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Performance of the EDACS-ADP incorporating high-sensitivity troponin assay:Do components of major adverse cardiac events matter?

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World Journal of Emergency Medicine 2024年第3期15卷 175-180页

作者： Yedalm yoo Shin Ahn Bora Chae Won Young Kim Department of Emergency Medicine Asan Medical CenterUniversity of Ulsan College of MedicineSeoul 05505Republic of Korea

BACKGROUND:The accelerated diagnostic protocol(ADP)using the Emergency Department Assessment of Chest pain Score(EDACS-ADP),a tool to identify patients at low risk of a major adverse cardiac event(MACE)among patients presenting with chest pain to the emergency department,was developed using a contemporary troponin assay.This study was performed to validate and compare the performance of the EDACS-ADP incorporating high-sensitivity cardiac troponin I between patients who had a 30-day MACE with and without unstable angina(MACE I and II,respectively).METHODS:A single-center prospective observational study of adult patients presenting with chest pain suggestive of acute coronary syndrome was performed.The performance of EDACS-ADP in predicting MACE was assessed by calculating the sensitivity and negative predictive value.RESULTS:Of the 1,304 patients prospectively enrolled,399(30.6%;95%confidence interval[95%CI]:27.7%–33.8%)were considered low-risk using the EDACS-ADP.Among them,the rates of MACE I and II were 1.3%(5/399)and 1.0%(4/399),respectively.The EDACS-ADP showed sensitivities and negative predictive values of 98.8%(95%CI:97.2%–99.6%)and 98.7%(95%CI:97.0%–99.5%)for MACE I and 98.7%(95%CI:96.8%–99.7%)and 99.0%(95%CI:97.4%–99.6%)for MACE II,respectively.CONCLUSION:EDACS-ADP could help identify patients as safe for early discharge.However,when unstable angina was added to the outcome,the 30-day MACE rate among the designated lowrisk patients remained above the level acceptable for early discharge without further evaluation.

关键词： Chest pain Major adverse cardiac event Acute coronary syndrome Emergency department

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Cnoidal waves and their soliton limits in single mode fiber lasers

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Photonics Research 2024年第3期12卷 543-551页

作者： XIAO HU TUPEI CHEN SEONGWOO yoo DINGYUAN TANG Julong College Shenzhen Technology UniversityShenzhen 518118China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore

Cnoidal waves are a type of nonlinear periodic wave solutions of the nonlinear dynamic equations.They are well known in fluid dynamics,but it is not the case in optics.In this paper we show both experimentally and numerically that cnoidal waves could be formed in a fiber laser either in the net normal or net anomalous cavity dispersion regime,especially because,as the pump power is increased,the formed cnoidal waves could eventually evolve into a train of bright(in the net anomalous cavity dispersion regime)or dark(in the net normal cavity dispersion regime)solitons.Numerical simulations of the laser operation based on the extended nonlinear Schrödinger equation(NLSE)have well reproduced the experimental observations.The result not only explains why solitons can still be formed in a fiber laser even without mode locking but also suggests a new effective way of automatic stable periodic pulse train generation in lasers with a nonlinear cavity.

关键词： fiber pump mode

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Arabidopsis STAY-GREEN2 Is a Negative Regulator of Chlorophyll Degradation during Leaf Senescence

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Molecular Plant 2014年第8期7卷 1288-1302页

作者： Yasuhito Sakuraba So-Yon Park Ye-Sol Kim Seung-Hyun Wang soo-cheul yoo Stefan Hortensteiner Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding Institute Research Institute of Agriculture and Life Sciences Seoul NationalUniversity Seoul 151-921 Korea Present address: Department of Plant Pathology Physiology and Weed Science Virginia Tech Blacksburg VA 24061 USA Present address: Advanced Radiation Technology Institute Korea Atomic Energy Research Institute Jeongeup 580-185 Korea Department of Plant & Environmental Science Hankyong National University Ansung 456-749 Korea Institute of Plant Biology University of Zurich CH-8008 Zurich Switzerland

