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2. Flexible and dependable manufacturing beyond xURLLC: A novel framework for communication-control co-design

Author

Han, Bin, Sun, Mu-Xia, Muk, Lai-Kan, Li, Yan-Fu, and Schotten, Hans D.

Subjects
Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Systems and Control
Abstract

Future Industrial 4.0 applications in the 6G era is calling for high dependability that goes far beyond the current ultra-reliable low latency communication (URLLC), and therewith proposed critical challenges to the communication technology. Instead of struggling against the physical and technical limits towards an extreme URLLC (xURLLC), communication-control co-design (CoCoCo) appears a more promising solution. This work proposes a novel framework of CoCoCo, which is not only enhancing the dependability of 6G industrial applications such as remote control, but also exhibiting rich potential in revolutionizing the future industry per openness and flexibility of manufacturing systems., Comment: To appear in the 22nd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2022) Workshops

Published
2022

4. Tenecteplase versus alteplase for thrombolysis in patients selected by use of perfusion imaging within 4·5 h of onset of ischaemic stroke (TASTE): a multicentre, randomised, controlled, phase 3 non-inferiority trial

Author

Phan, Timmy, Selmes, Christine, Lees, Kennedy, Kaste, Markku, MacIsaac, Rachael, Wellings, Tom, Loiselle, Andre, Pepper, Elizabeth, Miteff, Ferdi, Krishnamurthy, Venkatesh, Ang, Timothy, Alanati, Khaled, Gangadharan, Shyam, Zareie, Hossein, Starling, Rita, Dunkerton, Sophie, He, Jiacheng, Datta, Raka, Royan, Angela, Kerr, Erin, Kaauwai, Lara, Belevski, Linda, Ormond, Sally, Johnson, Annalese, Evans, Malcolm, Lachapelle, Nicole, Ombelet, Fouke, Bladin, Chris, Dewey, Helen, Wong, Joseph, Park, Peter, Cody, Ross, Tan, Peter, Callaly, Edward, Senanayake, Channa, Thomas, Grace, Liu, Jennifer, Busch, Tessa, Stuart, Narelle, Chung, Malcohm, Yassi, Nawaf, Valente, Michael, Sharobeam, Angelos, Cooley, Regan, Zhao, Henry, Alemseged, Fana, Williams, Cameron, Ng, Jo Lyn, Balabanski, Anna, dos Santos, Angela, Williamson, John, Pavlin-Premrl, Davor, Beharry, James, Ma, Margaret, Park, Ashley, Yan, Bernard, Hand, Peter, Jackson, David, McDonald, Amy, Fisicchia, Laura, Parsons, Nicola, Olenko, Liudmyla, Johns, Hannah, Guha, Prodipta, Rokaha, Birendra, Dhimal, Niruta, Harvey, Jackson, Cagi, Lavenia, Chia, Nicholas, Goh, Rudy, Palanikumar, Log, El-Masri, Shaddy, Mahadevan, Joshua, Kuranawai, Craig, Waters, Michael, Vallat, Wilson, Cheong, Eddie, Drew, Roy, Cordato, Dennis, McDougall, Alan, Cappelen-Smith, Cecilia, Venkat, Abhay, Edwards, Leon, Blair, Christopher, Thomas, James, Helou, Jacob, Green, Daniel, Nguyen, Tram, Pham, Timmy, Khan, Jasmeen, Miller, Megan, Loubiere, Laurence, Buck, Brian, Butcher, Ken, Fairall, Paige, Butt, Asif, Kalashyan, Hayrapet, Nomani, Ali, Lloret, Mar, Mishra, Sachin, Thirunavukkarasu, Sibi, Sivakumar, Leka, D'Souza, Atlantic, Tsai, Chon-Haw, Tseng, Billy, Tai, Iris, Chiang, I-Husan, Kuan, Angela, Tsai, Vivian, Hsu, Alice, Hsu, Sammi, Alchin, Deborah, Sanjuan, Estela, Fink, John, Wilson, Duncan, Mason, Deborah, Berry-Norohna, Alexander, Winders, Joel, Eagle, Jane, Green, Rosemary, Bremner, Kathleen, Celestino, Sherisse, Lee, Jiunn-Tay, Chou, Chung-Hsing, Tsai, Chia-Kuang, Sung, Yueh-Feng, Tsai, Chia-Lin, Lin, Yu-Kai, Kao, Hung-Wen, Vuong, Jason, Thirugnanachandran, Tharani, Hervet, Marie Veronic, Simmons, Karen, Sabet, Arman, Bailey, Peter, Urbi, Berzenn, Kurakose, Sumole, Martinez-Majander, Nicolas, Räty, Silja, Tiainen, Marjaana, Sibolt, Gerli, Ivanoff, Terhi, Sanz, Ana Calleja, García, Elisa Cortijo, De Lera Alfonso, Mercedes C., Araque, Maria Ester Ramos, Gómez, Alicia Sierra, Peñacoba, Gonzalo Valle, Vicente, Beatriz Gómez, Muñoz, Javier Reyes, Muñoz Rubio, Pedro Luis, Shah, Darshan, Harrison, Emma, Bendall, Carol, Subramanian, Ganesh, Jeng, Jiann-Shing, Tang, Sung-Chun, Tsai, Li-Kai, Yeh, Shin-Joe, Chen, Chih-Hao, Chung, Tai-Chun, Wong, Andrew, Muller, Claire, Skinner, Genevieve, Gunathilagan, Gunaratnam, Natarajan, Indira, Coutts, Shelagh, Menon, Bijoy, Kenney, Carol, Clarke, Brian, Ghatala, Rita, Mudd, Paul, Chen, Chih-Hung, Lemmens, Robin, Demeestere, Jelle, Mahant, Neil, Sun, Mu-Chien, Parsons, Mark W, Yogendrakumar, Vignan, Churilov, Leonid, Garcia-Esperon, Carlos, Campbell, Bruce C V, Russell, Michelle L, Sharma, Gagan, Chen, Chushuang, Lin, Longting, Chew, Beng Lim, Ng, Felix C, Deepak, Akshay, Choi, Philip M C, Kleinig, Timothy J, Cordato, Dennis J, Wu, Teddy Y, Fink, John N, Ma, Henry, Phan, Thanh G, Markus, Hugh S, Molina, Carlos A, Strbian, Daniel, Meretoja, Atte, Arenillas, Juan F, Buck, Brian H, Devlin, Michael J, Brown, Helen, Butcher, Ken S, O'Brien, Billy, Wijeratne, Tissa, Bivard, Andrew, Grimley, Rohan S, Agarwal, Smriti, Munshi, Sunil K, Donnan, Geoffrey A, Davis, Stephen M, Miteff, Ferdinand, Spratt, Neil J, and Levi, Christopher R

