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1. Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and Correspondences

Author

Vutukur, Shishir Reddy, Haugaard, Rasmus Laurvig, Huang, Junwen, Busam, Benjamin, and Birdal, Tolga

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Object pose distribution estimation is crucial in robotics for better path planning and handling of symmetric objects. Recent distribution estimation approaches employ contrastive learning-based approaches by maximizing the likelihood of a single pose estimate in the absence of a CAD model. We propose a pose distribution estimation method leveraging symmetry respecting correspondence distributions and shape information obtained using a CAD model. Contrastive learning-based approaches require an exhaustive amount of training images from different viewpoints to learn the distribution properly, which is not possible in realistic scenarios. Instead, we propose a pipeline that can leverage correspondence distributions and shape information from the CAD model, which are later used to learn pose distributions. Besides, having access to pose distribution based on correspondences before learning pose distributions conditioned on images, can help formulate the loss between distributions. The prior knowledge of distribution also helps the network to focus on getting sharper modes instead. With the CAD prior, our approach converges much faster and learns distribution better by focusing on learning sharper distribution near all the valid modes, unlike contrastive approaches, which focus on a single mode at a time. We achieve benchmark results on SYMSOL-I and T-Less datasets., Comment: Accepted to ECCV 2024

Published
2024

2. Alignist: CAD-Informed Orientation Distribution Estimation by Fusing Shape and Correspondences

Author

Vutukur, Shishir Reddy, Haugaard, Rasmus Laurvig, Huang, Junwen, Busam, Benjamin, Birdal, Tolga, Goos, Gerhard, Series 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, Leonardis, Aleš, editor, Ricci, Elisa, editor, Roth, Stefan, editor, Russakovsky, Olga, editor, Sattler, Torsten, editor, and Varol, Gül, editor

Published
2025
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3. MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images

Author

Huang, Junwen, Yu, Hao, Yu, Kuan-Ting, Navab, Nassir, Ilic, Slobodan, and Busam, Benjamin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category, hampering their scalability in real applications when confronted with previously unseen objects. In this paper, we propose MatchU, a Fuse-Describe-Match strategy for 6D pose estimation from RGB-D images. MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects. We rely on learning geometric 3D descriptors that are rotation-invariant by design. By encoding pose-agnostic geometry, the learned descriptors naturally generalize to unseen objects and capture symmetries. To tackle ambiguous associations using 3D geometry only, we fuse additional RGB information into our descriptor. This is achieved through a novel attention-based mechanism that fuses cross-modal information, together with a matching loss that leverages the latent space learned from RGB data to guide the descriptor learning process. Extensive experiments reveal the generalizability of both the RGB-D fusion strategy as well as the descriptor efficacy. Benefiting from the novel designs, MatchU surpasses all existing methods by a significant margin in terms of both accuracy and speed, even without the requirement of expensive re-training or rendering.

Published
2024

5. SSR-2D: Semantic 3D Scene Reconstruction from 2D Images

Author

Huang, Junwen, Artemov, Alexey, Chen, Yujin, Zhi, Shuaifeng, Xu, Kai, and Nießner, Matthias

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics with only 2D labeling which can be either manual or machine-generated. Our key technical innovation is to leverage differentiable rendering of color and semantics to bridge 2D observations and unknown 3D space, using the observed RGB images and 2D semantics as supervision, respectively. We additionally develop a learning pipeline and corresponding method to enable learning from imperfect predicted 2D labels, which could be additionally acquired by synthesizing in an augmented set of virtual training views complementing the original real captures, enabling more efficient self-supervision loop for semantics. As a result, our end-to-end trainable solution jointly addresses geometry completion, colorization, and semantic mapping from limited RGB-D images, without relying on any 3D ground-truth information. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations in predicting both geometry and semantics. To our knowledge, our method is also the first 2D-driven method addressing completion and semantic segmentation of real-world 3D scans simultaneously.

