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A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org . Application of CRISPR-Cas13d is limited by the inability to predict on- and off-targets. Here the authors perform CRISPR-Cas13d proliferation screens followed by modeling of Cas13d on- and off-targets; they design a deep learning model, DeepCas13, to predict the on-target activity of a gRNA.

Resistance to chemotherapy has been a major hurdle that limits therapeutic benefits for many types of cancer. Here we systematically identify genetic drivers underlying chemoresistance by performing 30 genome-scale CRISPR knockout screens for seven chemotherapeutic agents in multiple cancer cells. Chemoresistance genes vary between conditions primarily due to distinct genetic background and mechanism of action of drugs, manifesting heterogeneous and multiplexed routes towards chemoresistance. By focusing on oxaliplatin and irinotecan resistance in colorectal cancer, we unravel that evolutionarily distinct chemoresistance can share consensus vulnerabilities identified by 26 second-round CRISPR screens with druggable gene library. We further pinpoint PLK4 as a therapeutic target to overcome oxaliplatin resistance in various models via genetic ablation or pharmacological inhibition, highlighting a single-agent strategy to antagonize evolutionarily distinct chemoresistance. Our study not only provides resources and insights into the molecular basis of chemoresistance, but also proposes potential biomarkers and therapeutic strategies against such resistance. Chemoresistance limits the success of chemotherapy in patients with cancer. Here, the authors perform 30 genome wide CRISPR knockout screens to identify genes associated to resistance against commonly used chemotherapeutics across multiple cancer types, followed by 26 second round CRISPR screens to identify druggable targets of chemoresistance.

The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology.

Kazakh horses are renowned for their endurance and adaptability, with distinct muscle groups such as the longissimus dorsi (LD) and biceps femoris (BF) muscles serving specialized functions. However, the molecular mechanisms underlying the functional specialization of these muscles in Kazakh horses remain poorly understood. This study aims to address this gap by utilizing single-cell RNA sequencing (scRNA-seq) to investigate the transcriptomic differences between these muscle groups, with a focus on understanding their molecular adaptations. Our analysis revealed that the BF muscle, specialized for explosive movements, exhibited upregulation of genes associated with anaerobic metabolism, muscle contraction, and oxidative stress response, reflecting its reliance on glycolysis for sustained energy production. In contrast, the LD muscle, primarily responsible for postural support and endurance, showed a metabolic shift toward lipid utilization and energy production. Differential gene expression analysis also revealed distinct enrichment in biological pathways, with LD cells being enriched in pathways related to muscle contraction and calcium signaling, while BF cells were enriched in energy metabolism pathways. These findings provide valuable insights into the molecular adaptations of Kazakh horses’ muscle tissues, highlighting the functional specialization of LD and BF muscles and offering a foundation for future research on improving muscle performance and breeding programs in equines.

Environmental factors significantly affect the fatigue performance of weathering steel welded components in high-altitude, low-temperature corrosive environments. This study conducted multi-factor-coupled constant-amplitude fatigue tests on Q500qENH weathering steel V-groove welded joints and built an equivalent finite element model using test data to explore key influencing factors under multi-condition coupling. Results show that stress level most significantly affects fatigue performance, followed by corrosion duration, then ambient temperature, with influences decreasing in turn. Analyzing 18-day cyclic immersion corrosion morphology predicts 21-year outdoor corrosion in plateau regions, providing a reliable method for long-term exposure prediction. Finite element simulations confirm that low temperatures improve slightly corroded specimens’ fatigue performance by 20%, but damage accumulates before optimal service. This study offers key parameters for safe design of high-altitude weathering steel welded components.

In high-altitude corrosive environments, weathering steel is widely applied due to its excellent corrosion resistance. However, the welded joint regions, where the chemical composition and microstructure undergo changes, are susceptible to the corrosion-induced degradation of mechanical properties. This study investigates the corrosion–mechanical synergistic degradation behavior of a 16 mm thick Q500 qENH base metal and its V-type and Y-type welded joint specimens. Periodic immersion corrosion tests were conducted to simulate plateau atmospheric conditions, followed by mechanical performance evaluations. Corrosion metrics—including corrosion rate, cross-sectional loss, penetration depth, and corrosion progression speed—were analyzed in relation to mechanical indicators such as the fracture location, yield load, ultimate load, yield strength, and tensile strength at varying exposure durations. The results indicate that the corrosion process exhibits distinct layering, with a two-stage characteristic of rapid initial corrosion followed by slower progression. Welded joints consistently exhibit higher corrosion rates than the base metal, with the rate difference evolving nonlinearly in an “increase–decrease–stabilization” trend. After corrosion, the mechanical performance degradation of welded joint specimens is more severe than that of base metal specimens.

Aiming at the trajectory tracking control problem of wheeled mobile robots under unknown slip ratio conditions, this paper designs a trajectory tracking controller based on an improved rapid variable power reaching law and a sliding mode observer. First, a kinematic model of the wheeled mobile robot is established, explicitly considering the influence of slip ratio. Then, a sliding mode observer is developed for online estimation of the slip ratio, addressing the difficulty of direct slip ratio measurement. On this basis, a trajectory tracking controller is designed based on the improved rapid variable power reaching law, enabling fast tracking of multiple complex trajectories under slip conditions. Simulation and experimental results show that the proposed trajectory tracking controller not only effectively eliminates the influence of unknown slip disturbances on trajectory tracking, improving smoothness and tracking accuracy but also greatly accelerates the convergence process. The shortest convergence time is only 20.56% of that achieved by a fuzzy PID trajectory tracking controller and 61.43% of that achieved by a rapid double power reaching law trajectory tracking controller with a sliding mode observer.

