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CURRENT PROTOCOLS 2023年第10期3卷 e883页

作者： Yen, Allen Mateusiak, Chase Sarafinovska, Simona Gachechiladze, Mariam A. Guo, Juanru Chen, Xuhua Moudgil, Arnav Cammack, Alexander J. Hoisington-Lopez, Jessica Crosby, MariaLynn Brent, Michael R. Mitra, Robi D. Dougherty, Joseph D. Department of Genetics Washington University School of Medicine Saint Louis Missouri Department of Psychiatry Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Software Supervision Validation Visualization Writing - original draft Writing - review & editing Edison Family Center for Genome Sciences and Systems Biology Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Methodology Software Visualization Writing - original draft Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - original draft Writing - review & editing Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - review & editing Contribution: Data curation Methodology Software Writing - review & editing Contribution: Methodology Writing - review & editing Current address: Department of Ophthalmology Stanford University School of Medicine Stanford California Contribution: Conceptualization Methodology Software Validation Department of Neurology Washington University School of Medicine Saint Louis Missouri Current address: Department of Neurodegenerative Diseases University College London London United Kingdom Contribution: Conceptualization Methodology Validation Contribution: Investigation Writing - review & editing Department of Computer Science and Engineering Washington University Saint Louis Missouri Contribution: Funding acquisition Contribution: Conceptualization Funding acquisition Project administration Resources Software Supervision Writing - review & editing Intellectual and Developmental Disabilities Research Center Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Funding acquisition Project administration Resources Supervision Writing - origin

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. This article was corrected on 16 October 2023. See the end of the full text for details. Basic Protocol 1 : Preparation and delivery of Calling Cards reagents Support Protocol 1 : Next-generation sequencing quantification of barcode

关键词： calling cards epigenetics enhancer usage gene regulation transcription factor binding

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng Xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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Getting Ready for the European Health data Space (Ehds): The Iderha's Plan to Align with the Ehds Requirements for the Secondary Use of Health data

SSRN

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SSRN 2024年

作者： Hussein, Rada Balaur, Irina Burmann, Anja Ćwiek-Kupczyńska, Hanna Gadiya, Yojana Ghosh, Soumyabrata Jayathissa, Prabath Katsch, Florian Kremer, Andreas Lähteenmäki, Jaakko Meng, Zhaoling Morasek, Kathrin Rancourt, Rebecca C. Satagopam, Venkata Sauermann, Stefan Scheider, Simon Stamm, Tanja A. Muehlendyck, Christian Gribbon, Philip Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Luxembourg Centre for Systems Biology University of Luxembourg Luxembourg Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Discovery Research Screening Port Hamburg Germany Theodor Stern Kai 7 Frankfurt60590 Germany Bonn-Aachen International Center for Information Technology University of Bonn Bonn Germany Center for Medical Data Science Medical University of Vienna Vienna Austria ITTM S.A. Luxembourg VTT Technical Research Centre of Finland Ltd Espoo Finland Clinical Modeling and Evidence Integration Sanofi CambridgeMA United States Institute of Outcomes Research Center for Medical Data Science Medical University of Vienna Austria Ludwig Boltzmann Institute for Arthritis and Rehabilitation Vienna Austria Medical School Berlin Berlin Germany Faculty Life Science Engineering FH Technikum Wien Vienna Austria TU Dortmund Dortmund Germany Johnson & Johnson Medical GmbH Norderstedt Germany

Objective: The European Health data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulations will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a *** and Methods: In this study, we conducted a comprehensive review of the EHDS regulations and technical requirements for EHDS2. Mainly utilizing the results of the Joint Action Towards the European Health data Space (TEHDAS) project to identify the framework of IDERHA’s alignment with EHDS. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and ***: We identified the minimum-set of requirements for aligning IDERHA infrastructure with the planned EHDS regulations, including user journey, concepts, terminologies, and standards. The future IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) will be developed ***: The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability (DCAT-AP), enabling semantics interoperability (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the OHDSI OMOP CMD for representing the Patient-Generated Health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data ***: Collaboration, sharing lessons learned, and creating synergy among the current EHDS initiatives and projects are critical

