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Design and energy assessment of a new hybrid solar drying dome - Enabling Low-Cost, Independent and Smart Solar Dryer for Indonesia Agriculture 4.0

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

作者： F E Gunawan A S Budiman B Pardamean E Djuana S Romeli N Hananda C Harito D P B Aji D N N Putri Stevanus Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT. Impack Pratama Industri Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia. Mechanical Engineering Department. Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia

Solar dryer is typically used for agricultural purposes in Indonesia. There are many economically important crops requiring storage or drying under particular environmental conditions such as temperature and humidity. High temperatures inside solar dryer prevents the growth of microorganism, and quickly reduce moisture content from the substance. A hybrid solar dryer is generally considered to provide the most optimum solution, however solar panels may be expensive and they still only provide heat or energy in the daytime. Hence, we propose here a new kind of hybrid solar dryer for 24/7 optimum conditions for crops - enabled by recent advances in energy technologies as well as Industry 4.0. This study aims to create an efficient, affordable and a self-sufficient intelligent energy system that will be applied to agriculture for storage or drying purposes by measuring the energy needs for the optimal drying system. Therefore, it is crucial to estimate and assess the critical energy needs for such new systems in order to optimize and design such smart solar dryer (SSD) system especially for Indonesia's agricultural needs. We use design experience of our industry partner (PT Impack Pratama Industri, Indonesia) who has been working extensively on such solar dryer dome (SDD) based on polycarbonate material (only solar irradiation, no other technologies) and theoretical framework based on first principles in thermodynamics to estimate and assess critical energy needs for such dome with all the smart technologies. The calculation was performed based on Mollier diagram and the result still a rough estimation of energy required.

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MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics

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bioinformatics Advances 2023年第1期3卷

作者： Tsai, Dong-Ming Chang, Ching-Yao Lin, Shih-Ming Kuo, Tien-Chueh Wang, San-Yuan Chen, Guan-Yuan Kuo, Ching-Hua Tseng, Yufeng Jane Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei 106216 Taiwan The Metabolomics Core Laboratory Centers of Genomic and Precision Medicine National Taiwan University Taipei 100225 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 106216 Taiwan Master Program for Clinical Pharmacogenomics and Pharmacoproteomics School of Pharmacy Taipei Medical University Taipei 110301 Taiwan Forensic Medicine College of Medicine National Taiwan University Taipei 100225 Taiwan School of Pharmacy College of Medicine National Taiwan University Taipei 100225 Taiwan

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GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

arXiv

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arXiv 2023年

作者： Wu, Da Yang, Jingye Liu, Cong Hsieh, Tzung-Chien Marchi, Elaine Blair, Justin Krawitz, Peter Weng, Chunhua Chung, Wendy Lyon, Gholson J. Krantz, Ian D. Kalish, Jennifer M. Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Institute for Genomic Statistics and Bioinformatics University Hospital Bonn Rheinische Friedrich-Wilhelms-Universität Bonn Bonn Germany Department of Human Genetics New York State Institute for Basic Research in Developmental Disabilities Staten IslandNY United States Division of Human Genetics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Department of Pediatrics Boston Children’s Hospital Harvard Medical School BostonMA United States Biology PhD Program The Graduate Center The City University of New York New York United States Department of Genetics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pediatrics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data. © 2023, CC BY.

关键词： Diseases

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Inhibition of topoisomerase IIα and induction of DNA damage in cholangiocarcinoma cells by altholactone and its halogenated benzoate derivatives

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Biomedicine and Pharmacotherapy 2020年 127卷 110149-110149页

作者： Kitdumrongthum, Sarunya Reabroi, Somrudee Suksen, Kanoknetr Tuchinda, Patoomratana Munyoo, Bamroong Mahalapbutr, Panupong Rungrotmongkol, Thanyada Ounjai, Puey Chairoungdua, Arthit Toxicology Graduate Program Faculty of Science Mahidol University Bangkok Thailand Excellent Center for Drug Discovery (ECDD) Mahidol University Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Department of Physiology Faculty of Science Mahidol University Bangkok Thailand Department of Chemistry Faculty of Science Mahidol University Bangkok Thailand Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand

Topoisomerase IIα enzyme (Topo IIα) plays a critical function in DNA replication process and is considered to be a promising target of anti-cancer drugs. In the present study, we reported that the altholactone derivatives modified by adding a halogenated benzoate group showed greater inhibitory activity on Topo IIα enzyme in cell-free system concomitant with cytotoxicity against the CCA cell lines (KKU-M055 and KKU-M213) than those of the parent altholactone. However, the cytotoxic activities of four halogenated benzoate altholactone derivatives including iodo-, fluoro-, chloro-, and bromobenzoate derivatives (compound 1, 2, 3, and 4, respectively) were of equal potency. The fluorobenzoate derivative (compound 2) was chosen for investigating the underlying mechanism in CCA cells. Compound 2 arrested CCA cell cycle at sub G1 phase and induced apoptotic cell death. It markedly inhibited Topo IIα protein expression in both KKU-M055 and KKU-M213 cells, which was accompanied by DNA double-strand breaks demonstrated by an increase in phosphorylated H2A.X protein. Interestingly, KKU-M055 cells, which express higher Topo IIα mRNA compared to KKU-M213 cells, showed greater sensitivity to the compound, indicating the selectivity of the compound to Topo IIα enzyme. By computational docking analysis, the binding affinity of altholactone (-52.5 kcal/mol) and compound 2 (-56.7 kcal/mol) were similar to that of the Topo II poison salvicine (-53.7 kcal/mol). The aromatic moiety of both altholactones embedded in a hydrophobic pocket of Topo II ATPase domain. In addition, compound 2 induced the formation of linear DNA in Topo II-mediated cleavage assay. Collectively, our results demonstrate that the addition of fluorobenzoyl group to altholactone enhances potency and selectivity to inhibit type IIα topoisomerases. Atholactone and fluorobenzoate derivative act as Topo II cleavage complexes stabilizing compounds or Topo II poisons preferentially through binding at ATPase domain of Topo

关键词： Altholactone Cholangiocarcinoma Halogenated benzoate Topoisomerase IIα

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Distinguishing genetic correlation from causation across 52 diseases and complex traits (vol 50, pg 1728, 2018)

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NATURE GENETICS 2018年第12期50卷 1753-1753页

作者： O'Connor, Luke J. Price, Alkes L. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Graduate School of Arts and Sciences Cambridge MA USA Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA Program in Medical and Population Genetics Broad Institute Cambridge MA USA

Mendelian randomization, a method to infer causal relationships, is confounded by genetic correlations reflecting shared etiology. We developed a model in which a latent causal variable mediates the genetic correlation; trait 1 is partially genetically causal for trait 2 if it is strongly genetically correlated with the latent causal variable, quantified using the genetic causality proportion. We fit this model using mixed fourth moments \({\it{E}}({\it{\alpha }}_1^2{\it{\alpha }}_1{\it{\alpha }}_2)\) and \({\it{E}}\left( {{\it{\alpha }}_2^2{\it{\alpha }}_1{\it{\alpha }}_2} \right)\) of marginal effect sizes for each trait; if trait 1 is causal for trait 2, then SNPs affecting trait 1 (large \({\it{\alpha }}_1^2\)) will have correlated effects on trait 2 (large α₁α₂), but not vice versa. In simulations, our method avoided false positives due to genetic correlations, unlike Mendelian randomization. Across 52 traits (average n = 331,000), we identified 30 causal relationships with high genetic causality proportion estimates. Novel findings included a causal effect of low-density lipoprotein on bone mineral density, consistent with clinical trials of statins in osteoporosis.

关键词： Genetics Genome-wide association studies

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A Model of Visual Intelligent System for Genus Identification of Fish in the Siluriformes Order

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

作者： Taufik Budhi Pramono R Ardharyan Islamy Saprudin Joni Johanda Putra Teddy Suparyanto Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Aquaculture Study Program Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 Aquatic Resources Management Brawijaya University Malang East Java Indonesia Graduate Program Aquatic Resources Faculty of Fisheries and Marine Science Jenderal Soedirman University Purwokerto Central Java Indonesia 53123 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 Program Bina Nusantara University Jakarta Indonesia 11480

Ambiguity in the fish naming is present in several fish species database, especially for fish in Siluformes order. To fix the ambiguity, a visual intelligent system is needed to automate the fish naming correction in the database. In this study, we developed a deep-learning-based model as the core of the intelligent system. The proposed model achieved 89% accuracy for the classification of three genera in Siluformes order: Mystus, Hemibagrus, and Glyptothorax.

