities, human–wildlife conflicts have become increasingly frequent on the Qinghai–Tibet Plateau, threatening the living space of both herders and wildlife. This study centers on the Yushu Tibetan Autonomous Prefecture in Qinghai Province, integrating multi-source remote sensing data with field survey data, and employs the Maximum Entropy Model (MaxEnt) MaxEnt model and the BIOMOD2 framework to simulate high-risk areas for brown bear incidents. Results indicate that the BIOMOD2 ensemble model (EMca) achieved the highest predictive accuracy, with the Random Forest (RF) model demonstrating strong robustness among individual models. Digital Elevation Model (DEM), Soil Surface Moisture (SSM), Fractional Vegetation Cover (FVC), and Human Footprint (HFP) were identified as the primary factors influencing the spatial distribution of brown bear incidents. High-risk areas exhibited significant clustering, mainly concentrated in the southern and southeastern regions of Qumalai, Nangchen, and Chindu; the eastern part of Zadoi County; and the central and southern parts of Yushu City, particularly within the elevation range of 4304–4544 m, where human activity intensity is relatively low. The core high-risk zone is located along the Tongtian River in southern Qumalai County, demonstrating strong spatial connectivity. By investigating the spatial distribution patterns and driving mechanisms of brown bear incidents in Yushu Prefecture, this study offers some references for government agencies to formulate strategies that promote harmonious coexistence between humans and nature.

Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction. The first difficulty lies in the phenomenon of sample mismatch: self-supervised learning processes drug and target samples independently, while actual prediction requires the integration of drug-target pairs. Another challenge is the mismatch between the broadness of self-supervised learning objectives and the precision of biological mechanisms of drug-target affinity ( i.e ., the induced-fit principle). The former focuses on global feature extraction, while the latter emphasizes the importance of local precise matching. To address these issues, an adaptive self-supervised learning-based drug-target affinity prediction (ASSLDTA) was designed. ASSLDTA integrates a novel adaptive self-supervised learning (ASSL) module with a high-level feature learning network to extract the feature. The ASSL leverages a large amount of unlabeled training data to effectively capture low-level features of drugs and targets. Its goal is to maximize the retention of original feature information, thereby bridging the objective gap between self-supervised learning and drug-target affinity prediction and alleviating the sample mismatch problem. The high-level feature learning network, on the other hand, focuses on extracting effective high-level features for affinity prediction through a small amount of labeled data. Through this two-stage feature extraction design, each stage undertakes specific tasks, fully leveraging the advantages of each model while efficiently integrating information from different data sources, providing a more accurate and comprehensive solution for drug-target affinity prediction. In our experiments, ASSLDTA is much better than other deep methods, and the result of ASSLDTA is significantly increased by learning adaptive self-supervised learning-based features, which validates the effectiveness of our ASSLDTA.

Air pollution is a serious environmental health concern for humans and other living organisms. This study analyzes the spatial and temporal characteristics of air pollutant concentrations, changes in the degree of pollution, and the wavelet coherence of the air quality index (AQI) with pollutants in various monitoring stations. The analysis is based on long-term time series data (January 2016 to December 2023) of air pollutants (PM 2.5 , PM 10, and O 3 ) from Korla, an oasis city in the northeastern part of the Tarim Basin, China. The concentrations of PM 2.5 , PM 10, and O 3 in Korla showed a cyclical trend from 2016 to 2023; PM 10 concentrations exhibited all-season exceedance and PM 2.5 exhibited exceedance only in spring. PM 2.5 and PM 10 showed a seasonal distribution of spring > winter > fall > summer; O 3 concentrations showed a seasonal distribution of summer > spring > fall > winter. Strong positive wavelet coherence between PM and Air Quality Index (AQI) data series suggests that the AQI data series can effectively characterize fluctuating trends in PM concentrations. Moreover, PM 10 levels IV and VI were maintained at approximately 10%, indicating that sand and dust have a substantial influence on air quality and pose potential threats to the health of urban inhabitants. Based on the results of this study, future efforts must strengthen relative countermeasures for sand prevention and control, select urban greening species with anti-pollution capabilities, rationally expand urban green spaces, and restrict regulations for reducing particulate matter emissions within city areas.

The Liuwan burial complex is the largest known prehistoric clan-based cemetery in the upper Yellow River region, making its preservation vital for Chinese cultural heritage and sustainable local development. To address threats from unregulated agricultural activities and illegal looting, this study proposes a non-invasive preventive protection approach. Surface-visible tombs were identified using low-altitude UAV imagery and deep learning models (YOLOv8n, YOLOv5n, RT-DETR-l, and Hyper-YOLO). By incorporating environmental factors such as elevation, slope, aspect, distance to water, Topographic Wetness Index, and Topographic Position Index, potential tomb distributions were modeled on the Biomod2 platform and key environmental drivers were analyzed. Hyper-YOLO achieved the highest identification accuracy (94.4%). The optimal model, EMwmean (TSS = 0.492, AUC = 0.798), showed that high-potential tomb areas are mainly concentrated in the central region, with tombs preferring elevations of 1964–1978 m, south-facing slopes, and slopes of 13.14–19.19°. This study demonstrates the feasibility of using deep learning to identify surface-visible tombs and predict their potential distributions based on environmental characteristics, thereby providing priority references for heritage protection in Liuwan rather than a definitive inventory of all subsurface remains or cultural phases.

