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5,135 results on '"Lee, Meng"'

1. HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases

Author

Lee, Meng-Chieh, Zhu, Qi, Mavromatis, Costas, Han, Zhen, Adeshina, Soji, Ioannidis, Vassilis N., Rangwala, Huzefa, and Faloutsos, Christos

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source. However, many questions require both textual and relational information from SKB - referred to as "hybrid" questions - which complicates the retrieval process and underscores the need for a hybrid retrieval method that leverages both information. In this paper, through our empirical analysis, we identify key insights that show why existing methods may struggle with hybrid question answering (HQA) over SKB. Based on these insights, we propose HybGRAG for HQA consisting of a retriever bank and a critic module, with the following advantages: (1) Agentic, it automatically refines the output by incorporating feedback from the critic module, (2) Adaptive, it solves hybrid questions requiring both textual and relational information with the retriever bank, (3) Interpretable, it justifies decision making with intuitive refinement path, and (4) Effective, it surpasses all baselines on HQA benchmarks. In experiments on the STaRK benchmark, HybGRAG achieves significant performance gains, with an average relative improvement in Hit@1 of 51%.

Published
2024

2. Discovery and Extensive Follow-Up of SN 2024ggi, a nearby type IIP supernova in NGC 3621

Author

Chen, Ting-Wan, Yang, Sheng, Srivastav, Shubham, Moriya, Takashi J., Smartt, Stephen J., Rest, Sofia, Rest, Armin, Lin, Hsing Wen, Miao, Hao-Yu, Cheng, Yu-Chi, Aryan, Amar, Cheng, Chia-Yu, Fraser, Morgan, Huang, Li-Ching, Lee, Meng-Han, Lai, Cheng-Han, Liu, Yu Hsuan, K, Aiswarya Sankar., Smith, Ken W., Stevance, Heloise F., Wang, Ze-Ning, Anderson, Joseph P., Angus, Charlotte R., de Boer, Thomas, Chambers, Kenneth, Duan, Hao-Yuan, Erasmus, Nicolas, Gao, Hua, Herman, Joanna, Hou, Wei-Jie, Hsiao, Hsiang-Yao, Huber, Mark E., Lin, Chien-Cheng, Lin, Hung-Chin, Magnier, Eugene A., Man, Ka Kit, Moore, Thomas, Ngeow, Chow-Choong, Nicholl, Matt, Ou, Po-Sheng, Pignata, Giuliano, Shiau, Yu-Chien, Sommer, Julian Silvester, Tonry, John L., Wang, Xiao-Feng, Young, David R., Yeh, You-Ting, and Zhang, Jujia

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the discovery and early observations of the nearby Type II supernova (SN) 2024ggi in NGC 3621 at 6.64 +/- 0.3 Mpc. The SN was caught 5.8 (+1.9 -2.9) hours after its explosion by the ATLAS survey. Early-phase, high-cadence, and multi-band photometric follow-up was performed by the Kinder (Kilonova Finder) project, collecting over 1000 photometric data points within a week. The combined o- and r-band light curves show a rapid rise of 3.3 magnitudes in 13.7 hours, much faster than SN 2023ixf (another recent, nearby, and well-observed SN II). Between 13.8 and 18.8 hours after explosion SN 2024ggi became bluer, with u-g colour dropping from 0.53 to 0.15 mag. The rapid blueward evolution indicates a wind shock breakout (SBO) scenario. No hour-long brightening expected for the SBO from a bare stellar surface was detected during our observations. The classification spectrum, taken 17 hours after the SN explosion, shows flash features of high-ionization species such as Balmer lines, He I, C III, and N III. Detailed light curve modeling reveals critical insights into the properties of the circumstellar material (CSM). Our favoured model has an explosion energy of 2 x 10^51 erg, a mass-loss rate of 10^-3 solar_mass/yr (with an assumed 10 km/s wind), and a confined CSM radius of 6 x 10^14 cm. The corresponding CSM mass is 0.4 solar_mass. Comparisons with SN 2023ixf highlight that SN 2024ggi has a smaller CSM density, resulting in a faster rise and fainter UV flux. The extensive dataset and the involvement of citizen astronomers underscore that a collaborative network is essential for SBO searches, leading to more precise and comprehensive SN characterizations., Comment: 11 pages, 5 figures in manuscript, 6 pages in appendix, submitted to ApJL

Published
2024

3. TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly Detection

Author

Deforce, Boje, Lee, Meng-Chieh, Baesens, Bart, Asensio, Estefanía Serral, Yoo, Jaemin, and Akoglu, Leman

Subjects
Computer Science - Machine Learning
Abstract

Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neural networks have outstanding ability in modeling complex time series. Self-supervised models in particular tackle unsupervised TSAD by transforming the input via various augmentations to create pseudo anomalies for training. However, their performance is sensitive to the choice of augmentation, which is hard to choose in practice, while there exists no effort in the literature on data augmentation tuning for TSAD without labels. Our work aims to fill this gap. We introduce TSAP for TSA "on autoPilot", which can (self-)tune augmentation hyperparameters end-to-end. It stands on two key components: a differentiable augmentation architecture and an unsupervised validation loss to effectively assess the alignment between augmentation type and anomaly type. Case studies show TSAP's ability to effectively select the (discrete) augmentation type and associated (continuous) hyperparameters. In turn, it outperforms established baselines, including SOTA self-supervised models, on diverse TSAD tasks exhibiting different anomaly types., Comment: Accepted at NeurIPS workshop on Self-Supervised Learning

Published
2024

5. Stable LM 2 1.6B Technical Report

Author

Bellagente, Marco, Tow, Jonathan, Mahan, Dakota, Phung, Duy, Zhuravinskyi, Maksym, Adithyan, Reshinth, Baicoianu, James, Brooks, Ben, Cooper, Nathan, Datta, Ashish, Lee, Meng, Mostaque, Emad, Pieler, Michael, Pinnaparju, Nikhil, Rocha, Paulo, Saini, Harry, Teufel, Hannah, Zanichelli, Niccolo, and Riquelme, Carlos

Subjects
Computer Science - Computation and Language, Statistics - Machine Learning
Abstract

