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Comparative genomics based exploration of xenobiotic degradation patterns in Glutamicibacter, Arthrobacter, and Pseudarthrobacter isolated from diverse ecological habitats

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Heliyon 2024年第22期10卷 e40280页

作者： Ghimire, Nisha Kim, Byeollee Han, So-Ra Oh, Tae-Jin Department of Life Science and Biochemical Engineering Graduate School SunMoon University Asan 31460 South Korea Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan 31460 South Korea Genome-based BioIT Convergence Institute Asan 31460 South Korea Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan 31460 South Korea

Xenobiotics pose a substantial threat to environmental integrity by disrupting normal ecosystems. The genus Arthrobacter, known for its metabolic versatility can degrade several xenobiotic compounds. Arthrobacter strains have also undergone frequent taxonomic revisions and reclassifications to strains including Pseudarthrobacter and Glutamicibacter. Here, we present the complete genome sequence of Glutamicibacter protophormiae strain NG4, isolated from a coastal area surrounded by chemical plants. Further, through comparative genomics involving 55 strains from Glutamicibacter, Arthrobacter, and Pseudarthrobacter, we elucidated taxonomic relationships and xenobiotic degradation potential. Our genomics-based findings revealed a generally even distribution of xenobiotic-degrading genes and pathways among the studied strains. Glutamicibacter species emerged as potential candidate for steroid degradation. A significant number of host-specific and environmental isolates predominantly possessed pathways for 4-hydroxybenzoate (4-HB) degradation and only the environmental isolates possessed benzoate degradation pathway. Certain Arthrobacter and Pseudarthrobacter species isolated from the environmental settings were identified as potential degraders of toluene, xylene, and phenanthrene. Notably, most strains contained pathways for azathioprine, capecitabine, and 5-fluorouridine pharmaceutical drug metabolism. Overall, our findings shed light on microbial metabolic diversity among 55 strains isolated from diverse sources and hint the importance of strict environmental monitoring. Further, for the application of the putative xenobiotic degrading strains, experimental validation is required in the future. © 2024

关键词： Arthrobacter Glutamicibacter KEGG pathway Pseudarthrobacter Xenobiotic

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APPLICATION OF THE DIGITAL ANNEALER UNIT IN OPTIMIZING CHEMICAL REACTION CONDITIONS FOR ENHANCED PRODUCTION YIELDS

arXiv

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arXiv 2024年

作者： Li, Shih-Cheng Wang, Pei-Hwa Su, Jheng-Wei Chiang, Wei-Yin Huang, Shih-Hsien Lin, Yen-Chu Ou, Chia-Ho Chen, Chih-Yu Insilico Medicine Taiwan Ltd. Taipei City110208 Taiwan Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Quantum Information Center Chung Yuan Christian University Taoyuan City320314 Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City320314 Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung City900392 Taiwan

Finding appropriate reaction conditions that yield high product rates in chemical synthesis is crucial for the chemical and pharmaceutical industries. However, due to the vast chemical space, conducting experiments for each possible reaction condition is impractical. Consequently, models such as QSAR (Quantitative Structure-Activity Relationship) or ML (Machine Learning) have been developed to predict the outcomes of reactions and illustrate how reaction conditions affect product yield. Despite these advancements, inferring all possible combinations remains computationally prohibitive when using a conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU) to tackle these large-scale optimization problems more efficiently by solving Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models are constructed in this work: one using quantum annealing and the other using ML. Both models are built and tested on four high-throughput experimentation (HTE) datasets and selected Reaxys datasets. Our results suggest that the performance of models is comparable to classical ML methods (i.e., Random Forest and Multilayer Perceptron (MLP)), while the inference time of our models requires only seconds with a DAU. Additionally, in campaigns involving active learning and autonomous design of reaction conditions to achieve higher reaction yield, our model demonstrates significant improvements by adding new data, showing promise of adopting our method in the iterative nature of such problem settings. Our method can also accelerate the screening of billions of reaction conditions, achieving speeds millions of times faster than traditional computing units in identifying superior conditions. Therefore, leveraging the DAU with our developed QUBO models has the potential to be a valuable tool for innovative chemical synthesis. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Prior herpes zoster occurrence and high-dose corticosteroids increase herpes zoster risk in rheumatoid arthritis patients receiving janus kinase inhibitors in a retrospective and observational study