Published
2023

10. Temperature effects on the hydraulic properties of unsaturated rooted soils

Author

Wang, Hao, Chen, Rui, Leung, Anthony Kwan, and Huang, Junwen

Subjects
Hydrogeology -- Analysis, Soil moisture -- Analysis, Soil mechanics -- Analysis, Soils -- Thermal properties, Sanitary landfills -- Environmental aspects, Soil temperature -- Analysis, Earth sciences
Abstract

Soil temperature of vegetated landfill earthen covers varies with time during their service, due to heat released by the degradation of municipal solid waste. In this study, an experimental campaign to capture soil water-retention curve (SWRC) and hydraulic conductivity function (HCF) of unsaturated rooted soils was conducted at different temperatures (25, 45, and 60 [degrees]C). Simplified evaporation tests were conducted by subjecting soil specimens to a prolonged drying period at maintained temperature. Based on the responses of matric suction and water content, SWRC and HCF were determined by the instantaneous profile method. It was found that increasing temperature reduced water-retention capacity, due to root shrinkage, thermal expansion of liquid phase, and reduction in forces of capillarity and adsorption. Soil hydraulic conductivity was enhanced by the increase in soil temperature due to reduction of water viscosity and potential preferential flow at root channels induced by root shrinkage. Temperature effects on SWRC and HCF of rooted soils are suction-dependent. A new nonisothermal SWRC model was developed to capture the dual effects of roots and temperature. By using the same set of calibrated parameters, the model demonstrates its ability to predict SWRCs reasonably well within the range of temperature investigated. Key words: unsaturated soils, vegetation, temperature effects, soil water retention curve, hydraulic conductivity, 1. Introduction Landfilling is one of the most prevailing ways of waste treatment nowadays because of high feasibility, financially affordable cost, and relatively low environment risk. After waste layer being [...]

Published
2023
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11. StablePose: Learning 6D Object Poses from Geometrically Stable Patches

Author

Shi, Yifei, Huang, Junwen, Xu, Xin, Zhang, Yifan, and Xu, Kai

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A subnetwork is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation., Comment: CVPR 2021, 10 pages

Published
2021

12. SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images

Author

Shi, Yifei, Huang, Junwen, Zhang, Hongjia, Xu, Xin, Rusinkiewicz, Szymon, and Xu, Kai

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

We study the problem of symmetry detection of 3D shapes from single-view RGB-D images, where severely missing data renders geometric detection approach infeasible. We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects present in the input RGB-D image. Directly training a deep model for symmetry prediction, however, can quickly run into the issue of overfitting. We adopt a multi-task learning approach. Aside from symmetry axis prediction, our network is also trained to predict symmetry correspondences. In particular, given the 3D points present in the RGB-D image, our network outputs for each 3D point its symmetric counterpart corresponding to a specific predicted symmetry. In addition, our network is able to detect for a given shape multiple symmetries of different types. We also contribute a benchmark of 3D symmetry detection based on single-view RGB-D images. Extensive evaluation on the benchmark demonstrates the strong generalization ability of our method, in terms of high accuracy of both symmetry axis prediction and counterpart estimation. In particular, our method is robust in handling unseen object instances with large variation in shape, multi-symmetry composition, as well as novel object categories., Comment: 15 pages

Published
2020

19. High Maneuvering Control of Autonomous Vehicles Using Dual Heuristic Programming

Author

Jiang, Yan, Xu, Xin, Zhang, Xinglong, Huang, Junwen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wu, Meiping, editor, Niu, Yifeng, editor, Gu, Mancang, editor, and Cheng, Jin, editor

Published
2022
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20. Hydraulic Properties of Unsaturated Engineering Soils Vegetated with Vetiver Grass in Green Infrastructures

Author

Wang, Hao, Huang, Junwen, Chen, Rui, Li, Zhaofeng, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Zhu, Hong-Hu, editor, Garg, Ankit, editor, Zhussupbekov, Askar, editor, and Su, Li-Jun, editor

Published
2022
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21. Household income and maternal education in early childhood and risk of overweight and obesity in late childhood: Findings from seven birth cohort studies in six high-income countries

