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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

Procedia computer Science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

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Wheat Agricultural Residences for Bioenergy Generation in Morocco

Wheat Agricultural Residences for Bioenergy Generation in Mo...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Meisam Mahdavi Francisco Jurado Konrad Schmitt Stephen Bayne Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical & Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

Morocco is one of the important producers of wheat in Africa, in which this crop is the first agricultural product in the country. In spite of obtaining a huge quantity of wheat residues in the Moroccan agriculture sector, a small portion of this great bioenergy resource is reused for energy generation in the country. This is a big challenge for Morocco because its energy sector highly depends on costly fossil fuel imports. Furthermore, the country has to decrease harmful gases emission based on the Paris and Kyoto agreements. Regarding the importance of rainfall amount and water resources available for this agricultural product that is the main feedstock of Moroccan food, the use of wheat biomass to produce energy will remove high pressure from both the national energy and agriculture sectors. Besides that, the problems related to crop residue removal are reduced by using wheat biomass. Also, the use of wheat residues for energy generation pushes farmers to prevent the open firing of wheat straws and unnecessary air pollutants release. Thus, the present paper, for the first time, calculates possible energy production from wheat straws and root biomasses in Morocco, aiming to provide useful information for Morocco's energy sector.

关键词： Productivity Firing Plants (biology) Hydrogen Crops Air pollution Fossil fuels

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Disentangled Motion Modeling for Video Frame Interpolation 39

Disentangled Motion Modeling for Video Frame Interpolation

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Interpolation

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps

A Large Reservoir Computing Forecasting Method Based on Rand...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： Omid Orang Fabricio J. Erazo-Costa Petrônio C. L. Silva Guilherme de Alencar Barreto Frederico Gadelha Guimarães Department of Computer Science Universidade Federal de Minas Gerais Belo Horizonte Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Federal Institute of Northern Minas Gerais Januária Campus Brazil Department of Teleinformatics Engineering Universidade Federal do Ceará Fortaleza Brazil

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

This paper introduces a novel forecastings technique based on randomized fuzzy cognitive maps (FCM), called LRHFCM (or large reservoir of randomized high-order FCM) for predicting univariate time series. LR-HFCM is a hybrid method combining fuzzy time series (FTS), FCMs, and reservoir computing. It is a type of echo state network (ESN) consisting of the input layer, intermediate (or large reservoir) layer, and output layer, where LASSO regression is applied to train the output layer. The novelty of this approach is that the internal layer includes a very large reservoir, considering different combinations from the sets of concepts and order using a certain number of sub-reservoirs to capture different dynamics of input time series. It is important to highlight that the weights within each sub-reservoir are chosen randomly and remain constant throughout the training process. The validity of the LR-HFCM approach is evaluated across 15 different time series datasets. The results highlight the outperformance of the LR-HFCM technique in comparison to various baseline models.

关键词： Training Adaptation models Adaptive systems Computational modeling Time series analysis Fuzzy cognitive maps Reservoirs

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Analysis of Exposure to Electromagnetic Fields Due to Multiple Sources in a Classroom Environment

Analysis of Exposure to Electromagnetic Fields Due to Multip...

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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Norton Escopelli Soares Giovani Bulla Claudio E. Fernandez-Rodriguez Álvaro A. A. De Salles Department of Electrical Engineering Federal University of Rio Grande do Sul Porto Alegre Brazil Department of Electrical Engineering Federal Institute of Rio Grande do Sul Canoas Brazil Graduate Program on Electrical Engineering Federal University of Rio Grande do Sul Porto Alegre Brazil

This paper presents an analysis of the electromagnetic fields (EMF) exposure to multiple sources in a classroom environment. The research is motivated by the increasing use of electronic devices in educational environments and the need to estimate this. The methodology used involves simulating a classroom with students using laptops, which are considered as the main sources of electromagnetic radiation. The 1g and 10 g peak spacial Specific Absorption Rate (psSAR) are calculated for different parts of the human body, including the head, back, and hands. The results indicate that the psSAR is higher in the back, followed by the head and hands. When the free distance between desks is 250 mm, two-fold at the hands to forty-five-fold at the back greater psSAR are obtained when comparing to just one student with his laptop.

