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

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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BioBERT: A pre-trained biomedical language representation model for biomedical text mining

arXiv

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arXiv 2019年

作者： Lee, Jinhyuk Yoon, Wonjin Kim, Sungdong Kim, Donghyeon Kim, Sunkyu So, Chan Ho Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Clova AI Research Naver Corp Seong-Nam13561 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation: We make the pre-trained weights of BioBERT freely available at https://github. com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://***/dmis-lab/ biobert. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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Automating Care by Self-maintainability for Full Laboratory Automation

arXiv

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arXiv 2025年

作者： Ochiai, Koji Tahara-Arai, Yuya Kato, Akari Kaizu, Kazunari Kariyazaki, Hirokazu Umeno, Makoto Takahashi, Koichi Kanda, Genki N. Ozaki, Haruka Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research 6-7-1 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Bioinformatics Laboratory Institute of Medicine University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Ph.D. Program in Humanics School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Laboratory Automation Suppliers’ Association 2-1-8 Minatojima Minamimachi Chuo-ku Hyogo Kobe650-0047 Japan Cell Modeling and Simulation Group Exploratory Research Center on Life and Living Systems National Institutes of Natural Sciences 5-1 Higashiyama Myodaiji Aichi Okazaki444-8787 Japan Robotics Division YASKAWA Electric Corporation 2-1 Kurosaki Shiroishi Yahatanishi-ku Kitakyushu Fukuoka806-0004 Japan BAIKEIDO LLC 3-8-1 Asano Kokurakita-ku Kitakyushu Fukuoka802-0001 Japan Graduate School of Media and Governance Keio University 5322 Endo Fujisawa-shi Kanagawa252-0882 Japan Medical Research Laboratory Institute of Integrated Research Institute of Science Tokyo 1-5-45 Yushima Bunkyo-ku Tokyo113-8519 Japan Center for Artificial Intelligence Research University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

The automation of experiments in life sciences and chemistry has significantly advanced with the development of various instruments and AI technologies. However, achieving full laboratory automation, where experiments conceived by scientists are seamlessly executed in automated laboratories, remains a challenge. We identify the lack of automation in planning and operational tasks—critical human-managed processes collectively termed "care"—as a major barrier. Automating care is the key enabler for full laboratory automation. To address this, we propose the concept of self-maintainability (SeM): the ability of a laboratory system to autonomously adapt to internal and external disturbances, maintaining operational readiness akin to living cells. A SeM-enabled laboratory features autonomous recognition of its state, dynamic resource and information management, and adaptive responses to unexpected conditions. This shifts the planning and execution of experimental workflows, including scheduling and reagent allocation, from humans to the system. We present a conceptual framework for implementing SeM-enabled laboratories, comprising three modules—Requirement manager, Labware manager, and Device manager—and a Central manager. SeM not only enables scientists to execute envisioned experiments seamlessly but also provides developers with a design concept that drives the technological innovations needed for full automation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information management

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HATS: A hierarchical graph attention network for stock movement prediction

arXiv

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arXiv 2019年

作者： Kim, Raehyun So, Chan Ho Jeong, Minbyul Lee, Sanghoon Kim, Jinkyu Kang, Jaewoo Department of Computer Science and Engineering Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of

Many researchers both in academia and industry have long been interested in the stock market. Numerous approaches were developed to accurately predict future trends in stock prices. Recently, there has been a growing interest in utilizing graph-structured data in computer science research communities. Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy. First, the quality of collected information from different types of relations can vary considerably. No existing work has focused on the effect of using different types of relations on stock market prediction or finding an effective way to selectively aggregate information on different relation types. Furthermore, existing works have focused on only individual stock prediction which is similar to the node classification task. To address this, we propose a hierarchical attention network for stock prediction (HATS) which uses relational data for stock market prediction. Our HATS method selectively aggregates information on different relation types and adds the information to the representations of each company. Specifically, node representations are initialized with features extracted from a feature extraction module. HATS is used as a relational modeling module with initialized node representations. Then, node representations with the added information are fed into a task-specific layer. Our method is used for predicting not only individual stock prices but also market index movements, which is similar to the graph classification task. The experimental results show that performance can change depending on the relational data used. HATS which can automatically select information outperformed all the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Aggregates