Author

White, Pär Andersson, Awad, Yara Abu, Gauvin, Lise, Spencer, Nicholas James, McGrath, Jennifer J., Clifford, Susan A., Nikiema, Béatrice, Yang-Huang, Junwen, Goldhaber-Fiebert, Jeremy D., Markham, Wolfgang, Mensah, Fiona K., van Grieken, Amy, Raat, Hein, Jaddoe, V. W. V., Ludvigsson, Johnny, and Faresjö, Tomas

Published
2022
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22. Evaluation design of the patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard (InAdvance): a randomised controlled trial

Author

Yang-Huang, Junwen, Doñate-Martínez, Ascensión, Garcés, Jorge, Campos, Maria Soledad Gimenez, Romero, Raquel Valcarcel, López, Maria-Eugenia Gas, Fernandes, Adriano, Camacho, Mariana, Gama, Ana, Reppou, Sofia, Bamidis, Panagiotis D., Linklater, Gordon, Hines, Frances, Eze, Jude, and Raat, Hein

Published
2022
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24. Changes in Family Poverty Status and Child Health

Author

Yang-Huang, Junwen, primary, van Grieken, Amy, additional, You, Yueyue, additional, Jaddoe, Vincent W.V., additional, Steegers, Eric A., additional, Duijts, Liesbeth, additional, Boelens, Mirte, additional, Jansen, Wilma, additional, and Raat, Hein, additional

Published
2022
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25. SSR-2D: Semantic 3D Scene Reconstruction From 2D Images

Author

Huang, Junwen, Artemov, Alexey, Chen, Yujin, Zhi, Shuaifeng, Xu, Kai, and Niesner, Matthias

Abstract

Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics with only 2D labeling which can be either manual or machine-generated. Our key technical innovation is to leverage differentiable rendering of color and semantics to bridge 2D observations and unknown 3D space, using the observed RGB images and 2D semantics as supervision, respectively. We additionally develop a learning pipeline and corresponding method to enable learning from imperfect predicted 2D labels, which could be additionally acquired by synthesizing in an augmented set of virtual training views complementing the original real captures, enabling more efficient self-supervision loop for semantics. As a result, our end-to-end trainable solution jointly addresses geometry completion, colorization, and semantic mapping from limited RGB-D images, without relying on any 3D ground-truth information. Our method achieves state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations in predicting both geometry and semantics. To our knowledge, our method is also the first 2D-driven method addressing completion and semantic segmentation of real-world 3D scans simultaneously.

Published
2024
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27. Phase Transformation on Two-Dimensional MoTe 2 Films for Surface-Enhanced Raman Spectroscopy.

Author

Zhao, Caiye and Huang, Junwen

Subjects
*SERS spectroscopy, *CHEMICAL vapor deposition, *SUBSTRATES (Materials science), *TRANSITION metals, *DETECTION limit
Abstract

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have recently become attractive candidate substrates for surface-enhanced Raman spectroscopy (SERS) owing to their atomically flat surfaces and adjustable electronic properties. Herein, large-scale 2D 1T′- and 2H-MoTe2 films were prepared using a chemical vapor deposition method. We found that phase structure plays an important role in the enhancement of the SERS performances of MoTe2 films. 1T′-MoTe2 films showed a strong SERS effect with a detection limit of 1 × 10−9 M for the R6G molecule, which is one order of magnitude lower than that of 2H-MoTe2 films. We demonstrated that the SERS sensitivity of MoTe2 films is derived from the efficient photoinduced charge transfer process between MoTe2 and adsorbed molecules. Moreover, a prohibited fish drug could be detected by using 1T′-MoTe2 films as SERS substrates. Our study paves the way to the development and application of high-performance SERS substrates based on TMD phase engineering. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Biomimetic Modular Honeycomb with Enhanced Crushing Strength and Flexible Customizability.