Published
2024
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5. Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis

Author

Li, Xiang, Zhong, Aoxiao, Lin, Ming, Guo, Ning, Sun, Mu, Sitek, Arkadiusz, Ye, Jieping, Thrall, James, and Li, Quanzheng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various computer vision problems, there has been increasing work applying deep learning to medical image analysis. However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples. Specifically, annotation and labeling of the medical images is much more expensive and time-consuming than other applications and often involves manual labor from multiple domain experts. In this work, we propose a multi-stage, self-paced learning framework utilizing a convolutional neural network (CNN) to classify Computed Tomography (CT) image patches. The key contribution of this approach is that we augment the size of training samples by refining the unlabeled instances with a self-paced learning CNN. By implementing the framework on high performance computing servers including the NVIDIA DGX1 machine, we obtained the experimental result, showing that the self-pace boosted network consistently outperformed the original network even with very scarce manual labels. The performance gain indicates that applications with limited training samples such as medical image analysis can benefit from using the proposed framework., Comment: accepted by 8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017)

Published
2017
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7. Noncommutative Multi-Parameter Subsequential Wiener–Wintner-Type Ergodic Theorem.

Author

Sun, Mu and Zhang, Yinmei

Subjects
*ORLICZ spaces, *DYNAMICAL systems, *DENSITY
Abstract

This paper is devoted to the study of a multi-parameter subsequential version of the "Wiener–Wintner" ergodic theorem for the noncommutative Dunford–Schwartz system. We establish a structure to prove "Wiener–Wintner"-type convergence over a multi-parameter subsequence class Δ instead of the weight class case. In our subsequence class, every term of k ̲ ∈ Δ is one of the three kinds of nonzero density subsequences we consider. As key ingredients, we give the maximal ergodic inequalities of multi-parameter subsequential averages and obtain a noncommutative subsequential analogue of the Banach principle. Then, by combining the critical result of the uniform convergence for a dense subset of the noncommutative L p (M) space and the noncommutative Orlicz space, we immediately obtain the main theorem. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Temporal and Spatial Variations in Zebrafish Hairy/E(spl) Gene Expression in Response to Mib1-Mediated Notch Signaling During Neurodevelopment.

Author

Chen, Yi-Chieh, Hsieh, Fu-Yu, Chang, Chia-Wei, Sun, Mu-Qun, and Cheng, Yi-Chuan

Subjects
GENE expression, NEURAL development, BRAIN tumors, SPATIAL variation, NERVOUS system, NOTCH genes
Abstract