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Combining virtual screening with cis-/trans-cleavage enzymatic assays effectively reveals broad-spectrum inhibitors that target the main proteases of SARS-CoV-2 and MERS-CoV

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Antiviral research 2023年 216卷 105653页

作者： Yu-Jen Chang Uyen Nguyen Phuong Le Jia-Jun Liu Sin-Rong Li Shao-Ting Chao Hsueh-Chou Lai Yu-Feng Lin Kai-Cheng Hsu Chih-Hao Lu Cheng-Wen Lin The Ph.D. Program of Biotechnology and Biomedical Industry China Medical University Taichung Taiwan. Department of Medical Laboratory Science and Biotechnology China Medical University Taichung Taiwan Graduate Institute of Biological Sciences and Technology China Medical University Taichung Taiwan. Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan. Department of Laboratory Medicine China Medical University Hospital Taichung Taiwan. Department of Medical Laboratory Science and Biotechnology China Medical University Taichung Taiwan. Division of Hepato-Gastroenterology Department of Internal Medicine China Medical University Hospital Taichung Taiwan. Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan. Graduate Institute of Cancer Biology and Drug Discovery Taipei Medical University Taipei Taiwan. The Ph.D. Program of Biotechnology and Biomedical Industry China Medical University Taichung Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan. Electronic address: chlu@nycu.edu.tw. Graduate Institute of Biological Sciences and Technology China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan. Electronic address: cwlin@mail.cmu.edu.tw.

The main protease (M) of SARS-CoV-2 is essential for viral replication, which suggests that the M is a critical target in the development of small molecules to treat COVID-19. This study used an in-silico prediction approach to investigate the complex structure of SARS-CoV-2 M in compounds from the United States National Cancer Institute (NCI) database, then validate potential inhibitory compounds against the SARS-CoV-2 M in cis- and trans-cleavage proteolytic assays. Virtual screening of ∼280,000 compounds from the NCI database identified 10 compounds with highest site-moiety map scores. Compound NSC89640 (coded C1) showed marked inhibitory activity against the SARS-CoV-2 M in cis-/trans-cleavage assays. C1 strongly inhibited SARS-CoV-2 M enzymatic activity, with a half maximal inhibitory concentration (IC) of 2.69 μM and a selectivity index (SI) of >74.35. The C1 structure served as a template to identify structural analogs based on AtomPair fingerprints to refine and verify structure-function associations. M-mediated cis-/trans-cleavage assays conducted with the structural analogs revealed that compound NSC89641 (coded D2) exhibited the highest inhibitory potency against SARS-CoV-2 M enzymatic activity, with an IC of 3.05 μM and a SI of >65.57. Compounds C1 and D2 also displayed inhibitory activity against MERS-CoV-2 with an IC of <3.5 μM. Thus, C1 shows potential as an effective M inhibitor of SARS-CoV-2 and MERS-CoV. Our rigorous study framework efficiently identified lead compounds targeting the SARS-CoV-2 M and MERS-CoV M.

关键词： In-silico prediction MERS-CoV Main protease National cancer institute database SARS-CoV-2 cis-/trans-cleavage assay

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Improvement of Jakarta's air quality during large scale social restriction

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

作者： R Rahutomo K Purwandari J W C Sigalingging B Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level.

