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Biosensors 2023年第10期13卷 929-929页

作者： Chen, Hsiu-Mei Tsai, Yi-Hsuan Hsu, Chien-Yi Wang, Yong-Yi Hsieh, Cheng-En Chen, Jin-Hua Chang, Yu-Sheng Lin, Ching-Yu Department of Chemical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan Division of Cardiology and Cardiovascular Research Center Department of Internal Medicine Taipei Medical University Hospital Taipei11031 Taiwan Division of Cardiology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei Heart Institute Taipei Medical University Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan Office of Data Science Taipei Medical University Taipei11031 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City23561 Taiwan Division of Allergy Immunology and Rheumatology Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei11031 Taiwan

An effective early diagnosis is important for rheumatoid arthritis (RA) management. This study reveals a novel RA detection method using bacteriorhodopsin as a photoelectric transducer, a light-driven proton pump in purple membranes (PMs). It was devised by covalently conjugating a PM monolayer-coated electrode with a citrullinated-inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3)542–556 peptide that recognizes the serum RA-associated autoantibodies. The direct serum coating decreased the photocurrents in the biosensor, with the reduction in the photocurrent caused by coating with an RA-patient serum that is significantly larger than that with a healthy-control serum (38.1% vs. 20.2%). The difference in the reduction in the photocurrent between those two serum groups widened after the serum-coated biosensor was further labeled with gold nanoparticle (AuNP)-conjugated anti-IgA (anti-IgA-AuNP) (53.6% vs. 30.6%). Both atomic force microscopic (AFM) and Raman analyses confirmed the sequential peptide, serum, and anti-IgA-AuNP coatings on the PM-coated substrates. The reductions in the photocurrent measured in both the serum and anti-IgA-AuNPs coating steps correlated well with the results using commercial enzyme-linked immunosorbent assay kits (Spearman rho = 0.805 and 0.787, respectively), with both a sensitivity and specificity close to 100% in both steps. It was shown that an RA diagnosis can be performed in either a single- or two-step mode using the developed biosensor. © 2023 by the authors.

关键词： Peptides

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Hybrid Intelligent-Annotation Organ Segmentation on Medical datasets

Hybrid Intelligent-Annotation Organ Segmentation on Medical ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Jing Zhao Yidong Gu Gongye Di Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Ultrasound Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Beijing China Department of Medical Ultrasound Suzhou Municipal Hospital The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Jiangsu China Department of Ultrasonic The Affiliated Taizhou People's Hospital of Nanjing Medical University Taizhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound image segmentation is crucial for early disease detection and treatment planning but remains a challenging task due to the low contrast of organ boundaries and varying image quality. Current methods often require manual intervention or have limited accuracy. In this paper, we propose a novel hybrid framework that combines an automatic option polygon segment (AOPS) algorithm and a distributed- and memory-based evolution (DME) algorithm for precise ultrasound organ segmentation. Our pipeline consists of two cascaded stages: (1) a coarse segmentation step using the AOPS algorithm, which determines the number of vertices/clusters without human intervention, and (2) a refinement step using the DME algorithm to hunt for the optimal neural network, which is then used to represent a smooth, explainable mathematical expression of the organ boundary. We employ the fractional backpropagation learning network with L2 regularization (FBLN) for training and use the scaled exponential linear unit (SELU) activation function to address the vanishing gradient problem. This is a new attempt such a hybrid framework is applied to ultrasound organ segmentation tasks, and it demonstrates significant contributions in terms of accuracy, smoothness, and computational efficiency.

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Contour Detection from Ultrasound Kidney Images with A Coarse-to-Fine Approach

Contour Detection from Ultrasound Kidney Images with A Coars...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tao Peng Yidong Gu Yanqing Xu Caishan Wang Lei Zhang Jing Cai School of Future Science and Engineering Soochow University Suzhou China Department of Radiation Oncology UT Southwestern Medical Center Dallas TX USA Department of Medical Ultrasound the Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Suzhou Jiangsu China McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas Texas USA Department of Ultrasound the Second Affiliated Hospital of Soochow University Suzhou China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Kunshan Jiangsu China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong China