Author

Shen, Lumin, Wu, Yuanzhi, Ye, Tuo, Gao, Tianyu, Zheng, Shanmei, Long, Zhihao, Ren, Xi, Zhang, Huangyou, Huang, Junwen, and Liu, Kai

Subjects
HONEYCOMB structures, SPECIFIC gravity, LIMIT theorems, MODULAR forms, ENERGY dissipation
Abstract

The integration of biomimetic principles into the sophisticated design of honeycomb structures has gained significant traction. Inspired by the natural reinforcement mechanisms observed in tree stems, this research introduces localized thickening to the conventional honeycombs, leading to the development of variable-density honeycomb blocks. These blocks are strategically configured to form modular honeycombs. Initially, the methodology for calculating the relative density of the new design is meticulously detailed. Following this, a numerical model based on the plastic limit theorem, verified experimentally, is used to investigate the in-plane deformation models of modular honeycomb under the low- and high-velocity impact and to establish a theoretical framework for compressive strength. The results confirm that the theoretical predictions for crushing strength in the modular honeycomb align closely with numerical findings across both low- and high-velocity impacts. Further investigation into densification strain, energy absorption, and gradient strategy is conducted using both simulation and experimental approaches. The outcomes indicate that the innovative design outperforms conventional honeycombs by significantly enhancing the crushing strength under low-velocity impacts through the judicious arrangement of honeycomb blocks. Additionally, with a negligible difference in densification strains, the modular honeycomb demonstrates superior energy dissipation capabilities compared to its conventional counterparts. At a strain of 0.85, the modular honeycomb's energy absorption capacity improves by 36.68% at 1 m/s and 25.47% at 10 m/s compared to the conventional honeycomb. By meticulously engineering the arrangement of sub-honeycombs, it is possible to develop a modular honeycomb that exhibits a multi-plateau stress response under uniaxial and biaxial compression. These advancements are particularly beneficial to the development of auto crash absorption systems, high-end product transportation packaging, and personalized protective gear. [ABSTRACT FROM AUTHOR]

Published
2024
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29. A self‐learning human‐machine cooperative control method based on driver intention recognition.

Author

Jiang, Yan, Ding, Yuyan, Zhang, Xinglong, Xu, Xin, and Huang, Junwen

Subjects
REINFORCEMENT learning, LANE changing, ONLINE education, INTENTION, ALGORITHMS
Abstract

Human‐machine cooperative control has become an important area of intelligent driving, where driver intention recognition and dynamic control authority allocation are key factors for improving the performance of cooperative decision‐making and control. In this paper, an online learning method is proposed for human‐machine cooperative control, which introduces a priority control parameter in the reward function to achieve optimal allocation of control authority under different driver intentions and driving safety conditions. Firstly, a two‐layer LSTM‐based sequence prediction algorithm is proposed to recognise the driver's lane change (LC) intention for human‐machine cooperative steering control. Secondly, an online reinforcement learning method is developed for optimising the steering authority to reduce driver workload and improve driving safety. The driver‐in‐the‐loop simulation results show that our method can accurately predict the driver's LC intention in cooperative driving and effectively compensate for the driver's non‐optimal driving actions. The experimental results on a real intelligent vehicle further demonstrate the online optimisation capability of the proposed RL‐based control authority allocation algorithm and its effectiveness in improving driving safety. [ABSTRACT FROM AUTHOR]

Published
2024
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30. SIZ1-mediated SUMOylation of CPSF100 promotes plant thermomorphogenesis by controlling alternative polyadenylation.

Author

Yu, Zhibo, Wang, Jun, Zhang, Cheng, Zhan, Qiuna, Shi, Leqian, Song, Bing, Han, Danlu, Jiang, Jieming, Huang, Junwen, Ou, Xiaolin, Zhang, Zhonghui, Lai, Jianbin, Li, Qingshun Quinn, and Yang, Chengwei