Chlorophyll （Chl） degradation causes leaf yellowing during senescence or under stress conditions. For Chl breakdown, STAY-GREEN1 （SGR1） interacts with Chl catabolic enzymes （CCEs） and light-harvesting complex II （LHCII） at the thylakoid membrane, possibly to allow metabolic channeling of potentially phototoxic Chl breakdown intermediates. Among these Chl catabolic components, SGR1 acts as a key regulator of leaf yellowing. In addition to SGR1 （At4g22920）, the Arabidopsis thaliana genome contains an additional homolog, SGR2 （At4g11910）, whose biological function remains elusive. Under senescence-inducing conditions, SGR2 expression is highly up-regulated, similarly to SGR1 expression. Here we show that SGR2 function counteracts SGR1 activity in leaf Chl degradation; SGR2-overexpressing plants stayed green and the sgr2-1 knockout mutant exhibited early leaf yellowing under age-, dark-, and stress-induced senescence conditions. Like SGR1, SGR2 interacted with LHCII but, in contrast to SGR1, SGR2 interactions with CCEs were very limited. Furthermore, SGR1 and SGR2 formed homo- or heterodimers, strongly suggesting a role for SGR2 in negatively regulat- ing Chl degradation by possibly interfering with the proposed CCE-recruiting function of SGR1. Our data indicate an antagonistic evolution of the functions of SGR1 and SGR2 in Arabidopsis to balance Chl catabolism in chloroplasts with the dismantling and remobilizing of other cellular components in senescing leaf cells.

关键词： Arabidopsis thaliana stay-green SGR1 SGR2 chlorophyll degradation leaf senescence abiotic stress.

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Air-stable,all-dry transferred ReS_(2)/GaSe heterostructure-based NO_(2)gas sensor

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Journal of Materials Science & Technology 2024年第1期168卷 79-87页

作者： A.Venkatesan Hyeyoon Ryu Anupom Devnath Hyungyu yoo Seunghyun Lee Department of Electronic Engineering Kyung Hee UniversityYongin-Si 17104South Korea Department of Electronics and Information Convergence Engineering Kyung Hee UniversityYongin-Si 17104South Korea

Two-dimensional(2D)materials have gained considerable attention in chemical sensing owing to their naturally high surface-to-volume ratio.However,the poor response time and incomplete recovery re-strict their application in practical,high performance gas sensors.In this work,we fabricated air-stable ReS_(2)/GaSe heterostructure-based NO_(2)gas sensors with excellent gas sensing response,recovery,selectiv-ity and a low limit of detection(LOD)toward nitrogen dioxide(NO_(2)).The ReS_(2)/GaSe heterostructure was prepared via mechanical exfoliation and an all-dry transfer method.Before the sensing measurements,temperature-dependant transport measurements were carried out.The Schottky Barrier Height(SBH)of the ReS_(2)/GaSe heterostructure was calculated and the corresponding transport mechanisms were dis-cussed.The fabricated gas sensors showed a significant response enhancement with full reversibility to-ward ppm-level NO_(2)(response of∼17%at 3 ppm,a LOD of∼556 ppb)at an operating temperature of(33°C).In particular,the total response and recovery time of the ReS_(2)/GaSe was revealed to be less than 4 min(∼38 s and∼174 s,respectively)for the 250 ppm concentration,which is one of the best response and recovery time toward ppm-level NO_(2).The excellent sensing performances and recovery characteris-tics of the ReS_(2)/GaSe structure are attributed to its efficient charge separation,unique interlayer coupling and desirable band alignments.This atomically thin,ultrasensitive gas sensor that operates at room tem-perature is a strong technological contender to conventional metal oxide gas sensors,which often require elevated temperatures.

关键词： 2D materials All-dry transfer Gas sensor Van der Waals heterostructure ReS_(2)/GaSe Gas sensing response Transition metal dichalcogenide

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A novel deep learning framework with variational auto-encoder for indoor air quality prediction

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Frontiers of Environmental Science & Engineering 2024年第1期18卷 97-109页

作者： Qiyue Wu Yun Geng Xinyuan Wang Dongsheng Wang ChangKyoo yoo Hongbin Liu Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control College of Light Industry and Food EngineeringGuangxi UniversityNanning 530004China Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province Shandong Huatai Paper Co.Ltd.Dongying 257335China Department of Environmental Science and Engineering College of EngineeringKyung Hee UniversityYongin 446701Republic of Korea

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

关键词： Indoor air quality PM_(2.5)concentration Variational auto-encoder Latent variable Soft measurement modeling

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Defect Detection Model Using Time Series Data Augmentation and Transformation

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Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Natural Variation in OsPRR37 Regulates Heading Date and Contributes to Rice Cultivation at a Wide Range of Latitudes

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Molecular Plant 2013年第6期6卷 1877-1888页