Notch signaling is a conserved pathway crucial for nervous system development. Disruptions in this pathway are linked to neurodevelopmental disorders, neurodegenerative diseases, and brain tumors. Hairy/E(spl) (HES) genes, major downstream targets of Notch, are commonly used as markers for Notch activation. However, these genes can be activated, inhibited, or function independently of Notch signaling, and their response to Notch disruption varies across tissues and developmental stages. MIB1/Mib1 is an E3 ubiquitin ligase that enables Notch receptor activation by processing ligands like Delta and Serrate. We investigated Notch signaling disruption using the zebrafish Mib1 mutant line, mib1ta52b, focusing on changes in the expression of Hairy/E(spl) (her) genes. Our findings reveal significant variability in her gene expression across different neural cell types, regions, and developmental stages following Notch disruption. This variability questions the reliability of Hairy/E(spl) genes as universal markers for Notch activation, as their response is highly context-dependent. This study highlights the complex and context-specific nature of Notch signaling regulation. It underscores the need for a nuanced approach when using Hairy/E(spl) genes as markers for Notch activity. Additionally, it provides new insights into Mib1's role in Notch signaling, contributing to a better understanding of its involvement in Notch signaling-related disorders. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Review of 3D Face Reconstruction Based on Single Image

Author

HE Jia-yu, HUANG Hong-bo, ZHANG Hong-yan, SUN Mu-ye, LIU Ya-hui, ZHOU Zhe-hai

Subjects
3d face reconstruction, deep learning, convolutional neural networks, 3d morphable model, face recognition, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

In the field of computer vision,3D face reconstruction is a valuable research direction.High quality reconstruction of 3D faces can find applications in face recognition,anti-proofing,animation and medical cosmetology.In the last two decades,although great progress has been made 3D face reconstruction based on a single image,the results of reconstruction using traditionalalgorithms are still facing the challenge of facial expression,occlusion and ambient light,and there will be problems such as poor reconstruction accuracy and robustness.With the rapid development of deep learning in 3D face reconstruction,various methods which are superior to traditional reconstruction algorithms have emerged.Firstly,this paper focuses on deep-learning-based reconstruction algorithms.The algorithms are divided into four categories according to different network architecture,and the most popular methods are described in detail.Then commonly used 3D face data sets are introduced,and performance of representative methods are evaluated.Finally,conclusions and prospects of the single-image-based 3D face reconstruction are given.

Published
2022
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11. Noncommutative Ergodic Theorems for Connected Amenable Groups

Author

Sun, Mu

Subjects
Mathematics - Operator Algebras, Mathematics - Dynamical Systems
Abstract

This paper is devoted to the study of noncommutative ergodic theorems for connected amenable locally compact groups. For a dynamical system $(\mathcal{M},\tau,G,\sigma)$, where $(\mathcal{M},\tau)$ is a von Neumann algebra with a normal faithful finite trace and $(G,\sigma)$ is a connected amenable locally compact group with a well defined representation on $\mathcal{M}$, we try to find the largest noncommutative function spaces constructed from $\mathcal{M}$ on which the individual ergodic theorems hold. By using the Emerson-Greenleaf's structure theorem, we transfer the key question to proving the ergodic theorems for $\mathbb{R}^d$ group actions. Splitting the $\mathbb{R}^d$ actions problem in two cases according to different multi-parameter convergence types---cube convergence and unrestricted convergence, we can give maximal ergodic inequalities on $L_1(\mathcal{M})$ and on noncommutative Orlicz space $L_1\log^{2(d-1)}L(\mathcal{M})$, each of which is deduced from the result already known in discrete case. Finally we give the individual ergodic theorems for $G$ acting on $L_1(\mathcal{M})$ and on $L_1\log^{2(d-1)}L(\mathcal{M})$, where the ergodic averages are taken along certain sequences of measurable subsets of $G$., Comment: arXiv admin note: text overlap with arXiv:1602.00927

Published
2016

12. Noncommutative multi-parameter Wiener-Wintner type ergodic theorem

Author

Hong, Guixiang and Sun, Mu

Subjects
Mathematics - Operator Algebras, Mathematics - Dynamical Systems, Mathematics - Functional Analysis, 46L53, 46L55 (Primary), 47A35, 37A99 (Secondary)
Abstract

In this paper, we establish a multi-parameter version of Bellow and Losert's Wiener-Wintner type ergodic theorem for dynamical systems not necessarily being commutative. More precisely, we introduce a weight class $\mathcal{D}$, which is shown to strictly include the multi-parameter bounded Besicovitch weight class, thus including the set $$\Lambda_d=\left\{\{\lambda^{k_1}_1\dotsm\lambda^{k_d}_d\}_{(k_1,\dots,k_d)\in \mathbb{N}^d}:\quad (\lambda_1,\dots,\lambda_d) \in \mathbb{T}^d\right\};$$ then prove a multi-parameter Bellow and Losert's Wiener-Wintner type ergodic theorem for the class $\mathcal{D}$ and for noncommutative trace preserving dynamical system $(\mathcal{M},\tau,\mathbf{T})$. Restricted to consider the set $\Lambda_d$, we also prove a noncommutative multi-parameter analogue of Bourgain's uniform Wiener-Wintner ergodic theorem. The noncommutativity and the multi-parameter induce some difficulties in the proof. For instance, our arguments in proving the uniform convergence for a dense subset turn out to be quite different since the "pointwise" argument does not work in the noncommutative setting; to obtain the uniform convergence in the largest spaces, we show maximal inequality between the Orlicz spaces, which can not be deduced easily using classical extrapolation argument. Junge and Xu's noncommutative maximal inequalities with optimal order, together with the atomic decomposition of Orlicz spaces, play an essential role in overcoming the second difficulty.

