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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 956-962页

作者： Tianshan Zhang Bei Jiang Guang Yang Zhijie Bao Jiajie Peng AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shanxi China The National Engineering Laboratory for In tegrated Aerospace Ground Ocean Big Data Application Technology Shanxi China Tianjin second people's hospital Tianjin China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China

The CRISPR-Cas9 system, found across bacteria and archaea, enables efficient genome engineering in eukaryotic cells. There is a challenge that Cas9 guide RNA may cause off-target activities. Although numbers of methods have been proposed to predict off-target activities in guide RNA designing, this procedure involves numerous potential off-target sites, which causes label imbalance problem. To address this problem, we developed a deep learning framework, named CAF-Net (Cas9 Augmentation and Finetune Network), for predicting off-target activities of CRISPR-Cas9. First, we pretrain an embedding model to extract features from target and guide sequence pairs. Subsequently, data augmentation is applied to these features. And finally, the model is finetuned with synthetic samples. Evaluation results demonstrate its performance on published datasets.

关键词： RNA Bioinformatics Feature extraction DNA Genomics Convolutional neural networks Computational modeling Transformers Predictive models CRISPR

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Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

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IEEE Transactions on Fuzzy Systems 2025年

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

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Bs0→μ+μ− in a flavor violating extension of MSSM

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Physical Review D 2025年第3期111卷 035036-035036页

作者： Kai-Kai Meng Hai-Bin Zhang Jin-Lei Yang Department of Physics Hebei University Baoding 071002 China Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory Baoding 071002 China Hebei Research Center of the Basic Discipline for Computational Physics Baoding 071002 China Institute of Life Science and Green Development Hebei University Baoding 071002 China

B meson rare decays play a crucial role in exploring new physics beyond the standard model. In this study, we explore the rare decay process Bs0→μ+μ− in a flavor violating extension of the Minimal Supersymmetric Standard Model (MSSM), namely, the μ-from-ν SSM (μνSSM). Combined with the decay B¯→Xsγ, the numerical results indicate that the μνSSM can successfully accommodate the experimental data for Bs0→μ+μ− and additionally narrow down the parameter space.

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-MIMO Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis

arXiv

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arXiv 2025年

作者： Wu, Wenyi Zhang, Hao Wei, Zhisen Jing, Xiao-Yuan Zhang, Qinghua Wu, Songsong Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis School of Computer Guangdong University of Petrochemical Technology Maoming525000 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Fujian Province Zhangzhou363000 China

Source-free domain adaptation (SFDA) has been exploited for cross-domain bearing fault diagnosis without access to source data. Current methods select partial target samples with reliable pseudo-labels for model adaptation, which is sub-optimal due to the ignored target samples. We argue that every target sample can contribute to model adaptation, and accordingly propose in this paper a novel SFDA-based approach for bearing fault diagnosis that exploits both reliable and unreliable pseudo-labels. We develop a data-augmentation-based label voting strategy to divide the target samples into reliable and unreliable ones. We propose to explore the underlying relation between feature space and label space by using the reliable pseudo-labels as ground-truth labels, meanwhile, alleviating negative transfer by maximizing the entropy of the unreliable pseudo-labels. The proposed method achieves well-balance between discriminability and diversity by taking advantage of reliable and unreliable pseudo-labels. Extensive experiments are conducted on two bearing fault benchmarks, demonstrating that our approach achieves significant performance improvements against existing SFDA-based bearing fault diagnosis methods. Our code is available at https://***/BdLab405/SDALR Copyright © 2025, The Authors. All rights reserved.

关键词： Failure analysis

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Functional Bayesian Additive Regression Trees with Shape Constraints

arXiv

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arXiv 2025年

作者： Cao, Jiahao He, Shiyuan Zhang, Bohai Institute of Statistics and Big Data Renmin University of China Beijing100872 China School of Mathematics and Statistics Beijing Technology and Business University Beijing100048 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

Motivated by the great success of Bayesian additive regression trees (BART) on regression, we propose a nonparametric Bayesian approach for the function-on-scalar regression problem, termed as Functional BART (FBART). Utilizing spline-based function representation and tree-based domain partition model, FBART offers great flexibility in characterizing the complex and heterogeneous relationship between the response curve and scalar covariates. We devise a tailored Bayesian backfitting algorithm for estimating the parameters in the FBART model. Furthermore, we introduce an FBART model with shape constraints on the response curve, enhancing estimation and prediction performance when prior shape information of response curves is available. By incorporating a shape-constrained prior, we ensure that the posterior samples of the response curve satisfy the required shape constraints (e.g., monotonicity and/or convexity). Our proposed FBART model and its shape-constrained version are the new advances of BART models for functional data. Under certain regularity conditions, we derive the posterior convergence results for both FBART and its shape-constrained version. Finally, the superiority of the proposed methods over other competitive counterparts is validated through simulation experiments under various settings and analyses of two real datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Trees (mathematics)

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PEFN: A Patches Enhancement and Hierarchical Fusion Network for Robust Vehicle Re-Identification

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IEEE Internet of Things Journal 2025年

作者： He, Wenying Wang, Feiyu Bai, Yude Xiong, Neal N. Xu, Guangquan Guo, Fei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation Tianjin300130 China Tiangong University School of Software Tianjin300387 China Northeastern State University Department of Mathematics and Computer Science TahlequahOK74464 United States Tianjin300350 China Central South University School of Computer Science and Engineering Changsha410083 China