作者： Bon-Hyuk Koo soo-cheul yoo Joon-Woo Park Choon-Tak Kwon Byoung-Doo Lee Gynheung An Zhanying Zhang linjie Li Zichao Li Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding Institute and Research Institute for Agriculture and Life Sciences Seoul National UniversitySeoul 151-921 Korea Department of Plant Systems Biotech and Crop Biotech Institute Kyung Hee University Yongin 446-701 Korea Key Lab of Crop Heterosis and Utilization of Ministry of Education and Beijing Key Lab of Crop Genetic Improvement China Agriculture UniversityBeijing 100193 China Institute of Green Bio Science and Technology Seoul National University Pyeongchang 232-916 Korea

Heading date and photoperiod sensitivity are fundamental traits that determine rice adaptation to a wide range of geographic environments. By quantitative trait locus （QTL） mapping and candidate gene analysis using whole- genome re-sequencing, we found that Oryza sativa Pseudo-Response Regulator37 （OsPRR37; hereafter PRR37） is respon- sible for the Early heading7-2 （EH7-2）/Heading date2 （Hd2） QTL which was identified from a cross of late-heading rice ＇Milyang23 （M23）＇ and early-heading rice ＇H143＇. H143 contains a missense mutation of an invariantly conserved amino acid in the CCT （CONSTANS, CO-like, and TOC1） domain of PRR37 protein. In the world rice collection, different types of nonfunctional PRR37 alleles were found in many European and Asian rice cultivars. Notably, the japonica varieties harboring nonfunctional alleles of both Ghd7/Hd4 and PRR37/Hd2 flower extremely early under natural long-day condi- tions, and are adapted to the northernmost regions of rice cultivation, up to 53~ N latitude. Genetic analysis revealed that the effects of PRR37 and Ghd7 alleles on heading date are additive, and PRR37 down-regulates Hd3a expression to suppress flowering under long-day conditions. Our results demonstrate that natural variations in PRR37/Hd2 and GhdT/ Hd4 have contributed to the expansion of rice cultivation to temperate and cooler regions.

关键词： rice heading date quantitative trait locus natural variation Ghd7 OsPRR37.

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Spark plasma sintering of tungsten-based WTaVCr refractory high entropy alloys for nuclear fusion applications

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International Journal of Minerals,Metallurgy and Materials 2024年第1期31卷 146-154页

作者： Yongchul yoo Xiang Zhang Fei Wang Xin Chen Xing-Zhong Li Michael Nastasi Bai Cui Department of Mechanical&Materials Engineering University of Nebraska-LincolnLincolnNE 68588USA Nebraska Center for Materials and Nanoscience University of Nebraska-LincolnLincolnNE 68588USA Department of Nuclear Engineering Texas A&M UniversityCollege StationTX 77843USA

W-based WTaVCr refractory high entropy alloys (RHEA) may be novel and promising candidate materials for plasma facing components in the first wall and diverter in fusion reactors. This alloy has been developed by a powder metallurgy process combining mechanical alloying and spark plasma sintering (SPS). The SPSed samples contained two phases, in which the matrix is RHEA with a body-centered cubic structure, while the oxide phase was most likely Ta2VO6through a combined analysis of X-ray diffraction (XRD),energy-dispersive spectroscopy (EDS), and selected area electron diffraction (SAED). The higher oxygen affinity of Ta and V may explain the preferential formation of their oxide phases based on thermodynamic calculations. Electron backscatter diffraction (EBSD) revealed an average grain size of 6.2μm. WTaVCr RHEA showed a peak compressive strength of 2997 MPa at room temperature and much higher micro-and nano-hardness than W and other W-based RHEAs in the literature. Their high Rockwell hardness can be retained to at least 1000°C.

关键词： refractory high entropy alloy plasma-facing material fusion reactor spark plasma sintering

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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical Engineering Seoul National University IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Department of Chemical Engineering Stanford University Department of Mechanical Engineering Ajou University George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Toward a Learnable Climate Model in the Artificial Intelligence Era

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Advances in Atmospheric Sciences 2024年第7期41卷 1281-1288页

作者： Gang HUANG Ya WANG yoo-Geun HAM Bin MU Weichen TAO Chaoyang XIE State key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and TechnologyQingdao 266237China University of Chinese Academy of Sciences Beijing 100049China Department of Environmental Planning Graduate School of Environmental StudiesSeoul National UniversitySeoul 08826South Korea School of Software Engineering Tongji UniversityShanghai 200092China

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

关键词： artificial intelligence deep learning learnable climate model

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