We introduce StableLM 2 1.6B, the first in a new generation of our language model series. In this technical report, we present in detail the data and training procedure leading to the base and instruction-tuned versions of StableLM 2 1.6B. The weights for both models are available via Hugging Face for anyone to download and use. The report contains thorough evaluations of these models, including zero- and few-shot benchmarks, multilingual benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time of publishing this report, StableLM 2 1.6B was the state-of-the-art open model under 2B parameters by a significant margin. Given its appealing small size, we also provide throughput measurements on a number of edge devices. In addition, we open source several quantized checkpoints and provide their performance metrics compared to the original model., Comment: 23 pages, 6 figures

Published
2024

6. NetInfoF Framework: Measuring and Exploiting Network Usable Information

Author

Lee, Meng-Chieh, Yu, Haiyang, Zhang, Jian, Ioannidis, Vassilis N., Song, Xiang, Adeshina, Soji, Zheng, Da, and Faloutsos, Christos

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well? More specifically, do the graph structure and the node features carry enough usable information for the task? Our goals are (1) to develop a fast tool to measure how much information is in the graph structure and in the node features, and (2) to exploit the information to solve the task, if there is enough. We propose NetInfoF, a framework including NetInfoF_Probe and NetInfoF_Act, for the measurement and the exploitation of network usable information (NUI), respectively. Given a graph data, NetInfoF_Probe measures NUI without any model training, and NetInfoF_Act solves link prediction and node classification, while two modules share the same backbone. In summary, NetInfoF has following notable advantages: (a) General, handling both link prediction and node classification; (b) Principled, with theoretical guarantee and closed-form solution; (c) Effective, thanks to the proposed adjustment to node similarity; (d) Scalable, scaling linearly with the input size. In our carefully designed synthetic datasets, NetInfoF correctly identifies the ground truth of NUI and is the only method being robust to all graph scenarios. Applied on real-world datasets, NetInfoF wins in 11 out of 12 times on link prediction compared to general GNN baselines., Comment: Accepted to ICLR 2024 (Spotlight)

Published
2024

7. Descriptive Kernel Convolution Network with Improved Random Walk Kernel

Author

Lee, Meng-Chieh, Zhao, Lingxiao, and Akoglu, Leman

Subjects
Computer Science - Machine Learning
Abstract

Graph kernels used to be the dominant approach to feature engineering for structured data, which are superseded by modern GNNs as the former lacks learnability. Recently, a suite of Kernel Convolution Networks (KCNs) successfully revitalized graph kernels by introducing learnability, which convolves input with learnable hidden graphs using a certain graph kernel. The random walk kernel (RWK) has been used as the default kernel in many KCNs, gaining increasing attention. In this paper, we first revisit the RWK and its current usage in KCNs, revealing several shortcomings of the existing designs, and propose an improved graph kernel RWK+, by introducing color-matching random walks and deriving its efficient computation. We then propose RWK+CN, a KCN that uses RWK+ as the core kernel to learn descriptive graph features with an unsupervised objective, which can not be achieved by GNNs. Further, by unrolling RWK+, we discover its connection with a regular GCN layer, and propose a novel GNN layer RWK+Conv. In the first part of experiments, we demonstrate the descriptive learning ability of RWK+CN with the improved random walk kernel RWK+ on unsupervised pattern mining tasks; in the second part, we show the effectiveness of RWK+ for a variety of KCN architectures and supervised graph learning tasks, and demonstrate the expressiveness of RWK+Conv layer, especially on the graph-level tasks. RWK+ and RWK+Conv adapt to various real-world applications, including web applications such as bot detection in a web-scale Twitter social network, and community classification in Reddit social interaction networks., Comment: WWW 2024

Published
2024

9. A Review of Prospects and Opportunities in Disassembly with Human-Robot Collaboration

Author

Lee, Meng-Lun, Liang, Xiao, Hu, Boyi, Onel, Gulcan, Behdad, Sara, and Zheng, Minghui

Subjects
Computer Science - Robotics
Abstract

Product disassembly plays a crucial role in the recycling, remanufacturing, and reuse of end-of-use (EoU) products. However, the current manual disassembly process is inefficient due to the complexity and variation of EoU products. While fully automating disassembly is not economically viable given the intricate nature of the task, there is potential in using human-robot collaboration (HRC) to enhance disassembly operations. HRC combines the flexibility and problem-solving abilities of humans with the precise repetition and handling of unsafe tasks by robots. Nevertheless, numerous challenges persist in technology, human workers, and remanufacturing work, that require comprehensive multidisciplinary research to bridge critical gaps. These challenges have motivated the authors to provide a detailed discussion on the opportunities and obstacles associated with introducing HRC to disassembly. In this regard, the authors have conducted a thorough review of the recent progress in HRC disassembly and present the insights gained from this analysis from three distinct perspectives: technology, workers, and work.

Published
2023

23. NetEffect: Discovery and Exploitation of Generalized Network Effects

Author

Lee, Meng-Chieh, Shekhar, Shubhranshu, Yoo, Jaemin, and Faloutsos, Christos

Subjects
Computer Science - Social and Information Networks, Computer Science - Machine Learning
Abstract

Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks? The knowledge of GNE is valuable for various tasks like node classification, and targeted advertising. However, identifying GNE such as homophily, heterophily or their combination is challenging in real-world graphs due to limited availability of node labels and noisy edges. We propose NetEffect, a graph mining approach to address the above issues, enjoying the following properties: (i) Principled: a statistical test to determine the presence of GNE in a graph with few node labels; (ii) General and Explainable: a closed-form solution to estimate the specific type of GNE observed; and (iii) Accurate and Scalable: the integration of GNE for accurate and fast node classification. Applied on real-world graphs, NetEffect discovers the unexpected absence of GNE in numerous graphs, which were recognized to exhibit heterophily. Further, we show that incorporating GNE is effective on node classification. On a million-scale real-world graph, NetEffect achieves over 7 times speedup (14 minutes vs. 2 hours) compared to most competitors., Comment: Accepted to PAKDD 2024

