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Systematic literature review: Accelerate the rice production for global food security

AIP Conference Proceedings 2023年第1期2594卷

作者： N. Dominic T. W. Cenggoro B. Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

By 2050, about 70% of extra food production is needed to feed the world's population. As a majority staple food, the expansion of rice production should be accelerated. This systematic literature review aims to discover the factors that influence rice yields through a methodology that is partitioned into four main stages, i.e., query entry into multiple database sources, article title filtering, abstract filtering, and final article selection. The results show that genetics, irrigation system management, and farmers’ long-evolved local knowledge or experiences are the three big factors that elevate the rice production rates. Nevertheless, global warming is a serious challenge that should be deeply considered due to its great impact on reducing rice yields by up to 14.5%. Thus, the development of genetically modified rice varieties needs to be escalated while still maintaining other exogenous factors. This study contributes to providing a broader perspective for decision-makers and other relevant experts to accelerate rice production to achieve global food security.

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Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain

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细胞研究（英文版） 2022年第11期32卷 1008-1021页

作者： Yanxiao Zhang Maria Luisa Amaral Chenxu Zhu Steven Francis Grieco Xiaomeng Hou Lin Lin Justin Buchanan Liqi Tong Sebastian Preissl Xiangmin Xu Bing Ren Ludwig Institute for Cancer Research La JollaCAUSA School of Life Sciences Westlake UniversityHangzhouChina Bioinformatics and Systems Biology Graduate Program University of California San DiegoLa JollaCAUSA Ludwig Institute for Cancer Research La JollaCAUSA Department of Anatomy and Neurobiology School of MedicineUniversity of CaliforniaIrvineCAUSA Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany The Center for Neural Circuit Mapping University of CaliforniaIrvineCAUSA Department of Anatomy and Neurobiology School of MedicineUniversity of CaliforniaIrvineCAUSA Department of Cellular and Molecular Medicine University of California San Diego School of MedicineLa JollaCAUSA Ludwig Institute for Cancer Research La JollaCAUSA Center for Epigenomics University of California San DiegoLa JollaCAUSA.6Institute of Experimental and Clinical Pharmacology and ToxicologyFaculty of MedicineUniversity of FreiburgFreiburgGermany institute for Genomic Medicine University of California San DiegoLa JollaCAUSA

Loss of heterochromatin has been implicated as a cause of pre-mature aging and age-associated decline in organ functions in mammals;however,the specific cell types and gene loci affected by this type of epigenetic change have remained *** address this knowledge gap,we probed chromatin accessibility at single-cell resolution in the brains,hearts,skeletal muscles,and bone marrows from young,middle-aged,and old mice,and assessed age-associated changes at 353,126 candidate cis-regulatory elements(cCREs)across 32 major cell ***,we detected increased chromatin accessibility within specific heterochromatin domains in old mouse excitatory *** gain of chromatin accessibility at these genomic loci was accompanied by the cell-type-specific loss of heterochromatin and activation of LINE1 *** further confirmed the loss of the heterochromatin mark H3K9me3 in the excitatory neurons but not in inhibitory neurons or glial *** results reveal the cell-type-specific changes in chromatin landscapes in old mice and shed light on the scope of heterochromatin loss in mammalian aging.

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Questionnaire design for analyzing important risk factors of cervical cancer

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AIP Conference Proceedings 2023年第1期2594卷

作者： K. Purwandari D. Sudigyo A. S. Perbangsa A. Irwan B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Cervical cancer is Indonesia's second leading cause of death, following only breast cancer. According to the World Health Organization (WHO), non-communicable disorders will be blamed for 70% of all deaths and illnesses in 2020, while other disease types will account for 60%. Cervical cancer can be detected earlier, lowering the chance of death. This study aims to validate and check the pap smear test results obtained from the previously conducted pap smear screening program. The pap smear questionnaire developed would help in the compilation of a patient's habit history that has undergone a pap smear test during his life based on the factors associated with the occurrence of cervical cancer. Specific details, social-demographic features, diet, genital hygiene activities, reproductive background and behaviors, and health care and cancer prevention behavior are also included in the questionnaire template. The consequence of this design is the completeness of evidence on the theory of anomalies dependent on the findings of pap smear sampling.

