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Corrigendum to "SnO2-xNx based tpod nanostructure for SARS-CoV2 spike protein detection" [234 (2023) Oct 1, 116505]

Environmental research 2024年 242卷 117677页

作者： Nallin Sharma Chia-Hung Chi deepak dabur Andrew Chi-Chang Tsai Hui-Fen Wu Department of Chemistry National Sun Yat-Sen University Kaohsiung 70 Lien-Hai Road Kaohsiung 80424 Taiwan. International Ph.D. Program for Science National Sun Yat-Sen University Kaohsiung 80424 Taiwan. Jeenn Chwanq Enterprise Co. Ltd No. 14 Dazhu Road Kaohsiung 833 Taiwan. Department of Chemistry National Sun Yat-Sen University Kaohsiung 70 Lien-Hai Road Kaohsiung 80424 Taiwan School of Pharmacy College of Pharmacy Kaohsiung Medical University Kaohsiung 807 Taiwan Institute of Precision Medicine College of Medicine National Sun Yat-Sen University Kaohsiung 80424 Taiwan International Ph.D. Program for Science National Sun Yat-Sen University Kaohsiung 80424 Taiwan Institute of Medical Science and Technology National Sun Yat-Sen University Kaohsiung 80424 Taiwan. Electronic address: hwu@faculty.nsysu.edu.tw.

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Counting Roots of a Polynomial in a Convex Compact Region by Means of Winding Number Calculation via Sampling

arXiv

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arXiv 2019年

作者： Zaderman, Vitaly Zhao, Liang Ph.D. Program in Mathematics Graduate Center of the City University of New York New YorkNY10016 United States Department of Computer Science

In this paper, we propose a novel efficient algorithm for calculating winding numbers, aiming at counting the number of roots of a given polynomial in a convex region on the complex plane. This algorithm can be used for counting and exclusion tests in a subdivision algorithms for polynomial root-finding, and would be especially useful in application scenarios where high-precision polynomial coefficients are hard to obtain but we succeed with counting already by using polynomial evaluation with lower precision. We provide the pseudo code of the algorithm, proof of its correctness as well as estimation of its complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Winding

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Correction to: A mini‑review on plasticrusts: occurrence, current trends, potential threats, and recommendations for coastal sustainability

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Environmental monitoring and assessment 2024年第7期196卷 629页

作者： Minh-Ky Nguyen Md Refat Jahan Rakib Hoang-Lam Nguyen Chitsan Lin Guilherme Malafaia Abubakr M Idris Faculty of Environment and Natural Resources Nong Lam University of Ho Chi Minh City Hamlet 6 Linh Trung Ward Thu Duc City Ho Chi Minh City 700000 Vietnam. Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. Department of Fisheries and Marine Science Faculty of Science Noakhali Science and Technology University Sonapur Noakhali 3814 Bangladesh. Department of Civil Engineering McGill University Montreal Canada. Ph.D. Program in Maritime Science and Technology National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. ctlin@nkust.edu.tw. Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung 81157 Taiwan. ctlin@nkust.edu.tw. Post‑Graduation Program in Ecology Conservation and Biodiversity Federal University of Uberlandia Uberlandia MG Brazil. guilhermeifgoiano@***. Post‑Graduation Program in Biotechnology and Biodiversity Federal University of Goias Goiania GO Brazil. guilhermeifgoiano@***. Post‑Graduation Program in Conservation of Cerrado Natural Resources Goiano Federal Institute Urutai GO Brazil. guilhermeifgoiano@***. Department of Chemistry College of Science King Khalid University 62529 Abha Saudi Arabia. Research Center for Advanced Materials Science (RCAMS) King Khalid University 62529 Abha Saudi Arabia.

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Ultrafast circuit protection for unmanned systems

Ultrafast circuit protection for unmanned systems

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2019 International Conference on Communications, Information System, and computer Engineering, CISCE 2019

作者： Kao, Chih-Hong Huang, Sy-Ruen He, Bo-Rong Lin, Hsiung-Cheng School of Computer Science Minnan Normal University Lab of Granular Computing ZhangZhou Fujjan China Department of Electrical Engineering Feng Chia University Taichung Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering Chin-Yi University of Technology Taichung Taiwan

ISBN: (纸本)9781728136813

With increasing dependence on precision electronic equipment, self-protection is becoming essential. The protection circuits commonly used before were fuses and circuit breakers which, however, they are not enough for the current need of high-speed protection circuits and automated engineering logic. In this paper, we develop a new ultrahigh-speed protection circuit, which is over exceeded 20 times faster than traditional circuits and has the ability to self-recover when any failure occurs. With the new circuit developed, it is not necessary to do manual restart and will meet the future needs of protection for electronic precision equipment. This design provides greater convenience and reliability for the unmanned equipment for the future. © 2019 IEEE.

