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Learning management system for oil palm smallholder-owned plantations

AIP Conference Proceedings 2023年第1期2594卷

作者： E. Firmansyah D. Nurjannah S. I. Dinarti D. Sudigyo T. Suparyanto B. Pardamean Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Departement of Agricultural Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Departement of Agriculture Engineering Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The production gap between oil palm smallholder-owned plantations and corporations shows a real problem in the national palm oil industry. To reduce the gap, user-friendly assistance for smallholder-owned plantations is needed for the cultivation of sustainable palm oil production. Android-based software can be a platform for bridging the transfer of knowledge and technology, transfer of problems, and transfer of solutions between planters and experts. The platform development was formulated in five stages which began with Learning Management System (LMS) application development, identification of Good Agricultural Practices (GAP) and Best Management Practices (BMP) materials, development of digital contents, uploading the digital content to the LMS server, and dissemination. The curriculum in the application platform was prepared based on the needs of the growers according to the identification results of the farmers' profiles. The curriculum consists of GAP and BMP material blocks. Each block was divided into discussion topics. The digital contents related to GAP and BMP were set in the LMS, which was built using Moodle platform.

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Distal gut colonization by oral bacteria during intensive chemotherapy: direct evidence from strain-level analysis of paired samples

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NPJ biofilms and microbiomes 2025年第1期11卷 88页

作者： Suravi Raychaudhuri Hakan Gem Kevin Chung Jeffrey S McLean Kristopher A Kerns Meredith A J Hullar Elissa Elmorr Jacob B Appelbaum Mary-Elizabeth M Percival Roland B Walter Anna B Halpern Samuel S Minot Katie Kim Alexander S Zevin Armin Rashidi Clinical Research Division Fred Hutchinson Cancer Center Seattle WA USA. Division of Hematology and Medical Oncology Department of Medicine University of Washington Seattle WA USA. Department of Oral Medicine University of Washington Seattle WA USA. School of Dentistry University of Washington Seattle WA USA. Clinical Oral Microbiome Research Center University of Washington Seattle WA USA. Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA USA. Data Core Shared Resources Fred Hutchinson Cancer Center Seattle WA USA. Genomics and Bioinformatics Shared Resource Fred Hutchinson Cancer Center Seattle WA USA. Clinical Research Division Fred Hutchinson Cancer Center Seattle WA USA. arashidi@***. Division of Hematology and Medical Oncology Department of Medicine University of Washington Seattle WA USA. arashidi@***.

Oral bacteria have been found in the colon in pathologies such as inflammatory bowel disease. To ascertain niche coalescence, 2 elements are essential: (i) paired oral/fecal samples and (ii) strain-level resolution. We profiled the microbiota in 283 samples from 39 patients undergoing intensive chemotherapy at baseline (saliva: 49, plaque: 51, stool: 43), week 2 (saliva: 18, plaque: 17, stool: 17), week 3 (saliva: 18, plaque: 21, stool: 21), and week 4 (saliva: 8, plaque: 10, stool: 10) of chemotherapy. Through strain-level analysis of paired samples, we demonstrate strong evidence for a breakdown of niche separation in most patients. The extent of overlap increased with time, particularly in patients with intestinal mucositis. Our findings provide definitive evidence for ectopic colonization of the distal gut by oral bacteria in a disease state, likely facilitated by intestinal mucositis. Microbiota contribution by the mouth to the colon may have consequences for the host.

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TOWARDS EFFICIENT REAL-TIME VIDEO MOTION TRANSFER VIA GENERATIVE TIME SERIES MODELING

arXiv

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arXiv 2025年

作者： Haque, Tasmiah Syed, Md. Asif Bin Jeong, Byungheon Bai, Xue Mohan, Sumit Paul, Somdyuti Ahmed, Imtiaz Das, Srinjoy Department of Industrial and Management Systems Engineering West Virginia University MorgantownWV26505 United States Coupa Software San MateoCA United States Lyda Hill Department of Bioinformatics UT Southwestern Medical Center DallasTX United States Intel Corporation Santa ClaraCA United States Department of Artificial Intelligence Indian Institute of Technology Kharagpur West Bengal Kharagpur India School of Mathematical and Data Sciences Department of Industrial and Management Systems Engineering West Virginia University MorgantownWV26505 United States