Abstract

Under warm temperatures, plants adjust their morphologies for environmental adaption via precise gene expression regulation. However, the function and regulation of alternative polyadenylation (APA), an important fine-tuning of gene expression, remains unknown in plant thermomorphogenesis. In this study, we found that SUMOylation, a critical post-translational modification, is induced by a long-term treatment at warm temperatures via a SUMO ligase SIZ1 in Arabidopsis. Disruption of SIZ1 altered the global usage of polyadenylation signals and affected the APA dynamic of thermomorphogenesis-related genes. CPSF100, a key subunit of the CPSF complex for polyadenylation regulation, is SUMOylated by SIZ1. Importantly, we demonstrated that SUMOylation is essential for the function of CPSF100 in genome-wide polyadenylation site choice during thermomorphogenesis. Further analyses revealed that the SUMO conjugation on CPSF100 attenuates its interaction with two isoforms of its partner CPSF30, increasing the nuclear accumulation of CPSF100 for polyadenylation regulation. In summary, our study uncovers a regulatory mechanism of APA via SIZ1-mediated SUMOylation in plant thermomorphogenesis. In response to warm temperatures, the Arabidopsis SUMO ligase SIZ1 upregulates the SUMOylation of CPSF100 to suppress its association with two isoforms of CPSF30, leading to an increase in the nuclear accumulation of CPSF100. Subsequently, CPSF100 and CPSF30 collaborate to modulate the usage of poly(A) signals, thereby controlling the alternative polyadenylation of genes associated with warm temperature responses. [ABSTRACT FROM AUTHOR]

Published
2024
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34. Longitudinal Hypertension Outcomes at Four Student-Run Free Clinic Sites.

Author

Smith, Sunny D, Rojas, Steven M, Huang, Junwen, Yang, Kuangyi, and Vaida, Florin

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Health Disparities, Hypertension, Cardiovascular, Adolescent, Adult, Ambulatory Care Facilities, Blood Pressure, Female, Ill-Housed Persons, Humans, Longitudinal Studies, Male, Middle Aged, Patient Outcome Assessment, Retrospective Studies, Student Run Clinic, Students, Medical, Medical and Health Sciences, Education, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

Background and objectivesStudent-Run Free Clinics (SRFCs) play a significant role in medical education yet there is minimal information about patient outcomes over time in this setting. The purpose of this study was to assess longitudinal outcomes of hypertension management during routine care at four SRFC sites, while comparing control rates with national standards, and examining for characteristics independently associated with uncontrolled hypertension.MethodsThe authors conducted a retrospective medical record review of visits from January 2004 through April 2016 with an initial visit for hypertension and a follow-up visit 9 to 15 months later. Hypertension control was analyzed using descriptive statistics, paired t tests, logistic regression, and a longitudinal linear mixed-effects model.ResultsThis study included 496 patients and 4,798 visits. The mean age was 50.9 (SD 10.8) years old, 71.2% (346) were Latinos, 63.3% (314) were Spanish speaking, and 15.5% (72) were homeless. Mean blood pressure was reduced from 141.6 (SD 21.8)/85.1 (SD 13.2) to 132.1 (SD 17.3)/79.4 (SD10.8), a decrease of 9.5 (CI 7.4, 11.5)/5.7 (CI 4.4, 7.0) mmHg. Blood pressure was significantly reduced within the first month of treatment, and this reduction was sustained throughout the follow-up period. Using the National Health and Nutrition Examination Survey (NHANES) definition (

Published
2017

36. A Low Eosinophil to Platelet Ratio as a Worse Prognostic Index for Emergency Department Attendance in Acute Exacerbation of COPD

Author

Hu,Dapeng, Huang,Junwen, Zhao,Wenqu, Xu,Maosheng, Ma,Yanyan, Gong,Zhaoqian, Zhang,Qian, Zhao,Haijin, Hu,Dapeng, Huang,Junwen, Zhao,Wenqu, Xu,Maosheng, Ma,Yanyan, Gong,Zhaoqian, Zhang,Qian, and Zhao,Haijin