Published
2016

13. Accounting earnings and economic growth, trends, and challenges: a bibliometric approach

Author

Sun, Mu, Urquía Grande, María Elena, Chamizo-González, Julián, Del Campo Campos, Cristina, Sun, Mu, Urquía Grande, María Elena, Chamizo-González, Julián, and Del Campo Campos, Cristina

Abstract

In recent years, studies have been conducted to quantify the relationship between microeconomic and macroeconomic development. Macroeconomics is the orientation of microeconomic development. Existing research hopes to quantify the relationship between macroeconomics and micro-firms, rather than just focusing on economic indicators. And some empirical studies try to use the relationship between them to discuss its usefulness for micro-firm decision-making. This article focuses on applying and developing aggregate earnings in connecting microenterprise earnings and macroeconomic development. To achieve this goal, this research did a comprehensive bibliometric analysis on macro-accounting on the two most influential databases, namely, Web of Science and Scopus. It used the information visualization software VOSviewer to draw knowledge maps to sort research lines. We also analyzed the research hotspots of macro-accounting in recent years according to the year scale and combined it with the neural network PSO-LSTM model to predict their future development. It turns out that the research on aggregate earnings related to economic growth has become a research hotspot in recent years. Scopus research and development potential is better than Web of Science in this field., Depto. de Economía Financiera y Actuarial y Estadística, Depto. de Administración Financiera y Contabilidad, Fac. de Ciencias Económicas y Empresariales, TRUE, pub

Published
2024

14. The Prediction of GDP by Aggregate Accounting Information. A Neural Network Model

Author

Sun, Mu, Del Campo Campos, Cristina, Chamizo-González, Julián, Urquía Grande, María Elena, Sun, Mu, Del Campo Campos, Cristina, Chamizo-González, Julián, and Urquía Grande, María Elena

Abstract

Currently, accounting information plays a key role in the economic world and bridges the gap between macroeconomics and microeconomics. The existing literature corroborates that aggregate-level accounting earnings data encapsulates information regarding GDP growth. Nonetheless, accounting data derived from GDP components have not been given due consideration. Consequently, this research proposes an aggregate-level model grounded in the four components of the GDP income-based method, intending to assess the predictive power of aggregate-level accounting data concerning GDP. Furthermore, this paper scrutinizes the forecasting performance of a neural network model built upon the conventional linear regression framework. Finally, a comparison of the outcomes derived from both models is conducted. The findings reveal that both the present value model and the value-added model corroborate the notion that accounting information derived from the four components of the income-based GDP accounting framework encapsulates data pertinent to future GDP. Furthermore, the model demonstrates heightened sensitivity towards the performance of the subsequent second quarter's GDP. Among the variables, Depreciation and Income exhibit the most substantial impact, while Salaries exhibit the least impact. Concurrently, this research noted an improvement in the fitting performance of the neural network model as compared to that of the traditional linear model., Depto. de Economía Financiera y Actuarial y Estadística, Depto. de Administración Financiera y Contabilidad, Fac. de Ciencias Económicas y Empresariales, TRUE, pub

Published
2024

17. Area integral functions and $H^{\8}$ functional calculus for sectorial operators on Hilbert spaces

Author

Chen, Zeqian and Sun, Mu

Subjects
Mathematics - Functional Analysis
Abstract

Area integral functions are introduced for sectorial operators on Hilbert spaces. We establish the equivalence relationship between the square and area integral functions. This immediately extends McIntosh/Yagi's results on $H^{\8}$ functional calculus of sectorial operators on Hilbert spaces to the case when the square functions are replaced by the area integral functions., Comment: 7 pages

Published
2012

19. Distributed Real-Time Emulation of Formally-Defined Patterns for Safe Medical Device Control

Author

Sun, Mu and Meseguer, José

Subjects
Computer Science - Logic in Computer Science, Computer Science - Software Engineering, Design, Reliability, Verification
Abstract

Safety of medical devices and of their interoperation is an unresolved issue causing severe and sometimes deadly accidents for patients with shocking frequency. Formal methods, particularly in support of highly reusable and provably safe patterns which can be instantiated to many device instances can help in this regard. However, this still leaves open the issue of how to pass from their formal specifications in logical time to executable emulations that can interoperate in physical time with other devices and with simulations of patient and/or doctor behaviors. This work presents a specification-based methodology in which virtual emulation environments can be easily developed from formal specifications in Real-Time Maude, and can support interactions with other real devices and with simulation models. This general methodology is explained in detail and is illustrated with two concrete scenarios which are both instances of a common safe formal pattern: one scenario involves the interaction of a provably safe pacemaker with a simulated heart; the other involves the interaction of a safe controller for patient-induced analgesia with a real syringe pump., Comment: In Proceedings RTRTS 2010, arXiv:1009.3982

