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Adenine base editor incorporating the N-methylpurine DNA glycosylase MPGv3 enables efficient A-to-K base editing in rice

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Plant Communications 2023年第6期4卷 230-233页

作者： Xuemei Wu Bin Ren Lang Liu Shengqun Qiu Xin’ge Li Peijing Li Fang Yan Honghui Lin Xueping Zhou Dawei Zhang Huanbin Zhou Ministry of Education Key Laboratory for Bio-Resource and Eco-Environment College of Life ScienceState Key Laboratory of Hydraulics and Mountain River EngineeringSichuan UniversityChengdu 610064China State Key Laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijing 100193China Scientific Observing and Experimental Station of Crop Pests in Guilin Ministry of Agriculture and Rural AffairsGuilin 541399China Department of Plant Pathology China Agricultural UniversityBeijing 100193China State Key Laboratory of North China Crop Improvement and Regulation College of Plant ProtectionHebei Agricultural UniversityBaoding 071000China School of Life Science and Technology Hubei Engineering UniversityXiaogan 432000China State Key Laboratory of Rice Biology Institute of BiotechnologyZhejiang UniversityHangzhou 310058China Hainan Yazhou Bay Seed Lab/National Nanfan Research Institute(Sanya) Chinese Academy of Agricultural SciencesSanya 572024China

Dear Editor,Crop genetic diversity and elite agronomic traits are mainly caused by genetic variants,approximately half of which are single-nucleotide *** nucleotide substitution through CRISPR–Cas-mediated base editors has been widely used to correct defective alleles and create novel alleles by artificial evolution for rapid crop genetic *** 2017,cytosine base editors(CBEs)and adenine base editors(ABEs)have been successively developed in many plant species and have been continuously optimized to generate highly efficient C-to-T and A-to-G transitions,as well as by-product C-to-A/G conversion with low efficiency(Ren et al.,2018;Yan et al.,2021).

关键词： elite base editor

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AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource

arXiv

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arXiv 2024年

作者： Zhan, Wengyi Lin, Mingbao Lin, Chia-Wen Ji, Rongrong The Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China The Skywork AI Singapore The Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Taiwan

In an effort to improve the efficiency and scalability of single-image super-resolution (SISR) applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into any-scale, any-resource implementation. As a contrast to off-the-shelf methods that solve SR tasks across various scales with the same computing costs, our AnySR innovates in: 1) building arbitrary-scale tasks as any-resource implementation, reducing resource requirements for smaller scales without additional parameters;2) enhancing any-scale performance in a feature-interweaving fashion, inserting scale pairs into features at regular intervals and ensuring correct feature/scale processing. The efficacy of our AnySR is fully demonstrated by rebuilding most existing arbitrary-scale SISR methods and validating on five popular SISR test datasets. The results show that our AnySR implements SISR tasks in a computing-more-efficient fashion, and performs on par with existing arbitrary-scale SISR methods. For the first time, we realize SISR tasks as not only any-scale in literature, but also as any-resource. Our code is available at https://***/CrispyFeSo4/AnySR. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Efficient Finite-Difference Time-Domain Method for Solving Electromagnetic Scattering in Layered Anisotropies

Efficient Finite-Difference Time-Domain Method for Solving E...

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International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Yilin Kang Yuxian Zhang Xiaoli Feng Lixia Yang Zhixiang Huang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education and with the Information Materials and Intelligent Sensing Laboratory of Anhui Province Industry-Education-Research Institute of Advanced Materials and Technology for Integrated Circuits and with School of Electronic and Information Engineering Anhui University Hefei China

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

In this article, we propose an efficient numerical method for layered models based on the conventional finite difference time-domain (FDTD) method. The three-dimensional (3-D) layered model is compressed into one-dimensional (1-D) calculation, which improves the problem of large space memory and long running time for 3-D modeling while ensuring the accuracy and feasibility of the algorithm. On the one hand, by employing the transformation relationship between the transverse wave vector and the field in space, the expanded Maxwell's rotation equations are simplified, so that the equation system only retains the spatial transformation in the propagation direction. This processing allows for the calculation of the propagation characteristics of layered media without requiring more computational resources. On the other hand, to ensure the universality of the algorithm, a fully tensor anisotropic medium is selected for detecting the propagation data. At the same time, we design the layered structure and conduct the consistent 3-D modeling in the simulation software COMSOL. The results showed that our proposed efficient numerical algorithm greatly reduced the loss of computing resources while ensuring accuracy.

