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China’s reported cases of Human Immunodeficiency Virus (HIV) and AIDS increased from over 50000 in 2011 to more than 130000 in 2017, while AIDS related search indices on Baidu from 2.1 million to 3.7 million in the same time periods. In China, people seek AIDS related knowledge from Baidu which one of the world’s largest search engine. We study the relationship of national HIV surveillance data with the Baidu index (BDI) and use it to monitor AIDS epidemic and inform targeted intervention. After screening keywords and making index composition, we used seasonal autoregressive integrated moving average (ARIMA) modeling. The most correlated search engine query data was obtained by using ARIMA with external variables (ARIMAX) model for epidemic prediction. A significant correlation between monthly HIV/AIDS report cases and Baidu Composite Index ( r  = 0.845, P  < 0.001) was observed using time series plot. Compared with the ARIMA model based on AIDS surveillance data, the ARIMAX model with Baidu Composite Index had the minimal an Akaike information criterion (AIC, 839.42) and the most exact prediction (MAPE of 6.11%). We showed that there are close correlations of the same trends between BDI and HIV/AIDS reports cases for both increasing and decreasing AIDS epidemic. Therefore, the Baidu search query data may be a good useful indicator for reliably monitoring and predicting HIV/AIDS epidemic in China.

Currently, homosexual transmission has become one of the main routes of HIV-1 spread in China. Furthermore, about 80% Chinese men, who have sex with men (MSM), feel forced to enter eventually into heterosexual marriages due to the Chinese traditional marriage culture, which may cause HIV-1 infection in families. In this study, we identified HIV-1 transmission in a family and the direction of HIV-1 transmission from a MSM to his wife and infant, which indicated Chinese MSM may have become a potential bridge of HIV-1 transmission to their wives and children. Therefore, we need to develop more effective defence measures to prevent the spread of HIV-1 in MSM families in China.

HIV-1 evolved into various genetic subtypes and circulating recombinant forms (CRFs) in the global epidemic. The same subtype or CRF is usually considered to have similar phenotype. Being one of the world’s major CRFs, CRF01_AE infection was reported to associate with higher prevalence of CXCR4 (X4) viruses and faster CD4 decline. However, the underlying mechanisms remain unclear. We identified eight phylogenetic clusters of CRF01_AE in China and hypothesized that they may have different phenotypes. In the National HIV Molecular Epidemiology Survey, we discovered that people infected by CRF01_AE cluster 4 had significantly lower CD4 counts (391 vs. 470, P < 0.0001) and higher prevalence of X4-using viruses (17.1% vs. 4.4%, P < 0.0001) compared with those infected by cluster 5. In an MSM cohort, X4-using viruses were only isolated from seroconvertors in cluster 4, which was associated with low a CD4 count within the first year of infection (141 vs. 440, P = 0.003). Using a coreceptor binding model, we identified unique V3 signatures in cluster 4 that favor CXCR4 use. We demonstrate that the HIV-1 phenotype and pathogenicity can be determined at the phylogenetic cluster level in the same subtype. Since its initial spread to humans from chimpanzees, estimated to be the first half of the 20th century, HIV-1 continues to undergo rapid evolution in larger and more diverse populations. The divergent phenotype evolution of two major CRF01_AE clusters highlights the importance of monitoring the genetic evolution and phenotypic shift of HIV-1 to provide early warning of the appearance of more pathogenic strains.

HIV-1 evolved into various genetic subtypes and circulating recombinant forms (CRFs) in the global epidemic, with the same subtype or CRF usually having similar phenotype. Being one of the major CRFs, CRF01_AE infection was reported to associate with higher prevalence of CXCR4 (X4) viruses and faster CD4 decline. However, the underlying mechanisms remain unclear. We identified eight phylogenetic clusters of CRF01_AE in China and hypothesized that they may have different phenotypes. In the national HIV molecular epidemiology survey, we discovered that people infected by CRF01_AE cluster 4 had significantly lower CD4 count (391 vs. 470, p < 0.0001) and higher prevalence of predicted X4-using viruses (17.1% vs. 4.4%, p < 0.0001) compared to those infected by cluster 5. In a MSM cohort, X4-using viruses were only isolated from sero-convertors infected by cluster 4, which associated with rapid CD4 loss within the first year of infection (141 vs. 440, p = 0.01). Using co-receptor binding model, we identified unique V3 signatures in cluster 4 that favor CXCR4 usage. We demonstrate for the first time that HIV-1 phenotype and pathogenicity can be determined at the phylogenetic cluster level in a single subtype. Since its initial spread to human from chimpanzee in 1930s, HIV-1 remains undergoing rapid evolution in larger and more diverse population. The divergent phenotype evolution of two major CRF01_AE clusters highlights the importance in monitoring the genetic evolution and phenotypic shift of HIV-1 to provide early warning for the appearance of more pathogenic strains such as CRF01_AE cluster 4.

