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Medical Imaging 2025: Digital and Computational Pathology

作者： Fermin, Jamie L. Border, Samuel Naglah, Ahmed Shickel, Benjamin La Rosa, Patricio S. Tomaszewski, John E. Jain, Sanjay El-Achkar, Tarek M. Eadon, Michael T. Sarder, Pinaki Dept. of Electrical and Computer Engineering Univ. of Florida GainesvilleFL United States Dept. of Biomedical Engineering Univ. of Florida GainesvilleFL United States Univ. of Florida Intelligent Critical Care Center GainesvilleFL United States Seed Production Innovation Crop Science Division Bayer Company St. LouisMO United States Dept. of Pathology & Anatomical Sciences Univ. at Buffalo BuffaloNY United States Div. of Nephrology Dept. of Medicine Washington Univ. School of Medicine St. LouisMO United States Div. of Nephrology Dept. of Medicine. Indiana Univ School of Medicine IndianapolisIN United States Div. of Nephrology Hypertension and Renal Transplantation Quantitative Health Section Dept. of Medicine Univ. of Florida GainesvilleFL United States

ISBN: (纸本)9781510686045

Multi-omics data, such as 10X Genomics Visium (spatial transcriptomics), measure gene expressions, molecular pathway activities, and can predict cell types/states but are often expensive and inaccessible in clinical settings. Thus, despite the emergence of multi-omics technologies, histopathological assessments under brightfield microscopy remain the diagnostic gold standard. In this work, we examine machine learning-based pipelines for predicting cell types/states from brightfield h i stology i m ages u s ing s t ate-of-the-art (SOTA) d e ep l e arning (DL) models, aiming to enhance diagnostics and prognostics in clinical medicine. Our proposed pipeline consists of two stages: (1) an Image-To-Text retrieval Network (ITTN) that leverages the CONtrastive learning from Captions for Histopathology (CONCH) model to assign histopathological text prompt from brightfield h i stology i m age, a n d (2) a V i sion L a nguage M odel (V LM), w h ich i s b u ilt o n t h e same CONCH model used in ITTN but incorporates a regression head to predict cell type/state proportions based on the paired image and text inputs. During training, we classify the image into one of four structural types (glomerulus, tubules, vessels, and interstitium) using the ITTN. These classification l a bels a r e t h en u s ed to construct a new text prompt with a suitable histopathological description for each image in the test set. The new text prompt and raw image are used as paired inputs to the VLM to predict cell types/states. We also utilize SOTA models, such as CONCH (using only the vision encoder), ViT, and ResNet, which employ image-only inputs in separate regression pipelines. We experimented and tested our proposed pipelines on a set of 10X Visium formalin-fixed paraffin-embedded whole slides images of diabetic nephropathy samples collected at Indiana University. Our experiments yielded a mean squared error of 0.0027 for the proposed pipeline, showing improvements of 20.59%, 27.03%, and 32.50

关键词： Genes

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Automatic alignment and location of multiple fiducial marks

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International Journal of Mechatronics and Manufacturing Systems 2020年第2期13卷 125-152页

作者： Lin, Chuen-Horng Wen, Chih-Chin Chen, Jr-Wei Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd. Taichung Taiwan Mechatronics Section Metal Industries Research and Development Centre 1001 Kaohsiung Highway Kaohsiung811 Taiwan

This paper proposes a method for the automatic alignment and locating the position. This paper proposes methods for the alignment and location on two types of fiducial marks (FM) images. FM images of the first type include light-emitting diode (LED) wafer images, liquid crystal display (LCD) images and hand-held electronic device (HED) images. FM images of the second type include LCD cross images and LED square cross images. This paper proposes the artificial measurement method and the mechanical measurement method in order to make mechanical positioning-related decisions. To validate the properties and the robustness of the proposed methods. The results achieved by the proposed matching, alignment methods are compared with those achieved by manual image matching in order to evaluate the accuracy and performance of the alignment methods. computer image processing skills, appropriate charge coupled device (CCD) equipment, quantity, image capture technology and glazing mode are used to develop high speed, precision and stability automatic positioning technology in this paper. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Location

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Classifying hypertension mediated left ventricular hypertrophy phenotypes from the 12-lead electrocardiogram using machine learning

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European Heart Journal - Cardiovascular Imaging 2023年第SUPPLEMENT_1期24卷

作者： Naderi, H Ramirez, J Van Duijvenboden, S Ruiz Pujadas, E Aung, N Wang, L Chahal, C A A Lekadir, K Petersen, S E Munroe, P B Queen Mary University of London William Harvey Research Institute London United Kingdom of Great Britain & Northern Ireland University of Zaragoza Aragon Institute of Engineering Research Zaragoza Spain University of Barcelona Faculty of Mathematics and Computer Science Barcelona Spain Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom of Great Britain & Northern Ireland University of Pennsylvania Cardiac Electrophysiology Section Division of Cardiovascular Diseases Philadelphia United States of America

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Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network

Day-ahead Wind Power Predictions at Regional Scales: Post-pr...

