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arXiv 2023年

作者： Zhang, Shuo Xie, Lei Department of Computer Science Hunter College City University of New York United States Ph.D. Program in Computer Science Graduate Center City University of New York United States Helen & Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University United States

Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery. Copyright © 2023, The Authors. All rights reserved.

关键词： Proteins

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Nonlinear Twistronics for EM Waves

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IEEE Transactions on Antennas and Propagation 2025年

作者： Valagiannopoulos, Constantinos Alu, Andrea National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece City University of New York Photonics Initiative Advanced Science Research Center New YorkNY10031 United States City College of The City University of New York Department of Electrical Engineering New YorkNY10031 United States City University of New York Physics Program Graduate Center New YorkNY10016 United States

The electromagnetic (EM) interaction between two anisotropic nonlinear metasurfaces, under arbitrary rotations of their optical axes, is rigorously studied. Multistability conditions with respect to rotation angles between the two surfaces are determined and explored. The examined setup demonstrates opportunities for memory and storage since these in-plane rotations may act as continuous information carriers. The abrupt transitions of the system response, from one solution branch to the other, materialize the concept of twistronics engineering in the classical EM regime, sensitive to small angular changes. These findings can enhance the design of several photonic devices from ultra-sharp sensors and switches to analog information processors and optical memory elements. © 1963-2012 IEEE.

关键词： Optical bistability

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NIR-Responsive Methotrexate-Modified Iron Selenide Nanorods for Synergistic Magnetic Hyperthermic, Photothermal, and Chemodynamic Therapy

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ACS Applied Materials and Interfaces 2024年第20期16卷 25622-25636页

作者： Thirumurugan, Senthilkumar Muthiah, Kayalvizhi Samuvel Lin, Yu-Chien Dhawan, Udesh Liu, Wai-Ching Wang, An-Ni Liu, Xinke Hsiao, Michael Tseng, Ching-Li Chung, Ren-Jei No. 1 Section 3 Zhongxiao East Road Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom Faculty of Science and Technology Technological and Higher Education Institute of Hong Kong Hong Kong New Territories999077 Hong Kong Scrona AG Grubenstrasse 9 Zürich8045 Switzerland College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Genomics Research Center Academia Sinica Taipei115 Taiwan Department and Graduate Institute of Veterinary Medicine School of Veterinary Medicine National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei10608 Taiwan

Breast cancer is a malignant tumor with a high mortality rate among women. Therefore, it is necessary to develop novel therapies to effectively treat this disease. In this study, iron selenide nanorods (FeSe2 NRs) were designed for use in magnetic hyperthermic, photothermal, and chemodynamic therapy (MHT/PTT/CDT) for breast cancer. To illustrate their efficacy, FeSe2 NRs were modified with the chemotherapeutic agent methotrexate (MTX). MTX-modified FeSe2 (FeSe2-MTX) exhibited excellent controlled drug release properties. Fe2+ released from FeSe2 NRs induced the release of •OH from H2O2 via a Fenton/Fenton-like reaction, enhancing the efficacy of CDT. Under alternating magnetic field (AMF) stimulation and 808 nm laser irradiation, FeSe2-MTX exerted potent hyperthermic and photothermal effects by suppressing tumor growth in a breast cancer nude mouse model. In addition, FeSe2 NRs can be used for magnetic resonance imaging in vivo by incorporating their superparamagnetic characteristics into a single nanomaterial. Overall, we presented a novel technique for the precise delivery of functional nanosystems to tumors that can enhance the efficacy of breast cancer treatment. © 2024 The Authors. Published by American Chemical Society

关键词： Magnetic resonance imaging

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Utilizing Educational Robotics for Learning About Sustainability Issues

Utilizing Educational Robotics for Learning About Sustainabi...

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IEEE Latin American Robotics Symposium, LARS

作者： Andre Carlos Teixeira de Vasconcelos Almir Silva Silveira Raphael Melo de Oliveira Lucas Tejedor da Silva Giselle Medeiros Mesiara João Roberto de Toledo Quadros Master's Degree Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Department of Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil Sense & Learn (SELEA)21 Copenhagen University Denmark Department of Electronic Engineering/Pos-Graduate Program in Computer Science Celso Suckow da Fonseca Federal Technological Education Center Rio de Janeiro Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Nowadays, schools are trying to insert knowledge and learning about sustainability issues and environmental conservation in the academic context from an early age. It is also observed the use of information technology with new didactic and ludic resources that can encourage elementary and high school students to obtain knowledge about sustainability. To this end, this research presents some results using Educational Robotics, Gestalt, and information technology, which students received from a Brazilian public elementary school. In this study, after attending workshops on sustainability, the students developed robotic resources to help them with sustainability-related knowledge. The resources were tested according to their objectives: a mobile robot that recognizes the food and shows its footprint and a box with gas sensors to identify if food is in consumable condition or should be recycled. The experiment was very successful and is in a continuous application phase.

关键词： Conferences Education Robot sensing systems Object recognition Mobile robots Sustainable development Information technology

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RHC: A Dataset for In-Room and Out-Room Human Counting

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Procedia computer science 2021年 179卷 33-39页

作者： Bens Pardamean Hery Harjono Muljo Faizal Abid Herman Albert Susanto Tjeng Wawan Cenggoro Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://***/datasets/vt5c8h6kmh/1.

关键词： dataset human counting crowd counting in-room out-room

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Nonlocal Scatterer for Compact Wave-Based Analog Computing

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Physical Review Letters 2022年第7期128卷 073201-073201页

作者： Heedong Goh Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Analog computing based on wave interactions with metamaterials has been raising significant interest as a low-energy, ultrafast platform to process large amounts of data. Engineered materials can be tailored to impart mathematical operations of choice on the spatial distribution of the impinging signals, but they also require extended footprints and precise large-area fabrication, which may hinder their practical applicability. Here we show that the nonlocal response of a compact scatterer can be engineered to impart operations of choice on arbitrary impinging waves, and even to solve integro-differential equations, whose solution is observed in the scattered fields. The lack of strongly resonant phenomena makes the response robust, and the compact nature opens to scalability and cascading of these processes, paving the way to efficient, compact analog computers based on engineered microstructures.

关键词： Light propagation, transmission & absorption Mie scattering Scattering theory

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More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability

arXiv

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arXiv 2022年

作者： Ilmer, Ilia Ovchinnikov, Alexey Soto, Pedro Pogudin, Gleb Program in Computer Science Graduate Center CUNY New YorkNY United States Program in Mathematics CUNY Queens College New YorkNY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris Paris France

Structural global parameter identifiability indicates whether one can determine a parameter’s value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infinitely many values, such parameters are called non-identifiable. We present a procedure for accelerating a global identifiability query by eliminating algebraically independent non-identifiable parameters. Our proposed approach significantly improves performance across different computer algebra frameworks. © 2022, CC BY-SA.

关键词： Algebra

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Genome data mining approach to identify potential protein in crop plants

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment

AI-Based Feature Extraction and Cosine Similarity for Automa...

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Information Technology (ACIT)

作者： Bambang Dwi Wijanarko Bachtiar Rosilah Binti Hassan Dina Fitria Murad Ridho Bramulya Ihsan Yaya Heryadi Engkos Achmad Kuncoro Eka Julianti Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Master of English Education Graduate School Universitas Terbuka Jakarta Indonesia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Manajemen Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department Universitas Terbuka Jakarta Indonesia

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well.

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