Ultrasound kidney image segmentation presents significant challenges due to missing or ambiguous boundaries. In this study, we introduce a coarse-to-refinement approach incorporating four novel aspects. Firstly, we leverage the properties of a principal curve (PC) to automatically fine-tune the curve shape and employ a neural network's learning ability to reduce model error. Secondly, a deep fusion learning network is utilized for the coarse segmentation step, incorporating a parallel architecture to enhance deep-learning performance. Thirdly, addressing the limitation of standard PC-based methods in determining the number of vertices automatically, we propose an automatic searching polygon tracking method using a mean shift clustering-based approach to replace the projection and vertex extension step in standard PC-based methods. Lastly, we develop an explainable mathematical map function for the kidney contour, as denoted by the neural network output (i.e., optimized vertices), which aligns well with the ground truth contour. We conducted various experiments to evaluate our method's performance, demonstrating its effectiveness in ultrasound kidney image segmentation.

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CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data

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Nature Immunology 2025年第6期26卷 963-974页

作者： Zhu, Bokai Gao, Sheng Chen, Shuxiao Wang, Yuchen Yeung, Jason Bai, Yunhao Huang, Amy Y. Yeo, Yao Yu Liao, Guanrui Mao, Shulin Jiang, Zhenghui G. Rodig, Scott J. Wong, Ka-Chun Shalek, Alex K. Nolan, Garry P. Jiang, Sizun Ma, Zongming Ragon Institute of MGH MIT and Harvard Cambridge MA United States Broad Institute of MIT and Harvard Cambridge MA United States Massachusetts Institute of Technology Cambridge MA United States Department of Statistics and Data Science The Wharton School University of Pennsylvania Philadelphia PA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Computer Science City University of Hong Kong Hong Kong Dana-Farber Cancer Institute Boston MA United States Center of Hepato-Pancreato-Biliary Surgery The First Affiliated Hospital of Sun Yat-sen University Guangzhou China Division of Genetics and Genomics Boston Children’s Hospital Harvard Medical School Boston MA United States Program in Biological and Biomedical Sciences Harvard Medical School Boston MA United States Division of Gastroenterology/Liver Center Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States Department of Pathology Brigham and Women’s Hospital Harvard Medical School Boston MA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Chemistry Massachusetts Institute of Technology Cambridge MA United States Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA United States Department of Pathology Stanford University Stanford CA United States Department of Statistics and Data Science Yale University New Haven CT United States

Delineating cell populations is crucial for understanding immune function in health and disease. Spatial omics technologies offer insights by capturing three complementary domains: single-cell molecular biomarker expression, cellular spatial relationships and tissue architecture. However, current computational methods often fail to fully integrate these multidimensional data, particularly for immune cell populations and intrinsic functional states. We introduce Cell Local Environment and Neighborhood Scan (CellLENS), a self-supervised computational method that learns cellular representations by fusing information across three spatial omics domains (expression, neighborhood and image). CellLENS markedly enhances de novo discovery of biologically relevant immune cell populations at fine granularity by integrating individual cells’ molecular profiles with their neighborhood context and tissue localization. By applying CellLENS to diverse spatial proteomic and transcriptomic datasets across multiple tissue types and disease settings, we uncover unique immune cell populations functionally stratified according to their spatial contexts. Our work demonstrates the power of multi-domain data integration in spatial omics to reveal insights into immune cell heterogeneity and tissue-specific functions. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Estimating Effectiveness of Identifying Human Trafficking via data Envelopment Analysis

arXiv

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arXiv 2020年

作者： Dimas, Geri L. Khalkhali, Malak El Bender, Alex Maass, Kayse Lee Konrad, Renata A. Blom, Jeffrey S. Zhu, Joe Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute United States Business School Worcester Polytechnic Institute United States Mechanical and Industrial Engineering Department Northeastern University United States Love Justice International United States Business School Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute United States

Transit monitoring is a preventative approach used to identify possible cases of human trafficking prior to exploitation while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations at intercepting potential human-trafficking victims given the amount of resources (e.g. staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units (DMUs) from 7 stations over 13 quarters and considers three inputs, four outputs, and 3 homogeneity criteria. Using this model we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain. Copyright © 2020, The Authors. All rights reserved.

