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Purpose: Aiming to investigate the combined association of sleep quality and outdoor activity with the risk of asthma and allergic rhinitis in children. Patients and Methods: 16,936 children from kindergartens and primary schools in 13 administrative districts of Shanghai, China were involved in the analyses. The Children's Sleep Habits Questionnaire (CSHQ) and the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire were respectively used to evaluate the sleep quality, allergic rhinitis and asthma. Multivariable logistic regression models were used to analyze the correlation of sleep quality, outdoor activities and their combination with children's asthma and allergic rhinitis. Results: The overall prevalence of asthma and allergic rhinitis in our sampled children were 10.2% and 17.6%, respectively. Poor sleep quality (asthma: aOR, 1.45; 95% CI: 1.23 to 1.73; allergic rhinitis: aOR, 1.16; 95% CI: 1.03 to 1.31) and low outdoor activity (asthma: aOR, 1.30; 95% CI: 1.14 to 1.49, allergic rhinitis: aOR, 1.18; 95% CI: 1.07 to 1.32) were separately associated with higher risk of asthma and allergic rhinitis, and when the two were superimposed, an additive effect (asthma: aOR, 1.76; 95% CI: 1.30 to 2.39; allergic rhinitis: aOR, 1.46; 95% CI: 1.17 to 1.82) was revealed. These associations were independent of sleep duration, but being stronger in sleep sufficiency group. And after stratification by gender, premature birth and family history of allergy, similar associations were validated in general. Conclusion: Poor sleep quality and low levels of outdoor activity, when combined, have a stronger association with asthma and allergic rhinitis than each factor independently, and this relationship is not influenced by sleep duration. Our findings highlight the need for public health interventions that simultaneously address multiple lifestyle factors to reduce the risk of allergic diseases. [ABSTRACT FROM AUTHOR]

The Transformer has become pivotal for the integrated analysis of multi-source remote-sensing (RS) data in Earth observation, particularly in applications such as the fusion classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data. However, Transformers are often employed as effective feature extractors by adopting similar processing blocks for different modalities from multi-source sensors, overlooking differences in imaging principles and data characteristics. Moreover, in the feature extraction process across different sensor data, there is a lack of necessary cross-modal information interaction, leading to insufficient utilization of complementary information between different sensors and resulting in suboptimal fusion outcomes. In this paper, we propose an interactive Transformer and CNN network for the fusion classification of HSI and LiDAR data. Specifically, a heterogeneous three-branch network architecture is designed for HSI and LiDAR data, where Transformers and CNNs encapsulate global contextual spatial and spectral information for HSI and capture geometric elevation patterns for LiDAR data, respectively. Elevation-Spatial Interaction (ESI) and Spectral-Spatial Interaction (SSI) modules are then introduced for multi-stage feature interaction. ESI enables the CNN-Transformer network to focus on essential local elevation details while simultaneously modelling global contextual spatial information. SSI facilitates the Transformer-Transformer network to cyclically intertwine spectral and spatial information for long-range spectral-spatial feature fusion. Finally, the interacted elevation, spatial, and spectral features undergo the Gated Fusion module to achieve hierarchical fusion adaptively, resulting in an elevation-spatial-spectral representation. Experiments conducted on three benchmark HSI-LiDAR datasets demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter‐observer consistency. Methods: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U‐Net, ResUNet and U‐Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter‐observer was also evaluated. Results: The AttSEResUNet with post‐processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). Conclusion: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter‐observer. [ABSTRACT FROM AUTHOR]

Regulatory T (Treg) cells are involved in the antiviral immune response in patients with coronavirus disease 2019 (COVID-19); however, whether Treg cells are involved in the neutralizing antibody (nAb) response remains unclear. Here, we found that individuals who recovered from mild but not severe COVID-19 had significantly greater frequencies of Treg cells and lower frequencies of CXCR3+ circulating T follicular helper (cTfh) cells than healthy controls. Furthermore, the frequencies of Treg and CXCR3+ cTfh cells were negatively and positively correlated with the nAb responses, respectively, and Treg cells was inversely associated with CXCR3+ cTfh cells in individuals who recovered from mild COVID-19 but not in those with severe disease. Mechanistically, Treg cells inhibited memory B-cell differentiation and antibody production by limiting the activation and proliferation of cTfh cells, especially CXCR3+ cTfh cells, and functional molecule expression. This study provides novel insight showing that mild COVID-19 elicits concerted nAb responses, which are shaped by both Treg and Tfh cells. [ABSTRACT FROM AUTHOR]

Insecticide exposure may affect childhood asthma/wheezing, but evidence is scarce in low- and middle-income countries. We conducted a population-based cross-sectional study in Sanya, China. Generalized linear models were adopted to assess the associations of insecticide exposure with childhood asthma/wheezing, reported as odds ratios (ORs) and 95% confidence intervals (CIs). A subgroup analysis was performed to explore the possible effects of sociodemographic and environmental factors on these associations. The median age of the 9754 children was 6.7 years, and 5345 (54.8%) were boys. The prevalences of ever asthma (EA), ever wheezing (EW), and current wheezing (CW) were 7.4%, 5.3%, and 2.9%, respectively. We found a greater prevalence of childhood EA with insecticide exposure (OR = 1.18, 95% CI: 1.00, 1.38). Outdoor insecticide exposure was associated with elevated ORs for EA (1.24, 95% CI: 1.03, 1.50), EW (1.27, 95% CI: 1.03, 1.57), and CW (1.38, 95% CI: 1.04, 1.81). The p for the trend in insecticide exposure frequency was significant for EA (p = 0.001) and CW (p = 0.034). These adverse impacts were pronounced in girls who were exposed to low temperatures. Our findings suggest adverse effects of insecticide use, especially outdoors, on childhood asthma/wheezing. Further studies are warranted to verify this association and develop tailored prevention measures. [ABSTRACT FROM AUTHOR]

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications. [ABSTRACT FROM AUTHOR]