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Script identification is the first step in most multilingual text-processing systems. To improve the time efficiency of script identification algorithms, whether there is content written in a certain script in the text is first determined; if so, the content written in that script is then obtained. Then, it is determined whether the total length of the texts corresponding to the identified scripts is equal to the original text length; if so, the script identification process ends. Finally, considering the frequencies of various scripts on the Internet, those that are more common are prioritized during script identification. Based on these three approaches, an improved script identification algorithm was designed. A comparison experiment was conducted using sentence-level text corpora in 263 languages written in 26 scripts. The testing times of the newly proposed method were reduced by 9.35-fold, while the F1 score for script identification was slightly higher than those reported in our earlier studies. The method proposed in this study effectively improves the time efficiency of script identification algorithms.

Oropharyngeal mucosal epithelia of fetuses/neonates/infants and the genital epithelia of adults play a critical role in HIV-1 mother-to-child transmission and sexual transmission of virus, respectively. To study the mechanisms of HIV-1 transmission through mucosal epithelium, we established polarized tonsil, cervical and foreskin epithelial cells. Analysis of HIV-1 transmission through epithelial cells showed that approximately 0.05% of initially inoculated virions transmigrated via epithelium. More than 90% of internalized virions were sequestered in the endosomes of epithelial cells, including multivesicular bodies (MVBs) and vacuoles. Intraepithelial HIV-1 remained infectious for 9 days without viral release. Release of sequestered intraepithelial HIV-1 was induced by the calcium ionophore ionomycin and by cytochalasin D, which increase intracellular calcium and disrupt the cortical actin of epithelial cells, respectively. Cocultivation of epithelial cells containing HIV-1 with activated peripheral blood mononuclear cells and CD4+ T lymphocytes led to the disruption of epithelial cortical actin and spread of virus from epithelial cells to lymphocytes. Treatment of epithelial cells with proinflammatory cytokines tumor necrosis factor-alpha and interferon gamma also induced reorganization of cortical actin and release of virus. Inhibition of MVB formation by small interfering RNA (siRNA)-mediated silencing of its critical protein hepatocyte growth factor-regulated tyrosine kinase substrate (Hrs) expression reduced viral sequestration in epithelial cells and its transmission from epithelial cells to lymphocytes by ~60-70%. Furthermore, inhibition of vacuole formation of epithelial cells by siRNA-inactivated rabankyrin-5 expression also significantly reduced HIV-1 sequestration in epithelial cells and spread of virus from epithelial cells to lymphocytes. Interaction of the intercellular adhesion molecule-1 of epithelial cells with the function-associated antigen-1 of lymphocytes was important for inducing the release of sequestered HIV-1 from epithelial cells and facilitating cell-to-cell spread of virus from epithelial cells to lymphocytes. This mechanism may serve as a pathway of HIV-1 mucosal transmission.

Large language models (LLMs) demonstrate exceptional linguistic capabilities in text generation but remain prone to factual errors, particularly in specialized domains. Traditional factuality evaluation methods primarily rely on human annotation, which is costly, time-consuming, and difficult to generalize across different domains. To address these limitations, this study proposes an innovative multi-agent framework-T2F (Text-to-Factuality)-designed to automatically convert unstructured text into high-quality factuality evaluation datasets. T2F operates through the coordinated efforts of four specialized agents: Analysis, Information Extraction, Generation, and Validation. By systematically processing input data, T2F autonomously generates multiple types of assessment items-including single-choice questions, fill-in-the-blank questions, and true/false statements-without requiring human annotation, while maintaining strong cross-domain applicability. Experimental results demonstrate that T2F achieves data conversion success rates of 99% in the World Heritage domain, 98% in the Medical domain, and 85% in the Film domain. The generated data effectively assess LLMs’ factuality accuracy, highlighting T2F’s capability as a scalable and domain-agnostic factuality evaluation framework.

The random obstacles in the shallow sea environment, the irregular underwater pavement and the high quality requirements of multimedia transmission make the multimedia applications of shallow sea face the problems of high bit error rate, low transmission rate and low video quality. In order to solve these problems, this paper proposes a multimedia transmission mechanism and its architecture for wireless communication in shallow water based on time-frequency mode autonomous separation. Firstly, based on the complex and changeable seabed structure, the underwater biota movement track and the dynamic topology of end to end communication, a shallow sea wireless multimedia transmission system is constructed. Secondly, based on the performance of multimedia streaming in the time domain and frequency domain, a real-time multimedia transmission control mechanism for the time frequency separation of autonomous controlled multimedia signals from FS and TS is proposed. Finally, the simulation experiment and the field test results of shallow sea show that the proposed algorithm has superior performance in transmission rate, transmission efficiency, delivery rate and real-time performance.

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

Background Diabetic foot complications, including infections and osteomyelitis, pose significant health risks, with high prevalence and amputation rates. Differentiating diabetic foot infection (DFI) from osteomyelitis (OM) is challenging due to overlapping symptoms and limitations of current diagnostic methods. This study aimed to develop and validate an explainable machine learning (ML) model using routine blood biomarkers to improve differential diagnosis and provide a clinically accessible tool. Methods This retrospective, two-center study included 3,612 patients diagnosed with either DFI ( n  = 1,699) or OM ( n  = 1,913). Data from Center 1 ( n  = 3271) were used for model development (75% training, 25% internal validation), and data from Center 2 ( n  = 341) served as an independent external validation cohort. A robust feature selection pipeline identified the most predictive routine biomarkers. Multiple machine learning classifiers were trained and evaluated, with the top-performing model selected based on the area under the receiver operating characteristic curve (AUC), Brier score, and other key metrics. Explainable AI (XAI) techniques (SHAP, LIME) were used to ensure model transparency. A web-based calculator was developed for clinical translation. Results A LightGBM model using only six biomarkers—Age, HbA1c, Creatinine, Albumin, ESR, and Sodium—was selected as the final model. It achieved an AUC of 0.879 (95% CI 0.854–0.902) in internal validation and demonstrated excellent, generalizable performance in the external cohort with an AUC of 0.942 (95% CI 0.936–0.950). The model was well-calibrated and showed significant clinical utility in decision curve analysis. SHAP analysis quantified the specific contribution of each biomarker to individual predictions, enhancing interpretability. The final model was deployed as a user-friendly, publicly accessible web calculator. Conclusions An externally validated machine learning model based on six routine blood biomarkers can accurately and reliably differentiate DFI from OM. The model demonstrated high discriminative performance and clinical utility. Deployed as a transparent web calculator with integrated explainable AI, this low-cost tool has the potential to aid clinicians in diagnostic decision-making, particularly in resource-limited settings. Clinical trial number Not applicable.