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arXiv 2024年

作者： Akepati, Sri Vishnuvardhan Reddy Gupta, Nitant Jayaraman, Arthi Data Science Program University of Delaware NewarkDE19716 United States Department of Chemical and Biomolecular Engineering University of Delaware NewarkDE19716 United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States

Characterization of structural diversity within soft materials is key for engineering new materials for various applications. Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nm to microns). The typical output of a SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave. The scattered wavevector q is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While structures with isotropic spatial arrangement can be interpreted by analysis of azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic spatial arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles is complicated, and usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the ‘computational reverse engineering analysis for scatting experiments’ (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE uses a genetic algorithm for optimization and an artificial neural network (ANN) as the surrogate machine learning (ML) model for fast calculation of 1D ‘computed’ scattering profiles that are then compared to the experimental 1D scattering profiles in the optimization. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. Further, we use XGBoost as the surrogate ML model in CREASE-2D, in place of ANNs, to relate structural features to the I(q, θ) profile. The CREA

关键词： Inverse problems

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Base on GAN Combined with CNN Architecture to Generate Brain Stroke CT Images 24

Base on GAN Combined with CNN Architecture to Generate Brain...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Shih, Guan-Yu Ting, Hsien-Wei Chen, Yi-Xiang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Graduate Program inBiomedical Informatics Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

In recent years, many fields have expanded their research methods through the integration of artificial intelligence. In the current medical field, it is widely used in image recognition to diagnose patient symptoms, train clinical prediction models, and improve clinical workflow processes to avoid missing treatment opportunities. These methods have all achieved good results so far. Now, generative AI is also impacting the development of the medical field, not only generating prescriptions that match patient conditions but also being applied in generating medical images for data augmentation and simulating various pathological conditions, thereby aiding in training deep learning models. Stroke is a leading cause of death or disability, ranking as the second leading cause of death worldwide, next to cancer. If cancers were categorized individually, stroke would surpass all single diseases, making it a primary focus of research in brain disease treatment. There are already studies where generative AI has been used to create medical images, which are then manually evaluated by doctors to determine if they were generated by AI. In this project, we plan to generate CT images of strokes using generative AI and use a trained model (to diagnose stroke) to improve accuracy. Through this project, we hope to solve the problem of scarce stroke CT images while also providing support for training stroke-related models. © 2024 Copyright ACM.

关键词： Deep learning

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Constructing an Automated Method for Interpreting Alzheimer’s Disease Based on Deep Learning Models and Image Processing 24

Constructing an Automated Method for Interpreting Alzheimer...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Yi, Chia-Pin Ting, Hsien-Wei Yang, Tzu-Fang Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City242 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Alzheimer’s disease (AD) often presents only mild symptoms in its early stages, and as there is no direct diagnostic method currently available, many patients are diagnosed only after the condition has worsened. Considering that magnetic resonance imaging (MRI) can provide key information in the diagnosis of AD, but the condition may go undiagnosed under manual interpretation due to the subtleness of symptoms and the reliance on radiologists to interpret MRI images, this study aimed to develop an automated method for interpreting brain MR images, hoping to screen for potential AD patients. By combining image processing, the U-Net model, YOLOv8 model, various CNN model architectures, and ensemble data statistics, our method was able to identify features related to AD and predict whether a patient has the disease. Our method can process a complete set of MR images and automatically extract important slices for prediction, making it practical for medical applications. Furthermore, we tested the accuracy and reliability of the method with additional datasets. © 2024 Copyright ACM.

关键词： Magnetic resonance imaging

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HyperCRX 2.0: A Comprehensive and Automated Tool for Empowering GitHub Insights

HyperCRX 2.0: A Comprehensive and Automated Tool for Empower...

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International Conference on Software engineering - Companion, ICSE, Companion

作者： Yantong Wang Wei Wang Xiaoya Xia Yenan Tang Fenglin Bi Fanyu Han Jiaheng Peng Shengyu Zhao Lan You Data Science and Engineering East China Normal University Shanghai China Ant Group Open Source Program Office Hangzhou China Alibaba Group Hangzhou China School of Electronic and Information Engineering Tongji University Shanghai China School of Computer Science and Information Engineering Hubei University Wuhan China

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

HyperCRX is a browser extension designed for the GitHub platform, aimed at enhancing the open-source experience by providing in-depth insights into projects and developers. Unlike traditional tools, HyperCRX seamlessly integrates with GitHub, offering real-time analysis of project ecosystems, devel-oper contributions, and community activities. This helps users better understand and manage open-source projects. In this work, we have extended HyperCRX to deepen the level of insights and integrated LLMs capabilities to enable intelligent open-source operational support and automated project insight analysis. The plugin caters to a wide range of user groups, including developers, project maintainers, and open-source operators, helping them improve efficiency and make data-driven decisions. The source code for HyperCRX is open-sourced on GitHub at https://***/hypertrons/hypertrons-crx. A demonstration of the features is available at https://***/y4mCCfFVux0.

关键词： Source coding Ecosystems Real-time systems Browsers Software measurement Software development management Software engineering

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation

arXiv

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arXiv 2024年

作者： Islam, Md. Farhadul Zabeen, Sarah Manab, Meem Arafat Mahin, Mohammad Rakibul Hasan Mondal, Joyanta Jyoti Reza, Md. Tanzim Hasan, Md Zahidul Haque, Munima Sadeque, Farig Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Biotechnology Program Department of Mathematics and Natural Sciences School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer and Information Sciences University of Delaware United States School of Law and Government Dublin City University Ireland Gina Cody School of Engineering and Computer Science Concordia University Canada

The majority of medical images, especially those that resemble cells, have similar characteristics. These images, which occur in a variety of shapes, often show abnormalities in the organ or cell region. The convolution operation possesses a restricted capability to extract visual patterns across several spatial regions of an image. The involution process, which is the inverse operation of convolution, complements this inherent lack of spatial information extraction present in convolutions. In this study, we investigate how applying a single layer of involution prior to a convolutional neural network (CNN) architecture can significantly improve classification and segmentation performance, with a comparatively negligible amount of weight parameters. The study additionally shows how excessive use of involution layers might result in inaccurate predictions in a particular type of medical image. According to our findings from experiments, the strategy of adding only a single involution layer before a CNN-based model outperforms most of the previous *** Codes 68T45 © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia's Sugar Industry 7

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Utama, Ditdit Nugeraha Asrol, Muhammad Suharjito Harito, Christian Napthaleni Syafrudin, Muhammad Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Sejong University Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of

ISBN: (纸本)9798331519643

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89. © 2024 IEEE.

关键词： Sugar industry

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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IEEE International Conference on Big data

作者： Zheyi Qin Randy Paffenroth Anura P. Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Data Science Program Worcester Polytechnic Institute Worcester MA USA

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

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