Abstract

Dapeng Hu,1,2 Junwen Huang,1 Wenqu Zhao,1 Maosheng Xu,1 Yanyan Ma,1 Zhaoqian Gong,1 Qian Zhang,1 Haijin Zhao1 1Chronic Airways Diseases Laboratory, Department of Respiratory and Critical Care Medicine, NanFang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 2Department of Emergency Medicine, Dalian Municipal Friendship Hospital, Dalian, Liaoning, 116001, People’s Republic of ChinaCorrespondence: Haijin Zhao, Chronic Airway Diseases Laboratory, Department of Respiratory and Critical Care Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China, Tel/Fax +86-2062787112, Email zhjin@smu.edu.cnPurpose: Identifying prognosis for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is challenging. Eosinophils and platelet are involved in the development of COPD, which may predict adverse events. The objective of this study was to determine the effect of the eosinophil to platelet ratio (EPR) in predicting adverse events in patients with AECOPD who visited the emergency department.Patients and Methods: The records of patients with AECOPD treated at Dalian Municipal Friendship Hospital from January 2018 to December 2020 were retrospectively reviewed. The relationship between the clinical characteristics and EPR, as cut-off value of 0.755, was evaluated.Results: A total of 508 patients with an AECOPD (316 male, 192 female) were included. An optimal AUC cutoff of 0.755 for the EPR segregated the patients into 2 groups with significantly different mortality (25.3% vs 5.5%, P < 0.001). The same mortality risk with lower EPR was observed among the patients with emergency room attendance (35.6% vs 11.1%, P < 0.001). A model including EPR < 0.755, exacerbation history, PaO2 < 60mmHg, PaCO2 > 50 mm Hg, hypoalbuminemia and age ≥ 80 was developed to predict death risk and showed good performance.Conclusion: During severe COPD exacerbation

Published
2024

37. Early family socioeconomic status and asthma-related outcomes in school-aged children: Results from seven birth cohort studies

Author

Yang-Huang, Junwen, McGrath, Jennifer J., Gauvin, Lise, Nikiema, Beatrice, Spencer, Nicholas James, Abu Awad, Yara, Clifford, Susan, Markham, Wolfgang, Mensah, Fiona, Andersson White, Pär, Ludvigsson, Johnny, Faresjö, Tomas, Duijts, Liesbeth, van Grieken, Amy, Raat, Hein, Yang-Huang, Junwen, McGrath, Jennifer J., Gauvin, Lise, Nikiema, Beatrice, Spencer, Nicholas James, Abu Awad, Yara, Clifford, Susan, Markham, Wolfgang, Mensah, Fiona, Andersson White, Pär, Ludvigsson, Johnny, Faresjö, Tomas, Duijts, Liesbeth, van Grieken, Amy, and Raat, Hein

Abstract

Objective To examine the associations between maternal education and household income during early childhood with asthma-related outcomes in children aged 9-12 years in the UK, the Netherlands, Sweden, Australia, the USA and Canada.Methods Data on 31 210 children were obtained from 7 prospective birth cohort studies across six countries. Asthma-related outcomes included ever asthma, wheezing/asthma attacks and medication control for asthma. Relative social inequalities were estimated using pooled risk ratios (RRs) adjusted for potential confounders (child age, sex, mother ethnic background and maternal age) for maternal education and household income. The Slope Index of Inequality (SII) was calculated for each cohort to evaluate absolute social inequalities.Results Ever asthma prevalence ranged from 8.3% (Netherlands) to 29.1% (Australia). Wheezing/asthma attacks prevalence ranged from 3.9% (Quebec) to 16.8% (USA). Pooled RRs for low (vs high) maternal education and low (vs high) household income were: ever asthma (education 1.24, 95% CI 1.13 to 1.37; income 1.28, 95% CI 1.15 to 1.43), wheezing/asthma attacks (education 1.14, 95% CI 0.97 to 1.35; income 1.22, 95% CI 1.03 to 1.44) and asthma with medication control (education 1.16, 95% CI 0.97 to 1.40; income 1.25, 95% CI 1.01 to 1.55). SIIs supported the lower risk for children with more highly educated mothers and those from higher-income households in most cohorts, with few exceptions.Conclusions Social inequalities by household income on the risk of ever asthma, wheezing/asthma attacks, and medication control for asthma were evident; the associations were attenuated for maternal education. These findings support the need for prevention policies to address the relatively high risks of respiratory morbidity in children in families with low socioeconomic status., Funding Agencies|Canadian Institutes of Health Research [OCO-79897, MOP-89886, MSH-95353, ROG-110537]

