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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

Nature Communications 2025年第1期16卷 1-21页

作者： Leventhal, Matthew J. Zanella, Camila A. Kang, Byunguk Peng, Jiajie Gritsch, david Liao, Zhixiang Bukhari, Hassan Wang, Tao Pao, Ping-Chieh danquah, Serwah Benetatos, Joseph Nehme, Ralda Farhi, Samouil Tsai, Li-Huei dong, Xianjun Scherzer, Clemens R. Feany, Mel B. Fraenkel, Ernest MIT Ph.D. Program in Computational and Systems Biology Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Spatial Technology Platform Broad Institute of Harvard and MIT Cambridge MA United States Precision Neurology Program Brigham and Women’s Hospital and Harvard Medical school Boston MA United States APDA Center for Advanced Parkinson’s Disease Research Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Stephen and Denise Adams Center of Yale School of Medicine New Haven CT United States

despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate dNA damage in drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. © The Author(s) 2025.

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Transdermal nanolipoplex simultaneously inhibits subcutaneous melanoma growth and suppresses systemically metastatic melanoma by activating host immunity

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Nanomedicine: Nanotechnology, Biology, and Medicine 2023年 47卷 102628-102628页

作者： Chen, Chia-Hung Weng, Tzu-Han Chuang, Cheng-Hsun Huang, Kai-Yao Huang, Sih-Cheng Chen, Pin-Rong Huang, Hsiao-Hsuan Huang, Ling-Ya Shen, Pei-Chun Chuang, Po-Ya Huang, Hsiao-Yen Wu, Yi-Syuan Chang, Hao-Chiun Weng, Shun-Long Liao, Kuang-Wen Department of Medical Research Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan Dependent of Medical Education MacKay Memorial Hospital Taipei10449 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Industrial Development Graduate Program of College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Medicine MacKay Medical College New Taipei City25245 Taiwan Department of Obstetrics and Gynecology Hsinchu MacKay Memorial Hospital Hsinchu City30071 Taiwan MacKay Junior College of Medicine Nursing and Management Taipei City11260 Taiwan Drug Development and Value Creation Research Center College of Dental Medicine Kaohsiung Medical University School of Dentistry Graduate Institute of Medicine College of Medicine Center for Cancer Research Kaohsiung Medical University Kaohsiung City80708 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan Department of Biotechnology and Bioindustry Sciences National Cheng Kung University Tainan City70101 Taiwan Ph.D. Degree Program of Biomedical Science and Engineering National Yang Ming Chiao Tung University Hsinchu City30068 Taiwan

Benefit for clinical melanoma treatments, the transdermal neoadjuvant therapy could reduce surgery region and increase immunotherapy efficacy. Using lipoplex (Lipo-PEG-PEI-complex, LPPC) encapsulated doxorubicin (dOX) and carrying CpG oligodeoxynucleotide;the transdermally administered nano-liposomal drug complex (LPPC-dOX-CpG) would have high cytotoxicity and immunostimulatory activity to suppress systemic metastasis of melanoma. LPPC-dOX-CpG dramatically suppressed subcutaneous melanoma growth by inducing tumor cell apoptosis and recruiting immune cells into the tumor area. Animal studies further showed that the colonization and growth of spontaneously metastatic melanoma cells in the liver and lung were suppressed by transdermal LPPC-dOX-CpG. Furthermore, NGS analysis revealed IFN-γ and NF-κB pathways were triggered to recruit and activate the antigen-presenting-cells and effecter cells, which could activate the anti-tumor responses as the major mechanism responsible for the therapeutic effect of LPPC-dOX-CpG. Finally, we have successfully proved transdermal LPPC-dOX-CpG as a promising penetrative carrier to activate systemic anti-tumor immunity against subcutaneous and metastatic tumor. © 2022

关键词： Tumors

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Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

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Ecotoxicology and environmental safety 2024年 284卷 116885页

作者： Cheng-Hong Yang Chih-Hsien Wu Kuei-Hau Luo Huang-Chih Chang Sz-Chiao Wu Hung-Yi Chuang Department of Information Management Tainan University of Technology Tainan 71002 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 80708 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 80708 Taiwan. Electronic address: chyang@nkust.edu.tw. Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan. Electronic address: F108152123@nkust.edu.tw. Graduate Institute of Medicine College of Medicine Kaohsiung Medicine University Kaohsiung 80708 Taiwan. Electronic address: u107800007@kmu.edu.tw. Divisions of Pulmonary & Critical Care Medicine Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung 83341 Taiwan Ph.D Program in Environmental and Occupational Medicine and Research Center for Environmental Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Electronic address: kuan2101@yahoo.com.tw. Epidemiology in the Public Health Program College of Health Oregon State University Oregon 97331 USA. Electronic address: wusz550@***. Graduate Institute of Medicine College of Medicine Kaohsiung Medicine University Kaohsiung 80708 Taiwan Ph.D Program in Environmental and Occupational Medicine and Research Center for Environmental Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Public Health and Environmental Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan Department of Occupational and Environmental Medicine Kaohsiung Medicine University Hospital Kaohsiung Medicine University Kaohsiung 80708 Taiwan. Electronic address: ericch@kmu.edu.tw.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

