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Mixed effects of HDACi on host gene expression and their implications for HIV reactivation from latency

Journal of Virus Eradication 2015年 1卷 4-4页

作者： N. Beliakova-Bethell A. Mukim S. Deshmukh C.H. White C.H. Woelk C.A. Spina Department of Medicine University of California San Diego La Jolla CA USA VA San Diego Healthcare System San Diego CA USA Graduate Program in Bioinformatics and Systems Biology University of California San Diego La Jolla CA USA Faculty of Medicine University of Southampton UK Department of Pathology University of California San Diego La Jolla CA USA

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Information system model for maize research to anticipate the impact of climate change on Indonesian food security

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. S. Perbangsa F. A. Munawwar M. B. Pabendon B. Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Indonesia Cereals Research Institute Maros South Sulawesi 90514 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As a rich country in genetic resources, Indonesia has the potential for strengthening food security. Maize is one of the commodities that greatly affect food security in Indonesia. Maize is also widely used as animal feed. The high demand for maize requires efforts to increase the productivity of sustainable cultivation. Various research efforts that have been carried out require the support of a comprehensive information system. This study aimed to build a research model for maize genetic resource information systems that can help search for superior varieties. The data collection method used was collecting genotype, phenotype, climate data and interviewing the maize breeders or researchers. As for the design of the model, this study used the Object-Oriented Analysis & Design (OOAD) method with notation from Unified Modelling Language (UML). The result of this research was a web-based model of information systems for maize research.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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SpliceAPP: an interactive web server to predict splicing errors arising from human mutations

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BMC genomics 2024年第1期25卷 600页

作者： Ang-Chu Huang Jia-Ying Su Yu-Jen Hung Hung-Lun Chiang Yi-Ting Chen Yen-Tsung Huang Chen-Hsin Albert Yu Hsin-Nan Lin Chien-Ling Lin Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. Institute of Statistical Science Academia Sinica Taipei Taiwan. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. arith@gate.sinica.edu.tw. Institute of Molecular Biology Academia Sinica No. 128 Sec. 2 Academia Road Nangang District Taipei City 115014 Taiwan. mbcllin@gate.sinica.edu.tw. Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan. mbcllin@gate.sinica.edu.tw. Bioinformatics Program International Graduate Program Academia Sinica Taipei Taiwan. mbcllin@gate.sinica.edu.tw.

BACKGROUND:Splicing variants are a major class of pathogenic mutations, with their severity equivalent to nonsense mutations. However, redundant and degenerate splicing signals hinder functional assessments of sequence variations within introns, particularly at branch sites. We have established a massively parallel splicing assay to assess the impact on splicing of 11,191 disease-relevant variants. Based on the experimental results, we then applied regression-based methods to identify factors determining splicing decisions and their respective weights. RESULTS:Our statistical modeling is highly sensitive, accurately annotating the splicing defects of near-exon intronic variants, outperforming state-of-the-art predictive tools. We have incorporated the algorithm and branchpoint information into a web-based tool, SpliceAPP, to provide an interactive application. This user-friendly website allows users to upload any genetic variants with genome coordinates (e.g., chr15 74,687,208 A G), and the tool will output predictions for splicing error scores and evaluate the impact on nearby splice sites. Additionally, users can query branch site information within the region of interest. CONCLUSIONS:In summary, SpliceAPP represents a pioneering approach to screening pathogenic intronic variants, contributing to the development of precision medicine. It also facilitates the annotation of splicing motifs. SpliceAPP is freely accessible using the link https://***/SpliceAPP . Source code can be downloaded at https://***/hsinnan75/SpliceAPP .

关键词： Human mutations LASSO regression RNA splicing Splicing variant prediction

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Astrocytic Cebpd Regulates Pentraxin 3 Expression to Promote Fibrotic Scar Formation After Spinal Cord Injury

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Molecular neurobiology 2023年第4期60卷 2200-2208页

作者： Shao-Ming Wang Jung-Yu C Hsu Chiung-Yuan Ko Hsiang-En Wu Yu-Wei Hsiao Ju-Ming Wang Graduate Institute of Biomedical Sciences China Medical University Taichung 404333 Taiwan. wangs@mail.cmu.edu.tw. Neuroscience and Brain Disease Center China Medical University Taichung Taiwan. wangs@mail.cmu.edu.tw. Department of Neurology China Medical University Hospital Taichung Taiwan. wangs@mail.cmu.edu.tw. Department of Cell Biology and Anatomy College of Medicine National Cheng Kung University Tainan Taiwan. Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan. TMU Research Center of Neuroscience Taipei Medical University Taipei Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Taiwan. Cellular Pathobiology Section Integrative Neuroscience Research Branch Intramural Research Program National Institute on Drug Abuse NIH/DHHS Suite 3512 333 Cassell Drive Baltimore MD 21224 USA. Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan. Department of Biotechnology and Bioindustry Sciences College of Bioscience and Biotechnology National Cheng Kung University Tainan Taiwan. yumingw@mail.ncku.edu.tw. Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan. yumingw@mail.ncku.edu.tw. International Research Center for Wound Repair and Regeneration National Cheng Kung University Tainan Taiwan. yumingw@mail.ncku.edu.tw. Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. yumingw@mail.ncku.edu.tw. Institute of Bioinformatics and Biosignal Transduction College of Bioscience and Biotechnology National Cheng Kung University Tainan 701 Taiwan. yumingw@mail.ncku.edu.tw.

