MOTIVATION: DNA copy number variations (CNVs) exert a profound impact on major genetic disorders in humans. Although multiple sequencing technologies have become the first line of molecular diagnosis for CNVs, existing tools are unable to resolve the pathogenicity of CNVs directly from raw sequencing data. RESULTS: We developed CNVSeeker, a one-stop and easy-to-use pipeline that provides comprehensive analysis from raw sequencing data to variant interpretation reports, and supports multiple types of sequencing data including short-read data such as whole genome sequencing data and whole exome sequencing data, and long-read sequencing data from Pacific Biosciences HiFi platform or Oxford Nanopore Technologies platform. Through extensive benchmarking, CNVSeeker demonstrated comparable enhancement over the state-of-the-art methods for CNV calling. Moreover, CNVSeeker enables significantly precise variant classification with an accuracy of ∼87%. By applying CNVSeeker to 1946 individuals with autism spectrum disorder (ASD), a total of 133 ASD-associated CNVs in 122 patients were identified, yielding a diagnostic yield of ∼6.3%. Additionally, we have also provided a user-friendly webserver for intuitive visualization of results. This study highlights the potential of CNVSeeker to benefit clinicians and geneticists with limited bioinformatic skill by aiding them interpret CNVs directly from various types of raw sequencing data for auxiliary disease diagnosis. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at https://genemed.tech/cnvseeker and the open-source code can be found at https://github.com/lovelycatZ/CNVSeeker.

Accurately identifying variant hot and cold spots in human exonic regions is a crucial step in applying the PM1 and benign interpretation criteria of the ACMG-AMP guidelines. Addressing the issue that the existing tool HCSeeker relies heavily on the ClinVar database and has limited predictive performance in low-frequency variant regions, this study proposes HCSeer2-an innovative deep learning framework. By integrating the local feature extraction capability of CNN with the global dependency modeling of the Self-Attention mechanism, HCSeer2 achieves high-precision prediction of variant cold and hot spots across the entire human exome. HCSeer2 is trained on existing cold and hot spot data computed by tools such as HCSeeker. Through a multimodal feature fusion architecture, it simultaneously integrates sequence information and four types of genomic prior knowledge, effectively capturing the local aggregation patterns and global distribution rules of genomic variants. It enhances the understanding of variant clustering mechanisms in a self-supervised manner to predict variant cold and hot spots. Using this model, we identified 28,907 variant hot spots and 159,970 variant cold spots in the entire human exome, covering all human exonic regions. We also verified that the pathogenic potential of sites in hot spots is significantly higher than that in cold spots. This study not only provides scalable variant annotation resources for the clinical application of the ACMG-AMP guidelines but also offers new insights into the identification of genomic functional elements through the proposed multi-scale modeling approach. The code and data of HCSeer2 are freely available at https://github.com/xq-xia/HCSeer2.

The PS3 (Pathogenic Functional Evidence) and BS3 (Benign Functional Evidence) criteria in the American College of Medical Genetics and Genomics (ACMG) guidelines are critical for genetic variant classification. However, manual evaluation is time-consuming and prone to inter-laboratory inconsistencies, limiting the clinical interpretation of Variants of Uncertain Significance (VUS). Large Language Models (LLMs) offer potential for automated assessment, but their performance validation is hindered by the lack of standardized, high-quality datasets. This study introduces the PS3/BS3 Full-Chain Evidence Benchmark Dataset (PBD), the first manually curated dataset comprising 77 peer-reviewed publications, covering 266 cDNA variants (including duplicates) and their PS3/BS3 rating results. Adhering to ClinGen Sequence Variant Interpretation (SVI) standards, PBD includes structured, comprehensive evidence chains spanning genes, diseases, variants, experiments, and evidence ratings, designed to evaluate LLM capabilities in functional evidence extraction. We detail the dataset construction process, including literature screening, data extraction, standardization, and quality control, and developed a Python-based automated evaluation pipeline for reproducible, standardized analysis. Experiments using DeepSeek models$(1.5 \mathrm{b} / 7 \mathrm{b} / 14 \mathrm{b})$demonstrate PBD's potential in supporting automated variant interpretation. PBD provides a vital resource for bioinformatics and precision medicine, facilitating the development and standardization of variant classification tools. Data examples are available at https://github.com/User8588/PBD, with full data and code to be released upon paper acceptance.

Accurate spatial and motion understanding is critical for autonomous driving systems. While object-level perception models excel in structured environments, they struggle with open-set categories and often lack precise geometric representation. Occupancy-based, class-agnostic methods offer better scene expressiveness but typically ignore inter-agent interactions and fail to ensure physical consistency in motion predictions, limiting their reliability in complex traffic scenarios. In this paper, we propose LEGO-Motion, a novel class-agnostic motion prediction framework that bridges the gap between instance-level reasoning and occupancy-based modeling. Unlike conventional grid-based methods that treat each cell independently, LEGO-Motion introduces two key components: (1) the Interaction-Augmented Instance Encoder (IaIE), which models interactions among dynamic agents via cross-attention, and (2) the Instance-Enhanced BEV Encoder (IeBE), which improves motion consistency across instances through multi-stage feature fusion. These components enable our model to learn semantically coherent and physically plausible motion fields. Extensive experiments on the nuScenes dataset show that LEGO-Motion achieves a around 6% improvement in motion prediction accuracy over the previous state-of-the-art, while maintaining real-time inference at 21ms. Moreover, our method demonstrates strong generalization on a proprietary FMCW LiDAR benchmark. These results validate LEGO-Motion's effectiveness in capturing both global scene structure and fine-grained motion dynamics, making it a promising foundation for next-generation perception systems.

Functional near-infrared spectroscopy (fNIRS) can be used to measure cortical hemodynamics, with advantages such as non-invasiveness, high spatial resolution, wearability, ease of use and relatively low cost. These features make it potentially suitable for translational applications such as brain-computer interface (BCI), neurofeedback, and personalized healthcare. However, fNIRS signals are susceptible to motion artifacts (MAs), which can obscure physiological information. Herein, we propose to deploy a denoising autoencoder (DAE) network on an STM32 microcontroller for real-time multichannel MA removal at the edge. The DAE model was trained on fNIRS data augmented with simulated MAs. It was deployed on the edge device without any performance degradation, outperforming the conventional wavelet-based methods. With an inference time of 38 ms, this implementation is well-suited for real-time processing of multi-channel fNIRS data. Additionally, the low memory usage and CPU workload of the model make it ideal for deployment on diverse microcontroller platforms. This work holds the potential to enable the wider applications of wearable fNIRS in practice.

Extracting entity relations is vital in legal artificial intelligence. It automates the mining of triple data from vast legal texts. Current methods face challenges in inaccurately identifying legal named entity boundaries and extracting over-lapping relation triples from legal texts. We present KGNet, a model developed to address these issues effectively. Our approach introduces a Word Information Generator Based on BMES tagging combined with the Fusionformer module. This innovation enhances the incorporation of legal domain knowl-edge into text representations, improving the accuracy of entity recognition. Additionally, we utilize the GlobalPointer decoder, which redefines and decomposes relation triples, thus resolving the issue of overlapping entities. Performance evaluations on a specially constructed judicial document dataset show that KGNet achieves an F1 score of 66.7%, representing an average improvement of 15.3% over baseline models. These results confirm the effectiveness of KGNet in enhancing legal document processing.