Vehicle Re-Identification (Re-ID), which is a significant application in the Internet of Things, aims to accurately retrieve the remaining images of a given vehicle across different cameras views. The improvement in vehicle Re-ID performance largely stems from better addressing the issues of inter-class similarity and intra-class variance. Existing methods, relying solely on max or average pooling after using attention modules, fail to obtain significantly complete and pure global and local features, and neglect the false guidance that some unique individual information on images bring to re-identification. Moreover, models combining global and local features have shown good results in vehicle Re-ID, but these successes neglect the interaction between features across different convolutional layers, resulting in the loss of crucial details for vehicle Re-ID. To tackle these issues, we introduce a Patches Enhancement and hierarchical Fusion Network (PEFN) based on a multi-branch architecture, divided into a Global and Local Attention Supplement (GLAS) branch, and an Enhanced Hierarchical feature fusion (EnHi) branch. The GLAS branch, through the Identity-related Feature Remodeling (IDFR) module’s staged supplementation of spatial and channel features, has achieved the enhancement of both global and local features and effectively mitigated the negative impacts of individual information. The EnHi branch enhances the robustness of feature representation by interacting hierarchical features. Extensive experiments on two large-scale vehicle re-identification datasets demonstrate that our PEFN method outperforms state-of-the-art vehicle re-identification approaches. Specifically, without utilizing extra data and re-ranking, our model achieves 85.15% mAP on the VeRi776 dataset. © 2014 IEEE.

关键词： Reamers

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PAMPHLET:PAM Prediction HomoLogous-Enhancement Toolkit for precise PAM prediction in CRISPR-Cas systems

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Journal of Genetics and Genomics 2025年第2期52卷 258-268页

作者： Chen Qi Xuechun Shen Baitao Li Chuan Liu Lei Huang Hongxia Lan Donglong Chen Yuan Jiang Dan Wang Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhaiGuangdong 519087China School of Biology and Biological Engineering South China University of TechnologyGuangzhouGuangdong 510006China BGI Research HangzhouZhejiang 310030China BGI Research ShenzhenGuangdong 518083China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Laboratory of Integrative Biomedicine Department of BiologyFaculty of ScienceUniversity of CopenhagenCopenhagenDenmark Qingdao-Europe Advanced Institute for Life Sciences BGI ResearchQingdaoShandong 266555China School of Life Science Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiang 310024China STOmics Americas Ltd. 2904 Orchard PkwySan Jose CA 95134USA

CRISPR-Cas technology has revolutionized our ability to understand and engineer organisms,evolving from a singular Cas9 model to a diverse CRISPR toolbox.A critical bottleneck in developing new Cas proteins is identifying protospacer adjacent motif(PAM)*** to the limitations of experimental methods,bioinformatics approaches have become ***,existing PAM prediction programs are limited by the small number of spacers in CRISPR-Cas systems,resulting in low *** address this,we develop PAMPHLET,a pipeline that uses homology searches to identify additional spacers,significantly increasing the number of spacers up to *** is validated on 20 CRISPR-Cas systems and successfully predicts PAM sequences for 18 *** predictions are further validated using the DocMF platform,which characterizes protein-DNA recognition patterns via next-generation *** high consistency between PAMPHLET predictions and DocMF results for Cas proteins demonstrates the potential of PAMPHLET to enhance PAM sequence prediction accuracy,expedite the discovery process,and accelerate the development of CRISPR tools.

关键词： CRISPR-Cas Protospacer adjacentmotif Genome editing PAM prediction Computational framework

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Escalating variability of daily sea temperature decreases phytoplankton spring blooms

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Continental Shelf Research 2025年 290卷

作者： Sun, Zixuan Du, Yan Xiang, Dongdong Chongben Honors College Ocean University of China Qingdao 266100 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science School of Statistics East China Normal University Shanghai 200062 China

Increases in sea surface temperature (SST) and its variability (SSTV) in the context of climate change have a great impact on marine phytoplankton productivity, but their combined effects on phytoplankton biomass, especially at a daily scale, have been poorly assessed. Here, we used an extended autoregressive (EAR) model to assess the different roles of SST and SSTV on phytoplankton spring blooms (PSB) in the Yellow Sea, based on 25 years (1998–2022) of daily-scale high-resolution chlorophyll-a (Chl-a) data from satellites. The annual patterns of PSB indices (start, peak, and duration) clearly exhibited years which were weak (low peak: Chl-a <0.80 mg/m³;short duration: <15 days) and strong (high peak: Chl-a >1.0 mg/m³;long duration: >60 days). The EAR model detected the significant effects of SST and SSTV on PSB indices, with SSTV being the more important determinant. SST is highly related to the timing of PSB start and end, with the average SST of 9.5 ± 0.7 °C at the start and 13.2 ± 1.95 °C at the end. The amplitude of SSTV was negatively correlated with the daily increase in Chl-a, highlighting the role of SSTV in modulating the magnitude of PSB. At low SST, the deepening of the mixed layer prolonged the time for phytoplankton to equilibrate nutrient and light requirements, whereas at low SSTV, the rate of environmental change within the mixed layer was reduced, allowing phytoplankton to adapt to the environment more quickly. Since the accumulation of phytoplankton biomass depends largely on daily acclimated growth, our results suggest that increasing daily temperature anomalies and warming in future are detrimental to phytoplankton biomass accumulation, and may reduce the magnitude of PSB in mid-to high-latitude seas. © 2025 Elsevier Ltd

关键词： Chlorophyll-a Extended autoregressive model Phytoplankton acclimated growth Phytoplankton biomass Yellow sea

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