We propose a deep learning framework designed to significantly optimize bandwidth for motion-transfer-enabled video applications, including video conferencing, virtual reality interactions, health monitoring systems, and vision-based real-time anomaly detection. To capture complex motion effectively, we utilize the First Order Motion Model (FOMM), which encodes dynamic objects by detecting keypoints and their associated local affine transformations. These keypoints are identified using a self-supervised keypoint detector and arranged into a time series corresponding to the successive frames. Forecasting is performed on these keypoints by integrating two advanced generative time series models into the motion transfer pipeline, namely the Variational Recurrent Neural Network (VRNN) and the Gated Recurrent Unit with Normalizing Flow (GRU-NF). The predicted keypoints are subsequently synthesized into realistic video frames using an optical flow estimator paired with a generator network, thereby facilitating accurate video forecasting and enabling efficient, low-frame-rate video transmission. We validate our results across three datasets for video animation and reconstruction using the following metrics: Mean Absolute Error (MAE), Joint Embedding Predictive Architecture Embedding Distance (JEDi), Structural Similarity Index (SSIM), and Average Pair-wise Displacement (APD). Our results confirm that by utilizing the superior reconstruction property of the Variational Autoencoder, the VRNN integrated FOMM excels in applications involving multi-step ahead forecasts such as video conferencing. On the other hand, by leveraging the Normalizing Flow architecture for exact likelihood estimation, and enabling efficient latent space sampling, the GRU-NF based FOMM exhibits superior capabilities for producing diverse future samples while maintaining high visual quality for tasks like real-time video-based anomaly detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Latent Regression and Ordination Risk of Infectious Disease and Climate

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Procedia Computer Science 2021年 179卷 25-32页

作者： Rezzy Eko Caraka Rung Ching Chen Youngjo Lee Prana Ugiana Gio Arif Budiarto Bens Pardamean College of Informatics Chaoyang University of Technology 168 Jifong East Road Wufong Dist. Taichung City 41349 Taiwan (R.O.C.). Department of Statistics College of Natural Science Seoul National University Gwanak-gu Seoul 08826 Korea Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan 20222 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Indonesia.

Global warming arising from climate change can increase the spread of deadly diseases. Effort is needed to develop a set of policies for the government to stem or reduce health risks from global warming. The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. The results obtained that of the 17 types of diseases in Taiwan, that has the most significant loading factor is Amoebiasis, Malaria and Chikungunya. At the same time, climate variables that have the biggest most significant factor are Number day with max temp more than 30, Number day Temp more than 25, and Rainfall PH. Cronbach’s Alpha infectious disease 0.9696 and climate 0.2813. At the same time, the value of Dillon Goldstein’s rho infectious disease 0.974 and climate 0.6404, respectively.

关键词： Disease Counts Climate Latent PLS Regression

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Publisher Correction: GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

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Nature communications 2021年第1期12卷 5334页

作者： Hongyi Zhang Xiaowei Zhan Bo Li Lyda Hill Department of Bioinformatics UT Southwestern Medical Center Dallas TX USA. Department of Population and Data Science UT Southwestern Medical Center Dallas TX USA. Lyda Hill Department of Bioinformatics UT Southwestern Medical Center Dallas TX USA. bo.li@utsouthwestern.edu. Department of Immunology UT Southwestern Medical Center Dallas TX USA. bo.li@utsouthwestern.edu.

