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2 results on '"Xu, D. (Dan)"'

1. Childhood asthma outcomes during the COVID-19 pandemic:findings from the PeARL multinational cohort

Author

Papadopoulos, N. G. (Nikolaos G.), Mathioudakis, A. G. (Alexander G.), Custovic, A. (Adnan), Deschildre, A. (Antoine), Phipatanakul, W. (Wanda), Wong, G. (Gary), Xepapadaki, P. (Paraskevi), Abou-Taam, R. (Rola), Agache, I. (Ioana), Castro-Rodriguez, J. A. (Jose A.), Chen, Z. (Zhimin), Cros, P. (Pierrick), Dubus, J.-C. (Jean-Christophe), El-Sayed, Z. A. (Zeinab Awad), El-Owaidy, R. (Rasha), Feleszko, W. (Wojciech), Fierro, V. (Vincenzo), Fiocchi, A. (Alessandro), Garcia-Marcos, L. (Luis), Goh, A. (Anne), Hossny, E. M. (Elham M.), Huerta Villalobos, Y. R. (Yunuen R.), Jartti, T. (Tuomas), Le Roux, P. (Pascal), Levina, J. (Julia), Lopez Garcia, A. I. (Aida Ines), Ramos, A. M. (Angel Mazon), Morais-Almeida, M. (Mario), Murray, C. (Clare), Nagaraju, K. (Karthik), Nagaraju, M. K. (Major K.), Navarrete Rodriguez, E. M. (Elsy Maureen), Namazova-Baranova, L. (Leyla), Nieto Garcia, A. (Antonio), Pozo Beltran, C. F. (Cesar Fireth), Ratchataswan, T. (Thanaporn), Rivero Yeverino, D. (Daniela), Rodriguez Zagal, E. (Erendira), Schweitzer, C. E. (Cyril E.), Tulkki, M. (Marleena), Wasilczuk, K. (Katarzyna), and Xu, D. (Dan)

Subjects
immune system diseases, childhood asthma, coronavirus, COVID-19, respiratory tract diseases
Abstract

Background: The interplay between COVID-19 pandemic and asthma in children is still unclear. We evaluated the impact of COVID-19 pandemic on childhood asthma outcomes. Methods: The PeARL multinational cohort included 1,054 children with asthma and 505 non-asthmatic children aged between 4 and 18 years from 25 pediatric departments, from 15 countries globally. We compared the frequency of acute respiratory and febrile presentations during the first wave of the COVID-19 pandemic between groups and with data available from the previous year. In children with asthma, we also compared current and historical disease control. Results: During the pandemic, children with asthma experienced fewer upper respiratory tract infections, episodes of pyrexia, emergency visits, hospital admissions, asthma attacks, and hospitalizations due to asthma, in comparison with the preceding year. Sixty-six percent of asthmatic children had improved asthma control while in 33% the improvement exceeded the minimal clinically important difference. Pre-bronchodilatation FEV₁ and peak expiratory flow rate were improved during the pandemic. When compared to non-asthmatic controls, children with asthma were not at increased risk of LRTIs, episodes of pyrexia, emergency visits, or hospitalizations during the pandemic. However, an increased risk of URTIs emerged. Conclusions: Childhood asthma outcomes, including control, were improved during the first wave of the COVID-19 pandemic, probably because of reduced exposure to asthma triggers and increased treatment adherence. The decreased frequency of acute episodes does not support the notion that childhood asthma may be a risk factor for COVID-19. Furthermore, the potential for improving childhood asthma outcomes through environmental control becomes apparent.

Published
2021

2. Structured modeling of joint deep feature and prediction refinement for salient object detection

Author

Xu, Y. (Yingyue), Xu, D. (Dan), Hong, X. (Xiaopeng), Ouyang, W. (Wanli), Ji, R. (Rongrong), Xu, M. (Min), Zhao, G. (Guoying), Xu, Y. (Yingyue), Xu, D. (Dan), Hong, X. (Xiaopeng), Ouyang, W. (Wanli), Ji, R. (Rongrong), Xu, M. (Min), and Zhao, G. (Guoying)

Abstract

Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model. We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.

Published
2020

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