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Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators

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Applied Soft Computing 2023年第PartA期149卷

作者： Barhaghtalab, Mojtaba Hadi Sepestanaki, Mohammadreza Askari Mobayen, Saleh Jalilvand, Abolfazl Fekih, Afef Meigoli, Vahid Department of Electrical Engineering Faculty of Engineering University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin640301 Taiwan Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States Department of Electrical Engineering Faculty of Engineering Persian Gulf University Bushehr Iran

This paper proposes an adaptive fuzzy-neural inference system (ANFIS)-based control approach for a six degrees of freedom (6-DoF) robotic manipulator. Its main objective is to guarantee the error convergence of the controlled system in the presence of uncertainties and unknown disturbances. The suggested controller is a parallel combination of an ANFIS network with a proportional-integral-derivative (PID) controller. The ANFIS system is used as an estimator to approximate a part of the system and then applied as the feedback linearization in the suggested control structure. The convergence of system errors to zero was proven using Barbalat's lemma. The suggested control law combines the simplicity and ease of implementation of PID control with the estimation properties of ANFIS networks. The suggested approach was evaluated using a simulation study and further validated experimentally using the 6-DoF IRB-120 robotic manipulator (IRB-120-RM). The obtained results confirmed its superior performance and suitability for practical implementation to industrial actuators. © 2023 Elsevier B.V.

关键词： Feedback linearization

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Load Profile Effect on Shunt Capacitor Placement in Radial Distribution Feeders

Load Profile Effect on Shunt Capacitor Placement in Radial D...

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IEEE Kansas Power and Energy Conference

作者： Meisam Mahdavi Augustine Awaafo Konrad Schmitt Manohar Chamana Department of Electrical Engineering University of Jaén Linares Jaén Spain Electrical and Computer Engineering Department Texas Tech University Lubbock TX USA Renewable Energy Program Texas Tech University Lubbock USA

Capacitor placement in electric power systems is an effective way to minimize energy losses, especially in distribution systems, where power losses are more than transmission ones. On the other hand, time-varying load levels affect energy losses amount in such networks. Therefore, load profile has an important role in determining the amount of power losses and the optimal place of shunt capacitors in radial feeders. However, embedding load variability in the capacitor placement problem enhances the computational burden and processing time, while its ignorance leads to inaccurate calculation of energy losses and subsequent optimal locations of capacitor banks. Hence, most of specialized studies have ignored load changes in their capacitor placement models because of computational burden and processing time increase. Accordingly, the present paper intent to evaluate the effect of consumption patterns on capacitor placement proposals in order to understand the importance of load variability consideration in reducing power losses through reactive power compensation. The analysis has been carried out using a mathematical programming language in several distribution systems.

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Algorithm-Hardware Co-Design for Wearable BCIs: An Evolution from Linear Algebra to Transformers

Algorithm-Hardware Co-Design for Wearable BCIs: An Evolution...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sunyoung Park Wooseok Byun Minkyu Je Ji-Hoon Kim Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea SAPEON Korea Inc Seongnam Republic of Korea School of Electrical Engineering KAIST Daejeon Republic of Korea

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Recent advancements in brain-computer interface (BCI) technology for steady-state visual evoked potential (SSVEP)-based target identification have shifted from traditional linear algebra (LA) techniques to more sophisticated neural network (NN) approaches, driven by their increased accuracy and consistent performance across different subjects. However, adopting NN-based algorithms has introduced complexities in wearable BCI systems, mainly due to their extensive parameter sets that demand significant memory capacity. Moreover, the computational intensity of these models requires reevaluating hardware architectures. Additionally, the advent of Transformer-based models has further advanced the state of the art, providing even higher accuracy and reduced variability in cross-subject performance, placing greater demands on hardware resources. This paper provides an overview of recent algorithmic progress in SSVEP-based target identification. Also, it proposes considerations for the hardware architecture needed to efficiently support the computation of cutting-edge Transformer-based models in wearable BCIs from the perspective of algorithm-hardware co-design.

关键词： Visualization Accuracy Computational modeling computer architecture Artificial neural networks Linear algebra Transformers

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