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Evolutionary analysis and lineage designation of SARS-CoV-2 genomes

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Science Bulletin 2021年第22期66卷 2297-2311,M0004页

作者： Xiaolu Tang Ruochen Ying Xinmin Yao Guanghao Li Changcheng Wu Yiyuli Tang Zhida Li Bishan Kuang Feng Wu Changsheng Chi Xiaoman Du Yi Qin Shenghan Gao Songnian Hu Juncai Ma Tiangang Liu Xinghuo Pang Jianwei Wang Guoping Zhao Wenjie Tan Yaping Zhang Xuemei Lu Jian Lu State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijing 100871China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of ZoologyCenter for Excellence in Animal Evolution and GeneticsChinese Academy of SciencesKunming 650223China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China Yuxi Rongjian Information Technology Co. Ltd.Yuxi 653100China State Key Laboratory of Microbial Resources(SKLMR) The Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China The Microresource and Big Data Center The Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Key Laboratory of Combinatorial Biosynthesis and Drug Discovery Ministry of Education and Wuhan University School of Pharmaceutical SciencesWuhan 430071China Beijing Center for Disease Prevention and Control(CDC)&Research Center for Preventive Medicine of Beijing Beijing 100013China NHC Key Laboratory of Systems Biology of Pathogens and Christophe Mérieux Laboratory Institute of Pathogen BiologyChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China Key Laboratory of Respiratory Disease Pathogenomics Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China Key Laboratory of Synthetic Biology Institute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200032China NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China

The pandemic due to the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the etiological agent of coronavirus disease 2019(COVID-19),has caused immense global *** the rapid accumulation of SARS-CoV-2 genome sequences,however,thousands of genomic variants of SARSCoV-2 are now publicly *** improve the tracing of the viral genomes’evolution during the development of the pandemic,we analyzed single nucleotide variants(SNVs)in 121,618 high-quality SARS-CoV-2 *** divided these viral genomes into two major lineages(L and S)based on variants at sites 8782 and 28144,and further divided the L lineage into two major sublineages(L1 and L2)using SNVs at sites 3037,14408,and ***,we categorized them into 130 sublineages(37 in S,35 in L1,and 58 in L2)based on marker SNVs at 201 additional genomic *** lineage/sublineage designation system has a hierarchical structure and reflects the relatedness among the subclades of the major *** also provide a companion website(***)that allows users to visualize sublineage information and upload their own SARS-CoV-2 genomes for sublineage ***,we discussed the possible roles of compensatory mutations and natural selection during SARS-CoV-2’s *** efforts will improve our understanding of the temporal and spatial dynamics of SARS-CoV-2’s genome evolution.

关键词： COVID-19 Evolutionary analysis Compensatory advantageous mutation Adaptive evolution Lineage designation

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Adverse Health Correlates of Intimate Partner Violence against Older Women: Mining Electronic Health Records

arXiv

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arXiv 2022年

作者： Yılmaz, Serhan Günay, Erkan Lee, Da Hee Whiting, Kathleen Silver, Kristen Koyutürk, Mehmet Karakurt, Günnur Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States Şişli Hamidiye Etfal Training and Research Hospital Emergency Department Istanbul35371 Turkey Des Moines University Osteopathic Medicine and Public Health Des MoinesIA50312 United States Uniformed Services University Neuroscience Program WashingtonDC20814 United States University of Akron Department of Psychology AkronOH44325 United States Case Western Reserve University Center for Proteomics & Bioinformatics ClevelandOH44106 United States Case Western Reserve University Department of Psychiatry ClevelandOH44106 United States University Hospitals Family Medicine ClevelandOH44106 United States

Intimate partner violence (IPV) is often studied as a problem that predominantly affects younger women. However, studies show that older women are also frequently victims of abuse even though the physical effects of abuse are harder to detect. In this study, we mined the electronic health records (EHR) available through IBM Explorys to identify health correlates of IPV that are specific to older women. Our analyses suggested that diagnostic terms that are co-morbid with IPV in older women are dominated by substance abuse and associated toxicities. When we considered differential co-morbidity, i.e., terms that are significantly more associated with IPV in older women compared to younger women, we identified terms spanning mental health issues, musculoskeletal issues, neoplasms, and disorders of various organ systems including skin, ears, nose and throat. Our findings provide pointers for further investigation in understanding the health effects of IPV among older women, as well as potential markers that can be used for screening IPV. © 2022, CC BY-NC-SA.