Published
2010
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24. Renal function is associated with 1-month and 1-year mortality in patients with ischemic stroke

Author

Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyu, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Jeng, Jiann-Shing, Tang, Sung-Chun, Tsai, Li-Kai, Yeh, Shin-Joe, Chen, Chih-Hao, Tsai, Hsin-Hsi, Li, Jie-Yuan, Chen, Han-Jung, Lu, Kan, Hsu, Shih-Pin, Kuo, Hung-Chang, Tsou, Jung-Chi, Wang, Yan-Tang, Tai, Yi-Cheng, Hsieh, Meng-Tsang, Liliang, Po-Chao, Liang, Cheng-Loong, Wang, Hao-Kuang, Tsai, Yu-Tun, Wang, Kuo-Wei, Chen, Jui-Sheng, Chen, Po-Yuan, Wang, Yi-Ching, Chen, Chih-Hung, Sung, Pi-Shan, Hsieh, Han-Chieh, Su, Hui-Chen, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yeh, Hsu-Ling, Chang, Anna, Lin, Ching-Huang, Yen, Cheng-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Huang, Poyin, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Shen, Hsiu-Chu, Chen, An-Chih, Tsai, Shih-Jei, Lu, Tsong-Ming, Kung, Sheng-Ling, Lee, Mei-Ju, Chou, Hsi-Hsien, Chang, Wei-Lun, Chiu, Pai-Yi, Hsu, Min-Hsien, Chan, Po-Chi, Pan, Chau-Hsiung, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Chang, Wei-Chieh, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Denq, Jong-Chyou, Sun, Yu, Lu, Chien-Jung, Lin, Cheng-Huai, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Chan, Hoi-Fong, Lee, Siu-Pak, Sun, Ming-Hui, Ke, Li-Ying, Chen, Po-Lin, Lee, Yu-Shan, Sung, Sheng-Feng, Ong, Cheung-Ter, Wu, Chi-Shun, Hsu, Yung-Chu, Su, Yu-Hsiang, Hung, Ling-Chien, Lee, Jiunn-Tay, Lin, Jiann-Chyun, Hsu, Yaw-Don, Peng, Giia-Sheun, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu- Hua, Chiang, Chun-I., Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Chiang, Tsuey-Ru, Huang, Pai-Hao, Liao, Pin-Wen, Lee, Mei-Ching, Chen, Jen-Tse, Lie, Sian-King, Sun, Mu-Chien, Hsiao, Pi-Ju, Chen, Wei-Liang, Chen, Ta-Cheng, Chang, Chen-Shu, Lai, Chien-Hsu, Chuang, Chieh-Sen, Chen, Yen-Yu, Lin, Shinn-Kuang, Su, Yu-Chin, Shiao, Jen-Lun, Yang, Fu-Yi, Liu, Chih-Yang, Chiang, Han-Lin, Chen, Guei-Chiuan, Hsu, Po-Jen, Chang, Chun-Yuan, Lin, I.-sheng, Chien, Chung-Hsien, Chang, Yang-Chuang, Chen, Ping-Kun, Hsiao, Yu-Jen, Fang, Chen-Wen, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Hsu, Chih-Shan, Jhih, Ye-Jian, Zhuang, Hao-Yu, Pan, Yan-Hong, Shih, Shin-An, Chen, Chin-I., Sung, Jia-Ying, Weng, Hsing-Yu, Teng, Hao-Wen, Lee, Jing-Er, Huang, Chih-Shan, Chao, Shu-Ping, Yuan, Rey-Yue, Sheu, Jau-Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Lin, Ting-Chun, Yu, Shih-Chieh, Chen, Jiunn-Rong, Tsai, Song-Yen, Wei, Cheng-Yu, Hung, Chao-Hsien, Lee, Chia Fang, Yang, Sheng-Kung, Chen, Chih-Lin, Lin, Wei, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Chen, Pi-Tzu, Hu, Chaur-Jong, Chan, Lung, Chi, Nai-Fang, Chern, Chang-Ming, Lin, Chun-Jen, Wang, Shuu-Jiun, Hsu, Li-Chi, Wong, Wen-Jang, Lee, I-Hui, Yen, Der-Jen, Tsai, Ching-Piao, Kwan, Shang-Yeong, Soong, Bing-Wen, Chen, Shih-Pin, Liao, Kwong-Kum, Lin, Kung-Ping, Chen, Chien, Shan, Din-E., Fuh, Jong-Ling, Wang, Pei-Ning, Lee, Yi-Chung, Yu, Yu-Hsiang, Huang, Hui-Chi, Tsai, Jui-Yao, Wu, Ming-Hsiu, Chiang, Szu-Yi, Wang, Chiung-Yao, Hsu, Ming-Chin, Chen, Chien-Chung, Yeh, Po-Yen, Tsai, Yu-Tai, Wang, Ko-Yi, Chen, Tsang-Shan, Hsieh, Cheng-Yang, Chen, Wei-Fen, Yip, Ping-Keung, Wang, Vinchi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Liu, Yi-Chien, Chen, Shao-Yuan, Zhao, Zi-Hao, Wei, Zhi-Peng, Wu, Shey-Lin, Liu, Ching-Kuan, Lin, Ryh-Huei, Chu, Ching-Hua, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Tsai, Kun-Chang, Liao, Yi-Sheng, Tsai, Ming-Jun, Kao, Hsin-Yi, Wang, I-Kuan, Yen, Tzung-Hai, Tsai, I-Ju, Sung, Fung-Chang, and Hsu, Chung Y.