Astroglial-fibrotic scars resulted from spinal cord injury affect motor and sensory function, leading to paralysis. In particular, the fibrotic scar is a main barrier that disrupts neuronal regeneration after spinal cord injury. However, the association between astrocytes and fibrotic scar formation is not yet understood. We have previously demonstrated that the transcriptional factor Cebpd contributes to astrogliosis, which promotes glial scar formation after spinal cord injury. Herein, we show that fibrotic scar formation was decreased in the epicenter region in Cebpd mice after contusive spinal cord injury and astrocytic Cebpd promoted fibroblast migration through secretion of Ptx3. Furthermore, the expression of Mmp3 was increased under recombinant protein Ptx3 treatment in fibroblasts by observing microarray data, resulting in fibroblast migration. In addition, regulation of Mmp3 occurs through the NFκB signaling pathway by using an irreversible inhibitor of IκBα phosphorylation in pretreated fibroblasts. Of note, we used the synthetic peptide RI37, which blocks fibroblast migration and decreases fibroblast Mmp3 expression in IL-1β-treated astrocyte conditioned media. Collectively, our data suggest that fibroblast migration can be affected by astrocytic Cebpd through the Ptx3/NFκB/Mmp3 axis pathway and that the RI37 peptide may act as a therapeutic medicine to inhibit fibrotic scar formation after spinal cord injury.

关键词： Astrocyte Cebpd Fibrotic scar Ptx3 Spinal cord injury

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Maintaining the Quality and Aroma of Coffee with Fuzzy Logic Coffee Roasting Machine

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

作者： Harsawardana Bayu Samodro Bharuno Mahesworo Teddy Suparyanto Don Bosco Surya Atmaja Bens Pardamean Artificial Intelligence Research and Innovation Center for Agroindustry (AIRICA) Institut Pertanian STIPER Yogyakarta Indonesia 55281 Mechatronics and AI Research & Development Center Biotechnic Innovation Yogyakarta Indonesia 55281 Bioinformatics and 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

With the increasing global demands of coffee, Indonesia need to increase their coffee beans supply and improve the quality to maintain its position in the international coffee industry. One of the most important coffeepre-processing, coffee bean roasting, is under developed in Indonesia. Indonesia coffee farmers prefer to use the traditional cauldron to roast their coffee beans. Roasting coffee beans using traditional cauldron is inefficient and resulting unequal roasted level for the coffee beans. In this study, we proposed atemperature control system based on fuzzy logic for coffee roaster machine. The system controls the temperature in accordance with the demanded roasting level. A thermal camera is attached inside the roasting chamber to monitor the heaton the roasted coffee beans. The thermal camera is integrated with a stirring mechanism to equalise the heat among the beans.

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Sustainability of the Water Footprint of Various Soil Types on Oil Palm Plantations

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： Lisma Safitri Hermantoro Valensi Kautsar Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia School of Earth and Environment University of Leeds Woodhouse Leeds LS2 9J Bioinformatics and 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

There are significant studies that have quantified oil palm water footprint as an indicator of environmental sustainability but an estimation of water footprint under varying soil types furthermore is still limited. The objectives of the study were to estimate whether annual variations of soil type and yields significantly effects for the oil palm water footprint. The data from three types of soil (spodosol, inceptisol, ultisol) were collected from an oil palm plantation in Pundu village, Central Kalimantan, Indonesia. To perform the water footprint analysis we utilize water balance accounting equations via application Cropwat 8.0. From that, we determine the crop irrigation scheduling to compute the blue, green, and grey water footprint of oil palm fresh fruit bunch in the area. Our analysis found that the actual evapotranspiration of spodosol and inceptisol have the same value: 1242 mm/year whilst ultisol is 1239 mm/year. The total water footprint of oil palm varied considerably with the largest value being 1310.04 m3/ton for ultisol. The actual evapotranspiration of spodosol and inceptisol have the same value of 1242 mm/year whilst ultisol is 1239 mm/year. The higher production resulted in a lower water footprint and vice versa. Moreover, the total water footprint from ultisol soil type has the highest value due to the lowest yields. The difference in evapotranspiration value resulted in the insignificant value of total water footprint. The lower water availability, the lower water use, and the higher actual irrigation requirement in oil palm plantation yet showed the unnotable impact on water footprint in different soil types for the oil palm plantation.