关键词： Timing circuits

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Combinatorial Engineered Immuno-Fragments Targeting SARS-CoV-2 Nucleocapsid Protein for Epitope Complementarity

SSRN

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SSRN 2024年

作者： dlamini, Nhlanhla Benedict Lee, Chi-Hsin Wang, Wei-Chu Huang, Peng-Nien Leu, Sy-Jye Tseng, Sung-Nien Shih, Shin-Ru Chiang, Liao-Chun Mao, Yan-Chiao Tsai, Bor-Yu Yang, Yi-Yuan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei110301 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei110301 Taiwan Research Center for Emerging Viral Infections Chang Gung University Taoyuan333423 Taiwan Division of Infectious Diseases Department of Pediatrics Linkou Chang Gung Memorial Hospital Taoyuan333423 Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei110301 Taiwan Department of Microbiology and Immunology School of Medicine College of Medicine Taipei Medical University Taipei110301 Taiwan Department of Laboratory Medicine Linkou Chang Gung Memorial Hospital Taoyuan333423 Taiwan College of Life Sciences National Tsing Hua University Hsinchu300040 Taiwan Division of Clinical Toxicology Department of Emergency Medicine Taichung Veterans General Hospital Taichung407219 Taiwan Navi Bio-Therapeutics Inc. Taipei10351 Taiwan Core Laboratory of Antibody Generation and Research Taipei Medical University Taipei110301 Taiwan

Recombinant avian phage display immunoglobulin Y derivative single chain variable fragment (IgY-scFv) antibodies have evolved as robust single-chain antigen recognizing IgY analogues due to the advent of immuno-nanotechnology and other immunomolecular biotechnology applications. This academic study reports on the engineering and characterization of severe acute respiratory syndrome coronavirus 2 nucleocapsid (SARS-CoV-2 N) monoaffinity recombinant chicken IgY derivative single chain variable fragment (IgY-scFv) antibody fragments as versatile tools for surrogate immunological identification of the SARS-CoV-2 virus nuclear biomarker. To improve the immunopanning selection for specific binders, layer hens were immunised with the SARS-CoV-2 nucleocapsid protein to pre-enrich higher affinity B cell gene repertoires prior to library construction. Pre-enrichment humoral egg yolk polyclonal IgY antibodies were isolated following the PEG-8000 precipitation protocol. An experimental based modelling approach was applied in order to create IgY derivative N protein single chain variable fragments (IgY-scFv N) M13KO7 phage antibody libraries, an immune phage display library constructed system that supported the generation of functional anti-SARS-CoV-2 N protein IgY-scFv affinity monospecific fragment immunomolecules. After the immunopanning enrichment selection process three distinct positive sequence family IgY-scFv fragments obtained demonstrated epitope specific affinity (micromolar range kinetics) towards the targeted recombinant SARS-CoV-2 N protein and also positively identified native SARS-CoV-2 (COVId-19) N protein in formalin-fixed paraffin-embedded (FFPE) 293T cell pellets. © 2024, The Authors. All rights reserved.

关键词： Coronavirus

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Learning physical graph representations from visual scenes 20

Learning physical graph representations from visual scenes

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： daniel M. Bear Chaofei Fan damian Mrowca Yunzhu Li Seth Alter Aran Nayebi Jeremy Schwartz Li Fei-Fei Jiajun Wu Joshua B. Tenenbaum daniel L.K. Yamins Department of Psychology Stanford University and Wu Tsai Neurosciences Institute Stanford University Department of Psychology Stanford University and Department of Computer Science Stanford University Department of Computer Science Stanford University MIT CSAIL MIT Brain and Cognitive Sciences Neurosciences Ph.D. Program Stanford University Department of Computer Science Stanford University and Department of Psychology Stanford University MIT CSAIL and MIT Brain and Cognitive Sciences Department of Psychology Stanford University and Department of Computer Science Stanford University and Wu Tsai Neurosciences Institute Stanford University

ISBN: (纸本)9781713829546

Convolutional Neural Networks (CNNs) have proved exceptional at learning representations for visual object categorization. However, CNNs do not explicitly encode objects, parts, and their physical properties, which has limited CNNs' success on tasks that require structured understanding of visual scenes. To overcome these limitations, we introduce the idea of "physical Scene Graphs" (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts. Bound to each node is a vector of latent attributes that intuitively represent object properties such as surface shape and texture. We also describe PSGNet, a network architecture that learns to extract PSGs by reconstructing scenes through a PSG-structured bottleneck. PSGNet augments standard CNNs by including: recurrent feedback connections to combine low and high-level image information; graph pooling and vectorization operations that convert spatially-uniform feature maps into object-centric graph structures; and perceptual grouping principles to encourage the identification of meaningful scene elements. We show that PSGNet outperforms alternative self-supervised scene representation algorithms at scene segmentation tasks, especially on complex real-world images, and generalizes well to unseen object types and scene arrangements. PSGNet is also able learn from physical motion, enhancing scene estimates even for static images. We present a series of ablation studies illustrating the importance of each component of the PS-GNet architecture, analyses showing that learned latent attributes capture intuitive scene properties, and illustrate the use of PSGs for compositional scene inference.