Published
2022

24. Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining

Author

Yoo, Jaemin, Lee, Meng-Chieh, Shekhar, Shubhranshu, and Faloutsos, Christos

Subjects
Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract

How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself. Einstein said that we should "make everything as simple as possible, but not simpler." We rephrase it into the careful simplicity principle: a carefully-designed simple model can surpass sophisticated ones in real-world graphs. Based on the principle, we propose SlimG for semi-supervised node classification, which exhibits four desirable properties: It is (a) accurate, winning or tying on 10 out of 13 real-world datasets; (b) robust, being the only one that handles all scenarios of graph data (homophily, heterophily, random structure, noisy features, etc.); (c) fast and scalable, showing up to 18 times faster training in million-scale graphs; and (d) interpretable, thanks to the linearity and sparsity. We explain the success of SlimG through a systematic study of the designs of existing GNNs, sanity checks, and comprehensive ablation studies., Comment: Accepted to KDD 2023

Published
2022

25. Effects of endovascular therapy for mild stroke due to proximal or M2 occlusions: meta-analysis

Author

Lin, Chun-Hsien, Saver, Jeffrey L, Ovbiagele, Bruce, Tang, Sung-Chun, Lee, Meng, and Liebeskind, David S

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Stroke, Brain Disorders, Neurosciences, Rehabilitation, Humans, Brain Ischemia, Carotid Artery Diseases, Endovascular Procedures, Infarction, Middle Cerebral Artery, Ischemic Stroke, Thrombectomy, Treatment Outcome, Thrombolysis, Clinical sciences
Abstract

ObjectiveTo conduct a meta-analysis to evaluate the effect of endovascular therapy (EVT) in mild acute ischemic stroke (AIS) due to proximal versus distal occlusions.MethodsWe searched PubMed, Embase, the Cochrane Library, and clinicaltrials.gov from January 2000 to September 2021 to identify studies comparing EVT versus best medical management (BMM) in AIS with National Institutes of Health Stroke Scale (NIHSS) scores ≤5 due to proximal internal carotid artery (ICA) or M1 versus distal M2 occlusions. We included studies that reported the number of patients with proximal or distal occlusions separately and reported the number of patients with freedom of disability (modified Rankin Scale (mRS) score 0-1) or functional independence (mRS score 0-2) at 90 days in proximal or M2 occlusions, respectively. OR with 95% CI was used.ResultsWe identified six studies with 653 proximal ICA and M1 occlusion patients and 666 distal M2 occlusion patients. Pooled results showed EVT versus BMM was associated with a higher rate of being disability-free in patients with proximal occlusions (OR 1.58, 95% CI 1.09 to 2.30), but was associated with a lower rate of being disability-free in patients with M2 occlusions (OR 0.70, 95% CI 0.49 to 0.99). Effect of EVT in proximal versus distal occlusions was substantially different (P for interaction=0.002). A similar pattern was seen for functional independence (P for interaction=0.05).ConclusionsFor patients with mild AIS, observational data suggest that EVT may be beneficial for proximal ICA or M1 but not for distal M2 occlusions. Randomized trials are needed to confirm these findings.Prospero registration numberCRD42021281034.

Published
2023

26. Study of the Correlation Between Indoor Air Quality and Brain Waves of Elderly People in a Nursing Center

Author

Kuo, Yi Chun, Sun, Shih Chiao, Lee, Meng Chieh, Cheng, Tain Junn, Li, Gang, Series Editor, Filipe, Joaquim, Series Editor, Ghosh, Ashish, Series Editor, Xu, Zhiwei, Series Editor, Tsai, Tzu-wei, editor, Chen, Kuohsiang, editor, Yamanaka, Toshimasa, editor, Koyama, Shinichi, editor, Schütte, Simon, editor, and Mohd Lokman, Anitawati, editor

Published
2024
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27. A Study on the Relationship Between Indoor Thermal Comfort and the Physical and Psychological Perception of the Elderly

Author

Chang, Chung-Yi, Tan, Jenn-Ann, Lee, Meng-Chieh Jeffrey, Cheng, Tain-Junn, Li, Gang, Series Editor, Filipe, Joaquim, Series Editor, Ghosh, Ashish, Series Editor, Xu, Zhiwei, Series Editor, Tsai, Tzu-wei, editor, Chen, Kuohsiang, editor, Yamanaka, Toshimasa, editor, Koyama, Shinichi, editor, Schütte, Simon, editor, and Mohd Lokman, Anitawati, editor

Published
2024
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28. DiffFind: Discovering Differential Equations from Time Series

Author

Posam, Lalithsai, Shekhar, Shubhranshu, Lee, Meng-Chieh, Faloutsos, Christos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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29. NETEFFECT: Discovery and Exploitation of Generalized Network Effects

Author

Lee, Meng-Chieh, Shekhar, Shubhranshu, Yoo, Jaemin, Faloutsos, Christos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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34. Myocardial Injury in Patients with Hip Fracture: A HIP ATTACK Randomized Trial Substudy

