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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Bao, Jie Xiao, Anqi Han, Keyi Pei, Ziyu Fu, Lidan Tian, Jie Hu, Zhenhua CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi’an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9781510685987

Fluorescence molecular tomography (FMT) is a highly sensitive method for depicting the three-dimensional distribution of fluorescent targets within biological tissues. However, traditional FMT reconstruction can be affected by the severe photon scattering and the modeling errors of the photon propagation model, making it difficult to obtain accurate reconstruction results. Deep learning methods can directly learn the nonlinear mapping between the photon intensity on the surface and the internal light sources, providing a promising approach for addressing the inverse problem in FMT. Since the second near-infrared window (NIR-II, 1000-1700) fluorescence imaging has a higher tissue penetration depth and imaging contrast, this work constructs a deep learning model suitable for the fluorescence images in this spectrum. The model introduced in this study, akin to grasping the contextual nuances of word vectors in the domain of machine translation, endeavors to comprehend the process of photon transmission within biological tissues. Inspired by the transformer encoder, we proposed a network mainly consist of positional encoding, dual-head attention mechanism, residual connection, and fully connected layers. It has achieved significantly better results than comparative methods on the NIR-II dataset constructed by the Monte Carlo method. © 2025 SPIE.

关键词： Fluorescence imaging

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Two-Month Cognitive Changes Enhance Prediction of Nonremission in Clinical High-Risk Individuals

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Biological Psychiatry 2025年

作者： Zhang, TianHong Wei, YanYan Tang, XiaoChen Xu, LiHua Cui, HuiRu Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Tang, YingYing Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Longitudinal changes in cognitive function may be crucial in predicting clinical outcomes in clinical high-risk (CHR) individuals. In this study, we aimed to investigate the predictive value of baseline cognitive impairment and short-term cognitive changes for nonremission and conversion to psychosis in individuals at CHR for psychosis compared with healthy control individuals (HCs). Methods: This study used a multiple-group prospective design with a 3-year follow-up. CHR individuals and HCs were assessed at baseline and at a 2-month follow-up. Neuropsychological performance was evaluated using the Chinese version of the MATRICS (Measurement and Treatment research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery. Results: The study included 310 CHR individuals and 93 HCs. Significant improvements in predicting nonremission in CHR individuals were observed when incorporating cognitive changes over 2 months (area under the receiver operating characteristic curve [AUC] for baseline cognition, 0.690;AUC for changes, 0.819;z = 3.365, p < .001). Key predictors included the Hopkins Verbal Learning Test–Revised (β = 0.083, p = .003), Wechsler Memory Scale-III spatial span (β = 0.330, p < .001), and Brief Visuospatial Memory Test–Revised (β = 0.127, p < .001). Conversely, predicting conversion to psychosis showed no significant difference between baseline and 2-month cognitive changes (AUC for baseline cognition, 0.667;AUC for changes, 0.666;z = 0.021, p = .242). Conclusions: The findings underscore the importance of dynamic cognitive monitoring in CHR individuals. Short-term cognitive changes significantly enhanced the prediction of nonremission but did not add predictive value for conversion to psychosis beyond baseline assessments. Specific cognitive domains, such as verbal learning and working memory, were particularly valuable for predicting clinical outcomes. © 2025 Society of Biological Psychiatry

关键词： Cognition Functional outcome Prodromal psychosis Remission Transition Ultra-high risk

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Protecting centrosomes from fracturing enables efficient cell navigation

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science Advances 2025年第17期11卷

作者： Schmitt, Madeleine T. Kroll, Janina Ruiz-Fernandez, Mauricio J.A. Hauschild, Robert Ghosh, Shaunak Kameritsch, Petra Merrin, Jack Schmid, Johanna Stefanowski, Kasia Thomae, Andreas W. Cheng, Jingyuan Öztan, Gamze Naz Konopka, Peter Ortega, Germán Camargo Penz, Thomas Bach, Luisa Baumjohann, Dirk Bock, Christoph Straub, Tobias Meissner, Felix Kiermaier, Eva Renkawitz, Jörg Biomedical Center Walter Brendel Center of Experimental Medicine Institute of Cardiovascular Physiology and Pathophysiology Klinikum der Universität Ludwig Maximilians Universität München Munich Germany Institute of Science and Technology Austria Klosterneuburg Austria Institute Immune and Tumor Biology University of Bonn Bonn Germany Bioimaging Facility Biomedical Center Faculty of Medicine Ludwig Maximilians Universität München Munich Germany Institute of Innate Immunity Department of Systems Immunology and Proteomics Medical Faculty University of Bonn Bonn Germany Institute of Stem Cell Research Helmholtz Center Munich German Research Center for Environmental Health Munich Germany Physiological Genomics Biomedical Center Ludwig-Maximilians University Munich Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Medical Clinic III for Oncology Hematology Immuno-Oncology and Rheumatology University Hospital Bonn University of Bonn Bonn Germany Medical University of Vienna Institute of Artificial Intelligence Center for Medical Data Science Vienna Austria Bioinformatics Unit Biomedical Center Faculty of Medicine Ludwig Maximilians Universität München Munich Germany

