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NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas

Kaohsiung Journal of Medical sciences 2024年第5期40卷 445-455页

作者： Lim, Lee-Moay Lee, Yi-Chen Lin, Ting-Wei Hong, Zi-Xuan Hsu, Wei-Chi Ke, Hung-Lung Hwang, Daw-Yang Chung, Wen-Yu Li, Wei-Ming Lin, Hui-Hui Kuo, Hung-Tien Huang, A-Mei Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Anatomy School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institute Tainan Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Urology Ministry of Health and Welfare Pingtung Hospital Pingtung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Doctoral Degree Program in Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung Taiwan Department of Biochemistry School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Research Center for Environmental Medicine Kaohsiung Medical University Kaohsiung Taiwan

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan–Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC. © 2024 The Authors. The Kaohsiung Journal of Medical sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.

关键词： AKT-mTOR pathway neurotrophic receptor tyrosine kinase 3 (NTRK3) prognosis tropomyosin-related kinase (TrK) upper tract urothelial carcinoma (UTUC)

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

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Chemosphere 2024年 352卷 141484页

作者： Monteiro, Thalyssa Oliveira Alves, Pedro Augusto Araújo da Silva de Almeida Nava Barradas Filho, Alex Oliveira Villa-Vélez, Harvey Alexander Cruz, Glauber Postgraduate Program in Mechanical Engineering (PPGMEC) Department of Mechanics and Materials Federal Institute of Education Science and Technology of Maranhão (IFMA) Maranhão São Luís Brazil Postgraduate Program in Computer Science and Computational Mathematics (PPG-CCMC) Department of Computer Science University of São Paulo (USP) São Carlos São Paulo Brazil Data Analysis and Artificial Intelligence Laboratory (DARTi) Department of Computational Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Department of Chemical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Processes and Thermal Systems Laboratory (LPSisTer) Department of Mechanical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. H... 详细信息

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO₂, NO_x, SO_x, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO₂, NO, and SO₂) produced during the direct biomass combustion (N₂/O₂:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm⁻³ MAE, and 0.03–52.30 mg Nm⁻³ RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO₂, NO, and SO₂ emissions from NEB biomasses were approximately 0.01%, 10⁻⁶%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models. © 2024 Elsevier Ltd

关键词： Brazilian northeastern Environmental statistics Evaluation metrics Gaseous emissions Predictive models Thermoconversion

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent systems, ALIFE-CIS 2025

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis University of Michigan Department of Ecology and Evolutionary Biology Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and Ai in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Michigan State University Department of Computer Science and Engineering East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Heterogeneous download times in bandwidth-homogeneous BitTorrent swarms

arXiv

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arXiv 2021年

作者： Murai, Fabricio De Rocha, Antonio A.A. Figueiredo, Daniel R. De Souza E Silva, Edmundo A. COPPE/Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Computer Science Department Fluminense Federal University Niterói Brazil

Modeling and understanding BitTorrent (BT) dynamics is a recurrent research topic mainly due to its high complexity and tremendous practical efficiency. Over the years, different models have uncovered various phenomena exhibited by the system, many of which have direct impact on its performance. In this paper we identify and characterize a phenomenon that has not been previously observed: homogeneous peers (with respect to their upload capacities) experience heterogeneous download times. This behavior has direct impact on peer and system performance, such as high variability of download times, unfairness with respect to peer arrival order, bursty departures and content synchronization. Detailed packet-level simulations and prototype-based experiments on the Internet were performed to characterize this phenomenon. We also develop a mathematical model that accurately predicts the heterogeneous download rates of the homogeneous peers as a function of their content. In addition, we apply the model to calculate lower and upper bounds to the number of departures that occur in a burst. The heterogeneous download rates are more prevalent in unpopular swarms (very few peers). Although few works have addressed this kind of swarm, these by far represent the most common type of swarm in BT. © 2021, CC BY.

关键词： Functions

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Naked-eye observation of water-forming reaction on palladium etalon:transduction of gas-matter reaction into light-matter interaction

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PhotoniX 2023年第1期4卷 349-363页

作者： Jongsu Lee Eui-Sang Yu Taehyun Kim In Soo Kim Seok Chung Seung Jae Kwak Won Bo Lee Yusin Pak Yong-Sang Ryu Sensor System Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea Department of Electrical and Computer Engineering Seoul National UniversitySeoul 08826Republic of Korea Smart Materials Research Section Electronics and Telecommunications Research InstituteDaejeon 34129Republic of Korea Department of Micro/Nano Systems Korea University02841 SeoulRepublic of Korea Nanophotonics Research Center Korea Institute of Science and TechnologySeoul 02792Republic of Korea School of Mechanical Engineering Korea UniversitySeoul 02841Republic of Korea School of Chemical and Biological Engineering Seoul National UniversitySeoul 08826Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02481Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02481Republic of Korea

Palladium is the most prominent material in both scientific and industrial research on gas storage,purification,detection,and catalysis due to its unique properties as a catalyst and hydrogen *** the dynamic optical phenomena of palladium reacting with hydrogen,transduction of the gas-matter reaction into light-matter interaction is attempted to visualize the dynamic surface chemistry and reaction *** simple geometry of the metal-dielectric-metal structure,Fabry-Perot etalon,is employed for a colorimetric reactor,to display the catalytic reaction of the exposed gas via water-film/bubble formation at the dielectric/palladium *** adsorption/desorption behavior and catalytic reaction of hydrogen and oxygen on the palladium surface display highly repeatable and dramatic color changes based on two distinct water formation trends:the foggy effect by water bubbles and the whiteout effect by water film *** and experiments demonstrate the robustness of the proposed Fabry-Perot etalon as an excellent platform for monitoring the opto-physical phenomena driven by heterogeneous catalysis.

关键词： Palladium Hydrogen Oxygen Fabry-Perot resonators Colorimetric sensors Water-forming reaction

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TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs

arXiv

引用

arXiv 2022年

作者： Li, Alex J. Sundar, Vikram Grigoryan, Gevorg Keating, Amy E. Department of Chemistry Massachusetts Institute of Technology United States Computational and Systems Biology Program Massachusetts Institute of Technology United States Department of Computer Science Dartmouth College United States Departments of Biology and Biological Engineering Massachusetts Institute of Technology United States

Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in neural protein design frameworks. Our graph-based architecture, TERMinator, incorporates TERM-based and coordinate-based information and outputs a Potts model over sequence space. TERMinator outperforms state-of-the-art models on native sequence recovery tasks, suggesting that utilizing TERM-based and coordinate-based features together is beneficial for protein design. © 2022, CC BY.

关键词： Potts model

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