Published
2018
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25. Neuroendocrine Control of Drosophila Larval Light Preference

Author

Yamanaka, Naoki, Romero, Nuria M, Martin, Francisco A, Rewitz, Kim F, Sun, Mu, O’Connor, Michael B, and Léopold, Pierre

Subjects
Behavioral and Social Science, Neurosciences, Basic Behavioral and Social Science, Animals, Darkness, Drosophila Proteins, Drosophila melanogaster, Enzyme Activation, Escape Reaction, Insect Hormones, Larva, Light, Light Signal Transduction, Neurons, Neurosecretory Systems, RNA Interference, Receptor Protein-Tyrosine Kinases, General Science & Technology
Abstract

Animal development is coupled with innate behaviors that maximize chances of survival. Here, we show that the prothoracicotropic hormone (PTTH), a neuropeptide that controls the developmental transition from juvenile stage to sexual maturation, also regulates light avoidance in Drosophila melanogaster larvae. PTTH, through its receptor Torso, acts on two light sensors--the Bolwig's organ and the peripheral class IV dendritic arborization neurons--to regulate light avoidance. We found that PTTH concomitantly promotes steroidogenesis and light avoidance at the end of larval stage, driving animals toward a darker environment to initiate the immobile maturation phase. Thus, PTTH controls the decisions of when and where animals undergo metamorphosis, optimizing conditions for adult development.