关键词： data envelopment analysis

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SADDEL: Joint speech separation and denoising model based on multitask learning

arXiv

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arXiv 2020年

作者： Wu, Yuan-Kuei Tuan, Chao-I Lee, Hung-Yi Tsao, Yu Graduate Institute of Communication Engineering National Taiwan University Data Science Degree Program National Taiwan University Research Center for Information Technology Innovation Academia Sinica

Speech data collected in real-world scenarios often encounters two issues. First, multiple sources may exist simultaneously, and the number of sources may vary with time. Second, the existence of background noise in recording is inevitable. To handle the first issue, we refer to speech separation approaches, that separate speech from an unknown number of speakers. To address the second issue, we refer to speech denoising approaches, which remove noise components and retrieve pure speech signals. Numerous deep learning based methods for speech separation and denoising have been proposed that show promising results. However, few works attempt to address the issues simultaneously, despite speech separation and denoising tasks having similar nature. In this study, we propose a joint speech separation and denoising framework based on the multitask learning criterion to tackle the two issues simultaneously. The experimental results show that the proposed framework not only performs well on both speech separation and denoising tasks, but also outperforms related methods in most conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Source separation

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Aggregation of Sentiment Analysis Index with Hesitant Fuzzy Sets for Financial Time Series Forecasting

Aggregation of Sentiment Analysis Index with Hesitant Fuzzy ...

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AI IoT Congress (AIIoT), World

作者： Breno Costa Dolabela Dias Hossein Javedani Sadaei Petrônio Cândido De Lima e Silva Frederico Gadelha Guimarães UFMG Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Machine Intelligence and Data Science Laboratory (MINDS) Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto Federal do Norte de Minas Gerais Januaria Brazil

Sentiment analysis is an automatic technique to extract subjective information from texts, such as opinions and sentiments. For providing a time series forecasting using sentiment analysis, sentiment classifications of news and social media posts have to be aggregated into a single value to produce a time series with the same periodicity of the stock market prices, for example daily or hourly. In this paper, we adopt fuzzy linguistic values (and corresponding fuzzy sets) to represent prices and sentiments. Given the fuzzified sentiment index of each tweet, we proceed to an aggregation based on hesitant fuzzy sets, which aim to model the uncertainty caused by the hesitation that may arise in the attribution of degrees of membership of the elements to a fuzzy set. Having fuzzified the sentiment index and aggregated them within the same time period, we produce a fuzzified time series of sentiment data, which can be used as additional information for forecasting models. In this paper, we employ a multivariate fuzzy time series (FTS) method, namely Weighted Multivariate FTS (WMVFTS), as the machine learning model. For the experiments we collected tweets posted by Bloomberg and the closing prices of Standard & Poor's 500 Index and Nasdaq Composite Index. The main feature delivered by the proposed method is the capability of improving an FTS method by using hesitant information, such as the news posted on Twitter.

关键词： Fuzzy sets Sentiment analysis Uncertainty Social networking (online) Time series analysis Indexes Forecasting

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Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders

arXiv

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arXiv 2021年

作者： Gajamannage, Kelum Park, Yonggi Paffenroth, Randy Jayasumana, Anura P. Department of Mathematics and Statistics Texas A&M University–Corpus Christi Corpus ChristiTX78412 United States Department of Mathematical Sciences Department of Computer Science Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80525 United States

Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectories. Here, we present an extended deep autoencoder (DA) that we train only on fully observed segments of the trajectories by defining its loss function as the Hadamard product of a binary indicator matrix with the absolute difference between the outputs and the labels. The trajectories of the agents practicing collective motion is low-rank due to mutual interactions and dependencies between the agents that we utilize as the underlying pattern that our Hadamard deep autoencoder (HDA) codes during its training. The performance of our HDA is compared with that of a low-rank matrix completion scheme in the context of fragmented trajectory reconstruction. © 2021, CC BY.

关键词： Learning systems

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Correction: Study of bioaerosol disinfection kinetics and application of nonlinear regression modeling for optimization of TiO2-based photocatalytic disinfection process

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Nanotechnology for Environmental engineering 2023年第1期9卷 133-133页

作者： Nishad, Vartika Mandal, Chinmoy Sahu, Manoranjan Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Powai Mumbai India Interdisciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai India

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