The deubiquitinating enzyme USP1 (ubiquitin-specific protease 1) plays a role in the progression of various tumors, emerging as a potential therapeutic target. This study aimed to determine the role of USP1 as a therapeutic target in hepatocellular carcinoma (HCC). We detected USP1 expression in the tumor and adjacent tissues of patients with HCC using immunohistochemical staining. We evaluated the effect of the USP1 inhibitor ML-323 on HCC cell proliferation and cell cycle using a CCK-8 cell-counting kit and plate cloning assays, and propidium iodide, respectively. Apoptosis was detected by annexin V-FITC/Propidium Iodide (PI) staining and caspase 3 (casp3) activity. Transmission electron microscopy and LC3B immunofluorescence were used to detect autophagy. Western blotting was used to detect the accumulation of ubiquitinated proteins, the expression of endoplasmic reticulum (ER) stress-related proteins, and the AMPK-ULK1/ATG13 signaling pathway. We demonstrated that ML-323 inhibits the growth of HCC cells and induces G1 phase cell cycle arrest by regulating cyclin expression. ML-323 treatment resulted in the accumulation of ubiquitinated proteins, induced ER stress, and triggered Noxa-dependent apoptosis, which was regulated by the Activating Transcription Factor 4(ATF4). Moreover, active ER stress induces protective autophagy by increasing AMPK phosphorylation; therefore, we inhibited ER stress using 4-Phenylbutyric acid (4-PBA), which resulted in ER stress reduction, apoptosis, and autophagy in ML-323-treated HCC cells. In addition, blocking autophagy using the AMPK inhibitor compound C (CC), chloroquine (CQ), or bafilomycin A1 (BafA1) enhanced the cytotoxic effect of ML-323. Our findings revealed that targeting USP1 may be a potential strategy for the treatment of HCC.

We performed a theoretical study of the dephasing dynamics of a quantum two-state system under the influences of a non-equilibrium fluctuating environment. The effect of the environmental non-equilibrium fluctuations on the quantum system is described by a generalized random telegraph noise (RTN) process, of which the statistical properties are both non-stationary and non-Markovian. Due to the time-homogeneous property in the master equations for the multi-time probability distribution, the decoherence factor induced by the generalized RTN with a modulatable-type memory kernel can be exactly derived by means of a closed fourth-order differential equation with respect to time. In some special limit cases, the decoherence factor recovers to the expression of the previous ones. We analyzed in detail the environmental effect of memory modulation in the dynamical dephasing in four types of dynamics regimes. The results showed that the dynamical dephasing of the quantum system and the conversion between the Markovian and non-Markovian characters in the dephasing dynamics under the influence of the generalized RTN can be effectively modulated via the environmental memory kernel.

Low glucose is a common microenvironment for rapidly growing solid tumors, which has developed multiple approaches to survive under glucose deprivation. However, the specific regulatory mechanism remains largely elusive. In this study, we demonstrate that glucose deprivation, while not amino acid or serum starvation, transactivates the expression of DCAF1. This enhances the K48-linked polyubiquitination and proteasome-dependent degradation of Rheb, inhibits mTORC1 activity, induces autophagy, and facilitates cancer cell survival under glucose deprivation conditions. This study identified DCAF1 as a new cellular glucose sensor and uncovered new insights into mechanism of DCAF1-mediated inactivation of Rheb-mTORC1 pathway for promoting cancer cell survival in response to glucose deprivation.

Hepatitis E virus (HEV) is increasingly recognized as a cause of neurological disease beyond its hepatic manifestations. Neurological complications are the most frequently reported extrahepatic presentations and include both peripheral nervous system disorders, such as Guillain-Barré syndrome (GBS) and neuralgic amyotrophy (NA), and central nervous system (CNS) involvement, including encephalitis and myelitis, often in the absence of overt hepatitis. This review summarizes the clinical spectrum of HEV-associated neurological disease and integrates evidence from human studies and experimental models. Current evidence supports multifactorial pathogenesis, with direct viral neuroinvasion of the CNS and immune-mediated mechanisms predominating in peripheral neuropathies. Experimental and systems demonstrate that HEV can cross the blood-brain barrier (BBB) and replicate within neural tissues, providing biological plausibility for CNS involvement. By synthesizing clinical and experimental findings, this review highlights the dual pathogenic pathways underlying HEV-associated neurological injury and outlines key unresolved questions relevant to diagnosis, pathogenesis, and clinical management.

Identifying the spatiotemporal coupling characteristics of cropland-grain production-population is essential for the rational utilization of cropland and the evaluation of national and regional food security. Based on the grain production statistical data, GlobeLand30, and WorldPop data in the years 2000, 2010, and 2020, the spatiotemporal changes in China’s cropland area, grain production, and population and their coupling characteristics over the past two decades were detected at the grid level using the models of barycenter fitting and coupled dynamic analysis. The results showed that spatial change of cropland area in China was roughly characterized by the increase in the northwest and the decrease in the southeast; while grain production was characterized by an increase in the north and a decrease in the south, and population was roughly characterized by an increase in urban areas of the southeast coastal regions and a decrease in traditional agricultural areas. The barycenter of cropland area and that of grain production moved toward the northwest and the northeast, respectively, which mismatch the spatial pattern of hydro-thermal conditions of cropland resources in China and thus result in the increased risk of the national grain production system. Meanwhile, the barycenter of grain production and that of population continued to move in opposite directions overall, and the distances between their barycenters increased from 119.65 km in 2000 to 455.16 km in 2020, indicating that the phenomenon of ‘north-to-south grain diversion’ is intensifying. Our results highlight that the spatial mismatch of cropland-grain production-population in China has increased over the past two decades.