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RETRACTION: IspH inhibitors kill Gram-negative bacteria and mobilize immune clearance (Retraction of Vol 589, Pg 597, 2021)

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NATURE 2021年第7885期599卷 518-518页

作者： Singh, Kumar Sachin Sharma, Rishabh Reddy, Poli Adi Narayana Vonteddu, Prashanthi Good, Madeline Sundarrajan, Anjana Choi, Hyeree Muthumani, Kar Kossenkov, Andrew Goldman, Aaron R. Tang, Hsin-Yao Totrov, Maxim Cassel, Joel Murphy, Maureen E. Somasundaram, Rajasekharan Herlyn, Meenhard Salvino, Joseph M. Dotiwala, Farokh Vaccine and Immunotherapy Center The Wistar Institute Philadelphia PA USA Program in Molecular and Cellular Oncogenesis The Wistar Institute Philadelphia PA USA Molecular Screening and Protein Expression Facility The Wistar Institute Philadelphia PA USA Bioinformatics Facility The Wistar Institute Philadelphia PA USA Proteomics and Metabolomics Facility The Wistar Institute Philadelphia PA USA Molsoft San Diego CA USA

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Author Correction: AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples

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Experimental & molecular medicine 2025年第1期57卷 284页

作者： Hyeonseong Jeon Junhak Ahn Byunggook Na Soona Hong Lee Sael Sun Kim Sungroh Yoon Daehyun Baek Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Genome4me Inc. Seoul Republic of Korea. School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. AIGENDRUG Co. Ltd. Seoul Republic of Korea. Department of Software and Computer Engineering Ajou University Suwon Republic of Korea. Department of Computer Science and Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. baek@snu.ac.kr. Genome4me Inc. Seoul Republic of Korea. baek@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of Korea. baek@snu.ac.kr. Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Republic of Korea. baek@snu.ac.kr.

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Virtual Flavonoids Screening Identifies Potent Compounds Against Enterovirus A71 and Coxsackievirus A16

SSRN

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SSRN 2023年

作者： Sripattarphan, Amita Sanachai, Kamonpan Chavasiri, Warinthorn Wolschann, Peter Langer, Thierry Boonyasuppayakorn, Siwaporn Rungrotmongkol, Thanyada Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Khon Kaen University Khon Kaen40002 Thailand Center of Excellence in Natural Products Chemistry Department of Chemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Pharmaceutical Chemistry Faculty of Life Sciences University of Vienna Althanstraße 14 ViennaA-1090 Austria Institute of Theoretical Chemistry University of Vienna Vienna1090 Austria Center of Excellence in Applied Medical Virology Department of Microbiology Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok10330 Thailand

Worldwide outbreaks of hand, foot, and mouth disease (HFMD) are caused by enterovirus A71 (EV-A71) and coxsackievirus A16 (CV-A16). Since no anti-HFMD drugs are currently available, it is interesting to study potential viral inhibitors. Rupintrivir is a rhinovirus 3C protease (3Cpro) inhibitor with apparent inhibitory activity against HFMD. This study constructs pharmacophore models of the EV-A71 and CV-A16 3Cpro complexed with rupintrivir using molecular dynamics (MD) simulations. Both models had similar pharmacophore features, including hydrogen bond donors and acceptors and hydrophobic interactions. These pharmacophore models were then used as a template to screen 39 flavonoid compounds as potential novel inhibitors. Diosmin, epigallocatechin gallate (EGCG), and RTH-011 showed high binding affinities for EV-A71 and CV-A16 3Cpro. They formed hydrogen bonds with important surrounding residues in both proteins, including H40, L127, T142, A144, T145, H161, I162, G163, and G164. In addition, their effective concentrations against rhabdomyosarcoma (RD) cell infection by EV-A71 and CV-A16 were determined. EGCG had the highest half maximal effective concentration (EC50) of 12.86 ± 1.30 µM for EV-A71 and 15.54 ± 1.50 µM for CV-A16, while diosmin had EC50 of 21.01±1.57 µM for EV-A71 and 30.68 ± 3.25 µM for CV-A16. Both compounds were non-toxic in RD cells, with 50% cytotoxic concentrations of >100 µM for EGCG and >500 µM for diosmin. In addition, MD simulation analysis showed that EGCG had a higher binding affinity than diosmin, supported by its significantly lower solvated binding free energies and greater numbers of contact atoms and key binding residues. Moreover, previous studies reported EGCG’s inhibitory effect on other viruses, such as severe acute respiratory syndrome coronavirus 2. Therefore, our findings suggest that EGCG can effectively inhibit the EV-A71 and CV-A16 3Cpro. © 2023, The Authors. All rights reserved.