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Clinical Rheumatology 2024年第8期43卷 2503-2511页

作者： Chen, Po-Ku Chang, Shih-Hsin Chen, Yi-Ming Chen, Hsin-Hua Huang, Po-Hao Huang, Chien-Chung Yeo, Kai-Jieh Lan, Joung-Liang Chen, Der-Yuan Rheumatology and Immunology Center China Medical University Hospital No. 2 Yude Road Taichung 40447 Taiwan College of Medicine China Medical University Taichung Taiwan Ph.D. Program in Translational Medicine and Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan Division of Translational Medicine Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan School of Medicine National Yang-Ming Chiao Tung University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Big Data Center National Chung Hsing University Taichung Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan

Herpes zoster (HZ) risk is increased in rheumatoid arthritis (RA) patients receiving Janus kinase inhibitors (JAKi) therapy. Identifying and evaluating the risk factors of HZ development in patients receiving JAKi therapy would be clinically helpful. We investigated HZ's incidence rates (IR), identified the risk factors, and further assessed their influence on HZ development in RA patients undergoing JAKi therapy. We retrospectively evaluated 249 RA patients who received JAKi therapy between 2015 and 2023. data regarding clinical characteristics, HZ reactivation, HZ vaccination status, and concomitant medication use were collected. Among 249 JAKi-treated patients, 44 developed new-onset HZ (tofacitinib, 28/142;baricitinib, 6/35;upadacitinib,10/72), with an IR of 5.11/100patient-years. Multivariate analysis revealed significant predictors of HZ development: a long JAKi exposure period, prior HZ or COVID-19 history, and concomitant high-dose corticosteroids use. The interval between JAKi initiation and HZ development was significantly shorter in patients with prior HZ history than in those without (median, 6.5 months versus 33.5 months, p < 0.001), suggesting “biphasic” emergence of HZ. Only one patient who had experienced an HZ episode while receiving JAKi developed recurrent HZ. None of the seventeen patients immunized with the non-live recombinant zoster vaccine developed HZ. Our JAKi-treated patients had elevated HZ risks, a class effect across different JAKi. A long exposure period, prior history of HZ or COVID-19, and concomitant high-dose corticosteroid treatment may further increase the risk. The emergence of HZ shows a biphasic pattern: early HZ development in patients with prior HZ and late development in those without. (Table presented.) © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 2024.

关键词： Herpes zoster Janus kinase inhibitors Rheumatoid arthritis Risk factors Zoster vaccination

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory

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Combining Extreme Multi-label Classification and Principal Label Space Transformation for Cold Start Thread Recommendation 14

Combining Extreme Multi-label Classification and Principal L...

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14th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2019

作者： Jitharn, Kantarakotn Pacharawongsakda, Eakasit Big Data Engineering Program College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok Thailand

ISBN: (纸本)9781728156316

The recommendation system has been widely used in various areas, e.g., entertainment, education, and travel. However, this technique faces two main challenges which are Cold Start and High-Dimensionality problems. The cold start happens when the system does not have enough profile of new users;therefore, the system cannot recommend products to them. The second issue comes from the fact that there are a lot of distinct products or users to be recommended. Recently, Extreme Multi-label Classification (XMLC) has been applied to the recommendation system and addressed the Cold Start issue. However, the previous method still has a high-dimensionality issue. In this paper, we proposed a new approach, namely XMLC-PAO, which integrated label space reduction with XMLC. In more details, we transformed the recommendation problem to XMLC and applied Singular Value Decomposition (SVD) to generate reducing operator of label space (products' or users' label space). For the feature space, Deep Learning technique has been used to extract features from texts. From the experiments with Stackoverflow online forums dataset, we have found that the XMLC-PAO showed better performance in terms of RECALL@M and NDCG@M when the dimensions were reduced to 50% and 80% of the original size. © 2019 IEEE.