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An ensemble machine learning model generates a focused screening library for the identification of CdK8 inhibitors

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Protein science 2024年第6期33卷 e5007-e5007页

作者： Lin, Tony Eight Yen, dyan HuangFu, Wei-Chun Wu, Yi-Wen Hsu, Jui-Yi Yen, Shih-Chung Sung, Tzu-Ying Hsieh, Jui-Hua Pan, Shiow-Lin Yang, Chia-Ron Huang, Wei-Jan Hsu, Kai-Cheng Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei Taiwan Warshel Institute for Computational Biology The Chinese University of Hong Kong (Shenzhen) Guangdong Shenzhen China Biomedical Translation Research Center Academia Sinica Taipei Taiwan Division of Translational Toxicology National Institute of Environmental Health Sciences National Institutes of Health Durham NC United States School of Pharmacy College of Medicine National Taiwan University Taipei Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei Taiwan Cancer Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

The identification of an effective inhibitor is an important starting step in drug development. Unfortunately, many issues such as the characterization of protein binding sites, the screening library, materials for assays, etc., make drug screening a difficult proposition. As the size of screening libraries increases, more resources will be inefficiently consumed. Thus, new strategies are needed to preprocess and focus a screening library towards a targeted protein. Herein, we report an ensemble machine learning (ML) model to generate a CdK8-focused screening library. The ensemble model consists of six different algorithms optimized for CdK8 inhibitor classification. The models were trained using a CdK8-specific fragment library along with molecules containing CdK8 activity. The optimized ensemble model processed a commercial library containing 1.6 million molecules. This resulted in a CdK8-focused screening library containing 1,672 molecules, a reduction of more than 99.90%. The CdK8-focused library was then subjected to molecular docking, and 25 candidate compounds were selected. Enzymatic assays confirmed six CdK8 inhibitors, with one compound producing an IC₅₀ value of ≤100 nM. Analysis of the ensemble ML model reveals the role of the CdK8 fragment library during training. Structural analysis of molecules reveals the hit compounds to be structurally novel CdK8 inhibitors. Together, the results highlight a pipeline for curating a focused library for a specific protein target, such as CdK8. © 2024 The Authors. Protein science published by Wiley Periodicals LLC on behalf of The Protein Society.

关键词： CdK8 fragment machine learning molecular docking structure-based virtual screening

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Reduced miR-223 increases blood neutrophil extracellular trap and promotes skin inflammation in atopic dermatitis

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Allergy 2023年第12期78卷 3252-3254页

作者： Tsai-Ling Liao Chi-Chien Lin Yi-Hsing Chen Kuo-Tung Tang Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan. Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung Taiwan. Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan. Institute of Biomedical Science the iEGG and Animal Biotechnology Center National Chung-Hsing University Taichung Taiwan. Department of Medical Research China Medical University Hospital Taichung Taiwan. Department of Pharmacology College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. Division of Allergy Immunology and Rheumatology Taichung Veterans General Hospital Taichung Taiwan. Faculty of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan.

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Resin Cement Coating Reverts the detrimental Effect of Machining on the Flexural Fatigue Strength of Lithium disilicate Ceramic

SSRN

引用

SSRN 2022年

作者： May, Michele Mirian Machry, Renan Vaz Fraga, Sara de Andrade, Guilherme Schmitt Bottino, Marco Antonio Valandro, Luiz Felipe May, Liliana Gressler Post-Graduate Program in Dental Sciences Prosthetic Dentistry Unit Faculty of Odontology Federal University of Santa Maria RS Santa Maria Brazil Department of Conservative Dentistry Federal University of Rio Grande do Sul RS Porto Alegre Brazil Department of Dental Materials and Prosthodontics São Paulo State University SP São José dos Campos Brazil Department of Restorative Dentistry Federal University of Santa Maria RS Santa Maria Brazil Federal University of Santa Maria Faculty of Odontology Head of Ph.D.-M.Sci.D. Graduate Program in Oral Science Prosthodontics-Biomaterials Unit UFSM Campus 1000 Roraima Av. T Street Building 26F Room 2386 Rio Grande do Sul State Santa Maria97105-900 Brazil

