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SSRN 2024年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech BlacksburgVA24060 United States Fralin Life Science Institute Virginia Tech BlacksburgVA24060 United States Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24060 United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech BlacksburgVA24060 United States

The rise of antibiotic resistance (AR) poses substantial threats to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate continuous AR monitoring through longitudinal metagenomic data are lacking. To address this, we introduce Cyberinfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive ARG and taxonomic profiling, along with detection of anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and demonstrates its potential for informing effective and timely interventions to mitigate the spread and transmission of AR. CIWARS is broadly applicable to longitudinal metagenomic data from any environment and thus could be a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use when it is needed. The web server is freely available at https://***/. © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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Confidentiality. Integrity, and Availability (CIA) Approach for Private Dataset Management System Design

Confidentiality. Integrity, and Availability (CIA) Approach ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Dedy Ariansyah Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Dataset management systems are essential for assisting research and development (R&D) organizations in applying data governance protocols, especially in managing the utilization of datasets. In R&D, datasets are vital sources for producing analysis, machine learning prediction, and supporting decision-making. As such, it is important to deploy dataset management systems with considerations regarding the aspects of Confidentiality, Integrity, and Availability (CIA) which is a design principle that emphasized information protection and collaboration facilitation among dataset users. The study proposed an analysis and a design for a dataset management system that takes CIA aspects into account. In the future, the proposed design will be utilized as a benchmark for building a dataset management system. In this study, the Waterfall method was adopted. A use case diagram, an ERD (Entity Relationship Diagram), and an activity diagram were constructed in the design stage. In the project implementation stage, the Laravel framework including Laravel Jetstream was considered to be used.

关键词： Protocols Data security Decision making Organizations Machine learning Encryption Protection Information technology Research and development System analysis and design

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Estimating Causal Effects using a Multi-task Deep Ensemble

arXiv

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arXiv 2023年

作者： Jiang, Ziyang Hou, Zhuoran Liu, Yiling Ren, Yiman Li, Keyu Carlson, David Department of Civil and Environmental Engineering Duke University DurhamNC United States Department of Biostatistics and Bioinformatics Duke University School of Medicine DurhamNC United States Program in Computational Biology and Bioinformatics Duke University School of Medicine DurhamNC United States Department of Economics University of Michigan Ross School of Business Ann ArborMI United States Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Computer Science Duke University DurhamNC United States

A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap, we propose Causal Multi-task Deep Ensemble (CMDE), a novel framework that learns both shared and group-specific information from the study population. We provide proofs demonstrating equivalency of CDME to a multi-task Gaussian process (GP) with a coregionalization kernel a priori. Compared to multi-task GP, CMDE efficiently handles high-dimensional and multi-modal covariates and provides pointwise uncertainty estimates of causal effects. We evaluate our method across various types of datasets and tasks and find that CMDE outperforms state-of-the-art methods on a majority of these tasks. © 2023, CC BY.

关键词： Data handling

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Special issue on genome wide association study

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Quantitative Biology 2021年第2期9卷 105-106页

作者： Lin Hou Qiongshi Lu Can Yang Hongyu Zhao Center for Statistical Science Department of Industrial EngineeringMOE Key Laboratory of BioinformaticsSchool of Life SciencesTsinghua UniversityBeijing 100084China Department of Biostatistics and Medical Informatics Department of StatisticsCenter for Demography of Health and AgingUniversity of Wisconsin-MadisonWl 53726USA Department of Mathematics the Hong Kong University of Science and TechnologyHong KongChina Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Since the first success of genome wide association study(GWAS)was reported in 2005 by Hoh and colleagues on the identification of a major gene for age related macular degeneration,many thousands of published studies have reported reproducible association signals between hundreds of thousands of genetic variants and thousands of traits and diseases in the past 16 *** is a growing number of biobanks with up to one million participants developed across the world to facilitate genetic studies of complex traits through ***,many consortium projects have been initiated to collect diverse types of data that have proved very valuable for integrated analysis of GWAS *** rich data present both great opportunities and challenges to scientists on data management,computation,analysis,integration,and *** special issue of Quantitative Biology collects nine papers that cover major progresses,challenges,and future directions for GWAS analysis.

关键词： hundreds GWAS initiated

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Metabolic energy expenditure for time-varying isometric forces

arXiv

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arXiv 2024年

作者： Muralidhar, Sriram Sekaripuram Heitman, Kristen Walcott, Samuel C. Srinivasan, Manoj Mechanical and Aerospace Engineering The Ohio State University ColumbusOH43201 United States School of Health and Rehabilitation Sciences The Ohio State University ColumbusOH43201 United States Mathematical Sciences and Bioinformatics and Computational Biology Worcester Polytechnic Institute WorcesterMA01609 United States Program in Biophysics The Ohio State University ColumbusOH43201 United States