Author

Borges, Flavia K., Guerra-Farfan, Ernesto, Bhandari, Mohit, Patel, Ameen, Slobogean, Gerard, Feibel, Robert J., Sancheti, Parag K., Tiboni, Maria E., Balaguer-Castro, Mariano, Tandon, Vikas, Tomas-Hernandez, Jordi, Sigamani, Alben, Sigamani, Alen, Szczeklik, Wojciech, McMahon, Stephen J., Ślęczka, Pawel, Ramokgopa, Mmampapatla T., Adinaryanan, S., Umer, Masood, Jenkinson, Richard J., Lawendy, Abdel, Popova, Ekaterine, Nur, Aamer Nabi, Wang, Chew Yin, Vizcaychipi, Marcela, Biccard, Bruce M., Ofori, Sandra, Spence, Jessica, Duceppe, Emmanuelle, Marcucci, Maura, Harvey, Valerie, Balasubramanian, Kumar, Vincent, Jessica, Tonelli, Ana Claudia, Devereaux, P.J., Veevaete, Laurent, le Polain de Waroux, Bernard, Lavandʼhomme, Patricia, Cornu, Olivier, Tribak, Karim, Yombi, Jean C, Touil, Nassim, Bhutia, Jigme T, Clinckaert, Carol, De Clippeleir, Dirk, Reul, Maike, Patel, Ameen, Tandon, Vikas, Gauthier, Leslie P, Avram, Victoria RA, Winemaker, Mitchell, de Beer, Justin, Simpson, Diane L, Worster, Andrew, Alvarado, Kim A, Gregus, Krysten K, Lawrence, Kelly H, Leong, Darryl P, Joseph, Philip G, Magloire, Patrick, Deheshi, Benjamin, Bisland, Stuart, Wood, Thomas J, Tushinski, Daniel M, Wilson, David AJ, Haider, Shariq, Kearon, Clive, Bali, Kamal, Le Manach, Yannick, Adili, Anthony, Tiboni, Maria E, Neary, John D, Cowan, David D, Khanna, Vickas, Zaki, Amna, Farrell, Janet C, MacDonald, Anne Marie, Conen, David, Wong, Steven CW, Karbassi, Arsha, Wright, Douglas S, Shanthanna, Harsha, Coughlin, Ryan, Khan, Moin, Wikkerink, Spencer, Quraishi, Faraaz A, Lawendy, Abdel-Rahman, Kishta, Waleed, Schemitsch, Emil, Macleod, Mark D, Sanders, David W, Vasarhelyi, Edward, Dresser, George K, Tieszer, Christina, Inculet, Clayton, Gagnier, Joel, Schemitsch, Emil, Jenkinson, Richard J, Shadowitz, Steven, Lee, Jacques S, Choi, Stephen, Kreder, Hans J, Nousiainen, Markku, Kunz, Monica R, Tuazon, Ravianne, Shrikumar, Mopina, Ravi, Bheeshma, Wasserstein, David, Stephen, David JG, Nam, Diane, Henry, Patrick DG, Wood, Gavin CA, Mann, Stephen M, Jaeger, Melanie T, Sivilotti, Marco LA, Smith, Christopher A, Frank, Christopher C, Grant, Heather, Ploeg, Leone, Yach, Jeff D, Harrison, Mark M, Campbell, Aaron R, Bicknell, Ryan T, Bardana, Davide D, Feibel, Robert J, McIlquham, Katie, Gallant, Catherine, Halman, Samantha, Thiruganasambandamoorth, Venkatesh, Ruggiero, Sara, Hadden, William J, Chen, Brian PJ, Coupal, Stephanie A, McMahon, Stephen J, McLean, Lisa M, Shirali, Hemant R, Haider, Syed Y, Smith, Crystal A, Watts, Evan, Santone, David J, Koo, Kevin, Yee, Allan J, Oyenubi, Ademilola N, Nauth, Aaron, Schemitsch, Emil H, Daniels, Timothy R, Ward, Sarah E, Hall, Jeremy A, Ahn, Henry, Whelan, Daniel B, Atrey, Amit, Khoshbin, Amir, Puskas, David, Droll, Kurt, Cullinan, Claude, Payendeh, Jubin, Lefrancois, Tina, Mozzon, Lise, Marion, Travis, Jacka, Michael J, Greene, James, Menon, Matthew, Stiegelmahr, Robert, Dillane, Derek, Irwin, Marleen, Beaupre, Lauren, Coles, Chad P, Trask, Kelly, MacDonald, Shelley, Trenholm, JAI, Oxner, William, Richardson, CG, Dehghan, Niloofar, Sadoughi, Mehdi, Sharma, Achal, White, Neil J, Olivieri, Loretta, Hunt, Stephen B, Turgeon, Thomas R, Bohm, Eric R, Tran, Sarah, Giilck, Stephen M, Hupel, Tom, Guy, Pierre, OʼBrien, Peter J, Duncan, Andrew W, Crawford, Gordon A, Zhou, Junlin, Zhao, Yanrui, Liu, Yang, Shan, Lei, Wu, Anshi, Muñoz, Juan M, Chaudier, Philippe, Douplat, Marion, Fessy, Michel Henri, Piriou, Vincent, Louboutin, Lucie, David, Jean Stephane, Friggeri, Arnaud, Beroud, Sebastien, Fayet, Jean Marie, Landais, Paul, Li Leung, Frankie Ka, Fang, Christian Xinshuo, Hang Yee, Dennis King, Sancheti, Parag K, Pradhan, Chetan V, Patil, Atul A, Puram, Chetan P, Borate, Madhav P, Kudrimoti, Kiran B, Adhye, Bharati A, Dongre, Himanshu V, John, Bobby, Abraham, Valsamma, Pandey, Ritesh A, Rajkumar, Arti, George, Preetha E, Sigamani, Alen, Stephen, Manesh, Chandran, Nitheesh, Ashraf, Mohammed, Georgekutty, AM, Sulthan, Ahamad S, Adinarayanan, S, Sharma, Deep, Barnawal, Satish P, Swaminathan, Srinivasan, Bidkar, Prasanna U, Mishra, Sandeep K, Menon, Jagdish, M, Niranjan, ZK, Varghese, Hiremath, Santosh A, NC, Madhusudhan, Jawali, Abhijit, Gnanadurai, Kingsly R, George, Carolin E, Maddipati, Tatarao, KP, Mary, Sharma, Vijay, Farooque, Kamran, Malhotra, Rajesh, Mittal, Samarth, Sawhney, Chavi, Gupta, Babita, Mathur, Purva, Gamangati, Shivanand, Tripathy, Vijaylaxmi, Menon, Prem H, Dhillon, Mandeep S, Chouhan, Devendra K, Patil, Sharanu, Narayan, Ravi, Lal, Purushotham, Bilchod, Prashanth N, Singh, Surya U, Gattu, Uttam V, Dashputra, Ravi P, Rahate, Prashant V, Turiel, Maurizio, De Blasio, Giuseppe, Accetta, Riccardo, Perazzo, Paolo, Stella, Daniele, Bonadies, Marika, Colombo, Chiara, Fozzato, Stefania, Pino, Fabio, Morelli, Ilaria, Colnaghi, Eleonora, Salini, Vincenzo, Denaro, Giuseppe, Beretta, Luigi, Placella, Giacomo, Giardina, Giuseppe, Binda, Mirko, Marcato, Anna, Guzzetti, Luca, Piccirillo, Fabio, Cecconi, Maurizio, Khor, HM, Lai, Hou Yee, Kumar, CS, Chee, KH, Loh, PS, Tan, Kit Mun, Singh, Simmrat, Foo, Li Lian, Prakasam, Komella, Chaw, Sook Hui, Lee, Meng-Li, Ngim, Joanne HL, Boon, Huck Wee, Chin, Im Im, Kleinlugtenbelt, Ydo V, BM Landman, Ellie, Flikweert, Elvira R, Roerdink, Herbert W, Brokelman, Roy BG, Elskamp-Meijerman, Hannie F, Horst, Maarten R., MG Cobben, Jan-Hein, Umer, Masood, Begum, Dilshad, Anjum, Anila, Hashmi, Pervaiz M, Ahmed, Tashfeen, Rashid, Haroon U, Khattak, Mujahid J, Rashid, Rizwan H, Lakdawala, Riaz H, Noordin, Shahryar, Juman, Naveed M, Khan, Robyna I, Riaz, Muhammad M, Bokhari, Syedah S, Almas, Ayesha, Wahab, Hussain, Ali, Arif, Khan, Hammad N, Khan, Eraj K, Nur, Aamer Nabi, Janjua, Kholood A, Orakzai, Sajjad H, Khan, Abdus S, Mustafa, Khawaja J, Sohail, Mian A, Umar, Muhammad, Khan, Siddra A, Ashraf, Muhammad, Khan, Muhammad K, Shiraz, Muhammad, Furgan, Ahmad, Ślęczka, Paweł, Dąbek, Piotr, Kumoń, Adam, Satora, Wojciech, Ambroży, Wojciech, Święch, Mariusz, Rycombel, Jacek, Grzelak, Adrian, Kózka, Ilona Nowak-, Gucwa, Jaroslaw, Machala, Waldemar, Ramokgopa, Mmampapatla T, Firth, Gregory B, Karera, Mwalimu, Fourtounas, Maria, Singh, Virsen, Biscardi, Anna, and Muhammad

