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arXiv 2022年

作者： Yang, Zhenyu Lafata, Kyle Vaios, Eugene Hu, Zongsheng Mullikin, Trey Yin, Fang-Fang Wang, Chunhao Department of Radiation Oncology Duke University DurhamNC United States Medical Physics Graduate Program Duke Kunshan University Jiangsu Kunshan China Medical Physics Graduate Program Duke University DurhamNC United States Department of Radiology Duke University DurhamNC United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Radiation Physics The University of Texas MD Anderson Cancer Center HoustonTX United States The University of Texas MD Anderson Graduate School of Biomedical Science HoustonTX United States

Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU-Net model successfully achieved low uncertainty

关键词： Tumors

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Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Research Square

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Research Square 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, Di Zhang, Shuo Mura, Cameron Bourne, Philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVID-19 anti-infective polypharmacology

arXiv

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arXiv 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Autonomous Exploration Guided by Optimisation Metaheuristic

Autonomous Exploration Guided by Optimisation Metaheuristic

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International Conference on Control, Automation, Robotics and Vision

作者： Raphael Gomes Santos Edison Pignaton de Freitas Sen Cheng Paulo Rogerio de Almeida Ribeiro Alexandre Cesar Muniz de Oliveira Federal University of Maranhão São Luís MA Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Computer Engineering Federal University of Maranhão São Luís Maranhão Department of Informatics Federal University of Maranhão São Luís Maranhão

Autonomous exploration aims to develop mechanisms that enable a robot to navigate through an unknown environment, seeking to collect information that is relevant to its objective, with minimal or no human intervention. This article presents an autonomous exploration strategy based on a multimodal continuous utility function and optimization metaheuristic. The problem of autonomous exploration of mobile robots is formulated as an optimization problem, providing data for a Firefly-based algorithm that is able to search for points in the solution space, representing cells on the map being constructed, that best meet the exploration objectives. Simulations in a Robot Operating System environment showed that the proposed approach is able to guide robot through good trajectories, leading to full mapping, without any human intervention.

关键词： Linear programming Optimization Heuristic algorithms Robot kinematics Navigation Mobile robots

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Multi-criteria decision making on succesfull of online learning using AHP and regression

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Fatsyahrina Fitriastuti Uci Rahmalisa Abba Suganda Girsang Department of Informatics Engineering Janabadra University Yogyakarta Indonesia Department of Informatics Engineering STMIK Hang Tuah Pekan Baru Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This paper purposes comparing the view of system builder (expert system) using analitycal hierarchy process (AHP) and view of user (students) using regression method in elearning system STIKes-STMIK Hang Tuah Pekanbaru. This study uses some attributes to analyze and evaluate which affect the acceptance of elearning system success. In AHP, the attributes are categorized as criteria and grouped into some dimensions. These criteria and dimenssions are evaluated by some experts using analitycal hierarchy process (AHP) to assess priorities for elearning system. There are four dimensions, system quality (four criteria), learning information quality (three criteria), service quality (three criteria) and service support (three criteria) dimension. This system is also evaluated by filling out the questionnaire by students as user of elearning system using regression method. The attributes are used as independent variable, while the succesfull of elearning is dependent variable. The result of using AHP and regression system has a different. It also can show there are different view between users and system builder which should be considered by STIKes-STMIK Hang Tuah Pekanbaru.

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Data Processing of Functional Optical Microscopy for Neuroscience

arXiv

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arXiv 2022年

作者： Benisty, Hadas Song, Alexander Mishne, Gal Charles, Adam S. Yale Neuroscience New HavenCT06510 United States Max Planck Institute for Intelligent Systems Stuttgart Germany Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges. © 2022, CC BY-NC-ND.

关键词： Fluorescence microscopy

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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Generating Lymphoma Variability Through Adversarial Generation Models: A Limited Resource Approach for Synthetic Histopathology Image Generation and Classification

SSRN

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SSRN 2024年

作者： Tiwari, Ashutosh Saudarshan, Harsh Krisnawati, Dyah Ika Khafid, Muhamad Wardani, Erika Martining Mutalik, Chinmaya Chen, Chi-Long Kuo, Tsung-Rong International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495509 India Department of Nursing Faculty of Nursing and Midwifery Universitas Nahdlatul Ulama Surabaya East Java Surabaya60237 Indonesia Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Pathology School of Medicine College of Medicine and Taipei Medical University Hospital Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Precision Medicine and Translational Cancer Research Center Taipei Medical University Hospital Taipei11031 Taiwan Tulane University United States Graduate Institute of Clinical Medicine Taiwan

Lymphomas, a diverse group of blood cancers originating from the lymphatic system, present significant challenges for diagnosis and treatment due to their morphological and genetic differences. These variability obscures the development of personalized therapies and impacts disease outcomes. The scarcity of high-quality, diverse images of lymphoma cells is further hindered by patient privacy concerns, the rarity of certain types, and logistical challenges of collecting and processing biomedical images. Additionally, the costs and technical difficulties of microscopic imaging limit the scope of research. This paper addresses these challenges by utilizing generative adversarial networks (GANs) to generate synthetic yet realistic images of lymphoma histopathology and classify them in a low resource setting. Focusing specifically on chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and mantle cell lymphoma (MCL), we employed GANs to enhance the understanding of lymphoma variability and support advancements in digital cancer biology and pathology. This dual approach of image generation and classification aims to improve disease diagnosis and prognosis, ultimately contributing to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Lung cancer

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Design and Implementation Data Warehouse in Insurance Company

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Ryan Ari Setyawan Eko Prasetyo Abba Suganda Girsang Department of Informatics Engineering Janabadra University Yogyakarta Indonesia 55231. Computer Science Department Binus Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia.

Insurance company are certainly has a rich of data from their business process. One of the data is related to business process sales. From this sales data can be used to analyze the condition of a company whether the condition of the company is in a good condition or not. The purpose of this research is to develop a technique to analyze this data from the company. The method used to implement the data analysis in this paper is to design a data warehouse. Use ninestep methodology as the method for design data warehouse and Pentaho as a tool for ETL (Extract, Transform, Load), OLAP analysis and reporting data, the results of this research concluded that the implementation of data warehouse for perform data analysis and reporting better and more effective.

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