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A VLPs based vaccine protects against Zika virus infection and prevents cerebral and testicular damage

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NPJ vaccines 2025年第1期10卷 107页

作者： Nelson Côrtes Aline Lira Jaqueline D Q Silva Evelyn Carvalho Wasim A Prates-Syed Barbara Hamaguchi Ricardo Durães-Carvalho Andrea Balan Niels O S Câmara Otavio Cabral-Marques Norbert Pardi Ester C Sabino José E Krieger Gustavo Cabral-Miranda Department of Infectious Diseases and Tropical Medicine Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. Department of Clinical and Toxicological Analyses School of Pharmaceutical Sciences University of Sao Paulo São Paulo Brazil. Department of Biophysics São Paulo School of Medicine Federal University of São Paulo São Paulo SP Brazil. Department of Microbiology Immunology and Parasitology São Paulo School of Medicine Federal University of São Paulo (UNIFESP) São Paulo SP Brazil. Department of Morphology and Genetics Federal University of São Paulo São Paulo Brazil. Interunit Bioinformatics Graduate Program Institute of Chemistry University of Sao Paulo Sao Paulo Brazil. Applied Structural Biology Laboratory Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. Department of Medicine Division of Molecular Medicine Laboratory of Medical Investigation 29 Faculty of Medicine University of São Paulo São Paulo Brazil. DO'R Institute for Research São Paulo Brazil. Department of Microbiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. Laboratory of Genetics and Molecular Cardiology Clinical Hospital Faculty of Medicine University of São Paulo Heart Institute São Paulo Brazil. Department of Infectious Diseases and Tropical Medicine Laboratory of Medical Investigation 46 Faculty of Medicine University of São Paulo São Paulo Brazil. gcabral.miranda@usp.br. Department of Immunology Institute of Biomedical Sciences University of São Paulo São Paulo Brazil. gcabral.miranda@usp.br.

Still, Zika virus (ZIKV) infection poses a substantial public health risk, especially for pregnant women and their fetuses, as it can result in congenital abnormalities and fetal mortality during pregnancy. Despite significant advances in understanding and combating ZIKV, considerable challenges remain in the fight against this flavivirus. A crucial component of this effort is the development of vaccines, none of which have yet been licensed for human use. Here, we present a comprehensive study of a novel ZIKV vaccine candidate based on virus-like particles (VLPs), designed to provide broad immunological protection against viral infection combined with safety, without the need for additional adjuvants. A self-adjuvanted VLPs-based vaccine displaying the envelope protein domain III (EDIII) of ZIKV was built. The EDIII protein was expressed in E. coli and chemically conjugated to QβVLPs. Immunization of C57BL/6 mice with two doses of the EDIII-QβVLPs vaccine elicited strong EDIII-specific Th1-based immune response. Notably, the vaccine induced neutralizing antibodies and conferred protection in type I IFN receptor-deficient (G129) mice against ZIKV challenge. Furthermore, vaccinated male mice were protected from ZIKV-induced cerebral and testicular damage, critical concerns for ZIKV pathogenesis. These findings suggest that the EDIII-QβVLP vaccine is a promising candidate for preventing ZIKV infection, with potential applications in combatting this and other emerging flaviviruses.

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Addendum to “Hypothesis: a glycoprotein-degradation complex formed by protein–protein interaction involves cytoplasmic peptide: N -glycanase” [Biochem. Biophys. Res. Commun. 302 (2003) 1–5]

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Biochemical and Biophysical Research Communications 2003年第2期303卷 732-732页

作者： Tadashi Suzuki William J Lennarz Undergraduate Program for Bioinformatics and Systems Biology and Department of Biophysics and Biochemistry Graduate School of Science University of Tokyo and PRESTO Japan Science and Technology Corporation (JST) Hongo 7-3-1 Bunkyo-ku Tokyo 113-0033 Japan Department of Biochemistry and Cell Biology and the Institute for Cell and Developmental Biology State University of New York at Stony Brook Stony Brook NY 11794-5215 USA

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bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile Data on Nasopharyngeal Carcinoma

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Digdo Sudigyo Gisti Rahmawati Dicka W. Setiasari Risky H. Poluan Tjeng Wawan Cenggoro Arif Budiarto Alam Ahmad Hidayat Sri R. Indrasari Afiahayati Sofia M. Haryana Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Study Program of Biotechnology Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Medicine Public Health and Nursing Gadjah Mada University Yogyakarta Indonesia 55281 Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

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