Published
2024
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39. Mortality, perioperative complications and surgical timelines in hip fracture patients: Comparison of the Spanish with the non-Spanish Cohort of the HIP ATTACK-1 trial

Author

Veevaete, Laurent, Waroux, Bernard le Polain de, Lavand'homme, Patricia, Cornu, Olivier, Tribak, Karim, Yombi, Jean C, Touil, Nassim, Bhutia, Jigme T, Clinckaert, Carol, Clippeleir, Dirk De, Reul, Maike, Patel, Ameen, Tandon, Vikas, Gauthier, Leslie P, Avram, Victoria R.A., Winemaker, Mitchell, de Beer, Justin, Simpson, Diane L, Worster, Andrew, Alvarado, Kim A, Gregus, Krysten K, Lawrence, Kelly H, Leong, Darryl P, Joseph, Philip G, Magloire, Patrick, Deheshi, Benjamin, Bisland, Stuart, Wood, Thomas J, Tushinski, Daniel M., Wilson, David A.J., Haider, Shariq, Bali, Kamal, Kearon, Clive, Manach, Yannick Le, Adili, Anthony, Tiboni, Maria E, Neary, John D, Cowan, David D, Khanna, Vickas, Zaki, Amna, Farrell, Janet C, MacDonald, Anne Marie, Conen, David, Wong, Steven C.W., Karbassi, Arsha, Wright, Douglas S, Shanthanna, Harsha, Coughlin, Ryan, Khan, Moin, Wikkerink, Spencer, Quraishi, Faraaz A, Lawendy, Abdel-Rahman, Kishta, Waleed, Schemitsch, Emil, Carey, Timothy, Macleod, Mark D, Sanders, David W, Vasarhelyi, Edward, Bartley, Debra, Dresser, George K, Tieszer, Christina, Jenkinson, Richard J, Shadowitz, Steven, Lee, Jacques S, Choi, Stephen, Kreder, Hans J, Nousiainen, Markku, Kunz, Monica R, Tuazon, Ravianne, Shrikumar, Mopina, Ravi, Bheeshma, Wasserstein, David, Stephen, David J.G., Nam, Diane, Henry, Patrick D.G., Wood, Gavin C.A., Mann, Stephen M., Jaeger, Melanie T., Sivilotti, Marco L.A., Smith, Christopher A., Frank, Christopher C., Grant, Heather, Ploeg, Leone, Yach, Jeff D., Harrison, Mark M., Campbell, Aaron R., Bicknell, Ryan T., Bardana, Davide D., Feibel, Robert J., McIlquham, Katie, Gallant, Catherine, Halman, Samantha, Thiruganasambandamoorth, Venkatesh, Ruggiero, Sara, Hadden, William J., Chen, Brian P.J., Coupal, Stephanie A., McMahon, Stephen J., McLean, Lisa M., Shirali, Hemant R., Haider, Syed Y., Smith, Crystal A., Watts, Evan, Santone, David J., Koo, Kevin, Yee, Allan J., Oyenubi, Ademilola N., Nauth, Aaron, Schemitsch, Emil H., Daniels, Timothy R., Ward, Sarah E., Hall, Jeremy A., Ahn, Henry, Whelan, Daniel B., Atrey, Amit, Khoshbin, Amir, Puskas, David, Droll, Kurt, Cullinan, Claude, Payendeh, Jubin, Lefrancois, Tina, Mozzon, Lise, Marion, Travis, Jacka, Michael J., Greene, James, Menon, Matthew, Stiegelmahr, Robert, Dillane, Derek, Irwin, Marleen, Beaupre, Lauren, Coles, Chad P., Trask, Kelly, MacDonald, Shelley, Trenholm, J.A.I., Oxner, William, Richardson, C.G., Dehghan, Niloofar, Sadoughi, Mehdi, Sharma, Achal, White, Neil J., Olivieri, Loretta, Hunt, Stephen B., Turgeon, Thomas R., Bohm, Eric R., Tran, Sarah, Giilck, Stephen M., Hupel, Tom, Guy, Pierre, O'Brien, Peter J., Duncan, Andrew W., Crawford, Gordon A., hou, Junlin, Zhao, Yanrui, Liu, Yang, Shan, Lei, Wu, Anshi, Muñoz, Juan M., Chaudier, Philippe, Douplat, Marion, Fessy, Michel Henri, Piriou, Vincent, Louboutin, Lucie, David, Jean Stephane, Friggeri, Arnaud, Beroud, Sebastien, Fayet, Jean Marie, Landais, Paul, Leung, Frankie Ka Li, Fang, Christian Xinshuo, Yee, Dennis King Hang, Sancheti, Parag K., Pradhan, Chetan V., Patil, Atul A., Puram, Chetan P., Borate, Madhav P., Kudrimoti, Kiran B., Adhye, Bharati A., Dongre, Himanshu V., John, Bobby, Abraham, Valsamma, Pandey, Ritesh A., Rajkumar, Arti, George, Preetha E., Sigamani, Alen, Stephen, Manesh, Chandran, Nitheesh, Ashraf, Mohammed, Georgekutty, A.M., Sulthan, Ahamad S., Adinarayanan, S., Sharma, Deep, Barnawal, Satish P., Swaminathan, Srinivasan, Bidkar, Prasanna U., Mishra, Sandeep K., Menon, Jagdish, M, Niranjan, Zk, Varghese, Hiremath, Santosh A., Nc, Madhusudhan, Jawali, Abhijit, Gnanadurai, Kingsly R., George, Carolin E., Maddipati, Atarao, Kp, Mary, Sharma, Vijay, Farooque, Kamran, Malhotra, Rajesh, Mittal, Samarth, Sawhney, Chavi, Gupta, Babita, Mathur, Purva, Gamangati, Shivanand, Tripathy, Vijaylaxmi, Menon, Prem H., Dhillon, Mandeep S., Chouhan, Devendra K., Patil, Sharanu, Narayan, Ravi, Lal, Purushotham, Bilchod, Prashanth N., Singh, Surya U., Gattu, Uttam V., Dashputra, Ravi P., Rahate, Prashant V., Turiel, Maurizio, Blasio, Giuseppe De, Accetta, Riccardo, Perazzo, Paolo, Stella, Daniele, Bonadies, Marika, Colombo, Chiara, Fozzato, Stefania, Pino, Fabio, Morelli, Ilaria, Colnaghi, Eleonora, Salini, Vincenzo, Denaro, Giuseppe, Beretta, Luigi, Placella, Giacomo, Giardina, Giuseppe, Binda, Mirko, Marcato, Anna, Guzzetti, Luca, Piccirillo, Fabio, Cecconi, Maurizio, Khor, H.M., Lai, Hou Yee, Kumar, C.S., Chee, K.H., Loh, P.S., Tan, Kit Mun, Singh, Simmrat, Foo, Li Lian, Prakasam, Komella, Chaw, Sook Hui, Lee, Meng-Li, Ngim, Joanne H.L., Boon, Huck Wee, Chin, Im Im, Kleinlugtenbelt, Ydo V., Landman, Ellie B.M., Flikweert, Elvira R., Roerdink, Herbert W., Brokelman, Roy B.G., Elskamp-Meijerman, Hannie F., Horst, Maarten R., Cobben, Jan-Hein M.G., Umer, Masood, Begum, Dilshad, Anjum, Anila, Hashmi, Pervaiz M., Ahmed, Tashfeen, Rashid, Haroon U., Khattak, Mujahid J., Rashid, Rizwan H., Lakdawala, Riaz H., Noordin, Shahryar, Juman, Naveed M., Khan, Robyna I., Riaz, Muhammad M., Bokhari, Syedah S., Almas, Ayesha, Wahab, Hussain, Ali, Arif, Khan, Hammad N., Khan, Eraj K., Nur, Aamer Nabi, Janjua, Kholood A., Orakzai, Sajjad H., Khan, Abdus S., Mustafa, Khawaja J., Sohail, Mian A., Umar, Muhammad, Khan, Siddra A., Ashraf, Muhammad, Khan, Muhammad K., Shiraz, Muhammad, Furgan, Ahmad, Ślęczka, Paweł, Dąbek, Piotr, Kumoń, Adam, Satora, Wojciech, Ambroży, Wojciech, Święch, Mariusz, Rycombel, Jacek, Grzelak, Adrian, Nowak- Kózka, Ilona, Gucwa, Jaroslaw, Machala, Waldemar, Ramokgopa, Mmampapatla T., Firth, Gregory B., Karera, Mwalimu, Fourtounas, Maria, Singh, Virsen, Biscardi, Anna, Iqbal, Muhammad N., Campbell, Ryan J., Maluleke, Matimba L., Moller, Carien, Nhlapo, Lerato, Maqungo, Sithombo, Flint, Margot, Nejthardt, Marcin B., Chetty, Sean, Naidoo, Rubendren, Guerra-Farfan, Ernesto, Tomas-Hernandez, Jordi, Garcia-Sanchez, Yaiza, Clua, Miriam Garrido, Molero-Garcia, Vicente, Minguell-Monyart, Joan, Teixidor-Serra, Jordi, Villar-Casares, Maria del Mar, Marsa, Jordi Selga, Porcel-Vazquez, Juan A., Andres-Peiro, Jose-Vicente, Aguilar, Marc, Mestre-Torres, Jaume, Colomina, Maria J., Guilabert, Patricia, Gozalo, M Luisa Paños, Abarca, Luis, Martin, Nuria, Usua, Gemma, Martinez-Ripol, Pedro, Posada, M.A. Gonzalez, Lalueza-Broto, Pilar, Sanchez-Raya, Judith, Camarena, Jorge Nuñez, Fraguas-Castany, Antoni, Balaguer-Castro, Mariano, Torner, Pere, Jornet-Gibert, Montsant, Serrano-Sanz, Jorge, Cámara-Cabrera, Jaume, Salomó-Domènech, Mònica, Yela-Verdú, Christian, Peig-Font, Anna, Ricol, Laura, Carreras-Castañer, Anna, Martínez-Sañudo, Luis, Herranz, Susana, Feijoo-Massó, Carlos, Sianes-Gallén, Mònica, Castillόn, Pablo, Bernaus, Martí, Quintas, Saioa, Gόmez, Olga, Salvador, Jordi, Abarca, Javier, Estrada, Cristina, Novellas, Marga, Torra, Mercè, Dealbert, Alfred, Macho, Oscar, Ivanov, Alexia, Valldosera, Esther, Arroyo, Marta, Pey, Borja, Yuste, Antoni, Mateo, Llorenç, Caso, Julio De, Anaya, Rafael, Higa-Sansone, J.L., Millan, Angelica, Baños, Victoria, Herrera-Mateo, Sergio, Aguado, Hector, Martinez-Municio, Gonzalo, León, Ricardo, Santiago-Maniega, Silvia, Zabalza, Ana, Labrador, Gregorio, Guerado, Enrique, Ramirez-Roldan, Alicia, Cruz, Encarnacion, Cano, Juan R, Bogallo, Jose M, Sangasoongsong, Paphon, Kulachote, Noratep, Sirisreetreerux, Norachart, Pengrung, Nachapan, Chalacheewa, Theerawat, Arnuntasupakul, Vanlapa, Yingchoncharoen, Teerapat, Naratreekoon, Bundit, Kadry, Miriam A, Thayaparan, Surendini, Abdlaziz, Ihab, Aframian, Arash, Imbuldeniya, Arjuna, Movahedi, Roya, Omran, Sherif, Vizcaychipi, Marcela P, Correia, Patricia, Patil, Shashank, Haire, Kevin, Mayor, Amy S.E., Dillingham, Sally, Nicholson, Laura, Elnaggar, Mohamed, John, Joby, Nanjayan, Shashi K, Parker, Martyn J, O'Sullivan, Susan, Marmor, Meir T, Matityahu, Amir, McClellan, Robert T, Comstock, Curt, Ding, Anthony, Toogood, Paul, Slobogean, Gerard, Joseph, Katherine, O'Toole, Robert, Sciadini, Marcus, Ryan, Scott P, Clark, Molly E, Cassidy, Charles, Balonov, Konstantin, Bergese, Sergio D, Phieffer, Laura S, Gonzalez-Zacarias, Alicia A, Marcantonio, Andrew J, Borges, Flavia K., Bhandari, Mohit, Nuñez, Jorge H., Castillon, Pablo, De Caso Rodriguez, Julio, Aguado, Hector J., Popova, Ekaterine, Tonelli, Ana Claudia, Balasubramanian, Kumar, Vincent, Jessica, Harvey, Valerie, Kocaqi, Etri, and Devereaux, P.J.

Published
2024
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47. gen2Out: Detecting and Ranking Generalized Anomalies

Author

Lee, Meng-Chieh, Shekhar, Shubhranshu, Faloutsos, Christos, Hutson, T. Noah, and Iasemidis, Leon

Subjects
Computer Science - Machine Learning
Abstract

In a cloud of m-dimensional data points, how would we spot, as well as rank, both single-point- as well as group- anomalies? We are the first to generalize anomaly detection in two dimensions: The first dimension is that we handle both point-anomalies, as well as group-anomalies, under a unified view -- we shall refer to them as generalized anomalies. The second dimension is that gen2Out not only detects, but also ranks, anomalies in suspiciousness order. Detection, and ranking, of anomalies has numerous applications: For example, in EEG recordings of an epileptic patient, an anomaly may indicate a seizure; in computer network traffic data, it may signify a power failure, or a DoS/DDoS attack. We start by setting some reasonable axioms; surprisingly, none of the earlier methods pass all the axioms. Our main contribution is the gen2Out algorithm, that has the following desirable properties: (a) Principled and Sound anomaly scoring that obeys the axioms for detectors, (b) Doubly-general in that it detects, as well as ranks generalized anomaly -- both point- and group-anomalies, (c) Scalable, it is fast and scalable, linear on input size. (d) Effective, experiments on real-world epileptic recordings (200GB) demonstrate effectiveness of gen2Out as confirmed by clinicians. Experiments on 27 real-world benchmark datasets show that gen2Out detects ground truth groups, matches or outperforms point-anomaly baseline algorithms on accuracy, with no competition for group-anomalies and requires about 2 minutes for 1 million data points on a stock machine., Comment: In Proceedings of 2021 IEEE International Conference on Big Data (Big Data)

Published
2021

48. Comparing Low- or Standard-Dose Alteplase in Endovascular Thrombectomy: Insights From a Nationwide Registry

Author

Chen, Chih-Hao, Lee, Chung-Wei, Hsieh, Yi-Chen, Lin, Chun-Jen, Chen, Yu-Wei, Lin, Kuan-Hung, Sung, Pi-Shan, Tang, Chih-Wei, Chu, Hai-Jui, Tsai, Kun-Chang, Chou, Chao-Liang, Lin, Ching-Huang, Wei, Cheng-Yu, Yen, Shang-Yih, Chen, Po-Lin, Yeh, Hsu-Ling, Chan, Lung, Sung, Sheng-Feng, Lee, Meng, Liu, Hon-Man, Lin, Yen-Heng, Lee, I-Hui, Yeh, Shin-Joe, Lien, Li-Ming, Chiou, Hung-Yi, Lee, Jiunn-Tay, Tang, Sung-Chun, Jeng, Jiann-Shing, Tang, Sung-Chun, Jeng, Jiann-Shing, Lee, Chung-Wen, Chen, Chih-Hao, Lin, Yen-Heng, Yeh, Shin-Joe, Lee, Bo-Ching, Chung, Tai-Chun, Lin, Chun-Jen, Lee, I-Hui, Chi, Nai-Fang, Hsu, Li-Chi, Chung, Chih-Ping, Liu, Hung-Yu, Luo, Chao-Bao, Chang, Feng-Chi, Lin, Chung-Jung, Wu, Chia-Hung, Yu, Kai-Wei, Hwang, Hsuen-En, Lin, Te-Ming, Chen, Yu-Wei, Chen, Chi-Jen, Wang, Ching-Yi, Kuo, Yeh-Lin, Lu, Ping-Sheng, Chao, Yen-Tung, Su, Yi-Hsin, Lin, Pei-Ju, Chen, Yi-Chun, Fan, Li-Ling, Yang, Ju-Fang, Lin, Kuan-Hung, Lin, Chien-Jen, Yang, Sheng-Hsiang, Yang, Chun-Ming, Lin, Huey-Juan, Yeh, Poh-Shiow, Chang, Chia-Yu, Cheng, Tian-Junn, Lee, Wei-Jia, Ko, Ching-Chung, Tsui, Yu-Kun, Shih, Yun-Ju, Wu, Te-Chang, Sung, Pi-Shan, Chang Chun-Min Wang, Yu-Ming, Huang, Chih-Yuan, Chen, Chih-Hung, Hsieh, Meng-Tsang, Ou, Chang-Hsien, Lin, Wan-Ching, Chen, Li-Ching, Ann, Bi-Shin, Tang, Chih-Wei, Lai, Yen-Jun, Huang, Lih-Wen, Kuo, Ya-Ling, Peng, Szu-Hsiang, Pai Lin, Yi-Chun, Chu, Hai-Jui, Lin, Cheng-Huai, Sun, Yu, Lu, Chien-Jung, Lee, Chun-Yu, Liu, Chang-Hsiu, Tsai, Kun-Chang, Chen, Kuo-Wei, Tsai, Li-Kai, Hsiue, Yen-Chung, Cheng, Ya-Wen, Fu, Chuan-Hsiu, Chen, Wen-Yu, Chou, Chao-Liang, Po, Helen L., Lin, Ya-Ju, Hwang, Yung-Pin, Kuo, Shu-Fan, Huang, Chun-Chao, Jhou, Zong-Yi, Yu, Hui-Fen, Lin, Hsiao-Chu, Wei, Cheng-Yu, Chen, Chih-Lin, Wu, Pei-han, Tsai, Yi-Ching, Yen, Shang-Yih, Lee, Jiunn-tay, Chou, Chung-Hsing, Ko, Chien-An, Chen, Po-Lin, Tsuei, Yuang-Seng, Chen, Wen-Hsien, Liao, Nien-Chen, Liaw, Yeng-Fung, Yeh, Hsu-Ling, Lien, Li-Ming, Hsiao, Chen-Yu, Lin, Kuan-Yu, Yang, Tsui-Hua, Chan, Lung, Chen, Jia-Hung, Yu, Shun-Fan, Su, I-Chang, Lu, Yueh-Hsun, Sung, Sheng-Feng, Yang, Tzu-Hsien, Hsu, Yung-Chu, Su, Yu-Hsiang, Hung, Ling-Chien, Lin, Mao-Hsun, Su, Chien-Yu, Liu, Hon-Man, Huang, Yung-Chuan, Wan, Chih-Cheng, Lin, Ching-Huang, Yen, Cheng- Chang, Shih, Ching-Sen, Lin, Chun-Shien, Lee, Meng, Tsai, Yuan-Hsiung, Huang, Yen-Chu, Hung, Wei-Tse, and Lee, Jiann-Der

Published
2024
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49. Endovascular thrombectomy without versus with intravenous thrombolysis in acute ischemic stroke: a non-inferiority meta-analysis of randomized clinical trials

Author

Lin, Chun-Hsien, Saver, Jeffrey L, Ovbiagele, Bruce, Huang, Wen-Yi, and Lee, Meng

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Rehabilitation, Neurosciences, Brain Disorders, Clinical Research, Clinical Trials and Supportive Activities, Stroke, Brain Ischemia, Endovascular Procedures, Fibrinolytic Agents, Humans, Ischemic Stroke, Randomized Controlled Trials as Topic, Thrombectomy, Thrombolytic Therapy, Treatment Outcome, thrombectomy, thrombolysis, stroke, Clinical sciences
Abstract

ObjectiveTo conduct a meta-analysis of randomized trials to comprehensively compare the effect of endovascular thrombectomy (EVT) versus intravenous thrombolysis (IVT) plus EVT on functional independence (modified Rankin Scale (mRS) 0-2) after acute ischemic stroke due to large vessel occlusions (AIS-LVO).MethodsWe searched Pubmed, EMBASE, CENTRAL, and clinicaltrials.gov from January 2000 to February 2021 and abstracts presented at the International Stroke Conference in March 2021 to identify trials comparing EVT alone versus IVT plus EVT in AIS-LVO. Five non-inferiority margins established in the literature were assessed: -15%, -10%, -6.5%, -5%, and -1.3% for the risk difference for functional independence at 90 days.ResultsFour trials met the selection criteria, enrolling 1633 individuals, with 817 participants randomly assigned to EVT alone and 816 to IVT plus EVT. Crude cumulative rates of 90-day functional independence were 46.0% with EVT alone versus 45.5% with IVT plus EVT. Pooled results showed the risk difference of functional independence was 1% (95% CI -4% to 5%) between EVT alone versus IVT plus EVT. The lower 95% CI bound of -4% fell within the non-inferiority margins of -15%, -10%, -6.5%, and -5%, but not -1.3%. Pooled results also showed the risk difference between EVT alone versus IVT plus EVT was 1% (95% CI -3% to 5%) for mRS 0-1, and 1% (95% CI -1% to 3%) for symptomatic intracranial hemorrhage.ConclusionsThis meta-analysis suggests that EVT alone is non-inferior to IVT plus EVT for several, but not the most stringent, non-inferiority margins.

Published
2022

50. Modifications of lipid pathways restrict SARS-CoV-2 propagation in human induced pluripotent stem cell-derived 3D airway organoids

Author

Tsai, Ping-Hsing, Sun, Jun-Ren, Chien, Yueh, Chan, Man Sheung, Khor, Winnie, Yang, Hsin-Chou, Huang, Chih-Heng, Hsiung, Chia-Ni, Hwa, Teh-Yang, Lin, Yi-Ying, Yeh, Chih-Ling, Wang, Mong-Lien, Yang, Yi-Ping, Chen, Yuh-Min, Tsai, Fu-Ting, Lee, Meng-Shiue, Cheng, Yun-Hsiang, Tsai, Shan-Ko, Liu, Ping-Cheng, Chou, Shih-Jie, and Chiou, Shih-Hwa

Published
2024
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