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arXiv 2018年

作者： Kahrobaei, Delaram Mallahi-Karai, Keivan University of York Department of Computer Science United Kingdom City University of New York CUNY Graduate Center PhD Program in Computer Science New York United States New York University Computer Science and Engineering Department Department of mathematics and logistic Bremen28759 Germany

In this paper we will offer a new symmetric-key cryptographic scheme which is based on the existence of exponentially distorted subgroups in arithmetic groups. Aside from this, we will also provide new examples of distorted subgroups in SLn(Z[x]) which can be utilized for the same purpose. Copyright © 2018, The Authors. All rights reserved.

关键词： Cryptography

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Corrigendum to “Metal ratio and bimetal nanoarchitectonics of ammonia-based fluoride complex induced nickel hydroxide and manganese oxide composites as active materials of an energy storage device” [J. Energy Storage 80 (2024) 110316]

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Journal of Energy Storage 2025年 108卷

作者： Chuan-Chih Hsu You Zher Yu Chung-Hsien Wu Pin-Yan Lee Hung-Ming Chen Sadang Husain Chutima Kongvarhodom Yu-Cheng Hsiao Lu-Yin Lin Division of Cardiovascular Surgery Department of Surgery School of Medicine College of Medicine Taipei Medical University Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Medical University Hospital Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei 11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin 70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok 10140 Thailand Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei 11696 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Stanford Byers Center for Biodesign Stanford University CA 94305-5428 USA Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan

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Identification of gene expression signatures for psoriasis classification using machine learning techniques

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Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

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Solving the conjugacy decision problem via machine learning

arXiv

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arXiv 2017年

作者： GRYAK, JONATHAN HARALICK, ROBERT M. KAHROBAEI, DELARAM CUNY Graduate Center PhD Program in Computer Science City University of New York CUNY Graduate Center PhD Program in Computer Science City University of New York CUNY Graduate Center PhD Program in Computer Science and NYCCT Mathematics Department City University of New York

Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this paper, we seek to extend these techniques to finitely presented non-free groups, with a particular emphasis on polycyclic and metabelian groups that are of interest to non-commutative cryptography. As a prototypical example, we utilize supervised learning methods to construct classifiers that can solve the conjugacy decision problem, i.e., determine whether or not a pair of elements from a specified group are conjugate. The accuracies of classifiers created using decision trees, random forests, and N-tuple neural network models are evaluated for several non-free groups. The very high accuracy of these classifiers suggests an underlying mathematical relationship with respect to conjugacy in the tested groups.20F10, 68T05 Copyright © 2017, The Authors. All rights reserved.

关键词： Decision trees

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Hot air treatment: Alternative annealing of TiO2 nanoparticulate films without substrate deformation

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AIP Conference Proceedings 2020年第1期2279卷

作者： Arisara Panthawan Winai Thongpan Porntipa Pooseekheaw Nidchamon Jumrus Tewasin Kumpika Wattikon Sroila Ekkapong Kantarak Panupong Sanmuangmoon Adisorn Tuantranont Wiradej Thongsuwan Pisith Singjai 1PhD’s Degree Program in Materials Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 3Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 2Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand 4Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 5Thailand Organic and Printed Electronics Innovation Center National Electronics and Computer Technology Center National Science and Technology Development Agency (Klong Luang Pathumthani 12120 Thailand) 6Center of Advanced Materials of Printed Electronics and Sensors Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand

Titanium dioxide (TiO2) nanoparticulate films was successfully annealed on a glass substrate via alternative hot air treatment (HAT). Interestingly, HAT not only protects the glass substrate deformation but can also reduce cost and time in the annealing process. The annealed films using HAT at 500 °C for 10 min can be compared with the annealed films in conventional furnace heat treatment (FHT) at 500 °C for 60 min. The results showed the sizes of ∼7.9 nm are obtained after the films annealed with both annealing techniques. The surface roughness of the as-deposited, the annealed films using FHT and HAT were 17.37, 23.74 and 23.26 nm, respectively. The energy band gap of the as- deposited films, the annealed films using FHT and HAT were 3.24 eV, 3.1 and 3.19 eV, respectively. Moreover, the annealed films using FHT and HAT techniques show superhydrophilic with a water contact angle of 3.42° and 2.81°, while the as-deposited films was 8.93°. After aging time testing, superhydrophilicity of the annealed films using HAT is greater than FHT. The result is in good agreement with Ti wt% of the as-deposited, the annealed films using FHT and HAT left on the substrate to 0.15, 0.73 and 0.65 nm after testing simulation for 20 years.