关键词： Singular value decomposition

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programming Variational Quantum Circuits with Quantum-Train Agent

arXiv

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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

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Journal of Neuro-Oncology 2024年第1期166卷 167-174页

作者： Chou, Chi-Jen Yang, Huai-Che Chang, Po-Yao Chen, Ching-Jen Wu, Hsiu-Mei Lin, Chun-Fu Lai, I-Chun Peng, Syu-Jyun Division of Neurosurgery Department of Surgery Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan Department of Electrical Engineering National Central University Taoyuan Taiwan Department of Neurological Surgery University of Virginia Health System Charlottesville 22903 VA United States Department of Radiology Taipei Veterans General Hospital Taipei Taiwan Department of Heavy Particles & Radiation Oncology Taipei Veterans General Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep learning neural network Identification Metastatic brain tumors Perilesional edema Quantification

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A Clinical Decision Support System of Pressure Ulcers Tissue Classification 21

A Clinical Decision Support System of Pressure Ulcers Tissue...

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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Chang, Dun-Hao Chu, Po-Jui Li, Yi-Jhen Ning, Chun-Kai Chien, Ting-Ying Department of Information Management Yuan Ze University Taoyuan City Taiwan Department of Plastic and Aesthetic Surgery Far Eastern Memorial Hospital Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9781450389846

Pressure ulcers (PUs) are a common problem associated with great morbidity and cost. Because of the lack of professional personnel in the institutions or home care system, the diagnosis and treatment of PUs were sometimes delayed. We aim to develop an automatic tissue classification and severity evaluation system of PUs using AI technology. All PU images were collected from patients in the Far Eastern Memorial Hospital (FEMH) in recent 3 years and labeled according to different tissue types. The labeled images were used to train the tissue segmentation model with U-net convolutional neural network (CNN). The percentage of the different tissue can be calculated automatically and would be compared with the manually labeled one. The output of tissue classification was transformed to a clinical decision support system (CDSS) with a rule-base formula. Our study showed that proposed system can classify each tissue automatically and reach an accuracy about 93.6%. However, the CDSS's accuracy rate was only 64% for the referral suggestion and 62% for the care recommendation. Hence, the system should be modified with more data and information. © 2021 ACM.

关键词： Tissue

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Characterization of Rectangular Plates using Complex Natural Resonance

Characterization of Rectangular Plates using Complex Natural...

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IEEE International Symposium on Antennas and Propagation

作者： Siyuan Li Chad Owen Hargrave Hoi-Shun Lui School of Information Technology and Electrical Engineering (ITEE) The University of Queensland St. Lucia Australia Sustainable Mining Research Program Commonwealth Scientific and Industrial Research Organisation (CSIRO) Pullenvale Australia Global Big Data Technologies Centre School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia

ISBN: (纸本)9781728146713

The practicality of extracting natural resonant modes from the late-time target responses of conducting rectangular plates with various length-to-width ratios is studied using the matrix pencil method. The extracted resonant modes follow a similar behavior as the results obtained from a theoretical analysis reported in the literature. Our results show that it is feasible for these modes to be excited using a practical system, which opens the opportunities for automated target recognition.

关键词： Damping Target recognition Meetings Radar antennas Radar applications

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Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry

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Chinese Medical Journal 2023年第8期136卷 998-1000页

作者： Xueying Yang Lisheng Geng David Huang Kaiwen Li Hongqing Zhuang Jing Cai Ruijie Yang School of Physics Beihang UniversityBeijing 102206China Department of Radiation Oncology Peking University Third HospitalBeijing 100191China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Medical Physics Graduate Program Duke Kunshan UniversityKunshanJiangsu 215316China Medical Management Department CAS Ion Medical Technology Co.Ltd.Beijing 100190China Department of Health Technology and Informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

To the Editor:Due to the highly complex nature of intensity-modulated radiotherapy(IMRT)planning and delivery,patient specific quality assurance(PSQA)should be implemented to assure the reliability of treatment delive... 详细信息

关键词： IMRT treatment vivo

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