The centrosome is a microtubule orchestrator, nucleating and anchoring microtubules that grow radially and exert forces on cargos. At the same time, mechanical stresses from the microenvironment and cellular shape changes compress and bend microtubules. Yet, centrosomes are membraneless organelles, raising the question of how centrosomes withstand mechanical forces. Here, we discover that centrosomes can deform and even fracture. We reveal that centrosomes experience deformations during navigational pathfinding within motile cells. Coherence of the centrosome is maintained by Dyrk3 and cNAP1, preventing fracturing by forces. While cells can compensate for the depletion of centriolar-based centrosomes, the fracturing of centrosomes impedes cellular function by generating coexisting microtubule organizing centers that compete during path navigation and thereby cause cellular entanglement in the microenvironment. Our findings show that cells actively maintain the integrity of the centrosome to withstand mechanical forces. These results suggest that centrosome stability preservation is fundamental, given that almost all cells in multicellular organisms experience forces. © 2025 The Authors, some rights reserved;exclusive licensee American Association for the Advancement of science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

关键词： Cell culture

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Investigation of Weight Initialization Using Fibonacci Sequence on the Performance of Neural Networks 4

Investigation of Weight Initialization Using Fibonacci Seque...

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4th IEEE Pune Section International Conference, PuneCon 2021

作者： Mukherjee, Dipanwita Sinha Yeri, Naveen Gururaja Enterprise Analytics and Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India Head of Enterprise Analytics and Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9781665444798

Initializing weights are important for fast convergence and performance improvement of artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques. © 2021 IEEE.

关键词： Heuristic methods

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Undamped soliton-like domain wall motion in sliding ferroelectrics

arXiv

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arXiv 2025年

作者： Shi, Yubai Gao, Yuxiang He, Ri Wang, Hua Zhang, Binwen Zhong, Zhicheng CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Center for Quantum Matter School of Physics Zhejiang University Hangzhou310058 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Fujian Key Laboratory of Functional Marine Sensing Materials Center for Advanced Marine Materials and Smart Sensors College of Material and Chemical Engineering Minjiang University Fuzhou350108 China

Sliding ferroelectricity, which is a unique polarity recently discovered in bilayer van der Waals materials, achieves polarization switching through in-plane interlayer sliding. The unique mechanism, combining intralayer stiffness and interlayer slipperiness, leads to wider domain walls (DWs) and faster DW motion compared to conventional ferroelectrics. Herein, using machine-learning-assisted molecular dynamics simulations and field theory analysis, we find the DW in classical sliding ferroelectric bilayer 3R-MoS2 system exhibits uniformly accelerated motion under an external field, like the Newtonian particle with undamped motion. Remarkably, DW velocity remains constant even after the external field removal, completely deviating from the velocity breakdown observed in conventional ferroelectrics. We further propose an experimental approach to validate this undamped soliton-like DW behavior in sliding ferroelectric systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Path optimization method for the sign problem caused by the fermion determinant

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Physical Review D 2025年第9期111卷 094503-094503页

作者： Kazuki Hisayoshi Kouji Kashiwa Yusuke Namekawa Hayato Takase Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan

The path optimization method with machine learning is applied to the one-dimensional massive lattice Thirring model, which has the sign problem caused by the fermion determinant. This study aims to investigate how the path optimization method works for the sign problem. We show that the path optimization method successfully reduces statistical errors and reproduces the analytic results. We also examine an approximation of the Jacobian calculation in the learning process and show that it gives consistent results with those without an approximation.

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A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Lin, Tantao Ren, Zhijun Zhu, Linbo Zhu, Yongsheng Feng, Ke Ding, Weiping Yan, Ke Beer, Michael Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University Frontier Institute of Science and Technology Xi'an710049 China Xi'an Jiaotong University School of School of Chemical Engineering and Technology Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China Leibniz University Institute for Risk and Reliability Hannover30167 Germany University of Liverpool Department of Civil and Environmental Engineering LiverpoolL69 3BX United Kingdom Tongji University International Joint Research Center for Resilient Infrastructure International Joint Research Center for Engineering Reliability and Stochastic Mechanics Shanghai200092 China

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration. © 1963-2012 IEEE.