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International Conference on European Electricity Market, EEM

作者： Sukanta Basu Simon J. Watson Eric Lacoa Arends Bedassa Cheneka Faculty of Civil Engineering and Geosciences Delft University of Technology Delft The Netherlands Faculty of Aerospace Engineering Wind Energy Section Delft University of Technology Delft The Netherlands Faculty of Electrical Engineering Mathematics & Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9781728169194

ISBN: (纸本)9781728169200

A hybrid neural network model, comprising of a convolutional neural network and a multilayer perceptron network, has been developed for day-ahead forecasting of regional scale wind power production. This model requires operational weather forecasts as input and also has the capability to ingest data from ensemble forecasts. Even though the training of the model requires significant computational cost, the actual forecasting can be done within a few minutes on any recent personal computer. The proposed model has demonstrated noteworthy performance at a recent international forecasting competition.

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AB1556 MACHINE LEARNING APPROACH FOR CLASSIFICATION OF ARTHRITIS ACTIVITY STATE, USING DATA FROM A SINGLE ACCELEROMETER

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Annals of the Rheumatic Diseases 2023年 82卷 2012-2012页

作者： P. Mielnik A. Myhre Hjelle A. Traseth K. Tokarz B. Pollen M. Fojcik Helse Forde Section for Rheumatology； Department for Neurology Rheumatology and Physical Medicine Forde Norway Institute of Informatics Silesian University of Technology Department of Graphics Computer Vision and Digital Systems Gliwice Poland Western Norway University of Applied Sciences Faculty of Engineering and Science Forde Norway

Background Machine learning (ML) potential is not fully exploited in diagnostics and follow up of autoimmune inflammatory rheumatic disease (AIIRD). It is despite the broader use of ML in, e.g. imaging diagnostics. The specific tools for AIIRD are lacking. Objectives This is an interim analysis of data from the first checkpoint of the proof-of-concept study on using accelerometer (ACC) data in follow up of patients with AIIRD. The main goal of the study was to investigate the value of single ACC data in the classification of arthritis activity status. Methods Subjects with AIIRD are enrolled in the study when they start of new treatment due to the disease activity. Several comorbidities, such as severe neurological and cardiological disorders, as wells as impaired mobility, are part of the protocol exclusion criteria. Volunteers without AIIRD who fulfil other inclusion/exclusion criteria are included as controls. The study was approved by local competent authorities and informed consent was given by all participants. We used data collected up till the first study checkpoint. This analysis covers nine patients with AIIRD (5 rheumatoid and four psoriasis arthritis) and 13 controls. Five patients had 3 study visits, 1 had 2 visits and 3 only one. Controls have only one visit per protocol. We analysed ACC data from 3 minutes of clapping using a home-brewed device based on Arduino nano 33 BLE with 6-axis MTU by Nordic Semiconductor. Data was divided into 6-second chunks that were found optimal in our prior study. We conducted binary classification between any/ no arthritis in any of the upper extremities. We used accuracy and area under curve (AUC) as an efficacy function derived from the receiver operating characteristic curve (ROC). We extracted 54 features from 3 ACC axis signal. The features encompassed, among others, Fourier's components, autoregression coefficients, median absolute deviation (MAD), variance, Fourier's entropy, etc. We built linear discriminant anal

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Sharing a secret image in the cloud using two shadows

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International Journal of Network Security 2020年第4期22卷 551-560页

作者： Chen, Yu Lin, Jiang-Yi Chang, Chin-Chen Hu, Yu-Chen School of Information Science and Engineering Fujian University of Technology 33 Xuefu South Road Fuzhou350118 China Department of Computer Science Xiamen University of Technology 600 Ligong Road Xiamen361024 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhua Road Taichung40724 Taiwan Department of Computer Science and Information Management Providence University 200 Section 7 Taiwan Boulevard Shalu District Taichung43301 Taiwan

In this paper, we present a novel (2, 2) reversible secret image sharing scheme. Our scheme permits secret mes-sages to be shared with two participants by splitting the marked encrypted image into two shadows. The secret messages can be reconstructed if two participants collab-orate with each other. The proposed scheme chooses suit-able binary blocks of a cover image in which to embed the secret message and divides those blocks into two shadow blocks by executing a logical operation with all of the other binary blocks, thereby producing two shadows. In the data extraction procedure, the secret messages and the cover image can be reconstructed by the logical oper-ation of the corresponding binary blocks of the two shad-ows. A practical application is demonstrated by model-ing our scheme as a reversible watermarking scheme in the Cloud. The experimental results indicated that the proposed method is reversible and that it can restore the image and watermark properly. © 2020, Femto Technique Co., Ltd.

