As one of the most common and abundant post-transcriptional modifications, N⁶-methyladenosine (m⁶A) has been extensively studied for its essential regulatory role in gene expression and cell functions. The location of m⁶A RNA modification sites, however, remains a challenging problem, because of the inability to characterize m⁶A modified sites at a multi-scale level in their native RNA context. Here, we introduce an interpretability-guided invertible neural network (m⁶A-IIN), a deep learning model to accurately identify m⁶A RNA modification sites by integrating both primary and secondary structure information under an invertible coupling framework. Compared to existing methods, m⁶A-IIN achieves state-of-the-art performance in the prediction of m⁶A RNA modification sites across 11 benchmark datasets collected from different species and tissues. Furthermore, we find evidence indicating high consistency in methylation-related regions between primary and secondary structure of RNA, providing novel insights into m⁶A biology from the phylogenetic perspective. By analyzing conserved methylation-related regions identified by m⁶A-IIN across tissues, m⁶A-IIN facilitates the identification of novel pan-cancer genes, providing valuable contributions to cancer biology. Our results underscore the interpretability and predictive accuracy of m⁶A-IIN, opening an avenue towards the understanding of m⁶A RNA modification mechanisms.

Accurate and automated identification of organoids from bright-field images is essential for enabling high-throughput drug screening and precision medicine. Organoids, as 3-D in vitro cellular models, closely recapitulate the functional and structural characteristics of their tissue or organ of origin, presenting an unprecedented opportunity for biomedical research. However, the complexity of bright-field microscopy images, including heterogeneous backgrounds and diverse organoid morphologies, poses significant challenges for existing computational methods, often hindering robust feature extraction and high-throughput analysis. To address these issues at the intersection of computational vision and organoid biology, we propose FEMSSorg, a novel organoid recognition framework designed to adaptively aggregate multiscale scan-selected state space features through a fuzzy mixture-of-experts (FuzzyMoE) scoring mechanism. FEMSSorg introduces a fuzzy expert soft routing mechanism (fuzzy route), implemented via Fuzzy C-Means-based soft routing assignments, forming a new class of fuzzy MoE that leverages fuzzy expert clustering scores to dynamically integrate local (LocalSS) and global (GlobalSS) state space features. This approach enables effective balancing of global pixel dependencies and local texture information, thereby substantially reducing background interference and image noise in bright-field images and improving the accuracy of organoid identification. Furthermore, we incorporate a Dual Downsampling Adaptive Pooling Feature Fusion module, which combines original backbone features with parallel downsampled features and utilizes content-aware pooling for adaptive multilevel and multiscale feature fusion. Experimental results on multiclass organoid bright-field image datasets demonstrate that FEMSSorg achieves state-of-the-art performance in both organoid detection and morphological texture classification, highlighting its value as a robust computational tool for advancing real-time, high-throughput organoid research.

Dear Editor, This letter presents an organoid segmentation model based on multi-axis attention with convolution parallel block. MACPNet adeptly captures dynamic dependencies within bright-field microscopy images, improving global modeling beyond conventional UNet. It excels in sparse global interactions and concurrent computation, yielding enhanced segmentation. MACPNet stands out for its prowess in multi-scale data capture, aligned with diverse distance dependencies inherent in organoid images. Experimental results show that the proposed model outperforms several state-of-the-art methods as well as multiple baseline models in accurate organoid segmentation.

Atmospheric jets are pivotal components of atmospheric circulation, profoundly influencing surface weather patterns and the development of extreme weather events such as storms and cold waves. Accurate detection of the jet stream axis is indispensable for enhancing weather forecasting, monitoring climate change, and mitigating disasters. However, traditional methods for delineating atmospheric jets are plagued by inefficiency, substantial errors, and pronounced subjectivity, limiting their applicability in complex atmospheric scenarios. Current research on semi-supervised methods for extracting atmospheric jets remains scarce, with most approaches dependent on traditional techniques that struggle with stability and generalization. To address these limitations, this study proposes a semi-supervised jet stream axis extraction method leveraging an enhanced U-Net++ model. The approach incorporates improved residual blocks and enhanced attention gate mechanisms, seamlessly integrating these enhanced attention gates into the dense skip connections of U-Net++. Furthermore, it optimizes the consistency learning phase within semi-supervised frameworks, effectively addressing data scarcity challenges while significantly enhancing the precision of jet stream axis detection. Experimental results reveal the following: (1) With only 30% of labeled data, the proposed method achieves a precision exceeding 80% on the test set, surpassing state-of-the-art (SOTA) baselines. Compared to fully supervised U-Net and U-Net++ methods, the precision improves by 17.02% and 9.91%. (2) With labeled data proportions of 10%, 20%, and 30%, the proposed method outperforms the MT semi-supervised method, achieving precision gains of 9.44%, 15.58%, and 19.50%, while surpassing the DCT semi-supervised method with improvements of 10.24%, 16.64%, and 14.15%, respectively. Ablation studies further validate the effectiveness of the proposed method in accurately identifying the jet stream axis. The proposed method exhibits remarkable consistency, stability, and generalization capabilities, producing jet stream axis extractions closely aligned with wind field data.

Patient-derived organoids are increasingly being used in disease modeling, clinical research, drug development, and precision medicine. However, capturing complex organoid behaviors in dense three-dimensional culture environments remains challenging for manual workflows, and bright-field images often contain background artifacts that complicate quantitative analysis. To address this gap, we developed OrgLine, a quantitative analysis pipeline for organoids. Built on a large, curated bright-field image dataset, it couples a detector with a prompt-guided segmentation module. OrgLine recognizes organoids in bright-field images, supports quantitative characterization of morphological phenotypes across developmental stages, and enables accurate instance segmentation for downstream morphometric assessment. Together, these capabilities support more automated and quantitative organoid cultivation workflows.

Industrial robots have advanced rapidly over the past several decades and have played a significant role in industries such as aerospace, medicine, and manufacturing. However, uncalibrated robots can exhibit significant positioning errors and lack the precision required for high-accuracy applications. This limitation hinders their integration into state-of-the-art manufacturing processes. Although calibration techniques provide potential remedies, existing algorithms continue to face issues such as slow convergence and limited accuracy. This paper presents a novel robot arm calibration method that integrates an unscented Kalman filter with a newly developed Newton interpolation beetle antenna search (BAS) algorithm. This method offers two main advantages: it optimizes the traditional BAS algorithm to avoid local optima and significantly improves convergence speed, and it incorporates an unscented Kalman filter to reduce the influence of noise on measurement results. Experimental verification on ABB IRB120 industrial robot shows that compared with the advanced calibration algorithm, the maximum error of the proposed UKF-NIBAS algorithm is 10.25% lower than that of the most accurate LM algorithm, which can meet the needs of high-precision application scenarios.