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CT-based radiomics:predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 402-416页

作者： Jia Fu Mengjie Fang Zhiyong Lin Jianxing Qiu Min Yang Jie Tian Di Dong Yinghua Zou Department of Interventional Radiology and Vascular Surgery Peking University First HospitalBeijing 100043China Department of Radiology Peking University First HospitalBeijing 100043China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

This study aimed to comprehensively evaluate non-contrast computed tomography(CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis(ARAS)after percutaneous transluminal renal angioplasty(PTRA).A total of 52 patients were retrospectively recruited,and their clinical characteristics and pretreatment CT images were *** a median follow-up period of 3.7 mo,18 patients were confirmed to have benefited from the treatment,defined as a 20%improvement from baseline in the estimated glomerular filtration rate.A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype *** features,comprising 116 handcrafted features and 78 deep learning features,were extracted from the affected renal and perirenal adipose *** features from the latter were correlated with early outcomes,as determined by univariate analysis,and were visually represented in radiomics heatmaps and volcano *** using consensus clustering and the least absolute shrinkage and selection operator method for feature selection,five machine learning models were *** regression yielded the highest leave-one-out cross-validation accuracy of 0.780(95%CI:0.660-0.880)for the renal signature,while the support vector machine achieved 0.865(95%CI:0.769-0.942)for the perirenal adipose *** Additive exPlanations was used to visually interpret the prediction mechanism,and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature,*** analysis revealed that both signatures served as independent predictive *** combined,they achieved an area under the receiver operating characteristic curve of 0.888(95%CI:0.784-0.992),indicating that the imaging phenotypes from both regions complemented each *** conclusion,non-contrast CT-based radio

关键词： Atherosclerotic renal artery stenosis Percutaneous transluminal renal angioplasty Computed tomography Radiomics Deep learning Explainability

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Look Around Before Locating: Considering Content and Structure Information for Visual Grounding 39

Look Around Before Locating: Considering Content and Structu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Shiyi Zhao, Peizhi Zheng, Zhilong He, Peihang Cheng, Haonan Cai, Yi Huang, Qingbao School of Electrical Engineering Guangxi University Nanning China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Key Laboratory of Big Data and Intelligent Robot of Ministry of Education SCUT Guangzhou China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning China

ISBN: (纸本)157735897X

As a long-term challenge and fundamental requirement in vision and language tasks, visual grounding aims to localize a target referred by a natural language query. The regional annotations form a superficial correlation between the subject of expression and some common visual entities, which hinder models from comprehending the linguistic content and structure. However, current one-stage methods struggle to uniformly model the visual and linguistic structure due to the structural gap between continuous image patches and discrete text tokens. In this paper, we propose a semi-structured reasoning framework for visual grounding to gradually comprehend the linguistic content and structure. Specifically, we devise a cross-modal content alignment module to effectively align unlabeled contextual information into a stable semantic space corrected by token-level prior knowledge obtained with CLIP. A multi-branch modulated localization module is also established to obtain modulation grounding by linguistic structure. Through a soft split mechanism, our method can destructure the expression into a fixed semi-structure (i.e., subject and context) while ensuring the completeness of linguistic content. Our method is thus capable of building a semi-structured reasoning system to effectively comprehend the linguistic content and structure by content alignment and structure modulated grounding. Experimental results on five widely-used datasets validate the performance improvements of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantics

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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lncRNA7 and lncRNA2 modulate cell wall defense genes to regulate cotton resistance to Verticillium wilt (kiac041, 2022)

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PLANT PHYSIOLOGY 2022年第3期189卷 1881-1881页

作者： Zhang, Lin Liu, Jinlei Cheng, Jieru Sun, Quan Zhang, Yu Liu, Jinggao Li, Huimin Zhang, Zhen Wang, Ping Cai, Chaowei Chu, Zongyan Zhang, Xiao Yuan, Youlu Shi, Yuzhen Cai, Yingfan State Key Laboratory of Cotton Biology Henan Key Laboratory of Plant Stress Biology School of Life Sciences School of Computer and Information Engineering Henan University Kaifeng 475001 China College of life science and agricultural engineering Nanyang Normal University Nanyang 473000 China Chongqing Key Laboratory of Big Data for Bio Intelligence College of Bioinformation Chongqing University of Posts and Telecommunications Chongqing 400065 China Southern Plains Agricultural Research Center Agricultural Research Service USDA College Station Texas 77845 USA Kaifeng Academy of Agriculture and Forestry Kaifeng 475000 China State Key Laboratory of Cotton Biology Key Laboratory of Biological and Genetic Breeding of Cotton The Ministry of Agriculture Institute of Cotton Research Chinese Academy of Agricultural Sciences Anyang 455000 China