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[Applying the Concept of Grief Counseling in Caring for an Elderly Dialysis Patient With Bereavement Due to the Death of Spouse: A Nursing Care Experience]

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Hu li za zhi The journal of nursing 2018年第5期65卷 98-104页

作者： Pei-Yu Cheng Li Fang BSN RN Department of Nursing Pingtung Christian Hospital and Graduate Student Master of Science Program in Health Care Department of Nursing Meiho University Taiwan ROC. PhD RN Assistant Professor Department of Nursing Fooyin University Taiwan ROC. fangli72catholic36@***.

This article describes a 76-year-old dialysis patient who experienced widowhood. The nursing intervention occurred between April 2nd to June 28th, 2015. Using observation, interviews, medical records and health examinations, the authors assessed the patient's physical, psychological, social, intellectual, and spiritual status. The patient not only presented poor appetite, insomnia, hopelessness, and grief but had also attempted suicide. Thus, three health problems were identified, including dysfunctional grieving, hopelessness, and sleep pattern disturbance. Employing caring, active listening, and empathy, we helped the patient achieve the four tasks of Worden's Mourning: (1) to accept the reality of loss; (2) to work through the pain of grief; (3) to adjust to an environment in which the deceased is missing; and (4) to emotionally relocate the deceased and move on with life. With comprehensive and persistent nursing intervention, the patient got the support from her family members and eventually readapted to life without the deceased and continued to take hemodialysis and found new life motivation.

关键词： grief grief counseling hemodialysis widowhood

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

引用

arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Correction: CuI-mediated synthesis of 1-aryl-5,6,7-trimethoxybenzimidazoles as potent antitubulin agents

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RSC advances 2023年第22期13卷 14878页

作者： Cong-Min Peng Shih-Wei Wang Yi-Lin Hwang Wen-Chun Sun Li-Pin Chiu Yi-Ting Liu Yu-Wei Lai Hsueh-Yun Lee School of Pharmacy College of Pharmacy Taipei Medical University Taipei Taiwan hyl@tmu.edu.tw. Institute of Biomedical Sciences MacKay Medical College New Taipei City Taiwan. Department of Medicine MacKay Medical College New Taipei City Taiwan. School of Pharmacy College of Pharmacy Kaohsiung Medical University Kaohsiung Taiwan. Division of Colon and Rectal Surgery Department of Surgery MacKay Memorial Hospital Taipei Taiwan. Division of General Surgery Taipei City Hospital Chushing Branch Taipei Taiwan. General Education Center University of Taipei Taipei Taiwan. Division of Urology Taipei City Hospital Renai Branch Taipei Taiwan dai77@tpech.gov.tw. Department of Urology School of Medicine and Shu-Tien Urological Science Research Center National Yang Ming Chiao Tung University Taipei Taiwan. PhD Program in Drug Discovery and Development Industry College of Pharmacy Taipei Medical University Taipei Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1039/D3RA01927F.].

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Correction : IL-1R1 signaling in TBI: assessing chronic impacts and neuroinflammatory dynamics in a mouse model of mild closed-head injury

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Journal of neuroinflammation 2023年第1期20卷 287页

作者： Jonathan C Vincent Colleen N Garnett James B Watson Emma K Higgins Teresa Macheda Lydia Sanders Kelly N Roberts Ryan K Shahidehpour Eric M Blalock Ning Quan Adam D Bachstetter Department of Neuroscience University of Kentucky 741 S. Limestone St. Lexington KY 40536 USA. Spinal Cord and Brain Injury Research Center University of Kentucky Lexington KY USA. Sanders‑Brown Center on Aging University of Kentucky Lexington KY USA. MD/PhD Program University of Kentucky Lexington KY USA. Department of Cell Developmental and Integrative Biology University of Alabama at Birmingham Birmingham AL USA. Department of Pharmacology and Nutritional Sciences University of Kentucky Lexington KY USA. Department of Biomedical Science Charles E. Schmidt College of Medicine and Brain Institute Florida Atlantic University Jupiter FL USA. Department of Neuroscience University of Kentucky 741 S. Limestone St. Lexington KY 40536 USA. adam.bachstetter@uky.edu. Spinal Cord and Brain Injury Research Center University of Kentucky Lexington KY USA. adam.bachstetter@uky.edu. Sanders‑Brown Center on Aging University of Kentucky Lexington KY USA. adam.bachstetter@uky.edu.

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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