Published
2024
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43. Neurobiological, Psychosocial, and Behavioral Mechanisms Mediating Associations between Physical Activity and Psychiatric Symptoms in Youth in the Netherlands

Author

Rodriguez-Ayllon, Maria, Neumann, Alexander, Hofman, Amy, Voortman, Trudy, Lubans, David R., Yang-Huang, Junwen, Jansen, Pauline W., Raat, Hein, Vernooij, Meike W., Muetzel, Ryan L., Child and Adolescent Psychiatry / Psychology, Epidemiology, Clinical Psychology, and Radiology & Nuclear Medicine

Subjects
Psychiatry and Mental health, SDG 3 - Good Health and Well-being, Nutrition and Disease, Voeding en Ziekte, Life Science, Human medicine
Abstract

ImportanceUnderstanding the mechanisms by which physical activity is associated with a lower risk of psychiatric symptoms may stimulate the identification of cost-efficient strategies for preventing and treating mental illness at early life stages.ObjectiveTo examine neurobiological, psychosocial, and behavioral mechanisms that mediate associations of physical activity with psychiatric symptoms in youth by testing an integrated model.Design, setting, and participantsGeneration R is an ongoing prospective population-based cohort study collecting data from fetal life until young adulthood in a multiethnic urban population in the Netherlands. Pregnant women living in Rotterdam with an expected delivery date between April 2002 and January 2006 were eligible for participation along with their children born during this time. Data were collected at a single research center in the Erasmus Medical Center Sophia Children’s Hospital. For the current study, data were analyzed from 4216 children with complete data on both exposure and outcome at ages 6, 10, and 13 years. Data were analyzed from January 2021 to November 2022.ExposuresPhysical activity was ascertained at age 6 years (visit 1) via parent report and included weekly frequency and duration of walking or cycling to or from school, physical education at school, outdoor play, swimming, and sports participation.Main Outcomes and MeasuresPsychiatric symptoms (internalizing and externalizing symptoms) were assessed at age 6 years (visit 1) and at age 13 years (visit 3) using the Child Behavior Checklist. Several mechanisms were explored as mediators, measured at age 10 years (visit 2). Neurobiological mechanisms included total brain volume, white matter microstructure, and resting-state connectivity assessed using a 3-T magnetic resonance imaging scanner. Psychosocial mechanisms included self-esteem, body image, and friendship. Behavioral mechanisms included sleep quality, diet quality, and recreational screen time. Pearson correlations between physical activity measures and psychiatric symptoms were calculated, with false discovery rate correction applied to account for the number of tests performed. Mediation analyses were performed when a correlation (defined as false discovery rate P ResultsAmong the 4216 children included in this study, the mean (SD) age was 6.0 (0.4) years at visit 1, and 2115 participants (50.2%) were girls. More sports participation was associated with fewer internalizing symptoms (β for direct effect, −0.025; SE, 0.078; P = .03) but not externalizing symptoms. Self-esteem mediated the association between sports participation and internalizing symptoms (β for indirect effect, −0.009; SE, 0.018; P = .002). No evidence was found for associations between any other neurobiological, psychosocial, or behavioral variables. No association was found between other types of physical activity and psychiatric symptoms at these ages.Conclusions and RelevanceThe integrated model presented in this cohort study evaluated potential mechanisms mediating associations between physical activity and psychiatric symptoms in youth. Self-esteem mediated an association between sports participation in childhood and internalizing symptoms in adolescence; other significant mediations were not observed. Further studies might explore whether larger effects are present in certain subgroups (eg, children at high risk of developing psychiatric symptoms), different ages, or structured sport-based physical activity interventions.

Published
2023
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45. The smallest Schwarzite carbon with only heptagonal carbon rings.

