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

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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Takeover Performance and Workload under Varying Automation Levels, Time Budget and Road Curvature

Takeover Performance and Workload under Varying Automation L...

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2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2022

作者： Xu, Chengliang Li, Penghui Li, Yibing Merat, Natasha Lu, Ziwang Guo, Xing Tsinghua University State Key Laboratory of Automotive Safety and Energy School of Vehicle and Mobility Beijing China Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport School of Traffic and Transportation Beijing China University of Leeds Institute for Transport Studies Leeds United Kingdom Beijing Information Science and Technology University School of Mechanical and Electrical Engineering Beijing China

ISBN: (纸本)9781665409025

In SAE Level 2 (L2) and Level 3 (L3) automated driving, drivers are required to take over control from automation when reaching system boundaries. The length of time budget is the vital parameter that needs to be determined to ensure a safe takeover manner. Existing literature on this issue rarely considers potential interactions between time budget and other factors, and a variable time budget seemed to be paradoxical in critical unplanned transitions. To this end, this study investigated drivers' takeover performance and workload under varying automation levels, time budget and road curvature. We employed the operational design domain (ODD) exiting event as the transition scenario where the ego car was about to leave the highway. Results showed that drivers in L3 takeovers performed worse and elicited higher workload than L2 when entering a curved ramp with a limited time budget. Takeovers when entering a curved ramp yielded worse performance and higher workload, as compared to entering a straight ramp. Such detrimental effects caused by loss of situation awareness (SA) and change of road curvature can be mitigated by increasing the length of time budget. All these findings suggest that even in simplified ODD exiting scenarios, the length of time budget should vary according to the state of the driver and the environment. © 2022 IEEE.

关键词： Automation

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Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

Size-invariance matters: rethinking metrics and losses for i...

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Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

arXiv

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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An Enhanced Multi-Scale Collaborative Learning Network for Medical Image Segmentation in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Yang, Aimin Li, Jiahao Bai, Yunjie Li, Jie Yang, Xingwang North China University of Science and Technology College of Science Hebei Tangshan063210 China North China University of Science and Technology Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China North China University of Science and Technology Hebei Engineering Research Center of Iron Ore Optimization and Iron Pre-Process Intelligence Tangshan063210 China

Multi-organ segmentation in the abdomen is a key area in medical image segmentation and is essential for accurate diagnosis and treatment of diseases. The diversity of abdominal organs, differences in size, and ambiguous boundaries pose significant challenges to segmentation tasks, making it difficult for existing models to meet clinical demands in complex scenarios. To address this, this paper proposes a Multi-Scale Collaborative Learning Network (MSCL-Net) in conjunction with a medical Internet of Things (IoMT) platform, aiming to enhance the accuracy of abdominal organ segmentation. The introduction of IoMT technology dramatically expands the channels for obtaining medical image data. By collecting massive amounts of data in real-time from various scenarios, such as outpatient clinics and hospitals, doctors can quickly use the segmentation model to obtain preliminary segmentation results of CT images, which specialists can rapidly correct. This workflow improves clinical work efficiency and provides a large volume of highquality data for training the model, further enhancing its accuracy. Regarding model design, the Feature Space Module is used for global feature extraction, the Feature Extraction Module captures local semantic information at different scales, and the Feature Aggregation Module performs deep integration of local and global information. A considerable number of experimental results based on the Synapse dataset demonstrate that MSCL-Net outperforms existing advanced algorithms and achieves 81.71% in the Dice Similarity Coefficient (DSC) and 17.81 in the Hausdorff Distance at 95% (HD95) metrics, demonstrating its outstanding ability in medical image segmentation. © 2014 IEEE.

关键词： Diagnosis

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Robust Remote Sensing Scene Classification with Multi-View Voting and Entropy Ranking

arXiv

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arXiv 2023年

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

SSRN

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SSRN 2023年

作者： Kong, Lijun Fan, Lin Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China Nanjing University Nanjing210093 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel Neurotransmitter Biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. Beyond that, the multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based Neurotransmitter Biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 5.066 μM and 0.288 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Collaborative Ontology Matching With Dual Population Genetic Programming and Active Meta-Learning

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IEEE Transactions on Evolutionary Computation 2025年

作者： Xue, Xingsi Lin, Jerry Chun-Wei Jiang, Zhaohang Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Silesian University of Technology Department of Distributed Systems and IT Devices Akademicka Gliwice44-100 Poland Taiyuan University of Technology School of Electrical and Power Engineering Shanxi Taiyuan030024 China

Ontology provides a structured language to encapsulate domain-specific knowledge and harmonize diverse data. Ontology matching identifies similar entities in distinct ontologies, facilitating knowledge integration and information exchange. Similarity features are crucial for ontology matching by measuring entity resemblance, but noisy and redundant features can obscure relevant ones, reducing matching quality. To improve the accuracy of matching results, we propose a dual population genetic programming with an active meta-learning to build a high-quality similarity feature, which owns three novel components. First, a dual population genetic programming is developed to construct high-level similarity feature with a two-layer individual representation, a dual population based co-evolutionary mechanism, and a novel fitness function based on partial standard alignment. Second, a new active learning model is presented to update the partial standard alignment through an efficient interactive procedure, guiding the algorithm towards building more reliable similarity features. Finally, a weighted random forest meta-learning model is designed to train the expert vote aggregation model with their historical behaviors, and fine-tunes the model's performance with a compact genetic algorithm. Experimental results on the Ontology Alignment Evaluation Initiative's interactive matching tasks demonstrate that our method consistently achieves higher accuracy and better efficiency compared to advanced matching techniques across various expert error rates. © 1997-2012 IEEE.

关键词： Procedure oriented languages

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Linn: A Lightweight Image Hiding Method Based on Invertible Neural Network

SSRN

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SSRN 2024年

作者： Cheng, Dapeng Zhu, Minghui Mao, Yanyan Gao, Xiaolian Jing, Wanting Kong, Yue Li, Jinjiang School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai264005 China Yantai Key Laboratory of Big Data Modeling and Intelligent Computingy Shandong Yantai264005 China School of Information and Electronic Engineering Shandong Technology and Business University Shandong Yantai264005 China

Image steganography aims to conceal a secret image within a cover image, allowing only those who are informed can recover it. Compared to ordinary steganography, image steganography not only demands the recovered secret image to closely resemble the original image while keeping the stego image well concealed, but also requires a larger capacity. Existing image hiding methods based on invertible neural network have met this requirement, but they generally have a large number of parameters, the complexity of the model is high. In this paper, we propose a lightweight image hiding framework based on invertible neural network called LINN. This framework incorporates the idea of residuals, aiming to minimize the network depth while ensuring image quality. Additionally, it incorporates a spatial attention module, enabling adaptive identiffcation of regions more suitable for concealing secret images. We also try to ensure that the spatial domain and frequency domain of the original image and the image processed by the model are consistent through loss function. Experimental results demonstrate that the method proposed in this paper has stronger generalization ability and a smaller number of parameters, and the image quality generated is better than that generated by other existing methods, while having higher security. © 2024, The Authors. All rights reserved.

关键词： Image quality

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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