In plants, long noncoding RNAs (lncRNAs) regulate disease resistance against fungi and other pathogens. However, the specific mechanism behind this regulation remains unclear. In this study, we identified disease resistance-related lncRNAs as well as their regulating genes and assessed their functions by infection of cotton ( Gossypium ) chromosome segment substitution lines with Verticillium dahliae . Our results demonstrated that lncRNA7 and its regulating gene Pectin methylesterase inhibitor 13 ( GbPMEI13 ) positively regulated disease resistance via the silencing approach, while ectopic overexpression of GbPMEI13 in Arabidopsis ( Arabidopsis thaliana ) promoted growth and enhanced resistance to V. dahliae . In contrast, lncRNA2 and its regulating gene Polygalacturonase 12 ( GbPG12 ) negatively regulated resistance to V. dahliae . We further found that fungal disease-related agents, including the pectin-derived oligogalacturonide (OG), could downregulate the expression of lncRNA2 and GbPG12 , leading to pectin accumulation. Conversely, OG upregulated the expression of lncRNA7 , which encodes a plant peptide phytosulfokine (PSK-α), which was confirmed by lncRNA7 overexpression and Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS) experiments. We showed that PSK-α promoted 3-Indoleacetic acid (IAA) accumulation and activated GbPMEI13 expression through Auxin Response Factor 5. Since it is an inhibitor of pectin methylesterase (PME), GbPMEI13 promotes pectin methylation and therefore increases the resistance to V. dahliae . Consistently, we also demonstrated that GbPMEI13 inhibits the mycelial growth and spore germination of V. dahliae in vitro. In this study, we demonstrated that lncRNA7, lncRNA2, and their regulating genes modulate cell wall defense against V. dahliae via auxin-mediated signaling, providing a strategy for cotton breeding.

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Superhydrophobic Photothermal Coating Comprising Sio2-Grafted Fe3o4 and Ag-Decorated Reduced Graphene for Anti-Icing and De-Icing

SSRN

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SSRN 2024年

作者： Zhang, Pan Wu, Changmei Xie, Lan Xue, Bai Jiang, Yi Department of Polymer Materials and Engineering College of Materials and Metallurgy Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

Considering the current challenges associated with fossil fuel scarcity and ice accumulation on solid surfaces, we propose a sustainable superhydrophobic photothermal coating approach to harnesses solar energy for ice mitigation and de-icing. We have successfully integrated the Fe3O4@SiO2@FAS/Ag-rGO composites into an epoxy matrix to create a photothermal superhydrophobic coating. The innovative coating boasts outstanding superhydrophobicity with a water contact angle of 153.8°, resulting in a delayed freezing time of 1200 s, which is four times longer than that of a pure EP coating. Additionally, it exhibits an impressive photothermal effect, raising the surface temperature to 58.8 °C under 1 sun. The coating also demonstrates rapid de-icing capabilities, clearing ice in just 120 s under 40 mW/cm2. By leveraging the light-trapping effect, utilizing the plasma resonance of silver, and incorporating a high surface roughness micro-nano hierarchical structure, the coating achieves exceptional photothermal conversion efficiency and anti-icing performance. Moreover, the coating's chemical and mechanical durability enhance its practicality and potential utility across various applications. In conclusion, this innovative superhydrophobic coating with photothermal properties offers a promising solution for various practical applications in addressing challenges related to ice accumulation and removal. © 2024, The Authors. All rights reserved.