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Microchip-Based Structure Determination of Small Proteins Using Cryo-EM

SSRN

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SSRN 2020年

作者： Casasanta, Michael A. Jonaid, G.M. Kaylor, Liam Luqiu, William Y. Solares, Maria J. Schroen, Mariah L. Dearnaley, William J. Wilson, Jarad Dukes, Madeline J. Kelly, Deborah F. Department of Biomedical Engineering Pennsylvania State University University ParkPA16802 United States Materials Research Institute Pennsylvania State University University ParkPA16802 United States Bioinformatics and Genomics Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Molecular Cellular and Integrative Biosciences Graduate Program Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States RayBiotech Life Peachtree CornersGA30092 United States Applications Science Protochips Inc MorrisvilleNC27560 United States

Interest in cryo-Electron Microscopy (EM) imaging has skyrocketed in recent years due to its pristine views of macromolecules and materials. As advances in instrumentation and computing algorithms spurred this progress, there is renewed focus to address specimen-related challenges. Here we contribute a microchip-based toolkit to perform complementary structural and biochemical analysis on low molecular-weight proteins. As a model system, we used the SARS-CoV-2 nucleocapsid (N) protein (48 kDa) due to its stability and important role in therapeutic development. Cryo-EM structures of the N protein monomer revealed a flexible N-terminal “top hat” motif and a helical-rich C-terminal domain. To complement our structural findings, we engineered microchip-based immunoprecipitation assays that led to the discovery of the first antibody binding site on the N protein. The data also facilitated molecular modeling of a variety of pandemic and common cold-related coronavirus proteins. Such insights may guide future pandemic-preparedness protocols through immuno-engineering strategies to mitigate viral outbreaks. © 2020, The Authors. All rights reserved.

关键词： Proteins

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Acute Effects of Four Major Trace Amines on Zebrafish Behavioral, Neurochemical, and Neuroendocrine Responses

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Journal of Neurochemistry 2025年第6期169卷 e70116页

作者： Thalia M. Quintanilha Pietra M. Costa Ana L. S. Cardoso Gabrieli S. Battú Leonardo M. Bastos Bruno P. dos Santos Talise E. Müller Tiago F. de Oliveira Angelo Piato Allan V. Kalueff Murilo S. de Abreu Graduate Program in Health Sciences Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil Laboratory of Psychopharmacology and Behavior (LAPCOM) Department of Pharmacology Institute of Basic Health Sciences Federal University of Rio Grande do Sul Porto Alegre Brazil Institute of Experimental Medicine Almazov National Medical Research Centre Ministry of Healthcare of Russian Federation St. Petersburg Russia Institute of Translational Biomedicine St. Petersburg State University St. Petersburg Russia Suzhou Municipal Key Laboratory of Neurobiology and Cell Signaling School of Science Xi'an Jiaotong-Liverpool University Suzhou China Department of Biosciences and Bioinformatics School of Science Xi'an Jiaotong-Liverpool University Suzhou China

Trace amines are biologically active compounds endogenously synthesized in the brain in small amounts and structurally resembling biogenic amines. Acting via specific trace amine-associated receptors (TAARs), they induce robust behavioral and physiological effects in humans and animals. However, although TAAR ligands have recently been suggested as novel putative anxiolytics, their central effects and evolutionary conservation of activity remain poorly understood. Here, we evaluated the acute effects of four major trace amines (beta-phenylethylamine, tryptamine, tyramine, and octopamine) on zebrafish anxiety-like and social (shoaling) behavior, as well as neurochemical and neuroendocrine (cortisol) responses. Beta-phenylethylamine, at a low concentration (12 μg/L), caused overt anxiolytic-like effects and reduced brain acetylcholine levels; at a high concentration (1000 μg/L) increased zebrafish anxiety-like behavior and whole-body cortisol levels. Acute tryptamine exposure (7 mg/L) evoked an anxiogenic-like effect, reduced shoaling and social interaction, and elevated brain acetylcholine and whole-body cortisol. Acute exposure to tyramine (15 μg/L) and octopamine (125, 500, and 1500 μg/L) induced similar anxiogenic-like effects, accompanied by increased whole-body cortisol without altering brain acetylcholine levels. Collectively, these findings not only emphasize the important role of trace amines in brain and behavior but support the growing complexity of their CNS effects in vivo across taxa and highlight the relevance of zebrafish models for drug screening based on targeting brain TAARs.

关键词： acetylcholine anxiety behavior cortisol trace amines zebrafish

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