关键词： Solid modeling Three-dimensional displays Accuracy Tensors Software algorithms Vectors Mathematical models Numerical models Time-domain analysis Finite difference methods

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Learning Anatomical Semantics for Low-Dose Ct Denoising Via Pretrained Vision Models and Self-Supervised Learning

SSRN

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SSRN 2025年

作者： Hu, Chudi Wei, Meiyi Zhu, Yikun Chen, Gang The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University No. 299 Bayi Road Wuchang District Hubei Province Wuhan430072 China

Background: In order to reduce radiation dose and improve the diagnostic accuracy of computed tomography (CT), numerous studies in recent years have explored deep learning-based image denoising methods to suppress noise and artifacts induced by photon starvation and electronic noise. However, most existing methods supervise the denoising process using normal-dose CT images as a reference, neglecting the differences in noise levels among various anatomical structures in CT images, which results in over-smoothing in the denoised images. Methods: In this paper, we propose an anatomical semantics-based low-dose CT denoising method that leverages anatomical semantics for fine-grained denoising. Specifically, our approach comprises two components: a conventional denoising network for image denoising and an anatomy-aware network for extracting anatomical semantic features to guide the denoising process. To obtain comprehensive anatomical semantics, we extract general semantic features by leveraging a pretrained vision model and capture dataset-specific semantic features through self-supervised contrastive learning. Furthermore, to ensure consistency between the general semantic features and the dataset-specific semantic features, we introduce a semantic alignment module that maps both the general semantic features and the dataset-specific semantic features into a unified feature space. Results: Experimental results demonstrate that, compared with existing methods, our approach exhibits superior denoising performance on two public ***: The low-dose CT denoising method based on anatomical semantics proposed in this study significantly improves denoising performance by incorporating general semantic features and dataset-specific semantic features, demonstrating promising potential for improving diagnostic accuracy in clinical practice, reducing patient radiation exposure, and advancing research in medical image processing. © 2025, The Authors. All rights rese

关键词： Self-supervised learning

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Sensor-Based Authentication on Smartphones via Integrating Auxiliary Information

Sensor-Based Authentication on Smartphones via Integrating A...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yingjie Wang Ruimin Hu National Engineering Research Center for Multimedia Software Wuhan University Whuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Whuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the widespread use of smartphones, more and more private information is stored on the phone, and the loss or theft of the phone can lead to data leakage, theft of property, and other problems. Traditional active authentication methods based on passwords, faces, fingerprints, etc. authenticate only once at login, which still has some hidden dangers. Authentication methods based on behavioral biometrics such as walking gait, touch screen, keystroke, etc. can provide implicit and continuous authentication services, and thus have been widely studied recently. Considering the limited computational resources of smart devices, we first introduce the state-space model with linear model complexity, i.e., Mamba, to extract deep feature representations from raw signal sequences in the sensor-based authentication task. Then a series of auxiliary information from different domains are proposed and fused to the deep features to enhance the consistent semantic information and ultimately improve the discriminative ability of the model. Our model A-Mamba, i.e., Auxiliary-Mamba, is experimented on two public datasets. The experimental results show that our model outperforms existing approaches.

关键词： Biological system modeling Semantics Authentication Passwords Touch sensitive screens Feature extraction State-space methods Smart devices Smart phones Spectral analysis

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： Image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing Data mining Few shot learning Residual neural networks

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Grasp as you say: language-guided dexterous grasp generation 24

Grasp as you say: language-guided dexterous grasp generation

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yi-Lin Wei Jian-Jian Jiang Chengyi Xing Xian-Tuo Tan Xiao-Ming Wu Hao Li Mark Cutkosky Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Stanford University School of Computer Science and Engineering Sun Yat-sen University China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9798331314385