关键词： Air pollution Carbon monoxide Cognitive impairment dementia Ozone PM(2.5)

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. Macdermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, david L. Chodera, John d. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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Integration of MRI and somatosensory evoked potentials facilitate diagnosis of spinal cord compression

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Scientific Reports 2023年第1期13卷 1-12页

作者： Sun, Shu-Pin phang, Chun-Ren Tzou, Shey-Cherng Chen, Chang-Mu Ko, Li-Wei International Ph.D. Program in Interdisciplinary Neuroscience (UST) College of Biological Science and Technology National Yang Ming Chiao Tung University 734 Engineering Bldg. 5 1001 Daxue Road Hsinchu 30010 Taiwan Department of Medical Research National Taiwan University Hospital Hsin-Chu Branch Hsinchu 300 Taiwan Institute of Molecular Medicine and Bioengineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Department of Surgery College of Medicine and Hospital National Taiwan University No. 7 Zhongshan South Road Taipei 10002 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Institute of Electrical and Control Engineering Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Department of Biomedical Science and Environment Biology and the Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 807 Taiwan

This study aimed to integrate magnetic resonance imaging (MRI) and related somatosensory evoked potential (SSEP) features to assist in the diagnosis of spinal cord compression (SCC). MRI scans were graded from 0 to 3 according to the changes in the subarachnoid space and scan signals to confirm differences in SCC levels. The amplitude, latency, and time–frequency analysis (TFA) power of preoperative SSEP features were extracted and the changes were used as standard judgments to detect neurological function changes. Then the patient distribution was quantified according to the SSEP feature changes under the same and different MRI compression grades. Significant differences were found in the amplitude and TFA power between MRI grades. We estimated three degrees of amplitude anomalies and power loss under each MRI grade and found the presence or absence of power loss occurs after abnormal changes in amplitude only. For SCC, few integrated approach combines the advantages of both MRI and evoked potentials. However, integrating the amplitude and TFA power changes of SSEP features with MRI grading can help in the diagnosis and speculate progression of SCC. © 2023, The Author(s).

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OpenMM 8: Molecular dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, philip Wang, Yuanqing Zhang, Ivy Chodera, John d. de Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUdA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CdK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Self-Awareness for Autonomous systems

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PROCEEdINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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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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data-driven Two-Stage Framework for Identification and Characterization of different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry data

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Microbiology spectrum 2023年第3期11卷 e0347922页

作者： Chia-Ru Chung Hsin-Yao Wang Chun-Han Yao Li-Ching Wu Jang-Jih Lu Jorng-Tzong Horng Tzong-Yi Lee Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan. Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. Ph.D. Program in Biomedical Engineering Chang Gung University Taoyuan Taiwan. Department of Biomedical Sciences and Engineering National Central University Taoyuan Taiwan. College of Medicine Chang Gung University Taoyuan Taiwan. Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan Taiwan. Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan. Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan.

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALdI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALdI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALdI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALdI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including a

关键词： MALdI-TOF MS antimicrobial resistance cephalosporin cephalosporins fluoroquinolones machine learning matrix-assisted laser desorption ionization–time of flight mass spectrometry penicillin penicillins

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Comparative study of Cladophora sp. cellulose by using FTIR and XRd

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Journal of physics: Conference Series 2021年第1期1751卷

作者： S W Suciyati P Manurung S Sembiring R Situmeang Ph.D Student Doctoral Program of Mathematics and Natural Science Faculty of Mathematics and Natural Sciences University of Lampung Jl. Sumantri Brojonegoro no 1 Bandar Lampung Indonesia Department of Physics Faculty of Mathematics and Natural Sciences University of Lampung Jl. Sumantri Brojonegoro no 1 Bandar Lampung Indonesia Department of Chemical Faculty of Mathematics and Natural Sciences University of Lampung Jl. Sumantri Brojonegoro no 1 Bandar Lampung Indonesia

Cellulose Crystalline Cladophora sp. (CCC) was successfully isolated from the freshwater algae Cladophora sp. by used the hydrolysis method. The alkaline treatment can affect the structure of CCC that is showed by the spectrum of Fourier Transform Infrared (FTIR). The typical range for lignin does not appear here, which indicates the delignification process using NaOH plus hydrolysis of HCl has succeeded in removing lignin. The spectrum associated with functional group impurity (1800-1050 cm−1) with lipid, protein, and nucleid acid content are still seen after alkaline treatment and acid hydrolysis. Analysis of crystallinity with X-ray diffraction (XRd) data showed that CCC had the highest crystallinity index on Cd-K (93.4%), Cd-P (66.6%), and Cd-S (63.04%). The smallest particle size for each cellulose is Cd-K (9.5 nm), Cd-S (25.23 nm), and Cd-P (49.57 nm). Based on these results, the CCC production from Cladophora sp. using alkaline and acid hydrolysis treatment is enough to get samples with high crystallinity. The CCC product has the potential to be an excellent reinforcing material for biomaterial-based polymer materials.

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