Author

Hu, Meng, Huang, Junwen, Shi, Lu, Hua, Jing, Liu, Lingyu, He, Julong, and Ding, Jianning

Abstract

Carbon materials with full sp2-hybridized buckling is a major challenge pervading fundamental nanoscience and nanotechnology research. Carbon atoms that are sp2 hybridized prefer to form hexagonal rings, such as in carbon nanotubes and graphene, which are low-dimensional materials. The incorporation of heptagonal, octagonal, and/or larger rings into a hexagonal sp2 carbon meshwork has been identified as a strategy for assembling three-dimensional (3D) sp2 carbon crystals, and one of the typical representatives are Schwarzite carbons, which possess a negative surface Gaussian curvature as well as unique physical properties. Herein, a 3D Schwarzite carbon consisting of only sp2-buckled heptagonal carbon rings, which is referred to as Hepta-carbon, is proposed based on first-principles calculations. Hepta-carbon is mechanically and thermodynamically stable, and energetically more favourable than experimental graphdiyne, fullerene C20 and most Schwarzite carbons under ambient conditions. Molecular dynamics simulations indicate that Hepta-carbon exhibits high-temperature thermostability up to 1500 K. Band structure and mechanical property simulations indicate that Hepta-carbon is a semi-metallic material with electron conduction and exhibits impressive mechanical properties such as high strength with quasi-isotropy, high incompressibility similar to diamonds, elastic deformation behaviour under uniaxial stress, and high ductility. Hepta-carbon presents a porous network with a low mass density of 1.84 g cm−3 and connected channels with diameters of 3.3–6.1 Å. Theoretical simulations of gas adsorption energy demonstrate that Hepta-carbon can effectively adsorb and stabilize greenhouse gases, including N2O, CO2, CH4, and SF6. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Corrigendum to: “Mitoquinone ameliorated airway inflammation by stabilizing β-catenin destruction complex in a steroid-insensitive asthma model” [Biomed. Pharmacother. vol. 162 (2023) 114680]

Author

Huang, Junwen, primary, Chen, Ying, additional, Peng, Xianru, additional, Gong, Zhaoqian, additional, Wang, Yanhong, additional, Li, Yuemao, additional, Xu, Maosheng, additional, Ma, Yanyan, additional, Yu, Changhui, additional, Cai, Shaoxi, additional, Zhao, Wenqu, additional, and Zhao, Haijin, additional

Published
2023
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49. Factors of heavy social media use among 13-year-old adolescents on weekdays and weekends

Author

You, Yue Yue, Yang-Huang, Junwen, Raat, Hein, van Grieken, Amy, You, Yue Yue, Yang-Huang, Junwen, Raat, Hein, and van Grieken, Amy

Abstract

Background: Few studies have investigated which factors were related to adolescents’ social media use. This study aimed to evaluate which factors were associated with heavy social media use on weekdays and weekends among 13-year-old adolescents. Methods: We analyzed data from 3727 children from the Generation R Study, a population-based cohort study in the Netherlands. Associations of demographic factors (child age, sex, ethnic background, and family situation), socioeconomic position (parental educational level, parental employment status, and net household income), screen-based behaviors (computer playing and TV viewing), and the home environment (communication, supervision, and restriction) with adolescents’ heavy social media use (≥ 2 hours/day) were assessed separately on weekdays and weekends. Multivariate logistic regression analysis was applied. Results: The prevalence of heavy social media use was 37.7% on a weekday and 59.6% on a weekend day. Being a girl, living in a one-parent family, and more time spent playing on the computer were associated with heavy social media use on weekdays and weekends (all P < 0.05). Low socioeconomic position adolescents (low parental educational level and low household income) were more likely to show heavy social media use only on weekends (all P < 0.05). Children whose social media use was restricted by parents on weekdays or children whose social media use was supervised by parents on weekends had lower odds of heavy social media use (all P < 0.05). Conclusions: Being a girl, living in a one-parent family, or having a longer computer playing time were associated with heavy social media use on weekdays and weekends. More studies are needed to understand the factors associated with heavy social media use and the impact of heavy social media use on child health.

Published
2023

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