This paper explores a novel task "Dexterous Grasp as You Say" (DexGYS), enabling robots to perform dexterous grasping based on human commands expressed in natural language. However, the development of this field is hindered by the lack of datasets with natural human guidance; thus, we propose a language-guided dexterous grasp dataset, named DexGYSNet, offering high-quality dexterous grasp annotations along with flexible and fine-grained human language guidance. Our dataset construction is cost-efficient, with the carefully-design hand-object interaction retargeting strategy, and the LLM-assisted language guidance annotation system. Equipped with this dataset, we introduce the DexGYSGrasp framework for generating dexterous grasps based on human language instructions, with the capability of producing grasps that are intent-aligned, high quality and diversity. To achieve this capability, our framework decomposes the complex learning process into two manageable progressive objectives and introduce two components to realize them. The first component learns the grasp distribution focusing on intention alignment and generation diversity. And the second component refines the grasp quality while maintaining intention consistency. Extensive experiments are conducted on DexGYSNet and real world environments for validation.

关键词：

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DialogREC+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs

arXiv

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arXiv 2023年

作者： Xiong, Yiyun Dai, Mengwei Li, Fei Fei, Hao Li, Bobo Wu, Shengqiong Ji, Donghong Teng, Chong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computing National University of Singapore Singapore Singapore

Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogREC+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogREC+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks. © 2023, CC BY-NC-ND.

关键词： Extraction

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Rotation Exploration Transformer for Aerial Person Re-identification

Rotation Exploration Transformer for Aerial Person Re-identi...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Lei Wang Quan Zhang Junyang Qiu Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pazhou Lab (HuangPu) Guangzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Aerial person re-identification (AReID) focuses on accurately matching target person images within a UAV camera network. Challenges arise due to the broad field of view and arbitrary movement of UAVs, leading to foreground target rotation and background style variation. Existing AReID methods have provided limited solutions for the former, while the latter remains largely unexplored. This paper propose a Rotation Exploration Vision Transformer (RoExViT) to tackle the aforementioned dual challenges. Specifically, we design Multiple Rotation Tokens (MRT) to explore diverse rotational representations at the feature level, addressing foreground target rotation. To handle background style variation, we propose Cross-Camera Similarity (CCS) loss to effectively minimize the view gap among different cameras. Furthermore, we propose Iteratively Adaptive Batch Construction (IABC) strategy to mitigate overfitting on small datasets. Extensive experiments show that our method outperforms the state-of-the-art methods on PRAI-1581 and UAV-Human while also exhibting outstanding performance on Market1501.

关键词： Computer vision Transformers Cameras Autonomous aerial vehicles Identification of persons

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ON SCHRÖDINGERIZATION BASED QUANTUM ALGORITHMS FOR LINEAR DYNAMICAL SYSTEMS WITH INHOMOGENEOUS TERMS

arXiv

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arXiv 2024年

作者： Jin, Shi Liu, Nana Ma, Chuwen School of Mathematical Sciences Shanghai Jiao Tong University Institute of Natural Sciences Shanghai Jiao Tong University Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Institute of Natural Sciences Shanghai Jiao Tong University Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China

We analyze the Schrödingerization method for quantum simulation of a general class of non-unitary dynamics with inhomogeneous source terms. The Schrödingerization technique, introduced in [S. Jin, N. Liu and Y. Yu, Phys. Rev. Lett., 133 (2024), 230602], transforms any linear ordinary and partial differential equations with non-unitary dynamics into a system under unitary dynamics via a warped phase transition that maps the equations into a higher dimension, making them suitable for quantum simulation. This technique can also be applied to these equations with inhomogeneous terms modeling source or forcing terms, or boundary and interface conditions, and discrete dynamical systems such as iterative methods in numerical linear algebra, through extra equations in the system. Difficulty arises with the presence of inhomogeneous terms since they can change the stability of the original system. In this paper, we systematically study—both theoretically and numerically—the important issue of recovering the original variables from the Schrödingerized equations, even when the evolution operator contains unstable modes. We show that, even with unstable modes, one can still construct a stable scheme;however, to recover the original variable, one needs to use suitable data in the extended space. We analyze and compare both the discrete and continuous Fourier transforms used in the extended dimension and derive corresponding error estimates, which allow one to use the more appropriate transform for specific equations. We also provide a smoother initialization for the Schrödingerized system to gain higher-order accuracy in the extended space. We homogenize the inhomogeneous terms with a stretch transformation, making it easier to recover the original variable. Our recovery technique also provides a simple and generic framework to solve general ill-posed problems in a computationally stable way. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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