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Experimental modeling of Alzheimer's disease: Translational lessons from cross-taxon analyses

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Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第5期21卷 e70273页

作者： Konstantin B Yenkoyan Maria M Kotova Kirill V Apukhtin David S Galstyan Tamara G Amstislavskaya Tatyana Strekalova Murilo S de Abreu Vergine A Chavushyan Lee Wei Lim Longen Yang Denis D Rosemberg Allan V Kalueff Neuroscience Laboratory COBRAIN Center Yerevan State Medical University after M. Heratsi Yerevan Armenia. Neuroscience Program Sirius University of Science and Technology Sochi Russia. Institute of Translational Biomedicine St. Petersburg State University St. Petersburg Russia. Institute of Experimental Medicine Almazov National Medical Research Centre Ministry of Healthcare of Russian Federation St. Petersburg Russia. Scientific Research Institute of Neurosciences and Medicine Novosibirsk Russia. Research Institute of General Pathology and Pathophysiology Moscow Russia. Graduate Program in Health Sciences Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil. Department of Biosciences and Bioinformatics School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Suzhou Municipal Key Laboratory of Neurobiology and Cell Signaling School of Science Xi'an Jiaotong-Liverpool University Suzhou China. Laboratory of Experimental Neuropsychobiology Department of Biochemistry and Molecular Biology Natural and Exact Sciences Center Federal University of Santa Maria Santa Maria Brazil. Graduate Program in Biological Sciences: Toxicological Biochemistry Federal University of Santa Maria Santa Maria Brazil. The International Zebrafish Neuroscience Research Consortium (ZNRC) Slidell Louisiana USA.

Alzheimer's disease (AD) is a severely debilitating neurodegenerative disease with a rapidly increasing global prevalence, poorly understood causes, and no efficient treatments. Experimental models are valuable for studying AD pathogenesis, including amyloid beta and tau accumulation, synaptic dysfunction, and neuroinflammation. While no model fully reproduces the disease, we take an evolutionary biology approach to discuss available models across taxa, from mammals (rodents, primates) to zebrafish, Drosophila melanogaster, and Caenorhabditis elegans. Evaluating their strengths and limitations provides insight into disease mechanisms and may refine research strategies for improved diagnostics and therapeutic screening. Traditional models have significantly contributed to AD research, yet their translational limitations highlight the need for physiologically relevant alternatives. Integrating humanized rodent models, zebrafish, organoids, and induced pluripotent stem cell-based systems-along with advances in bioengineering and genetic editing-may offer a more comprehensive framework to bridge the gap between preclinical research and clinical application. HIGHLIGHTS: Experimental models across rodents, primates, zebrafish, fruit flies, and worms provide key insights into Alzheimer's disease (AD). Cross-taxon comparisons assess strengths and weaknesses in AD models. Evolutionary biology approaches refine experimental strategies for AD research. Diverse animal models improve understanding of AD pathogenesis. Cross-species models enhance diagnostics and therapeutic strategy development.

关键词： Alzheimer's disease animal models cross‐taxon analyses evolutionary psychiatry translation medicine

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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia

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IOP Conference Series: Earth and Environmental Science 2021年第1期729卷

作者： I Nurlaila A A Hidayat B Pardamean Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 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

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated.

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PapSmear Image Recording System for Artificial Intelligence Data Collection

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

作者： Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 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

Cervical cancer is one of the most leading causes of death for women worldwide. To reduce the mortality caused by cervical cancer, early screening techniques such as pap smear need to be carried out more extensively. Forthat, the availability of an automatic screening system is essential. In this paper, we proposed a system that can collect the dataset needed to train an Artificial Intelligence (AI) system for the automatic pap smear screening system. The proposed system can be integrated seamlessly to the current pap smear result recording procedure; hence avoiding any possible complication.

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A Design of Deep Learning Experimentation for Fruit Freshness Detection

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

作者： Febrian Valentino Tjeng Wawan Cenggoro Bens 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 and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow easily in *** have many good nutrients such as vitamins, proteins and others. But the fruit also has a period where the fruit is said to be fresh *** this time there are still many fruit supplier companies that send fruit unfit for consumption due to lack of accuracy in the process of sorting the fruit when the fruit is taken from the plantation and the entry of other fruit into an improper packaging. Thus, it makes detecting food spoilage from the production stage to consumption is very important. We propose a design of computer vision-based technique usingdeep learning with the Convolutional Neural Network (CNN) model to detect fruit freshness. The specially designed CNN model is then evaluated with public datasets of fruits fresh and rotten for classification derived from Kaggle.

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