关键词： Records management

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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From sequence to protein structure and conformational dynamics with AI/ML

arXiv

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arXiv 2025年

作者： Ille, Alexander M. Anas, Emily Mathews, Michael B. Burley, Stephen K. Rutgers Cancer Institute Rutgers The State University of New Jersey NewarkNJ United States College of Computing Georgia Institute of Technology AtlantaGA United States Department of Medicine Rutgers New Jersey Medical School NewarkNJ United States School of Graduate Studies Rutgers The State University of New Jersey NewarkNJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey PiscatawayNJ United States Collaboratory Rutgers The State University of New Jersey PiscatawayNJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New BrunswickNJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California-San Diego La Jolla San DiegoCA United States

The 2024 Nobel Prize in Chemistry was awarded in part for protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and 3D structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments (MSAs). The deterministic relationship between sequence and structure was hypothesized over half a century ago with profound implications for the biological sciences ever since. Based on this relationship, we hypothesize that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting ensembles of protein structures (i.e., distinct conformations). Accordingly, we conceptualized an AI/ML model architecture which may be trained on sequence data in combination with conformationally-sensitive structure data, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Sequence-informed prediction of protein structural dynamics has the potential to emerge as a transformative capability across the biological sciences, and its implementation could very well be on the horizon. © 2025, CC BY-NC-ND.

关键词： Conformations

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Comprehensive Comparison of Early Relapse and End-Stage Relapsed Refractory Multiple Myeloma

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BLOOD 2020年 136卷 1-1页

作者： Poos, Alexandra M. Giesen, Nicola Catalano, Calogerina Paramasivam, Nagarajan Huebschmann, Daniel John, Lukas Baumann, Anja Hochhaus, Jason Mueller-Tidow, Carsten Sauer, Sandra Huhn, Stefanie Schlesner, Matthias Brors, Benedikt Goldschmidt, Hartmut Raab, Marc S. Weinhold, Niels Department of Internal Medicine V Heidelberg University Hospital Heidelberg Germany Clinical Cooperation Unit (CCU) Molecular Hematology/Oncology German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant Heidelberg Germany Computational Oncology Molecular Diagnostics Program National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ) Heidelberg Germany Department of Pediatric Immunology Hematology and Oncology Heidelberg University Hospital Heidelberg Germany Heidelberg Institute for Stem Cell Technology and Experimental Medicine gGmbH (HI-STEM) Heidelberg Germany Core Center Heidelberg German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Germany Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany

Introduction: Treatments incorporating autologous transplantation (ASCT), proteasome inhibitors (PIs), and immunomodulatory drugs (IMIDs) induce deep and durable remissions in most multiple myeloma (MM) patients, resulting in prolonged survival. Yet, patients who suffer from early relapse within 2 years of treatment initiation or become refractory to PIs and IMIDs still have a dismal outcome. The mutational landscape in early relapsed MM (ERMM) and relapsed/refractory MM (RRMM) has been comprehensively described. Several aberrations are associated with these two types of high-risk disease but little is known about the biological difference between them. To this end, we have comparatively and sequentially analyzed whole genome and RNA sequencing data from ERMM and RRMM patients. Methods: We included 32 patients who had relapsed within 2 years of first-line therapy (ERMM group, 30 after ASCT). Samples were collected at first relapse. Paired baseline samples were available from 17 patients. For the RRMM group, we included 43 patients with a median of 5 prior lines of therapy (range 2 - 13; 88% with ASCT), who had relapsed after PIs and IMiDs. For 22 of them consecutive tumor samples were available. Sequencing data were pre-processed using in-house pipelines. Mutations, indels, translocations, and copy number variants were called using Platypus, SOPHIA and ACESeq. Mutational signatures were identified with MMSig and subclonal structures with SciClone. Differential gene expression was assessed using DESeq, gene set enrichment analysis was performed with hypeR, and gene fusions were detected with Arriba. Results: Nonsynonymous mutations occurred more frequently in RRMM (median=180) compared to ERMM at first relapse (median=62, p<0.001). While TP53 mutations were more often seen in ERMM (31% of cases vs. 21% in RRMM), NRAS mutations were enriched in RRMM (37% vs. 22%). Bi-allelic inactivation of TP53 , RB1 , or CDKN2C was more frequent in ERMM (44% vs. 30% in RRMM). In 11/

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