Published
2013

26. Cholesterol Levels Are Associated with 30-day Mortality from Ischemic Stroke in Dialysis Patients

Author

Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyn, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Jeng, Jiann-Shing, Tang, Sung-Chun, Tsai, Li-Kai, Yeh, Shin-Joe, Hsu, Shih-Pin, Chen, Han-Jung, Chang, Cheng-Sen, Kuo, Hung-Chang, Lee, Lian-Hui, Tsui, Huan-Wen, Tsou, Jung-Chi, Wang, Yan-Tang, Tai, Yi-Cheng, Tsai, Kun-Chang, Chen, Yen-Wen, Lu, Kan, Liliang, Po-Chao, Tsai, Yu-Tun, Liang, Cheng-Loong, Wang, Kuo-Wei, Wang, Hao-Kuang, Chen, Jui-Sheng, Chen, Po-Yuan, Chye, Cien-Leong, Tzeng, Wei-Jie, Wu, Pei-Hua, Chen, Chih-Hung, Sung, Pi-Shan, Hsieh, Han-Chieh, Su, Hui-Chen, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yeh, Hsu-Ling, Chang, Anna, Lin, Ching-Huang, Yen, Cheng-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Huang, Poyin, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Shen, Hsiu-Chu, Chen, An-Chih, Tsai, Shih-Jei, Lu, Tsong-Ming, Kung, Sheng-Ling, Lee, Mei-Ju, Chou, Hsi-Hsien, Chi, Hsin-Yi, Pan, Chou-Hsiung, Chan, Po-Chi, Hsu, Min-Hsien, Chang, Wei-Lun, Huang, Zhi-Zang, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Denq, Jong-Chyou, Sun, Yu, Lu, Chien-Jung, Lin, Cheng-Huai, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Chan, Hoi-Fong, Lee, Siu-Pak, Sun, Ming-Hui, Ke, Li-Ying, Chen, Po-Lin, Lee, Yu-Shan, Sung, Sheng-Feng, Ong, Cheung-Ter, Wu, Chi-Shun, Hsu, Yung-Chu, Su, Yu-Hsiang, Hung, Ling-Chien, Lee, Jiunn-Tay, Lin, Jiann-Chyun, Hsu, Yaw-Don, Peng, Giia-Sheun, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu-Hua, Chiang, Chun-I, Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Chiang, Tsuey-Ru, Lee, Mei-Ching, Huang, Pai-Hao, Lie, Sian-King, Liao, Pin-Wen, Chen, Jen-Tse, Sun, Mu-Chien, Lai, Tien-Pao, Chen, Wei-Liang, Chen, Yen-Chun, Chen, Ta-Cheng, Wang, Wen-Fu, Lee, Kwo-Whei, Chang, Chen-Shu, Lai, Chien-Hsu, Shih, Siao-Ya, Chuang, Chieh-Sen, Chen, Yen-Yu, Chen, Chien-Min, Lin, Shinn-Kuang, Su, Yu-Chin, Hsiao, Cheng-Lun, Yang, Fu-Yi, Liu, Chih-Yang, Chiang, Han-Lin, Chang, Chun-Yuan, Lin, I-sheng, Chien, Chung-Hsien, Chang, Yang-Chuang, Chen, Ping-Kun, Chiu, Pai-Yi, Hsiao, Yu-Jen, Fang, Chen-Wen, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Hsu, Chih-Shan, Jhih, Ye-Jian, Zhuang, Hao-Yu, Pan, Yan-Hong, Shih, Shin-An, Chen, Chin-I, Sung, Jia-Ying, Weng, Hsing-Yu, Teng, Hao-Wen, Lee, Jing-Er, Huang, Chih-Shan, Chao, Shu-Ping, Yuan, Rey-Yue, Sheu, Jau- Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Lin, Ting-Chun, Yu, Shih-Chieh, Chen, Jiunn-Rong, Tsai, Song-Yen, Wei, Cheng-Yu, Yang, Chao-Nan, Hung, Chao-Hsien, Shih, Ian, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Chen, Pi-Tzu, Hu, Chaur-Jong, Chi, Nai-Fang, Chan, Lung, Chern, Chang-Ming, Lin, Chun-Jen, Wang, Shuu-Jiun, Hsu, Li-Chi, Wong, Wen-Jang, Lee, I-Hui, Yen, Der-Jen, Tsai, Ching-Piao, Kwan, Shang-Yeong, Soong, Bing-Wen, Chen, Shih-Pin, Liao, Kwong-Kum, Lin, Kung-Ping, Chen, Chien, Shan, Din-E, Fuh, Jong-Ling, Wang, Pei-Ning, Lee, Yi-Chung, Yu, Yu-Hsiang, Huang, Hui-Chi, Tsai, Jui-Yao, Wu, Ming-Hsiu, Chen, Shi-Cheng, Chiang, Szu-Yi, Wang, Chiung-Yao, Hsu, Ming-Chin, Chen, Chien-Chung, Yeh, Po-Yen, Tsai, Yu-Tai, Wang, Ko-Yi, Chen, Tsang-Shan, Yip, Ping-Keung, Wang, Vinchi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Chen, Chih-Hao, Liu, Yi-Chien, Chen, Shao-Yuan, Zhao, Zi-Hao, Wei, Zhi-Peng, Wu, Shey-Lin, Liu, Ching-Kuan, Lin, Ryh-Huei, Chu, Ching-Hua, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Liao, Yi-Sheng, Tsai, Ming-Jun, Kao, Hsin-Yi, Wang, I.-Kuan, Yen, Tzung-Hai, Muo, Chih-Hsin, Ma, Henry, Wen, Chi-Pang, Sung, Fung-Chang, and Hsu, Chung Y.

Published
2017
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27. The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.

Author

Xu, Site and Sun, Mu

Abstract

There are limited data available regarding the connection between hypertension and heavy metal exposure. The authors intend to establish an interpretable machine learning (ML) model with high efficiency and robustness that identifies hypertension based on heavy metal exposure. Our datasets were obtained from the US National Health and Nutrition Examination Survey (NHANES, 2013–2020.3). The authors developed 5 ML models for hypertension identification by heavy metal exposure, and tested them by 10 discrimination characteristics. Further, the authors chose the optimally performing model after parameter adjustment by Genetic Algorithm (GA) for identification. Finally, in order to visualize the model's ability to make decisions, the authors used SHapley Additive exPlanation (SHAP) and Local Interpretable Model‐Agnostic Explanations (LIME) algorithm to illustrate the features. The study included 19 368 participants in total. A best‐performing eXtreme Gradient Boosting (XGB) with GA for hypertension identification by 16 heavy metals was selected (AUC: 0.774; 95% CI: 0.772–0.776; accuracy: 87.7%). According to SHAP values, Barium (0.02), Cadmium (0.017), Lead (0.017), Antimony (0.008), Tin (0.007), Manganese (0.006), Thallium (0.004), Tungsten (0.004) in urine, and Lead (0.048), Mercury (0.035), Selenium (0.05), Manganese (0.007) in blood positively influenced the model, while Cadmium (−0.001) in urine negatively influenced the model. Study participants' hypertension associated with heavy metal exposure was identified by an efficient, robust, and interpretable GA‐XGB model with SHAP and LIME. Barium, Cadmium, Lead, Antimony, Tin, Manganese, Thallium, Tungsten in urine, and Lead, Mercury, Selenium, Manganese in blood are positively correlated with hypertension, while Cadmium in blood is negatively correlated with hypertension. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Formal Specification of Button-Related Fault-Tolerance Micropatterns

Author

Sun, Mu, Meseguer, José, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Escobar, Santiago, editor

Published
2014
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32. Tf2O/DMSO-mediated dual activation of aryl phosphinate to access various aryl phosphonates.