关键词： Information fusion

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An integrated consensus-oriented decision-making framework for exploring the barriers and applications of medical digital twins

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Expert Systems with Applications 2025年 284卷

作者： Wu, Qun Tan, Weiqi Zhou, Ligang Deveci, Muhammet Ding, Weiping Martínez, Luis Pamucar, Dragan School of Mathematical Sciences Anhui University Hefei230601 China Anhui University Center for Applied Mathematics Anhui University Anhui Hefei230601 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey Royal School of Mines Imperial College London LondonSW7 2AZ United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China Faculty of Data Science City University of Macau 999078 China Department of Computer Science University of Jaén Jaén23071 Spain Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia

With improvements in medical data collection technology and the refinement of medical big data, digital twin (DT) technology has increasingly been applied in the medical field. A DT is a digital representation of physical objects, processes, and systems. The rapid iteration of technologies such as mathematical modeling, artificial intelligence, cloud computing, blockchain, and the Internet of Things has made the development of medical digital twins (MDTs) possible, showing immense potential for growth in the medical industry. Although MDT application has evolved considerably, it still faces numerous barriers, and specific MDT application fields remain unclear. This research presents a novel integrated consensus-oriented MCGDM framework under uncertain linguistic environments for exploring MDT barriers and applications. First, the linguistic 2-tuple representation model is used to quantify the uncertain linguistic semantics in the decision-making procedure. Second, a two-stage 2-tuple linguistic consensus optimization model with minimum adjustment rules is presented to aggregate individual opinions to derive more scientific and efficient group opinions. Third, the objective and reasonable Criteria Importance Through Inter-criteria Correlation (CRITIC) method combined with subjective weights and the flexible and reliable double normalization-based multiple aggregation (DNMA) method are incorporated into the 2-tuple linguistic environment to prioritize the MDT barriers and application fields, respectively. Finally, a case study with a comprehensive evaluation system to prioritize MDT barriers and application fields is utilized to test the effectiveness and practicality of the presented decision support framework. The results show that technology and talent shortage (0.2153) has the highest levels among all the barriers, and prognosis prediction and chronic disease management (0.5474) is the most pervasive and easy-to-implement MDT application field. The findings of thi

关键词： Risk perception

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rcsb-api: Python Toolkit for Streamlining Access to RCSB Protein data Bank APIs: rcsb-api: Python Toolkit to Access *** APIs

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Journal of Molecular Biology 2025年 168970页

作者： Piehl, Dennis W. Vallat, Brinda Truong, Ivana Morsy, Habiba Bhatt, Rusham Blaumann, Santiago Biswas, Pratyoy Rose, Yana Bittrich, Sebastian Duarte, Jose M. Segura, Joan Bi, Chunxiao Myers-Turnbull, Douglas Hudson, Brian P. Zardecki, Christine Burley, Stephen K. Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States

The Protein data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein data Bank *** research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support *** users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (***/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences. © 2025 The Author(s)

关键词： bioinformatics FAIR principles RCSB Protein data Bank structural biology

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Preprocessing Application for Car Insurance Claim Classification Model

Preprocessing Application for Car Insurance Claim Classifica...

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2021 International Conference on artificial intelligence and Big data Analytics, ICAIBDA 2021

作者： Gunadi, Farhan Fauzi, Muhammad Firdaus, Bagas Helen, Afrida Universitas Padjadjaran Research Center for Artificial Intelligence and Big Data Department of Computer Science Sumedang Indonesia

ISBN: (纸本)9781665408905

The use of data in various industry sectors becomes a necessity in predicting or deciding on one of them is the use of car insurance data related to claims made by vehicle owners. The data can later be used by insurance companies to be analyzed related to car insurance claims both from the owner's side of the car and from the condition of the car. This paper will discuss the preprocessing data on auto insurance claim data in America with the aim of later making a data model so that it can be used next to see the accuracy of the data processed classification. The results of processing data and data classification can help car insurance companies in deciding a policy or problem that occurs accurately and measurable. The data used in this paper is data that still has a missing value. Therefore, data cleaning is done by cleaning, filtering, and combining these data. The study used the Car Insurance Claim data dataset downloaded on kaggle's website. The results showed that the JRIP algorithm had the best accuracy of 83.09 percent (before the preprocessing dataset) and 83.14 percent (after the preprocessing dataset was applied) in the 10 fold cross-validation test mode. With an increased level of accuracy, the data can be better used again as an example for forecasting tau as a reference company to trigger something related to the data. © 2021 IEEE.

关键词： Statistical tests

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