关键词： Image watermarking

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Identification of combinatorial mutations associated with colistin resistance in Shewanella algae

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Microbes and Infection 2023年第7期25卷 105143页

作者： Huang, Yao-Ting Mao, Yan-Chiao Tseng, Chien-Hao Liu, Chia-Wei Liu, Po-Yu Department of Computer Science and Information Engineering National Chung Cheng University Daxue Road Section 1 Minxiong Township Chiayi County 62102 Taiwan Division of Clinical Toxicology Department of Emergency Medicine Taichung Veterans General Hospital 1650 Taiwan Boulevard Section 4 Xitun District Taichung 40705 Taiwan Division of Infectious Diseases Department of Internal Medicine Taichung Veterans General Hospital 1650 Taiwan Boulevard Section 4 Xitun District Taichung 40705 Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University 145 Xingda Rd South District Taichung 40227 Taiwan Ph.D. Program in Translational Medicine National Chung Hsing University 145 Xingda Rd South District Taichung 40227 Taiwan Department of Post-Baccalaureate Medicine National Chung Hsing University 145 Xingda Rd South District Taichung 40227 Taiwan Genomic Center for Infectious Diseases Taichung Veterans General Hospital 1650 Taiwan Boulevard Section 4. Xitun District Taichung 40705 Taiwan

Colistin is a last-resort antibiotic used to treat infections caused by drug-resistant gram-negative bacteria. However, the genetic mechanisms underlying colistin resistance in Shewanella algae are not well understood. In this study, we sequenced and compared the genomes of 23 mcr-negative colistin-resistant and sensitive S. algae samples from various sources. We applied a computational approach to identify combinatorial mutations associated with colistin resistance. Our analysis revealed a combination of three mutations (PmrB 451, PmrE168, PmrH292) that were strongly associated with colistin resistance in S. algae. This study provides insights into the genetic mechanisms of colistin resistance in S. algae and demonstrates the utility of a computational approach for identifying epistatic interactions among mutations. Identifying the genetic mutations responsible for colistin resistance in S. algae can inform the development of new treatments or strategies to combat infections caused by this emerging pathogen. © 2023 Institut Pasteur

关键词： Drug-resistant gram-negative bacteria Machine learning Whole genome sequencing

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The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII (vol 2024, baae069, 2024)

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DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2024年第2024期2024卷 baae069页

作者： Islamaj, Rezarta Lai, Po-Ting Wei, Chih-Hsuan Luo, Ling National Center for Biotechnology Information (NCBI) National Library of Medicine (NLM) National Institutes of Health (NIH) 8600 Rockville Pike Bethesda MD 20894 United States School of Computer Science and Technology Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian 116024 China Department of Electronics Telecommunications and Informatics (DETI) Institute of Electronics and Informatics Engineering of Aveiro (IEETA) University of Aveiro Campus Universitário de Santiago Aveiro 3810-193 Portugal Departamento de Informática Faculdade de Ciências da Universidade de Lisboa Edifício C6 Campo Grande Lisbon 1749-016 Portugal Department of Computer Science and Information Engineering National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Republic of China Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City 32001 Taiwan Republic of China Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd. Nangang District Taoyuan City 115201 Taiwan Republic of China School of Information Sciences University of Illinois at Urbana-Champaign 614 E. Daniel St Champaign IL 61820 United States Higher School of Economics University 20 Myasnitskaya st Moscow 101000 Russia Kazan Federal University 18 Kremlevskaya st Kazan Russia 420008 Russia Miin Wu School of Computing National Cheng Kung University No. 1 University Road Tainan 701 Taiwan Republic of China School of Computing Technologies RMIT University 124 La Trobe Street Melbourne Victoria 3000 Australia

The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available athttps://***/pub/lu/BC8-BioRED-track/andhttps://***/competitions/13377andhttps://***/competitions/*** URL:https://***/pub/lu/BC8-BioRED-track/https://***/competitions/13377https://***/competitions/13378