关键词： Electronic data interchange

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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A mobile cloud-based ehealth scheme

arXiv

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arXiv 2020年

作者： Liu, Yihe Abbasi, Aaqif Afzaal Aghaei, Atefeh Abbasi, Almas Mosavi, Amir Shamshirband, Shahab Al-qaness, Mohammed A.A. Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AI Neijiang Normal University Neijiang641100 China Department of Software Engineering Foundation University Islamabad44000 Pakistan Department of Communication Faculty of Social Sciences University of Tehran Tehran*** Iran Department of Computer Science International Islamic University Islamabad46000 Pakistan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Kalman Kando Faculty of Electrical Engineering Obuda University Budapest1034 Hungary School of the Built Environment Oxford Brookes University OxfordOX3 0BP United Kingdom Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam School of Computer Science Wuhan University Wuhan430072 China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial *** Codes 68T05 Copyright © 2020, The Authors. All rights reserved.

关键词： Signal analysis

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Robust frequent directions with application in online learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

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Application of Improved Mask R-CNN Algorithm Based on Gastroscopic Image in Detection of Early Gastric Cancer

Application of Improved Mask R-CNN Algorithm Based on Gastro...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Zhi-Heng Cui Qin-Yan Zhang Jing-Wei Zhang Xue Sun Qing Wang Yi Lei Lin Zang Li Zhao Ji-Jiang Yang School of Modern Posts Beijing University of Posts and Telecommunications Beijing China VIP Department and Family Medicine Department Chinese Academy of Medical Sciences Beijing Hospital Beijing Hospital National Center of Gerontology Institute of Geriatric Medicine Beijing China Department of Automation Tsinghua University Beijing China Faculty of Information Technology School of Software Engineering Beijing University of Technology Beijing China Pharmacovigilance Research Center for information technology and Data Science Cross-strait Tsinghua Research Institute Xiamen China Department of Gastroenterology Beijing Hospital National Center of Gerontology Chinese Academy of Medical Sciences Beijing Hospital Institute of Geriatric Medicine Beijing China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Gastroscopy is an important step in the diagnosis of early gastric cancer. However, because the morphological manifestations of early gastric cancer are not obvious, endoscopists need long-term specialized training and experience accumulation to correctly identify early cancer through magnification gastroscopy. In this paper, the data set of gastroscopy image is collected and enhanced, and target detection method is combined with gastroscopy image. The Mask R-CNN+BiFPN model was proposed to enhance the feature fusion and improve the detection effect of early gastric cancer lesions. Compared with Mask R-CNN, the improved Mask R-CNN model has better performance, with the sensitivity and specificity of 91.67% and 88.95% in accurately labeled gastroscopic datasets, respectively, showing a good segmentation effect for surface swelling lesions.

关键词： Training Image segmentation software algorithms Surface morphology Object detection Sensitivity and specificity Feature extraction

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Corrigendum: “Provably-secure electronic cash based on certicateless partially-blind signatures”

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Electronic Commerce Research and Applications 2013年第1期12卷 60-60页

作者： Lei Zhang Futai Zhang Bo Qin Shubo Liu East China Normal University Software Engineering Institute Shanghai Key Laboratory of Trustworthy Computing Shanghai China Universitat Rovira i Virgili Department of Computer Engineering and Mathematics UNESCO Chair in Data Privacy Tarragona Catalonia Spain Nanjing Normal University School of Computer Science and Technology China Xi’an University of Technology School of Science Department of Maths China Wuhan University School of Computer Science Wuhan China

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Micro-Expression Recognition based on 2D Gabor Filter and Sparse Representation

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Journal of Physics: Conference Series 2017年第1期787卷

作者： Hao Zheng Key Laboratory of Trusted Cloud Computing and Big Data Analysis School of Information Engineering Nanjing XiaoZhuang University Nanjing China MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Micro-expression recognition is always a challenging problem for its quick facial expression. This paper proposed a novel method named 2D Gabor filter and Sparse Representation (2DGSR) to deal with the recognition of micro-expression. In our method, 2D Gabor filter is used for enhancing the robustness of the variations due to increasing the discrimination power. While the sparse representation is applied to deal with the subtlety, and cast recognition as a sparse approximation problem. We compare our method to other popular methods in three spontaneous micro-expression recognition databases. The results show that our method has more excellent performance than other methods.

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