Muscles consume metabolic energy (ATP) to produce force. A mathematical model for energy expen- diture can be useful in estimating real-time costs of movements or to predict energy optimal movements. Metabolic cost models developed so far have predominantly aimed at dynamic movement tasks, where me- chanical work dominates. Further, while it is known that both force magnitude and rate of change of force (force rate) affect metabolic cost, it is not known how these terms interact, or if the force rate dependence can be a consequence of the force dependence. Here, we performed extensive human subject experiments, involving each subject over 5 hours of metabolic trials, which systematically changed the mean forces and forces rates so as to characterize a holistic relation for metabolic cost based on both force and force rate - or analogously, torque and torque rate. Our experiments involved humans producing symmetric or asym- metric sinusoidal forces with different means, amplitudes, frequencies, and rise and fall periods. We showed that the metabolic cost can be well-approximated by a sum of power law functions of torque and torque rate. We found that the metabolic cost scales non-linearly with joint torque (with exponent 1 = 1.36) and non-linearly with torque rate (with exponent 2 = 2.5). Surprisingly, the data suggested that the cost was roughly four times higher for decreasing the torque than increasing, mirroring the analogous ratio between the cost of positive and negative work. Using these metabolic cost relations, we show that if the metabolic cost scales with particular exponents with muscle force and force rates, the same exponents will be observed in multi-joint tasks with multiple muscles. Our new metabolic cost model involving both force and force rate will potentially allow better predictions of energy optimal movements and thus inform wearable robot design and analysis. © 2024, CC BY.

关键词：

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

引用

IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Characterization of Antioxidative Flavonoids in Wild and Cultivated Soybean by Ion-Filtering Strategy-Combined Lc-Hrmsn Analysis

SSRN

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SSRN 2024年

作者： No, Kyoung Tai Kim, Kwan Joong Cho, Min Hyung Lee, Sangwon Ha, Jang-Ho Chu, Sung-June Chung, Gyuhwa Kim, Dae-Ok Bioinformatics & Molecular Design Research Center Incheon21983 Korea Republic of Department of Biotechnology Chonnam National University Yeosu59626 Korea Republic of Graduate School of Biotechnology Kyung Hee University Yongin17104 Korea Republic of Interdisciplinary Graduate Program Department of Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Yonsei University Korea Republic of

To profiling flavonoids in wild soybean (WB;Glycine soja Sieb & Zucc.) and cultivated soybean (CB;Glycine max (L.) Merr.), liquid chromatography-high resolution mass spectrometry (LC-HRMS), antioxidant assays, and chemometrics method were used. Total 70 flavonoids were tentatively identified with ion filter strategies containing precursor ion list, mass defect filter, nitrogen rule filter, and diagnostic ion filter from 20,999 features in WB and CB. Semi-quantification result revealed that WB has 1.4-, 93.1-, 6.3-, and 1.9-fold higher contents in isoflavone, flavan, procyanidin, and flavone than that of CB. In antioxidant assays, WB showed 1.89- to 5.83-fold higher antioxidant capacities than CB in four antioxidant capacity assays. In orthogonal partial least squares discriminant analysis, WB and CB were clearly classified, and 31 characteristic flavonoids were identified by variable importance in projection. Furthermore, epicatechin and procyanidins of WB showed significantly positive correlation with antioxidant capacities by Pearson's correlation. © 2024, The Authors. All rights reserved.

关键词： Flavonoids

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Twitter-based text classification using svm for weather information system 6

Twitter-based text classification using svm for weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Rahutomo, Reza Sigalingging, Join W. C. Ajy Kusuma, Mahisa Prasetyo, Aji Pardamean, Bens Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia BMKG Meteorological Climatological and Geophysical Agency Database Center Division Jakarta10720 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

The popularity of social media amplified the amount of text data that is used to enrich text classification research with machine learning approach. The modernization in weather information system is needed to produce real-time weather information using the involvement of Twitter users. GetOldTweets was used to cover data acquisition from Twitter. Support Vector Machine (SVM) was deployed as a machine learning model for text classification. The result was delivered in Esri Maps features. It facilitated a social sensing for Indonesia weather information system by using Twitter posts from Indonesian users with 93% accuracy. © 2021 IEEE.

关键词： Data acquisition

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Pseudomonas aeruginosa-derived DnaJ induces TLR2 expression through TLR10-mediated activation of the PI3K-SGK1 pathway in macrophages

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Microbes and Infection 2025年第4期27卷 105481页

作者： Lee, Jaehoo Jin, Yongxin Wu, Weihui Lee, Yeji Ha, Un-Hwan Department of Biotechnology and Bioinformatics Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong 30019 South Korea State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education Department of Microbiology Nankai University Tianjin 300071 China

TLR2 is a key component of the innate immune system, responsible for recognizing Gram-positive bacterial components and initiating inflammatory signaling cascades that activate defense responses. However, little is known about the regulatory effects of Pseudomonas aeruginosa (P. aeruginosa) on TLR2 expression. In this study, we investigated the potential link between P. aeruginosa-derived DnaJ and TLR2 expression in macrophages, as well as the activation of downstream signaling pathways. Our findings revealed that DnaJ significantly induced TLR2 expression in a dose- and time-dependent manner, predominantly affecting TLR2 with minimal impact on other TLRs, such as TLR4 and TLR5, which detect bacterial PAMPs. The DnaJ-mediated TLR2 induction was driven by activation of the PI3K-SGK1 signaling pathway, with TLR10 playing a crucial role in facilitating these effects. This increase in TLR2 expression led to enhanced production of inflammatory cytokines in response to secondary Staphylococcus aureus infections, indicating a role in boosting host defense mechanisms. In conclusion, these findings suggest that P. aeruginosa-derived DnaJ promotes TLR2 expression via TLR10-mediated activation of the PI3K-SGK1 pathway, thereby enhancing host immune responses against Gram-positive bacterial infections. © 2025 Institut Pasteur

关键词： DnaJ Pseudomonas aeruginosa SGK1 TLR10 TLR2

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia Computer Science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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