关键词： Coatings

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Evolving Effective Drug Therapies with Multi-objective Genetic Algorithm 16th

Evolving Effective Drug Therapies with Multi-objective Genet...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Weng, Shaowei Chu, Shu-Chuan Nguyen, Trong-The Dao, Thi-Kien School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

The development of effective drug therapies is a complex and multifaceted problem involving various biological, chemical, and computational challenges. Traditional drug development methods are often time-consuming and costly, necessitating innovative approaches. Multi-objective genetic algorithms (MOGAs) have emerged as a powerful tool for optimizing drug design by simultaneously considering multiple conflicting objectives, such as efficacy, safety, and cost. This paper reviews the application of MOGAs in drug discovery, highlighting their principles, advantages, and case studies, and discussing future directions in the field. Enhancing MOGAs in drug discovery are also outlined, emphasizing the integration with machine learning, advancements in computational infrastructure, and the potential for personalized medicine. The paper concludes by affirming the significant promise of MOGAs in accelerating the development of safe and effective drug therapies, thereby addressing critical needs in modern pharmacology. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drug discovery

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AMOD-Net: Atention-based Multi-Scale Object Detection Network for X-Ray Baggage Security Inspection 5

AMOD-Net: Atention-based Multi-Scale Object Detection Networ...

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5th International Conference on Computer Science and Artificial Intelligence, CSAI 2021

作者： Zhu, Xiaoke Zhang, Jitong Chen, Xiaopan Li, Danyang Wang, Yufei Zheng, Minghao School of Computer and Information Engineering Henan University China Henan Key Laboratory of Big Data Analysis and Processing Henan University China

ISBN: (纸本)9781450384155

X-ray baggage security checking is an extremely important task, which can detect various dangerous objects in airports, stations and other public places to prevent crimes and protect personal safety. However, at present, most of the recognition is done manually, which is inefficient and error-prone. As a complementary, object detection algorithm is beneficial to avoiding errors caused by manual detection. Although the universal object detection is well developed and the performance of the universal detectors is very advanced, the performance of these detectors in X-ray image detection is mediocre. In this paper, we propose an Attention-based Multi-Scale Object Detection Network (called AMOD-Net) for X-ray baggage security inspection. To solve the problems of stacking and occlusion existed in the X-ray baggage image, we design a channel selection attention module for AMOD-Net. To make better use of the feature information, we construct a deep feature fusion structure for AMOD-Net. Experiments on the X-ray baggage dataset demonstrate that our approach achieves very competitive results. © 2021 ACM.

关键词： Object detection

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Analysis of Schema Formation in Genetic Algorithms: A Review 16th

Analysis of Schema Formation in Genetic Algorithms: A Review

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Zhang, Tiancong Chu, Shu-Chuan Weng, Shaowei Dao, Thi-Kien Nguyen, Trong-The School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qing-Dao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam

ISBN: (纸本)9789819615346

Genetic algorithms (GAs) are a powerful class of optimization techniques inspired by the principles of natural selection and genetics. One of the theoretical cornerstones of GAs is schema theory, which provides a framework for understanding how building blocks of solutions, known as schemas, are propagated and combined through generations. This review paper aims to provide a comprehensive theoretical analysis of schema formation in genetic algorithms. It delves into the foundational principles of schema theory, including the schema theorem and its implications for the efficiency and behavior of GAs. The paper also explores advanced topics such as schema disruption mechanisms, the impact of multimodal and epistatic fitness landscapes, and the integration of schema theory with hybrid approaches. Additionally, practical implications and real-world applications of schema theory are discussed through case studies and best practices. By elucidating the mechanisms of schema formation and their influence on genetic algorithm performance, this review seeks to offer valuable insights and directions for future research. Ultimately, this theoretical exploration aims to bridge the gap between abstract concepts and practical application, contributing to the advancement of genetic algorithms as robust optimization tools. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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Chinese Control and Decision Conference, CCDC

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Carbon monoxide is a colorless and odorless gas that is extremely dangerous. Carbon monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight conditions. As a result, the human body can only inhale it passively. Carbon monoxide inhalation can deplete the body's oxygen supply, causing symptoms such as nausea, dizziness, headaches, and, in severe cases, death. In response to the event's insidiousness, urgency, and gravity. Simultaneously, because the current carbon monoxide monitoring system is not portable, it is proposed to design a microcontroller as the core, with a concentration collection module, a wireless transmission module, an LCD module, and so on. Interact with information between intelligent monitoring systems via wireless transmission module and mobile phone APP. The system consists of a microcontroller, concentration acquisition module, wireless transmission module, and LCD display module. And the performance of the system is tested by changing the environmental conditions, while a control test is conducted. The results show that the system has a fast and stable transmission rate, small size, and wireless monitoring function. Users can be informed of environmental conditions via their mobile phones, providing early warning and quick alarms.

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