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Cell Biomaterials 2025年

作者： Anthony O. Omole Hannah S. Newton Edward Cedrone Kimia Nematpour Shaojun Xie Yongmei Zhao Bao Tran Marina A. Dobrovolskaia Nicole F. Steinmetz Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California San Diego La Jolla San Diego CA 92093 USA Shu and K.C. Chien and Peter Farrell Collaboratory University of California San Diego La Jolla San Diego CA 92093 USA Center for Nano-ImmunoEngineering University of California San Diego La Jolla San Diego CA 92093 USA Moores Cancer Center University of California San Diego La Jolla San Diego CA 92093 USA Nanotechnology Characterization Lab Cancer Research Technology Program Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA Sequencing Facility Bioinformatics Group Biomedical Informatics and Data Science Directorate Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA CCR Sequencing Facility Cancer Research Technology Program Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute Frederick MD 21702 USA Department of Bioengineering University of California San Diego La Jolla San Diego CA 92093 USA Department of Radiology University of California San Diego La Jolla San Diego CA USA Institute for Materials Discovery and Design University of California San Diego La Jolla San Diego CA 92093 USA Center for Engineering in Cancer Institute of Engineering Medicine University of California San Diego La Jolla San Diego CA 92093 USA

Cowpea mosaic virus (CPMV) is a plant virus with potent anti-tumor efficacy. Intratumoral CPMV, but not cowpea chlorotic mottle virus (CCMV), reprograms the tumor microenvironment priming durable, systemic anti-tumor immunity. Here, we performed a side-by-side mechanism of action comparison between CPMV and CCMV. Through immune-multiplexing and trafficking studies we observed that CPMV and CCMV are taken up by innate immune cells at similar rates but are processed differently. While CCMV induces proinflammatory interleukins 22, 23, and 27, CPMV is a strong inducer of type I, II, and III interferons, which contribute to the activation and enhancement of immune cell function. We observed that CPMV RNAs activate Toll-like receptor (TLR) 7 upon delivery—and not CCMV RNAs; CPMV RNAs persist longer within cells compared with CCMV. Taken together, this work identifies determinants for CPMV’s unique mechanism for intratumoral immunotherapy. It also broadens our understanding of the utility of plant viruses for biomedical applications.

关键词： plant viruses intratumoral immunotherapy cowpea mosaic virus cowpea chlorotic mottle virus cancer nanotechnology

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Author Correction: Widespread exposure to SARS-CoV-2 in wildlife communities

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Nature communications 2024年第1期15卷 6940页

作者： Amanda R Goldberg Kate E Langwig Katherine L Brown Jeffrey M Marano Pallavi Rai Kelsie M King Amanda K Sharp Alessandro Ceci Christopher D Kailing Macy J Kailing Russell Briggs Matthew G Urbano Clinton Roby Anne M Brown James Weger-Lucarelli Carla V Finkielstein Joseph R Hoyt Department of Biological Sciences Virginia Tech Blacksburg VA USA. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. Department of Biomedical Sciences and Pathobiology Virginia Tech Blacksburg VA USA. Translational Biology Medicine and Health Graduate Program Virginia Tech Roanoke VA USA. Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA USA. Department of Biochemistry Virginia Tech Blacksburg VA USA. Data Services University Libraries Virginia Tech Blacksburg VA USA. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. Academy of Integrated Science Virginia Tech Blacksburg VA USA. Department of Biological Sciences Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. finkielc@vt.edu. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. finkielc@vt.edu. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Academy of Integrated Science Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Department of Biological Sciences Virginia Tech Blacksburg VA USA. hoytjosephr@***.

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An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter 3

An Improved Version of Texture-based Foreground Segmentation...

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3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Muchtar, Kahlil Rahman, Faris Cenggoro, Tjeng Wawan Budiarto, Arif Pardamean, Bens Nodeflux Inc. Jakarta Indonesia Syiah Kuala University Aceh Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Textures

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Transplantation for Congenital Sideroblastic Anaemia Is Feasible and Offers Outcomes Comparable to Other Transfusion Dependent Anaemias. a Joint Retrospective Study of the Paediatric Diseases and Severe Aplastic Anaemia Working Parties (PDWP/SAAWP) of EBMT

