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5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement

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Digitizing Farmers' Land Data Collection Systems in Indonesia with the Use of Tani Millenial Apps 4

Digitizing Farmers' Land Data Collection Systems in Indonesi...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Bimantio, Mohammad Prasanto Putra, Dian Pratama Suparyanto, Teddy Ferhat, Amallia Nugraha, Nanda Satya Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Food and Agricultural Technology Yogyakarta Indonesia Institute of Agriculture STIPER Department of Agrotechnology Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Institute of Agriculture STIPER Department of Agricultural Socio-Economics Yogyakarta Indonesia Institute of Agriculture STIPER Department of Forestry Yogyakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods. © 2022 IEEE.

关键词： Decision making

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon Shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling

arXiv

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arXiv 2024年

作者： Liu, Zhuoyi Grigas, Alex T. Sumner, Jacob Knab, Edward Davis, Caitlin M. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs Nr that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, Nr/N 0.08, where N is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo. © 2024, CC BY.

关键词： Conformations

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Structural insights into the role of N-terminal integrity in PhoSL for core-fucosylated N-glycan recognition

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International Journal of Biological Macromolecules 2024年 255卷 128309页

作者： Lou, Yuan-Chao Tu, Cheng-Fen Chou, Chun-Chi Yeh, Hsin-Hong Chien, Chia-Yu Sadotra, Sushant Chen, Chinpan Yang, Ruey-Bing Hsu, Chun-Hua Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Department of Agricultural Chemistry National Taiwan University Taipei106 Taiwan Chemical Biology and Molecular Biophysics Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu300 Taiwan Biomedical Translation Research Center Academia Sinica Taipei115 Taiwan Ph.D. Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei110 Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei106 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei106 Taiwan Center for Computational and Systems Biology National Taiwan University Taipei106 Taiwan

PhoSL (Pholiota squarrosa Lectin) has an exceptional binding affinity for biomolecules with core-fucosylated N-glycans. This modification involves the addition of fucose to the inner N-acetylglucosamine within the N-glycan structure and is known to influence many physiological processes. Nevertheless, the molecular interactions underlying high-affinity binding of native PhoSL to core-fucosylated N-glycans remain largely unknown. In this study, we devised a strategy to produce PhoSL with the essential structural characteristics of the native protein (n-PhoSL). To do so, a fusion protein was expressed in E. coli and purified. Then, enzymatic cleavage and incubation with glutathione were utilized to recapitulate the native primary structure and disulfide bonding pattern. Subsequently, we identified the residues crucial for n-PhoSL binding to core-fucosylated chitobiose (N2F) via NMR spectroscopy. Additionally, crystal structures were solved for both apo n-PhoSL and its N2F complex. These analyses suggested a pivotal role of the N-terminal amine in maintaining the integrity of the binding pocket and actively contributing to core-fucose recognition. In support of this idea, the inclusion of additional residues at the N-terminus considerably reduced binding affinity and PhoSL cytotoxicity toward breast cancer cells. Taken together, these findings can facilitate the utilization of PhoSL in basic research, diagnostics and therapeutic strategies. © 2023 Elsevier B.V.

关键词： Proteins

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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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In vitro activity of imipenem/relebactam, meropenem/vaborbactam and comparators against Enterobacterales from patients with intra-abdominal infections: Results of the study for Monitoring Antimicrobial Resistance Trends (SMART) in Taiwan, 2020

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Journal of Microbiology, Immunology and Infection 2023年第1期56卷 75-83页

作者： Lee, Yu-Lin Ko, Wen-Chien Hsueh, Po-Ren Department of Internal Medicine Changhua Christian Hospital Changhua Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Department of Internal Medicine Chung Shan Medical University Hospital Taichung Taiwan Department of Medicine College of Medicine National Cheng Kung University Tainan Taiwan Departments of Laboratory Medicine and Internal Medicine China Medical University Hospital Taichung Taiwan School of Medicine China Medical University Taichung Taiwan Ph.D Program for Aging School of Medicine China Medical University Taichung Taiwan Departments of Laboratory Medicine and Internal Medicine National Taiwan University Hospital National Taiwan University College of Medicine Taipei Taiwan