Biomedical research requires large, diverse samples to produce unbiased results. Retrospective data harmonization is often used to integrate existing datasets to create these samples, but the process is labor-intensive. Automated methods for matching variables across datasets can accelerate this process, particularly when harmonizing datasets with numerous variables and varied naming conventions. Research in this area has been limited, primarily focusing on lexical matching and ontology-based semantic matching. We aimed to develop new methods, leveraging large language models (LLMs) and ensemble learning, to automate variable matching. This study utilized data from two GERAS cohort studies (European [EU] and Japan [JP]) obtained through the Alzheimer's Disease (AD) Data Initiative's AD workbench. We first manually created a dataset by matching 347 EU variables with 1322 candidate JP variables and treated matched variable pairs as positive instances and unmatched pairs as negative instances. We then developed four natural language processing (NLP) methods using state-of-the-art LLMs (E5, MPNet, MiniLM, and BioLORD-2023) to estimate variable similarity based on variable labels and derivation rules. A lexical matching method using fuzzy matching was included as a baseline model. In addition, we developed an ensemble-learning method, using the Random Forest (RF) model, to integrate individual NLP methods. RF was trained and evaluated on 50 trials. Each trial had a random split (4:1) of training and test sets, with the model's hyperparameters optimized through cross-validation on the training set. For each EU variable, 1322 candidate JP variables were ranked based on NLP-derived similarity scores or RF's probability scores, denoting their likelihood to match the EU variable. Ranking performance was measured by top-n hit ratio (HR-n) and mean reciprocal rank (MRR). E5 performed best among individual methods, achieving 0.898 HR-30 and 0.700 MRR. RF performed better than E5 on all metrics over 50 trials (P < 0.001) and achieved an average HR-30 of 0.986 and MRR of 0.744. LLM-derived features contributed most to RF's performance. One major cause of errors in automatic variable matching was ambiguous variable definitions. NLP techniques (especially LLMs), combined with ensemble learning, hold great potential in automating variable matching and accelerating biomedical data harmonization.

Although the Kazakh horse is a dual-purpose breed renowned for both milk and meat production, the extent to which surgical castration alters gene expression in its muscles has not yet been fully elucidated. In this study, left longissimus dorsi muscle (LDM) samples were obtained from six Kazakh stallions (W group) and six Kazakh geldings (S group) to comparatively evaluate meat quality parameters, examine histological characteristics in tissue sections, and apply transcriptomic profiling to comprehensively explore the principal regulatory pathways and candidate genes through which surgical castration modulates LDM growth. The results demonstrated that surgical castration did not induce significant alterations in meat color or pH-related parameters. However, cooking loss and shear force values were markedly diminished, accompanied by a marked decrease in muscle fiber cross-sectional area. Transcriptomic analysis identified 848 differentially expressed genes (DEGs) in total, comprising 415 upregulated and 433 markedly downregulated DEGs, which were predominantly enriched in key biological pathways, including actin cytoskeleton regulation. Moreover, eleven core candidate genes, including MYL2, MYL3, and TNNI1, were further screened and identified.

This study aimed to assess the anti-inflammatory properties of mare milk by analyzing immune markers in mice following gavage of mare milk. Metagenomic sequencing was employed to examine variations in the composition and functional profiles of the intestinal microbiota across different experimental groups. Bacterial diversity, abundance, and functional annotations of gut microbiota were evaluated for each group. The results show that, compared to the control group, the mare milk group exhibited a significant decrease in the pro-inflammatory cytokine IL-6 levels and a significant increase in secretory immunoglobulin A (SIgA) levels (p < 0.05). The fermented mare milk group and the pasteurized fermented mare milk group demonstrated a significant downregulation of the pro-inflammatory cytokines TNF-α and IL-1β, along with a significant increase in the anti-inflammatory cytokine IL-10 levels (p < 0.05). Additionally, metagenomic analysis revealed that both the mare milk and fermented mare milk groups were able to regulate the imbalance of the intestinal microenvironment by improving the diversity of the gut microbiota and reshaping its structure. Specifically, the mare milk group enhanced gut barrier function by increasing the abundance of Bacteroides acidifaciens, while the fermented mare milk group increased the proportion of Bacillota and the relative abundance of beneficial bacterial genera such as Faecalibaculum and Bifidobacterium. KEGG pathway annotation highlighted prominent functions related to carbohydrate and amino acid metabolism, followed by coenzyme and vitamin metabolism activities. In conclusion, mare milk and its fermented products demonstrate anti-inflammatory effects, particularly in modulating immune responses and inhibiting inflammatory cascades. Additionally, the administration of mare milk enhances the composition and metabolic activity of intestinal microbiota in mice, supporting intestinal microecological balance and overall gut health, and offering valuable insights for the development of mare milk-based functional foods.