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BLOOD 2020年 136卷 45-47页

作者： de la Fuente, Josu Eikema, Dirk-Jan Bosman, Paul Wynn, Robert F. Diaz, Miguel Shaw, Peter J. Al-Seraihy, Amal Ar, Muhlis Cem Mohamed, Mohamed Salaheldin Bron, Dominique de Heredia, Cristina Diaz Gedde-Dahl, Tobias Gibson, Brenda Handgretinger, Rupert Gribben, Professor John G. Kerre, Tessa Kitra-Roussou, Vassiliki Lenhoff, Stig Moraleda, Jose Maria Martinez, Antonio Perez Moppett, John Sengeloev, Henrik Tbakhi, Abdelghani Veelken, Hendrik Verburgh, Estelle Visani, Giuseppe Risitano, Antonio Dufour, Carlo Corbacioglu, Selim De latour, Regis Peffault Centre for Haematology Department of Immunology and Inflammation Imperial College London London United Kingdom LUMC Dept. Medical Statistics & Bioinformatics Leiden Netherlands EBMT Data Office Leiden Netherlands Royal Manchester Children's Hospital Manchester United Kingdom HOSPITAL NiÑO JESUS MADRID ESP The Children's Hospital at Westmead Sydney AUS King Faisal Hospital and Research Centre Riyadh Saudi Arabia Department of Internal Medicine Division of Hematology Istanbul University-Cerrahpasa Cerrahpasa Medical Faculty Istanbul Turkey King Faisal Specialist Hospital and Research Center Jeddah SAU Dpt of Hematology Institut Jules Bordet ULB 1 Rue Heger Bordet Belgium Hospital Universitari Vall d'Hebron Vall d'Hebron Barcelona Spain Department of Hematology Oslo University Hospital Rikshospitalet and Institute of Clinical Medicine University of Oslo Oslo Norway Department of Hematology Royal Hospital for Children Glasgow GBR Hematology / Oncology Children's University Hospital Tubingen Germany Barts Cancer Institute Queen Mary University of London London United Kingdom Department of Hematology Ghent University Hospital Ghent Belgium St. Sophia' Children's Hospital Athene Greece Skanes University Hospital Lund Sweden Hematopoietic Transplant and Cellular Therapy Unit Instituto Murciano de Investigación Biosanitaria IMIB-Arrixaca Virgen de la Arrixaca University Hospital University of Murcia Murcia Spain Murcia Spain Hospital Infantil Universitario Niño Jesús Madrid ESP French Reference Center for Aplastic Anemia and Paroxysmal Nocturnal Hemoglobinuria Assistance Publique - Hôpitaux de Paris Université de Paris Paris France

Congenital sideroblastic anaemias (CSA) are a rare group of disorders characterized by the presence of pathologic iron deposits within the mitochondria of erythroid precursors (ring sideroblasts) in the bone marrow due to heterogenous germline mutations leading to defects in mitochondrial heme synthesis, iron-sulfur (Fe-S) cluster biogenesis, or protein synthesis. Patients present with anaemia and relative reticulocytopenia, and systemic iron overload secondary to chronic ineffective erythropoiesis, leading to end-organ damage. The disease is heterogenous underlying the genetic variability and the variable response to treatment. Although a number of CSA patients have received a bone marrow transplant, the outcomes and toxicities are not known. This status makes it very difficult to understand the role of BMT in the management of CSA. A search in the EBMT database identified 28 patients receiving a HSCT for CSA between 1998 to 2018 by 24 participating centres. The median year of transplantation was 2014 (IQR 2004-2016). The distribution was equal between males (n=14) and females (n=14). The median age at transplantation was 7 years of age (3-10 years). Fifteen patients had a sibling HSCT (88%), one a family matched donor HSCT (6%) and one an unrelated matched (6%), the type of transplant being unknown in others (n=11). The source of stem cells was bone marrow in 20 cases (74%), peripheral blood in 4 cases (15%), cord blood in 2 (7%) and combined bone marrow and cord in one (4%). Five cases had a Bu/Cy based conditioning regimen, 4 had Bu/fludarabine based regimen and three fludarabine/treosulfan based conditioning with the rest having a variety of approaches. Eighty-six percent of cases had serotherapy with ATG or alemtuzumab. The median follow-up was 31.6 months (95% CI, 12.2-74.1%). The overall survival at 12 and 24 months was 88% (76-100) and 82% (66-99), respectively (figure 1). The median neutrophil engraftment was 18 (15-21) days and platelet engraftment >20 x

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Utilizing mobile-based deep learning model for managing video in knowledge management system

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2018年第6期12卷 62-73页

作者： Prabowo, Harjanto Cenggoro, Tjeng Wawan Budiarto, Arif Perbangsa, Anzaludin Samsinga Muljo, Hery Harjono Pardamean, Bens Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia

Knowledge Management (KM) system is a core feature in facilitating intellectual growth in organization. However, there are numerous difficulties in maintaining a reliable KM system. One of the challenges is to manage knowledge materials in video format. A video file contains complex data that lead to the difficulties in managing them. Without an intelligent system, managing videos for KM requires a laborious effort. In this paper, an intelligent framework for KM system, embedded with deep learning model, is proposed. The use of the deep learning model alleviates the heavy burden of video materials management in KM system. To enhance the agility of the system, mobile-based deep learning model is utilized in the framework. © 2018, International Association of Online Engineering.