Author

Yue, Hui-Qi, Shi, Da-Wei, Li, Ming, Gao, Si-Qi, Sun, Mu-Xin, Zhang, Shun, Yang, Shang-Dong, and Yang, Bin

Subjects
METALS, PHOSPHONATES
Abstract

A metal-free method for the dual activation of aryl phosphinate has been developed; the P–H and P–O bonds are sequentially activated by the Tf2O/DMSO system. Without the requirement of metals and unstable P-reagents, this one-pot procedure provides a convenient and practical access to a variety of aryl phosphonates. A mechanism involving twice generation of electrophilic P-species and two SN-processes is proposed on the basis of the control experiments. [ABSTRACT FROM AUTHOR]

Published
2023
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34. Derivation and Validation of a Discharge Disposition Predicting Model after Acute Stroke

Author

Jeng, Jiann-Shing, Yeh, Shin-Joe, Tsai, Li-Kai, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yuan, Rey-Yue, Hu, Chaur-Jong, Sheu, Jau-Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Chung, Wen-Ting, Sung, Jia-Ying, Chen, Chin-I, Weng, Hsing-Yu, Han, Yu-Hsuan, Huang, Chun-Ping, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Lo, Yuk-Keung, Lin, Yu-Te, Lin, Ching-Hwung, Lin, Jiann-Chyun, Hsu, Yaw-Don, Denq, Jong-Chyou, Peng, Giia-Sheun, Lee, Jiunn-Tay, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu-Hua, Chiang, Chun-I, Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyn, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Sun, Ming-Hui, Yu, Shih-Chieh, Ke, Li-Ying, Lai, Ming-Liang, Chen, Chih-Hung, Chen, Jiunn-Rong, Tsai, Song-Yen, Chiang, Tsuey-Ru, Lee, Mei-Ching, Lin, Shinn-Kuang, Tseng, Yuan-Fu, Lee, Jing-Er, Hsiao, Cheng-Lun, Liang, Jen-Feng, Chen, Chiu-Mei, Wu, Chao-Hsin, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Smith, Eric E., Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Hu, Han-Hwa, Wong, Wen-Jang, Luk, Yun-On, Chern, Chang-Ming, Hsu, Li-Chi, Chung, Chih-Ping, Sun, Mu-Chien, Wu, Shey-Lin, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Chan, Lung, Lee, Siu-Pak, Sun, Yu, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Lin, Cheng-Huai, Lu, Chien-Jung, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Chen, Po-Lin, Lee, Yu-Shan, Chen, Shu-Yi, Chen, Han-Jung, Hsu, Shih-Pin, Chang, Cheng-Sen, Kuo, Hung-Chang, Wu, Shu-Lung, Tsui, Huan-Wen, Tsou, Jung-Chi, Chou, Feng-Hsiung, Lin, Pei-Jung, Tung, Chin-Sung, Lu, Kan, Liliang, Po-Chou, Tsai, Yu-Duan, Liang, Cheng-Loong, Wang, Kuo-Wei, Wang, Hao-Kuang, Chen, Te-Yuan, Hsin, Yue-Loong, Lin, Chih-Yuan, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Tsai, Kun-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Lin, I-Sheng, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Po, Helen L., Lin, Ya-Ju, Tu, Hao-Chieh, Tang, Sung-Chun, Lin, Fu-Yu, Chen, Ping-Kun, Chiu, Pai-Yi, Yip, Ping-Keung, Wang, Vin-Chi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Chen, Chih-Hao, Liu, Yi-Chien, Yeh, Chung-Hsin, Pan, Chou-Hsiung, Jih, Shin-Yi, Chan, Po-Chi, Hsu, Min-Hsien, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Wei, Cheng-Yu, Lee, Jun-Yu, Lin, Ryh-Huei, Chu, Ching-Hua, Hsu, Ming-Chin, Lin, Feng-Jenq, Chen, Pi-Tzu, Mou, Chih-Hsin, Chang, Chun-Yuan, Chen, An-Chih, Hsiao, Yu-Jen, Lin, Ching-Huang, Hsu, Chung-Y., and Sung, Fung-Chang

Published
2015
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45. A Formal Pattern Architecture for Safe Medical Systems

Author

Sun, Mu, Meseguer, José, Sha, Lui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Ölveczky, Peter Csaba, editor

Published
2010
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