SHARPIN is a tumor‐associated gene involved in the growth and proliferation of many tumor types. A function of SHARPIN in cholangiocarcinoma (CCA) is so far unclear. Here, we studied the role and function of SHARPIN in CCA and revealed its relevant molecular mechanism. The expression of SHARPIN was analyzed in cholangiocarcinoma tissues from patients using immunohistochemistry, quantitative PCR, and western blot analysis. Expression of SHARPIN was suppressed/overexpressed by siRNA silencing or lentiviral overexpression vector, and the effect on cell proliferation was determined by the CCK‐8 assay and flow cytometry. Accumulation of reactive oxygen species was measured with MitoTracker, and JC‐1 staining showed mitochondrial fission/fusion and mitochondrial membrane potential changes as a result of the silencing or overexpression. The ferroptosis marker solute carrier family 7 member 11 (SLC7A11), glutathione peroxidase 4 (GPX4), and the antioxidant enzymes superoxide dismutase 1 (SOD‐1) and SOD‐2 were analyzed by western blot. The results showed that SHARPIN expression was increased in CCA tissue, and this was involved in cell proliferation. SHARPIN silencing resulted in accumulated reactive oxygen species, reduced mitochondrial fission, and a reduced mitochondrial membrane potential. Silencing of SHARPIN inhibited the ubiquitination and degradation of p53, and downregulated levels of SLC7A11, GPX4, SOD‐1, and SOD‐2, all of which contributed to excessive oxidative stress that leads to ferroptosis. Overexpression of SHARPIN would reverse the above process. The collected data suggest that in CCA, SHARPIN‐mediated cell ferroptosis via the p53/SLC7A11/GPX4 signaling pathway is inhibited. Targeting SHARPIN might be a promising approach for the treatment of CCA. SHARPIN‐mediated cell ferroptosis via the p53/SLC7A11/GPX4 signaling pathway is inhibited. Targeting SHARPIN might be a promising approach for the treatment of CCA.

With the advancement of precision agriculture, variable-rate spraying (VRS) technology has demonstrated significant potential in enhancing pesticide utilization efficiency and promoting environmental sustainability, particularly in orchard applications. As a critical medium for pesticide transport, the dynamic structural characteristics of orchard canopies exert a profound influence on spraying effectiveness. This review systematically summarizes recent progress in the dynamic perception and modeling of orchard canopies, with a particular focus on key sensing technologies such as LiDAR, Vision Sensor, multispectral/hyperspectral sensors, and point cloud processing techniques. Furthermore, it discusses the construction methodologies of static, quasi-dynamic, and fully dynamic canopy modeling frameworks. The integration of canopy sensing technologies into VRS systems is also analyzed, including their roles in spray path planning, nozzle control strategies, and precise droplet transport regulation. Finally, the review identifies key challenges—particularly the trade-offs between real-time performance, seasonal adaptability, and modeling accuracy—and outlines future research directions centered on multimodal perception, hybrid modeling approaches combining physics-based and data-driven methods, and intelligent control strategies.

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems.

Based on the current issues of difficulty in clearing intra-row weeds in orchards, inaccurate sensor detection, and the inability to adjust the row spacing depth, this study designs an intelligent intra-row obstacle avoidance and weeding machine for orchards. We designed the weeding machine’s sensor device, depth-limiting device, row spacing adjustment mechanism, joystick-based obstacle avoidance mechanism, weeding shovel, and hydraulic system. The sensor device integrates non-contact sensors and a mechanical tactile structure, which overcomes the instability of non-contact detection and avoids the risk of collision obstacle avoidance by the weeding parts. The weeding shovel can be adapted to the environments of orchards with small plant spacing. The combination of the sensor device and the obstacle avoidance mechanism realizes flexible obstacle avoidance. We used Ansys Workbench to conduct static and vibration modal analyses on the chassis of the in-field weeding machine. On this basis, through topology optimization, the chassis quality of the weeding machine is reduced by 8%, which realizes the goal of light weight and ensures the stable operation of the machinery. To further optimize the weeding operation parameters, we employed the Box–Behnken design response surface analysis, with weeding coverage as the optimization target. We systematically explored the effects of forward speed, hydraulic cylinder extension speed, and retraction speed on the weeding efficiency. The optimal operational parameter combination determined by this study for the weeding machine is as follows: forward speed of 0.5 m/s, hydraulic cylinder extension speed of 11.5 cm/s, and hydraulic cylinder retraction speed of 8 cm/s. Based on the theoretical analysis and scenario simulations, we validated the performance of the weeding machine through field experiments. The results show that the weeding machine, while exhibiting excellent obstacle avoidance performance, can achieve a maximum weeding coverage of 84.6%. This study provides a theoretical foundation and technical support for the design and development of in-field mechanical weeding, which is of great significance for achieving intelligent orchard management and further improving fruit yield and quality.

Electrostatic spraying technology can improve the efficiency of pesticide deposition on the surface of leaves and reduce the environmental pollution caused by pesticide drift, which has an important prospect in agricultural pesticide application. To improve the deposition and penetration of droplets in the crop canopy, we designed and optimized an air-assisted electrostatic nozzle and conducted the spraying performance experiment. Parameters, such as charge-to-mass ratio (CMR) and particle size, were tested and analyzed to obtain the suitable operating parameters of nozzle. The results proved that the improved air-assisted electrostatic nozzle has good atomization and chargeability. There is a good charging effect with a charging voltage of 3,000–5,000 V, the CMR increased 127.8% from 0.86 to 1.97 mC/kg as the charge voltage increases from 1,000 to 4,000 V, at an air pressure of 1.0 bar and liquid flow rate of 200 ml/min. Furthermore, we designed a multi-factor orthogonal experiment, which was conducted using a four-factor, three-level design to investigate the effects of operational parameters and canopy characteristics on droplet deposition and penetration. Analysis of variance (ANOVA) and F-test were performed on the experiment results. The results showed that the factor effect on droplet penetration, in descending order, was as follows: spray distance, leaf area index, air pressure, and air pressure × spray distance. The factor effect on abaxial leaf deposition, in descending order, was as follows: air pressure, spray distance, air pressure × charge voltage, spray distance × charge voltage, and charge voltage. For optimal droplet penetration and abaxial leaf deposition, option A 3 B 1 D 2 (air pressure 1.5 bar, spray distance 0.2 m, charge voltage 2,500 V) is recommend. The spray nozzle atomization performance and deposition regulation were studied by experimental methods to determine the optimal values of operating parameters to provide a reference for electrostatic spray system development.

Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery.