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158. Prototypical Representation Learning for Relation Extraction

Author

Ding, Ning, Wang, Xiaobin, Fu, Yao, Xu, Guangwei, Wang, Rui, Xie, Pengjun, Shen, Ying, Huang, Fei, Zheng, Hai-Tao, and Zhang, Rui

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abundant label noise and complicated expressions in human language. This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data that are effective in different settings, including supervised, distantly supervised, and few-shot learning. Instead of solely relying on the supervision from noisy labels, we propose to learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Prototypes are representations in the feature space abstracting the essential semantics of relations between entities in sentences. We learn prototypes based on objectives with clear geometric interpretation, where the prototypes are unit vectors uniformly dispersed in a unit ball, and statement embeddings are centered at the end of their corresponding prototype vectors on the surface of the ball. This approach allows us to learn meaningful, interpretable prototypes for the final classification. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art models. We further demonstrate the robustness of the encoder and the interpretability of prototypes with extensive experiments., Comment: Accepted by ICLR 2021

Published
2021

159. Learning Purified Feature Representations from Task-irrelevant Labels

Author

Li, Yinghui, Wang, Chen, Li, Yangning, Zheng, Hai-Tao, and Shen, Ying

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

Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks. In this paper, we propose a novel learning framework called PurifiedLearning to exploit task-irrelevant features extracted from task-irrelevant labels when training models on small-scale datasets. Particularly, we purify feature representations by using the expression of task-irrelevant information, thus facilitating the learning process of classification. Our work is built on solid theoretical analysis and extensive experiments, which demonstrate the effectiveness of PurifiedLearning. According to the theory we proved, PurifiedLearning is model-agnostic and doesn't have any restrictions on the model needed, so it can be combined with any existing deep neural networks with ease to achieve better performance. The source code of this paper will be available in the future for reproducibility., Comment: Accepted by IJCNN 2022. arXiv admin note: substantial text overlap with arXiv:2011.08470

Published
2021

160. Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation

Author

Feng, Lingyun, Qiu, Minghui, Li, Yaliang, Zheng, Hai-Tao, and Shen, Ying

Subjects
Computer Science - Computation and Language
Abstract

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment for data-scarce domain BERT knowledge distillation, by learning a cross-domain manipulation scheme that automatically augments the target with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced selector to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on four different tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ${\sim}13.3\%$) when only a few labeled examples available., Comment: AAAI2021

Published
2021

161. Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Author

Jiehao Bi, Ang Jin, Chi Chen, and Shen Ying

Subjects
lunar rovers, Mars rovers, human–robot interaction, enhanced rendering, permanently shadowed region (PSR), rocks, Science
Abstract

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover’s interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover’s traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover’s path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.

Published
2024
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162. Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Author

Li, Yinghui, Liu, Ruiyang, Zhang, ZiHao, Ding, Ning, Shen, Ying, Tao, Linmi, and Zheng, Hai-Tao

Subjects
Computer Science - Machine Learning
Abstract

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility., Comment: An updated version of this work has been available at arXiv:2102.10955

Published
2020

163. Preexisting autoantibodies to type I IFNs underlie critical COVID-19 pneumonia in patients with APS-1

Author

Bastard, Paul, Orlova, Elizaveta, Sozaeva, Leila, Lévy, Romain, James, Alyssa, Schmitt, Monica M, Ochoa, Sebastian, Kareva, Maria, Rodina, Yulia, Gervais, Adrian, Le Voyer, Tom, Rosain, Jérémie, Philippot, Quentin, Neehus, Anna-Lena, Shaw, Elana, Migaud, Mélanie, Bizien, Lucy, Ekwall, Olov, Berg, Stefan, Beccuti, Guglielmo, Ghizzoni, Lucia, Thiriez, Gérard, Pavot, Arthur, Goujard, Cécile, Frémond, Marie-Louise, Carter, Edwin, Rothenbuhler, Anya, Linglart, Agnès, Mignot, Brigite, Comte, Aurélie, Cheikh, Nathalie, Hermine, Olivier, Breivik, Lars, Husebye, Eystein S, Humbert, Sébastien, Rohrlich, Pierre, Coaquette, Alain, Vuoto, Fanny, Faure, Karine, Mahlaoui, Nizar, Kotnik, Primož, Battelino, Tadej, Podkrajšek, Katarina Trebušak, Kisand, Kai, Ferré, Elise MN, DiMaggio, Thomas, Rosen, Lindsey B, Burbelo, Peter D, McIntyre, Martin, Kann, Nelli Y, Shcherbina, Anna, Pavlova, Maria, Kolodkina, Anna, Holland, Steven M, Zhang, Shen-Ying, Crow, Yanick J, Notarangelo, Luigi D, Su, Helen C, Abel, Laurent, Anderson, Mark S, Jouanguy, Emmanuelle, Neven, Bénédicte, Puel, Anne, Casanova, Jean-Laurent, and Lionakis, Michail S

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Infectious Diseases, Rare Diseases, Pneumonia, Pneumonia & Influenza, Autoimmune Disease, Clinical Research, Lung, Adolescent, Adult, Autoantibodies, COVID-19, Child, Female, Humans, Interferon Type I, Male, Middle Aged, Polyendocrinopathies, Autoimmune, SARS-CoV-2, Young Adult, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Patients with biallelic loss-of-function variants of AIRE suffer from autoimmune polyendocrine syndrome type-1 (APS-1) and produce a broad range of autoantibodies (auto-Abs), including circulating auto-Abs neutralizing most type I interferons (IFNs). These auto-Abs were recently reported to account for at least 10% of cases of life-threatening COVID-19 pneumonia in the general population. We report 22 APS-1 patients from 21 kindreds in seven countries, aged between 8 and 48 yr and infected with SARS-CoV-2 since February 2020. The 21 patients tested had auto-Abs neutralizing IFN-α subtypes and/or IFN-ω; one had anti-IFN-β and another anti-IFN-ε, but none had anti-IFN-κ. Strikingly, 19 patients (86%) were hospitalized for COVID-19 pneumonia, including 15 (68%) admitted to an intensive care unit, 11 (50%) who required mechanical ventilation, and four (18%) who died. Ambulatory disease in three patients (14%) was possibly accounted for by prior or early specific interventions. Preexisting auto-Abs neutralizing type I IFNs in APS-1 patients confer a very high risk of life-threatening COVID-19 pneumonia at any age.

Published
2021

164. A novel ADE system with tunable droplet size and ejection height.

Author

Shen, Shih-Hung, Lian, Han-Wei, Huang, Yu-Wen, Chu, Yu-Chun, Liu, Pu-Chun, Lin, Fu-Sung, Shen, Ying-Ting, and Huang, Chih-Hsien

Subjects
ACOUSTIC surface waves, GATE array circuits
Abstract

Acoustic droplet ejection (ADE) has been proven to move liquids in droplet form from a container to mid-air. However, the droplet sizes produced using the traditional ADE setups are proportional to their heights if the physical framework is unchanged. This limitation obstructs further employment of ADE technology since having the exact sizes of droplets at a specific location or different sizes at different locations is required for many applications. To overcome this limitation and enable more possible applications of ADE, this study proposed an innovative ADE configuration that could manipulate the size and height of the ADE droplets with only electrical signals. To achieve this, a low-voltage driving period and a pinhole structure were added to create a water mound before ejection. First, simulations were conducted to validate the proposed method and find the parameters of the novel ADE setup. After that, a driving circuit featuring a high-voltage pulser and a field-programmable gate array was built. However, a 3D spherical resin model was printed to focus the acoustic wave on the water surface, and a cover with a pinhole was added to create water mound. To observe the behavior of the droplets, a recording system and detection algorithm were developed to capture and identify the dimension/height of the droplets, respectively. Finally, the proposed ADE configuration successfully manipulated the droplet size at the same ejecting height under three driving voltages (65, 70, and 75 V) and pinhole diameters (3.7, 4, and 4.4 mm). [ABSTRACT FROM AUTHOR]

Published
2024
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170. Application of Bimetal Composite Pipe in Hutubi Gas Storage

Author

YANG Zhaoyan, SHEN Ying, YANG Zhong, DENG Jing, XU Mingquan, LI Yongkang

Subjects
bimetallic composite pipe, injection production pipeline, base course, lining, co2 corrosion, gas storage, Materials of engineering and construction. Mechanics of materials, TA401-492, Technology
Abstract

In order to study the corrosion problem of the CO2 in natural gas，the corrosivity of medium under different working conditions was analyzed based on the corrosion mechanism.Targeted corrosion test was carried out before material selection.The base and lining materials of bimetallic composite pipe were optimized and explored from the aspects of design and construction.Based on the actual application situation on site，a complete application framework of bimetallic composite pipe was developed.

Published
2023
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171. Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

Author

Daniela Matuozzo, Estelle Talouarn, Astrid Marchal, Peng Zhang, Jeremy Manry, Yoann Seeleuthner, Yu Zhang, Alexandre Bolze, Matthieu Chaldebas, Baptiste Milisavljevic, Adrian Gervais, Paul Bastard, Takaki Asano, Lucy Bizien, Federica Barzaghi, Hassan Abolhassani, Ahmad Abou Tayoun, Alessandro Aiuti, Ilad Alavi Darazam, Luis M. Allende, Rebeca Alonso-Arias, Andrés Augusto Arias, Gokhan Aytekin, Peter Bergman, Simone Bondesan, Yenan T. Bryceson, Ingrid G. Bustos, Oscar Cabrera-Marante, Sheila Carcel, Paola Carrera, Giorgio Casari, Khalil Chaïbi, Roger Colobran, Antonio Condino-Neto, Laura E. Covill, Ottavia M. Delmonte, Loubna El Zein, Carlos Flores, Peter K. Gregersen, Marta Gut, Filomeen Haerynck, Rabih Halwani, Selda Hancerli, Lennart Hammarström, Nevin Hatipoğlu, Adem Karbuz, Sevgi Keles, Christèle Kyheng, Rafael Leon-Lopez, Jose Luis Franco, Davood Mansouri, Javier Martinez-Picado, Ozge Metin Akcan, Isabelle Migeotte, Pierre-Emmanuel Morange, Guillaume Morelle, Andrea Martin-Nalda, Giuseppe Novelli, Antonio Novelli, Tayfun Ozcelik, Figen Palabiyik, Qiang Pan-Hammarström, Rebeca Pérez de Diego, Laura Planas-Serra, Daniel E. Pleguezuelo, Carolina Prando, Aurora Pujol, Luis Felipe Reyes, Jacques G. Rivière, Carlos Rodriguez-Gallego, Julian Rojas, Patrizia Rovere-Querini, Agatha Schlüter, Mohammad Shahrooei, Ali Sobh, Pere Soler-Palacin, Yacine Tandjaoui-Lambiotte, Imran Tipu, Cristina Tresoldi, Jesus Troya, Diederik van de Beek, Mayana Zatz, Pawel Zawadzki, Saleh Zaid Al-Muhsen, Mohammed Faraj Alosaimi, Fahad M. Alsohime, Hagit Baris-Feldman, Manish J. Butte, Stefan N. Constantinescu, Megan A. Cooper, Clifton L. Dalgard, Jacques Fellay, James R. Heath, Yu-Lung Lau, Richard P. Lifton, Tom Maniatis, Trine H. Mogensen, Horst von Bernuth, Alban Lermine, Michel Vidaud, Anne Boland, Jean-François Deleuze, Robert Nussbaum, Amanda Kahn-Kirby, France Mentre, Sarah Tubiana, Guy Gorochov, Florence Tubach, Pierre Hausfater, COVID Human Genetic Effort, COVIDeF Study Group, French COVID Cohort Study Group, CoV-Contact Cohort, COVID-STORM Clinicians, COVID Clinicians, Orchestra Working Group, Amsterdam UMC Covid-19 Biobank, NIAID-USUHS COVID Study Group, Isabelle Meyts, Shen-Ying Zhang, Anne Puel, Luigi D. Notarangelo, Stephanie Boisson-Dupuis, Helen C. Su, Bertrand Boisson, Emmanuelle Jouanguy, Jean-Laurent Casanova, Qian Zhang, Laurent Abel, and Aurélie Cobat

Subjects
Rare variants, COVID-19, Immunity, Type I interferon, Medicine, Genetics, QH426-470
Abstract

Abstract Background We previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15–20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in ~ 80% of cases. Methods We report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded. Results No gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5–528.7, P = 1.1 × 10−4) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR = 3.70[95%CI 1.3–8.2], P = 2.1 × 10−4). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR = 19.65[95%CI 2.1–2635.4], P = 3.4 × 10−3), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR = 4.40[9%CI 2.3–8.4], P = 7.7 × 10−8). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD] = 43.3 [20.3] years) than the other patients (56.0 [17.3] years; P = 1.68 × 10−5). Conclusions Rare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old.

Published
2023
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172. Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents

Author

Chen, Daoyuan, Li, Yaliang, Lei, Kai, and Shen, Ying

Subjects
Computer Science - Computation and Language
Abstract

Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts. In this paper, we consider the problem of \textit{shifted label distribution}, which is caused by the inconsistency between the noisy-labeled training set subject to external knowledge graph and the human-annotated test set, and exacerbated by the pipelined entity-then-relation extraction manner with noise propagation. We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents. To handle noisy instances in a fine-grained manner, each agent in the cooperative group evaluates the instance by calculating a continuous confidence score from its own perspective; To leverage the correlations between these two extraction tasks, a confidence consensus module is designed to gather the wisdom of all agents and re-distribute the noisy training set with confidence-scored labels. Further, the confidences are used to adjust the training losses of extractors. Experimental results on two real-world datasets verify the benefits of re-labeling noisy instance, and show that the proposed model significantly outperforms the state-of-the-art entity and relation extraction methods., Comment: Accepted by ACL 2020

Published
2020

173. Sector connectedness in the Chinese stock markets

Author

Shen, Ying-Ying, Jiang, Zhi-Qiang, Ma, Jun-Chao, Wang, Gang-Jin, and Zhou, Wei-Xing

Subjects
Quantitative Finance - Risk Management
Abstract

Uncovering the risk transmitting path within economic sectors in China is crucial for understanding the stability of the Chinese economic system, especially under the current situation of the China-US trade conflicts. In this paper, we try to uncover the risk spreading channels by means of volatility spillovers within the Chinese sectors using stock market data. By applying the generalized variance decomposition framework based on the VAR model and the rolling window approach, a set of connectedness matrices is obtained to reveal the overall and dynamic spillovers within sectors. It is found that 17 sectors (mechanical equipment, electrical equipment, utilities, and so on) are risk transmitters and 11 sectors (national defence, bank, non-bank finance, and so on) are risk takers during the whole period. During the periods with the extreme risk events (the global financial crisis, the Chinese interbank liquidity crisis, the Chinese stock market plunge, and the China-US trade war), we observe that the connectedness measures significantly increase and the financial sectors play a buffer role in stabilizing the economic system. The robust tests suggest that our results are not sensitive to the changes of model parameters. Our results not only uncover the spillover effects within the Chinese sectors, but also highlight the deep understanding of the risk contagion patterns in the Chinese stock markets., Comment: 17 pages, 7 figures, and 3 tables

Published
2020

174. A multi-agent ontologies-based clinical decision support system

Author

Shen, Ying, Armelle, Jacquet-Andrieu, and Colloc, Joël

Subjects
Computer Science - Artificial Intelligence
Abstract

Clinical decision support systems combine knowledge and data from a variety of sources, represented by quantitative models based on stochastic methods, or qualitative based rather on expert heuristics and deductive reasoning. At the same time, case-based reasoning (CBR) memorizes and returns the experience of solving similar problems. The cooperation of heterogeneous clinical knowledge bases (knowledge objects, semantic distances, evaluation functions, logical rules, databases...) is based on medical ontologies. A multi-agent decision support system (MADSS) enables the integration and cooperation of agents specialized in different fields of knowledge (semiology, pharmacology, clinical cases, etc.). Each specialist agent operates a knowledge base defining the conduct to be maintained in conformity with the state of the art associated with an ontological basis that expresses the semantic relationships between the terms of the domain in question. Our approach is based on the specialization of agents adapted to the knowledge models used during the clinical steps and ontologies. This modular approach is suitable for the realization of MADSS in many areas., Comment: in French

Published
2020

176. An automatic high throughput colony counter based on Unet++ segmentation model

Author

SHEN Ying, XIE Feng, WANG Yulin, TAN Bo, and FAN Jinxu

Subjects
colony counting, automatic, high throughput, unet++ image segmentation model, Food processing and manufacture, TP368-456
Abstract

Objective: To improve the detection efficiency of the aerobic plate counter, to meet the requirements of quality management system of the inspection and testing institutions and the application of laboratory LIMS system. Methods: It was used to continuously collected of colonies image through the GigE industrial camera, variable lens and multiple light sources combined lighting system, at the same time the Unet++ segmentation model was used for image recognition processing and colony counting. Results: The colony counter had the characteristics of high collection efficiency, it only took 38 s to complete the image acquisition of one plate. It only took 3~5 s to complete the whole process of image transmission and colony identification and counting of one plate, and it had fast data processing speed and good transmission performance. Compared with the counting method required by the current standard, the error rate of the counting result was less than 8%, and it had high accuracy of counting results and good repeatability. At the same time, it realized the automatic processing of the original data of the aerobic plate count detection. Conclusion: The equipment can not only carry out high-throughput image acquisition, automatic image processing and colony counting, but also can realize the fusion with the laboratory LIMS system, effectively improve the work efficiency. Meanwhile, it can ensure the data traceability, reduce the work intensity of the test personnel, meet the error requirements of the aerobic plate count method.

Published
2023
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177. SARS-CoV-2 activates the TLR4/MyD88 pathway in human macrophages: A possible correlation with strong pro-inflammatory responses in severe COVID-19

Author

Sabina Sahanic, Richard Hilbe, Christina Dünser, Piotr Tymoszuk, Judith Löffler-Ragg, Dietmar Rieder, Zlatko Trajanoski, Anne Krogsdam, Egon Demetz, Maria Yurchenko, Christine Fischer, Michael Schirmer, Markus Theurl, Daniela Lener, Jakob Hirsch, Johannes Holfeld, Can Gollmann-Tepeköylü, Carl P. Zinner, Alexandar Tzankov, Shen-Ying Zhang, Jean-Laurent Casanova, Wilfried Posch, Doris Wilflingseder, Guenter Weiss, and Ivan Tancevski

Subjects
SARS-CoV-2, Macrophages, Innate immunity, Toll-like receptors, COVID-19, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: Toll-like receptors (TLRs) play a pivotal role in the immunologic response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Exaggerated inflammatory response of innate immune cells, however, may drive morbidity and death in Coronavirus disease 19 (COVID-19). Objective: We investigated the engagement of SARS-CoV-2 with TLR4 in order to better understand how to tackle hyperinflammation in COVID-19. Methods: We combined RNA-sequencing data of human lung tissue and of bronchoalveolar lavage fluid cells derived from COVID-19 patients with functional studies in human macrophages using SARS-CoV-2 spike proteins and viable SARS-CoV-2. Pharmacological inhibitors as well as gene editing with CRISPR/Cas9 were used to delineate the signalling pathways involved. Results: We found TLR4 to be the most abundantly upregulated TLR in human lung tissue irrespective of the underlying pathology. Accordingly, bronchoalveolar lavage fluid cells from patients with severe COVID-19 showed an NF-κB-pathway dominated immune response, whereas they were mostly defined by type I interferon signalling in moderate COVID-19. Mechanistically, we found the Spike ectodomain, but not receptor binding domain monomer to induce TLR4-dependent inflammation in human macrophages. By using pharmacological inhibitors as well as CRISPR/Cas9 deleted macrophages, we identify SARS-CoV-2 to engage canonical TLR4-MyD88 signalling. Importantly, we demonstrate that TLR4 blockage prevents exaggerated inflammatory responses in human macrophages infected with different SARS-CoV-2 variants, including immune escape variants B.1.1.7.-E484K and B.1.1.529 (omicron). Conclusion: Our study critically extends the current knowledge on TLR-mediated hyperinflammatory responses to SARS-CoV-2 in human macrophages, paving the way for novel approaches to tackle severe COVID-19. Take-home message: Our study combining human lung transcriptomics with functional studies in human macrophages clearly supports the design and development of TLR4 - directed therapeutics to mitigate hyperinflammation in severe COVID-19.

Published
2023
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178. Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering

Author

Deng, Yang, Lam, Wai, Xie, Yuexiang, Chen, Daoyuan, Li, Yaliang, Yang, Min, and Shen, Ying

Subjects
Computer Science - Computation and Language
Abstract

Community question answering (CQA) gains increasing popularity in both academy and industry recently. However, the redundancy and lengthiness issues of crowdsourced answers limit the performance of answer selection and lead to reading difficulties and misunderstandings for community users. To solve these problems, we tackle the tasks of answer selection and answer summary generation in CQA with a novel joint learning model. Specifically, we design a question-driven pointer-generator network, which exploits the correlation information between question-answer pairs to aid in attending the essential information when generating answer summaries. Meanwhile, we leverage the answer summaries to alleviate noise in original lengthy answers when ranking the relevancy degrees of question-answer pairs. In addition, we construct a new large-scale CQA corpus, WikiHowQA, which contains long answers for answer selection as well as reference summaries for answer summarization. The experimental results show that the joint learning method can effectively address the answer redundancy issue in CQA and achieves state-of-the-art results on both answer selection and text summarization tasks. Furthermore, the proposed model is shown to be of great transferring ability and applicability for resource-poor CQA tasks, which lack of reference answer summaries., Comment: Accepted by AAAI 2020 (oral)

Published
2019

184. Lenvatinib plus pembrolizumab versus lenvatinib plus placebo for advanced hepatocellular carcinoma (LEAP-002): a randomised, double-blind, phase 3 trial

Author

Strasser, Simone, Thompson, Alexander, Roohullah, Aflah, Sievert, William, Andelkovic, Vladimir, Knox, Jennifer, Davies, Janine, Asselah, Jamil, Brahmania, Mayur, Majeed, Habeeb, Villanueva, Luis, Yanez Weber, Patricio, Garrido, Marcelo, Pizarro, Gonzalo Ignacio, Yanez, Nicolas, Xu, Ruocai, Ou, Shuangyan, Ren, Zhenggang, Pan, Hongming, Meng, Zhiqiang, Gu, Kangsheng, Chen, Xi, Zhang, Tao, Hao, Chunyi, Cao, Peiguo, Guo, Yabing, Qin, Shukui, Xiao, Juxiang, Fang, Weijia, Wang, Xin, Bai, Yuxian, Chen, Xiaoming, Yan, Dong, Zhao, Hong, Ying, Jieer, Bonilla, Carlos, Urrego, Olga, Zambrano, Angela, Lema, Mauricio, Restrepo Gutierrez, Juan Carlos, Cardona, Andres, Franco Millan, Sandra, Oscar, Madiedo, Ramos, Victor, Merle, Philippe, LeSourd, Samuel, Edeline, Julien, Bronowicki, Jean-Pierre, Bourliere, Marc, Cattan, Stephane, Bouattour, Mohamed, Mineur, Laurent, Regnault, Helene, Dauvois, Barbara, Schulze, Kornelius, Folprecht, Gunnar, Geier, Andreas, Waidmann, Oliver, Finkelmeier, Fabian, Venerito, Marino, Berres, Marie-Luise, Berg, Thomas, Lange, Christian, Schmidt, Hartmut, Waldschmidt, Dirk T, Bitzer, Michael, McDermott, Ray, Duffy, Austin, Masi, Gianluca, Zagonel, Vittorina, Tonini, Giuseppe, Piscaglia, Fabio, Gori, Stefania, Rizzo, Mimma, Biscaldi, Elisa, Foltran, Luisa, Cabibbo, Giuseppe, Ikeda, Masafumi, Kudo, Masatoshi, Aikata, Hiroshi, Numata, Kazushi, Marusawa, Hiroyuki, Kato, Naoya, Kurosaki, Masayuki, Morimoto, Manabu, Yamashita, Tatsuya, Koga, Hironori, Masaki, Tsutomu, Tateishi, Ryosuke, Inaba, Yoshitaka, Arakawa, Tomohiro, Suzuki, Yoshiyuki, Akua, Norio, Kobayashi, Masahiro, Hosaka, Tetsuya, Nakamuta, Makoto, Oza, Noriko, Kondo, Shunsuke, Furuse, Junji, Nagashima, Fumio, Kitano, Masayuki, Takaguchi, Koichi, Ryoo, Baek-Yeol, Lim, Ho Yeong, Kim, Jee Hyun, Kim, Tae-You, Kim, Han Sang, Hernandez Hernandez, Carlos, Motola Kuba, Daniel, Segura Gonzalez, Manuel, Cabrera Luviano, Jesus, Huitzil Melendez, Fidel, Ramirez Godinez, Francisco, Silva-Bravo, Fernando, Gane, Edward, Stedman, Catherine, Kwiatkowski, Mariusz, Wyrwicz, Lucjan, Nowakowska-Zajdel, Ewa, Kraj, Leszek, Janczewska, Ewa, Surma-Wlodarczyk, Renata, Breder, Valeriy, Sekacheva, Marina, Orlova, Rashida, Vasilyev, Alexander, Zukov, Ruslan, Vladimirov, Vladimir, Minguez, Beatriz, Matilla Pena, Ana, Fernandez Castroagudin, Javier, Romero Gomez, Manuel, Luis Lledo, Jose, Rubin, Angel, Garcia Sanchez, Araceli, Calleja Panero, Jose, Yu, Ming-Chin, Shen, Ying-Chun, Huang, Yi-Hsiang, Jeng, Long-Bin, Chang, Ting-Tsung, Yang, Sheng-Shun, Chen, Shinn-Cherng, Dechaphunkul, Arunee, Tanwandee, Tawesak, Leerapun, Apinya, Seker, Mesut, Harputluoglu, Hakan, Cicin, Irfan, Oksuzoglu, Berna, Bilici, Mehmet, Sezgin Goksu, Sema, Artac, Mehmet, Cil, Timucin, Yalcin, Suayib, Meyer, Tim, Sarker, Debashis, Rao, Ankit, Palmer, Daniel, Evans, Jeff, Hubner, Richard, Finn, Richard, Ulahannan, Susanna, Zakari, Ahmed, Feun, Lynn, Al-Rajabi, Raed, Sung, Max, He, Aiwu, Kardosh, Adel, Goyal, Lipika, Li, Daneng, Choi, Minsig, Posada, Juan, Dodlapati, Jyothi, Shroff, Rachna, Agrawal, Saurabh, Gopaluni, Srivalli, Akce, Mehmet, Alese, Olatunji, Baron, Ari, Dunne, Richard, Loaiza-Bonilla, Arturo, Frenette, Catherine, Llovet, Josep M, Kumada, Hiromitsu, Cheng, Ann-Lii, Galle, Peter R, Kaneko, Shuichi, Wang, Anran, Mody, Kalgi, Dutcus, Corina, Dubrovsky, Leonid, Siegel, Abby B, and Finn, Richard S

Published
2023
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187. SARS-CoV-2 activates the TLR4/MyD88 pathway in human macrophages: A possible correlation with strong pro-inflammatory responses in severe COVID-19

Author

Sahanic, Sabina, Hilbe, Richard, Dünser, Christina, Tymoszuk, Piotr, Löffler-Ragg, Judith, Rieder, Dietmar, Trajanoski, Zlatko, Krogsdam, Anne, Demetz, Egon, Yurchenko, Maria, Fischer, Christine, Schirmer, Michael, Theurl, Markus, Lener, Daniela, Hirsch, Jakob, Holfeld, Johannes, Gollmann-Tepeköylü, Can, Zinner, Carl P., Tzankov, Alexandar, Zhang, Shen-Ying, Casanova, Jean-Laurent, Posch, Wilfried, Wilflingseder, Doris, Weiss, Guenter, and Tancevski, Ivan

Published
2023
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198. Abstract 18753: Dynamic Phenotypic Transition of Smooth Muscle Cells During Human Ascending Thoracic Aortic Disease Progression: From Compensation in Aortic Aneurysm to Decompensation in Aortic Dissection

Author

Li, Yanming, Zhang, Chen, Vasquez, Hernan, Li, Yang, Chakraborty, Abhijit, Rebello, Kimberly, Xu, Samantha, Zhang, Lin, Sawada, Hisashi, Zhou, Zhen, Chen, Rui, li, yumei, Saeys, Yvan, eisenberg, steven, Lu, Hong S, Cassis, Lisa A, Coselli, Joseph S, Daugherty, Alan, Milewicz, Dianna M, Shen, Ying H, and Lemaire, Scott A

Published
2023
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