Background: Multi-drug resistant Enterobacterales is a growing health threat. Imipenem/relebactam and meropenem/vaborbactam, are not clinically used in Taiwan and the susceptibility is lack from routine laboratory tests. Methods: Broth microdilution method was used to determine the minimum inhibitory concentrations (MICs) and interpreted according to the Clinical and Laboratory Standards Institute (CLSI) breakpoints criteria. Isolates that were not susceptible to imipenem (MIC ≥ 2 mg/L), imipenem/relebactam (MIC ≥ 2 mg/L), or ceftolozane-tazobactam (MIC ≥ 4 mg/L) were selected for further molecular testing for genes encoding extended-spectrum beta-lactamases (ESBLs), AmpC β-lactamases, and carbapenemases by multiplex PCR assays. Results: A total of 290 Enterobacterales isolates from 9 participating hospitals were collected in 2020. Escherichia coli (n = 135, 46.6%) and Klebsiella pneumoniae (n = 88, 30.3%) were two leading pathogens of all Enterobacterales isolates. The antimicrobial agents with susceptibility rates more than 90% included amikacin (99.3%, 288/290), ertapenem (90.0%, 261/290), meropenem (97.2%, 282/290), imipenem/relebactam (94.8%, 275/290) and meropenem/vaborbactam (99.3%, 288/290). K. pneumoniae isolates were less susceptible to ertapenem, imipenem, meropenem, piperacillin-tazobactam and ceftozolane/tazobactam than E. coli (all p < 0.05). ESBL, AmpC, and carbapenemase were detected in 40.5% (17/42), 45.2% (19/42) and 11.9% (5/42) among tested isolates, respectively. The 5 carbapenemase genes included 4 bla_KPC and 1 bla_IMP. The imipenem-non-susceptible isolates (n = 30) had higher susceptibility rates to meropenem/vaborbactam (93.3%, 28/30) than imipenem/relebactam (50%, 12/30) (p < 0.05). Conclusions: Imipenem/relebactam and meropenem/vaborbactam had excellent efficacy against Enterobacterales isolates. Meropenem/vaborbactam allowed better salvage therapy for carbapenem-resistant Enterobacterales infections. © 2022

关键词： Carbapenem resistance Cross resistance Enterobacterales Multidrug resistance (MDR) Novel β-lactam combination agents

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Towards High Generalization Performance on Electrocardiogram Classification

Towards High Generalization Performance on Electrocardiogram...

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2021 Computing in Cardiology, CinC 2021

作者： Han, Hyeongrok Park, Seongjae Min, Seonwoo Choi, Hyun-Soo Kim, Eunji Kim, Hyunki Park, Sangha Kim, Jinkook Park, Junsang An, Junho Lee, Kwanglo Jeong, Wonsun Chon, Sangil Ha, Kwonwoo Han, Myungkyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of HUINNO Co. Ltd Seoul Korea Republic of LG AI Research Seoul Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of Department of Biological Sciences Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665479165

Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. The characteristics of ECG vary from dataset to dataset for various reasons (i.e., hospital, race, etc.). Therefore, it is important for a model to have high generalization performance consistently over all datasets. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2021, we present a model developed to classify cardiac abnormalities from 12 lead and reduced-lead ECGs. In particular, to upgrade our previous model for improving generalization performance, we newly adopt constant-weighted cross-entropy loss, additional features, Mixup augmentation, and squeeze/excitation block, OneCycle learning rate scheduler, which are selected via evaluation of generalization performance using leave-one-dataset-out cross-validation setting. With the present model, our DSAILSNU team has received challenge scores of 0.55, 0.58, 0.58, 0.57 and 0.57 (ranked 2nd, 1st, 1st, 2nd, 2nd out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set, respectively. The present model achieves higher generalization performance over all versions of the hidden test set than the model submitted last year. © 2021 Creative Commons.

关键词： Deep learning

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