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Background: The outbreak of Coronavirus Disease 2019(COVID-19) caused psychological stress in Chinese adults population. But we are unaware of whether the pandemic causes psychological stress on children. Methods: We used the Children's Impact of Event Scale questionnaire (CRIES-13) to investigate the degree of Post-traumatic Stress (PTSD) symptoms caused by the pandemic in students selected from schools in Sichuan, Jiangsu, Henan, Yunnan, and Chongqing provinces of China. Results: A total of 7769 students(3692 male and 4077 female), aged 8–18 years, were enrolled in the study, comprising 1214 in primary schools, 2799 in junior high schools and 3756 in senior high schools. A total of 1639 students (21.1%) had severe psychological stress reactions. A large proportion of senior high school students (23.3%) experienced severe psychological stress, and they had the highest median total CRIES-13 score. Female students were more likely to experience severe psychological stress and had higher median CRIES-13 total scores than males. Conclusion: COVID-19 has placed psychological stresses on primary and secondary school students in China. These stresses are more likely to reach severe levels among female students and senior high school students. [ABSTRACT FROM AUTHOR]

• A method for locating wildlife crossings on roads in China was explored. • The Linkage Mapper model was used to simulate potential migration corridors. • Three scenarios of road barrier sections were defined based on the gazelle home range. • Preliminary monitoring shows a 62.5% usage of crossing structures by gazelle. Despite limited research on the topic, understanding the barrier effect of roads on wildlife habitats in China is crucial for the placement of wildlife crossing structures. First, a habitat suitability evaluation model for goitered gazelle (Gazella subgutturosa) was established. Then, using the Linkage Mapper model, potential migration corridors were simulated along the expressway. The migration corridors were then overlaid with the crossing structures to evaluate the barrier effects of the expressway. A total of 12 barrier segments were identified by the results, eight of which were found to have crossing structures, and preliminary monitoring indicated that five of the bridges have been used by goitered gazelle (62.5%). Based on the results, recommendations for the mitigation of the barrier effect of expressways on goitered gazelle are provided. [ABSTRACT FROM AUTHOR]

[Display omitted] • The validity for HSI system in detecting Camellia oleifera mildewing was proved. • First two PC score images were used for exploratory visual detection. • PLS-DA, KNN, and CART were individually used to develop classification models. • Seven mildewing-related wavelengths selected by CARS were the most effective. • Pixel-wise classification maps were successfully built to visualize the mildewing. Camellia oleifera fruit is a kind of popular woody oil crops in China, which is susceptible to natural mildew damage during storage. This problem significantly depreciates its value and poses a potential threat to human health. A hyperspectral imaging (HSI) system covered visible and near-infrared (Vis-NIR, 400–1000 nm) range was employed to identify Camellia oleifera fruit with three different natural mildew degrees (slight, moderate, and severe). Spectra were primarily extracted from representative regions of interest (ROIs), and principal component analysis (PCA) of extracted spectra revealed that PC 1 and PC 2 were effective for the identification. Hyperspectral images were then processed using PC transformation for exploratory visual detection by displaying PC 1 and PC 2 score images. Three modeling methods including partial least squares-discriminant analysis (PLS-DA), k-nearest neighbor (KNN), and classification and regression tree (CART) were subsequently evaluated in terms of their capacity to establish the natural mildew degree estimation model developed by full spectra. Raw spectra without any preprocessing constructed the optimal PLS-DA model with the highest 90.8% correct classification rate (CCR) of success in external prediction set. After that, key wavelengths selected by competitive adaptive reweighted sampling (CARS) algorithm built an optimal simplified model, achieving the best performance with the highest CCR of 83.3%, AUC value of 0.89, and Kappa coefficient of 0.75 in prediction set. Therefore, CARS-PLS-DA model was finally employed to visually classify the three different natural mildew degrees. As a result, the general natural mildew degrees of Camellia oleifera samples were readily discernible in pixel-wise manner by generating classification maps. The overall results illustrated that HSI offered an alternative way in detecting and visualizing natural mildew degrees of Camellia oleifera fruit. [ABSTRACT FROM AUTHOR]