The aim of this study was to assess the effect of resin-cement coating with high and low viscosities on the flexural fatigue strength of machined lithium disilicate glass-ceramic. discs (IPS *** CAd;Ivoclar Vivadent) were prepared and divided according to the surface condition (machining (M) – CEREC inLab;and polishing (P) – laboratory procedures), resin-cement coating (with or without) and cement viscosity (high (H) and low (L)). The ceramic bonding surface was etched/primed by a one-step primer application (Monobond Etch & Prime;Ivoclar Vivadent) followed by resin-cement application (Variolink N Base + High or Low viscosity catalyst;Ivoclar Vivadent), which was pressed over a glass sheet under loading and photo-activated. Biaxial flexural fatigue strength was evaluated on a piston-on-three-ball set by the step-test method (n = 15) (initial stress: 60 MPa;incremental steps: 20 MPa;10,000 cycles/step, at 20 Hz). Weibull statistics were used for fatigue data. Contact angle, topographic and fractographic analysis were also performed. Machining produced statistically lower contact angle than polishing and a significant detrimental effect on the fatigue behavior (σ0M = 247.2 [246.9 – 268.3];σ0P = 337.4 [297.8 – 382.4]). Machined groups followed by resin-cement coating (σ0MH = 297.9 [276.0 – 321.5];σ0ML = 301.2 [277.1 – 327.4]) behaved similarly to the polished and coated groups (σ0ph = 342.0 [308.9 – 378.5];σ0PL = 357.3 [324.7 – 393.1]), irrespective of the cement viscosity. Cement coating reverts the detrimental effects of the machining on the fatigue strength of lithium disilicate glass-ceramic. High and low viscosity cements behave similarly in the improvement of CAd-CAM lithium disilicate fatigue strength. © 2022, The Authors. All rights reserved.

关键词： Coatings

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Radiomics in liver research: A paradigm shift in disease detection and staging

European Journal of Radiology Artificial Intelligence

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European Journal of Radiology Artificial Intelligence 2025年 2卷 100016-100016页

作者： Le, Minh Huu Nhat Kha, Hien Quang Tran, Nghia Minh Nguyen, phat Ky Huynh, Han H. Huynh, phat Kim Lam, Han Le, Nguyen Quoc Khanh International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan AIBioMed Research Group Taipei Medical University Taipei 110 Taiwan Translational Science and Precision Medicine Talent Cultivation Program College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan International Master Program for Translational Science College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Department of Industrial and Systems Engineering North Carolina A&T State University USA Industrial & Management Systems Engineering Department University of South Florida Tampa FL 33620 USA In-Service Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei 110 Taiwan

AbstractThis narrative review explores radiomics in the broader context of liver disease research, an interdisciplinary field bridging medical imaging, oncology, and data science. It begins with an introduction to data analysis techniques, from foundational methods like descriptive statistics, comparative and regression analysis, and survival analysis, to advanced approaches like machine learning (ML), meta-analysis, and bioinformatics. Contextualizing these methodologies within liver disease research enhances our understanding of liver diseases. The focus is on radiomics, an emerging field, which is first placed in historical context before its applications are explained across fatty liver diseases (FLd), fibrosis, cirrhosis, and liver cancers. The role of radiomics in early detection, disease staging, and outcome prediction, particularly as a non-invasive alternative to liver biopsy, is analyzed in-depth. Current imaging modalities for liver cancer diagnosis are also examined, highlighting their advantages and limitations. Alongside radiomics, these imaging tools enable comprehensive liver assessment. Lastly, the integration of radiomics with other data types, especially genomic data, is explored, emphasizing its role in a holistic approach to liver disease management. The review concludes with a forward-looking perspective on future directions and advancements in radiomics and liver cancer research, addressing the challenges and opportunities in the field. It underscores the transformative impact of these developments on liver cancer research, patient care, personalized medicine, and clinical decision-making.

关键词： AFP alpha-fetoprotein AI artificial intelligence AST aspartate transaminase AUC area under the curve CCrCC clear cell renal cell carcinoma CEUS contrast-enhanced ultrasound CNNs convolutional neural networks dCNN deep convoluted neural network dOR diagnostic odds ratio FLd fatty liver disease GWAS genome-wide association studies HCC hepatocellular carcinoma MAFLd metabolic associated fatty liver disease ML machine learning MRE magnetic resonance elastography NAFLd nonalcoholic fatty liver disease NASH nonalcoholic steatohepatitis NPV negative predictive value PPV positive predictive value VCTE vibration-controlled transient elastography Radiomics Liver disease Cancer Non-invasive diagnostic techniques Presicion medicine deep learning Machine learning

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