关键词： Molecular dynamics

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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq

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Nature Communications 2025年第1期16卷 1-21页

作者： Zhang, Hefei Li, Xuhang Song, Dongyuan Yukselen, Onur Nanda, Shivani Kucukural, Alper Li, Jingyi Jessica Garber, Manuel Walhout, Albertha J. M. Department of Systems Biology University of Massachusetts Chan Medical School Worcester MA United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles CA United States Via Scientific Inc. Cambridge MA United States Department of Genomics and Computational Biology University of Massachusetts Chan Medical School Worcester MA United States Department of Statistics and Data Science Department of Biostatistics Department of Computational Medicine and Department of Human Genetics University of California Los Angeles CA United States Department of Genetics and Genome Sciences University of Connecticut Health Center Farmington CT United States Pathology Beth Israel Deaconess Medical Center Harvard Medical School Boston MA United States

Transcriptomes provide highly informative molecular phenotypes that, combined with gene perturbation, can connect genotype to phenotype. An ultimate goal is to perturb every gene and measure transcriptome changes, however, this is challenging, especially in whole animals. Here, we present ‘Worm Perturb-Seq (WPS)’, a method that provides high-resolution RNA-sequencing profiles for hundreds of replicate perturbations at a time in living animals. WPS introduces multiple experimental advances combining strengths of Caenhorhabditis elegans genetics and multiplexed RNA-sequencing with a novel analytical framework, EmpirDE. EmpirDE leverages the unique power of large transcriptomic datasets and improves statistical rigor by using gene-specific empirical null distributions to identify DEGs. We apply WPS to 103 nuclear hormone receptors (NHRs) and find a striking ‘pairwise modularity’ in which pairs of NHRs regulate shared target genes. We envision the advances of WPS to be useful not only for C. elegans, but broadly for other models, including human cells. © The Author(s) 2025.

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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年第5期25卷 e622-e634.e6页

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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The development of video analysis instrument to determine teacher's character

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AIP Conference Proceedings 2023年第1期2594卷

作者： Y. B. Gromang M. A. Rudhito D. Sudigyo T. Suparyanto B. Pardamean Department of Mathematics Education Universitas Sanata Dharma Paingan Maguwoharjo Depok Sleman Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This study aims to develop a valid and reliable video analysis instrument to determine teachers’ character in the opening lesson in terms of its utterances and produce video analysis on micro-teaching that will be used in training related to Artificial Intelligence (AI). The type of research used is research and development. The subject of this research is the micro-teaching video of class E students’ batch 2019, Mathematics Education Study program at Sanata Dharma University. The product of this research is a video analysis instrument to find out the character of the prospective teachers, such as confidence, enthusiasm, and happiness in terms of voice utterances. This study uses the ADDIE research model. The results show that the video analysis instrument has a validity value of 4.80 which means it's very valid. Video analysis instrument to find out the enthusiastic character has a value of validity of 4.70, which means it's very valid. Then, the video analysis instrument to find out the character of happiness has a validity value of 4.60 which means it's very valid. The practicality of the developed video analysis instrument meets the required criteria with a practicality value of 4.31. Overall, the video analysis instrument developed belongs to an effective category.

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Pixelsteganalysis: Destroying hidden information with a low degree of visual degradation

arXiv

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arXiv 2019年

作者： Jung, Dahuin Bae, Ho Choi, Hyun-Soo Yoon, Sungroh Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Bioinformatics Seoul National University

Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorithms. Our framework is the first one to disturb covert communications and transactions via the recent deep learning-based steganography algorithms. We first extract a sophisticated pixel distribution of the potential stego image from the auto-regressive model induced by deep learning. Using the extracted pixel distributions, we detect whether an image is the stego or not at the pixel level. Each pixel value is adjusted as required and the adjustment induces an effective removal of the secret image. Because the decoding method of deep learning-based steganography algorithms is approximate (lossy), which is different from the conventional steganography, we propose a new quantitative metric that is more suitable for measuring the accurate effect. We evaluate our method using three public benchmarks in comparison with a conventional steganalysis method and show up to a 20% improvement in terms of decoding rate. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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