关键词： Knowledge management

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Feasibility of multiomics tumor profiling for guiding treatment of melanoma

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Nature medicine 2025年 2025 May 27页

作者： Nicola Miglino Nora C Toussaint Alexander Ring Ximena Bonilla Marina Tusup Benedict Gosztonyi Tarun Mehra Gabriele Gut Francis Jacob Stephane Chevrier Kjong-Van Lehmann Ruben Casanova Andrea Jacobs Sujana Sivapatham Laura Boos Parisa Rahimzadeh Manuel Schuerch Bettina Sobottka Natalia Chicherova Shuqing Yu Rebekka Wegmann Julien Mena Emanuela S Milani Sandra Goetze Cinzia Esposito Jacobo Sarabia Del Castillo Anja L Frei Marta Nowak Anja Irmisch Jack Kuipers Monica-Andreea Baciu-Drăgan Pedro F Ferreira Franziska Singer Anne Bertolini Michael Prummer Ulrike Lischetti Rudolf Aebersold Marina Bacac Gerd Maass Holger Moch Michael Weller Alexandre P A Theocharides Markus G Manz Niko Beerenwinkel Christian Beisel Lucas Pelkmans Berend Snijder Bernd Wollscheid Viola Heinzelmann Bernd Bodenmiller Mitchell P Levesque Viktor H Koelzer Gunnar Rätsch Reinhard Dummer Andreas Wicki Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. NEXUS Personalized Health Technologies ETH Zurich Zurich Switzerland. SIB Swiss Institute of Bioinformatics Lausanne Switzerland. Swiss Data Science Center SDSC Zurich Switzerland. Department of Computer Science Institute of Machine Learning ETH Zurich Zurich Switzerland. Department of Dermatology University Hospital Zurich University of Zurich Zurich Switzerland. Department of Biomedicine University Hospital Basel and University of Basel Basel Switzerland. Department of Quantitative Biomedicine University of Zurich Zurich Switzerland. Department of Biology RWTH Aachen Aachen Germany. Department of Pathology and Molecular Pathology University of Zurich and University Hospital Zurich Switzerland. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. Department of Health Sciences and Technology ETH Zurich Zurich Switzerland. ETH PHRT Swiss Multi-Omics Center (SMOC) ETH Zurich Zurich Switzerland. Department of Molecular Life Sciences University of Zurich Zurich Switzerland. Roche Pharmaceutical Research and Early Development Roche Innovation Center Zurich Switzerland. Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland. Roche Diagnostics GmbH MWG Penzberg Germany. Department of Neurology University Hospital and University of Zurich Zurich Switzerland. Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland. Biomedical Informatics University Hospital Zurich Zurich Switzerland. AI Center at ETH Zurich ETH Zurich Zurich Switzerland. Department of Biology ETH Zurich Zurich Switzerland. Department of Medical Oncology and Hematology University of Zurich and University Hospital Zurich Switzerland. andreas.wicki@usz.ch.

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. *** identifier: NCT06463509 .

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Ecological Show Cave and Wild Cave: Negative Binomial Gllvm's Arthropod Community Modelling 3

Ecological Show Cave and Wild Cave: Negative Binomial Gllvm'...

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3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Caraka, Rezzy Eko Shohaimi, Shamarina Kurniawan, Isma Dwi Herliansyah, Riki Budiarto, Arif Sari, Shinta Purnama Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia Department of Statistics Padjadjaran University Indonesia Department of Biology UIN Sunan Gunung Djati Bandung Indonesia Kalimantan Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ecology is a branch of biology that studies the interaction and relationship between organisms and their environment. Abundance, distribution of organisms and patterns of biodiversity are great interests for many ecologists. One of interesting ecosystems to be studied is a cave. A cave has a typical environment character with a vulnerable ecosystem. Many caves in Indonesia, particularly in Gunungsewu karst area have been developed into tourist objects (show caves) and managed imprudently. Such cave management has potential to harm the environment and leads to ecosystem destruction. Arthropods are the most abundance fauna in cave that play critical roles in maintaining cave ecosystems equilibrium. In the heart of statistical ecology, we need to analyze the differences on Arthropods community and abiotic (climatic-edaphic) parameters among show caves and wild caves. Statistical techniques are needed for the extraction of such information. GLLVM is one method that is able to explain spatial-based information and is particularly suitable for ecology. In this paper, we use negative binomial models to see the differences on spatial patterns of predator and decomposer Arthropods, also characteristic of edaphic and climatic in each cave. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Caves

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IoT Solution for Intelligent Pond Monitoring

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Usin Darmalim Ferdinan Darmalim Sutristo Darmalim Alam Ahmad Hidayat Arif Budiarto Bharuno Mahesworo Bens Pardamean Mega Medica Pharmaceuticals Jakarta Indonesia 11840 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond.

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The need to implement FAIR principles in biomolecular simulations

arXiv

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arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber

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IOP Conference Series: Earth and Environmental Science 2020年第1期426卷

作者： Harsawardana Reza Rahutomo Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Teddy Suparyanto Don Bosco Surya Atmaja Bayu Samoedro Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data.

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