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Magnetic, electrical, and dielectric properties of microwave-assisted synthesis of Bi 0.6 Sr 0.4 FeO 3 nanoparticles for wastewater treatments: Electro-degradation of Rhodamine B dye

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Ceramics International 2025年

作者： Mohamed Abdel-Megid Mohamed E. Eissa Ahmed T. Mosleh Nourhan A.M. Ragab Heba Y. Zahran Ibrahim S. Yahia Elbadawy A. Kamoun M.H. Ghozza Chemistry Department College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11623 Saudi Arabia Nanotechnology Section Egyptian Company for Carbon Materials El-Sheraton/El-Nozha Cairo 11757 Egypt Laboratory of Nano-Smart Materials for Science and Technology (LNSMST) Department of Physics Faculty of Science King Khalid University P.O. Box 9004 Abha Saudi Arabia Department of Chemistry College of Science King Faisal University Al-Ahsa 31982 Saudi Arabia Basic Science Department Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Bi 0.6 Sr 0.4 FeO 3 (BSF) ceramics was synthesized using various methods to compare their surface morphology, dielectric, and magnetic characteristics. For 0, 5, 10, 15, and 20 min, the mixed aqueous solutions were subjected to 700 W of microwave radiation, as coded (BSF0, BSF5, BSF10, BSF15 and BSF20). SEM findings demonstrated that the duration of microwave radiation exposure impacted significantlly the size and shape of produced nanostructures. Additionally, it was observed that when the microwave duration increased, the average particle size of nanostructures dropped. BSF samples showed a transition from tetragonal to cubic with microwave assist duration time. Notably, a significant portion of substantial dielectric constant (10 3 –10 4 ) at lower frequencies is caused by heterogeneous electronic microstructure at grain contacts. The mechanically alloyed materials exhibited a much-decreased grain boundary resistivity, compared to solid-state and chemically-routed samples. σ Dc as a function of temperature depicts the transition point which was observed near 340 K and shifted to low-temperature exposure time increases. σ Dc also falled to 50 % of its value with the influence of microwave time, while E a (0.09–0.37 eV) increased, except BSF10 sample. σ AC increased with increasing frequency, but the effect of microwave time on σ AC is still insignificant, where ε ′ falled to 50 % of its value with 20 min. Magnetization and coercivity affected significantly by microwave time, where corecivity increaseed 150 %, and magnetization increased 400 % with exposure to microwave for BSF20. Signatures of strong ferromagnetism at room temperature were observed in some of samples. Bi 0.6 Sr 0.4 FeO 3 , a bismuth perovskite, is a good option for data storage applications, because of its strong coercivity and saturation magnetization. Electrocatalytic tests demonstrated that BSF10 perovskite exhibited the highest activity, achieving 98.18 %, while BSF0 achieved 54.04 % of rhodam

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TRU-NET: A deep learning approach to high resolution prediction of rainfall

arXiv

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arXiv 2020年

作者： Adewoyin, Rilwan A. Dueben, Peter Watson, Peter He, Yulan Dutta, Ritabrata Department of Computer Science University of Warwick United Kingdom Department of Computer Science and Engineering Southern University of Science and Technology China Earth System Modelling Section The European Centre for Medium-Range Weather Forecasts United Kingdom School of Geographical Sciences University of Bristol United Kingdom Department of Statistics University of Warwick United Kingdom

Climate models (CM) are used to evaluate the impact of climate change on the risk of floods and heavy precipitation events. However, these numerical simulators produce outputs with low spatial resolution that exhibit difficulties representing precipitation events accurately. This is mainly due to computational limitations on the spatial resolution used when simulating multi-scale weather dynamics in the atmosphere. To improve the prediction of high resolution precipitation we apply a Deep Learning (DL) approach using input data from a reanalysis product, that is comparable to a climate model's output, but can be directly related to precipitation observations at a given time and location. Further, our input excludes local precipitation, but includes model fields (weather variables) that are more predictable and generalizable than local precipitation. To this end, we present TRU-NET (Temporal Recurrent U-Net), an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers to effectively model multi-scale spatio-temporal weather processes. We also propose a non-stochastic variant of the conditional-continuous (CC) loss function to capture the zero-skewed patterns of rainfall. Experiments show that our models, trained with our CC loss, consistently attain lower RMSE and MAE scores than a DL model prevalent in precipitation downscaling and outperform a state-of-the-art dynamical weather model. Moreover, by evaluating the performance of our model under various data formulation strategies, for the training and test sets, we show that there is enough data for our deep learning approach to output robust, high-quality results across seasons and varying regions. Copyright © 2020, The Authors. All rights reserved.

关键词： Weather forecasting

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