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ObjectiveTo construct and validate a deep learning-based bone age prediction model for children living in both plain and highland regions.MethodsA model named "ethnicity vision gender-bone age net (EVG-BANet)" was trained using three datasets, including the Radiology Society of North America (RSNA) dataset [training set(n=12 611), validation set (n=1425), test set (n=200)], the Radiological Hand Pose Estimation (RHPE) dataset[training set (n=5491), validation set (n=713), test set (n=79)], and a self-established dataset[training set (n=825), test set (n=351)], and it was validated using an external test set. Self-established dataset retrospectively recruited 1176 left-hand DR images of children from Peking Union Medical College Hospital (n=745, all were Han) and Tibet Autonomous Region People's Hospital (n=431, 114 were Han, 317 were Tibetan). External test set included images from People's Hospital of Nagqu (n=256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators.ResultsEVG-BANet exhibited MAD of 0.34 and 0.52 years in RSNA and RHPE test sets, respectively. In the self-established test set, the model achieved MAD of 0.47 years (95% CI: 0.43-0.50) with accuracy within 1 year of 97.72% (95% CI: 95.56-99.01%). For the external test set, MAD was 0.53 years(95% CI: 0.48-0.58), with accuracy within 1 year of 89.45% (95% CI: 85.03-92.93).ConclusionEVG-BANet demonstrated high accuracy in bone age prediction, and therefore can be applied in children living in both plain and highland.

Promoting agricultural green development is crucial for achieving agricultural modernization and ecological civilization. Based on the current status and problems of agricultural green development in China, this study analyzes the internal mechanism by which the digital technology enables the agricultural green development from four dimensions: factor allocation, industrial synergy, information sharing, and information feedback effects. Combined with current application practices of the digital technology in agricultural enterprises, this study explores the internal logic of enabling agricultural green development through digital technology and proposes a dual-driving force mechanism: market and government regulation. In terms of market regulation, the agricultural green development is driven by means of active pollution and carbon reduction at the production end and rising demand for green agricultural products at the consumer side. In terms of government regulation, the agricultural green development is incentivized and guided through measures such as policy formulation and economic incentives, data sharing and financial support, and scientific innovation and network publicity. These measures can help realize the all-round, entire-process greening of agricultural production, ecology, and life, and achieve the sustainable and high-quality development of agriculture.

Cultivated land is the most valuable agricultural resource and the most important factor of production. The protection of cultivated land quality is not only the foundation of national food security but also a strategic issue related to the sustainable development of the economy and society as a whole. This paper uses survey data from Jiangxi Province's "Double Hundred and Thousand" in 2023 and empirically analyzes the impact of digital literacy on farmers' cultivated land quality protection behaviors and its mechanism by using the O-probit model and the mediating effect model. The results show that digital literacy can effectively promote farmers' cultivated land quality protection behaviors. Furthermore, digital literacy can indirectly promote farmers' cultivated land quality protection behaviors through cultivated land protection cognition and digital use ability. Heterogeneity analysis indicates that farmers are more inclined to participate in cultivated quality protection behaviors In the context of better soil fertility conditions and higher digital literacy. Therefore, it is necessary to continuously improve the digital literacy of farmers, improve their awareness of cultivated land protection, and continuously strengthen their digital use ability. [ABSTRACT FROM AUTHOR]

Objectives To investigate the bone age (BA) characteristics of children living in high-altitude regions and determine the impact of altitude on the development of BA. Methods From June 2014 to July 2022, 1318 children with left-hand-wrist radiographs were retrospectively enrolled from 3 different geographical altitudes (Beijing 43.5 m above sea level [asl], Lhasa 3650 m asl, and Nagqu 4500 m asl). The predicted age difference (PAD), defined as the difference between BA and chronologic age (CA), was considered the indicator for delayed or advanced growth. The PAD of children from the 3 regions in total and according to different age groups, genders, and ethnicities were compared. The linear regression model was used to assess the effect of altitude on PAD. Results A total of 1284 children (CA: 12.00 [6.45-15.72] years; male: 837/1284, 65.2%) were included in the study with 407 from Beijing, 491 from Lhasa, and 386 from Nagqu. The PAD for Beijing, Lhasa, and Nagqu were 0.1 [−0.30 to 0.65], −0.40 [−1.20 to 0.27], and −1.42 [−2.32 to −0.51] years, respectively. A linear regression analysis showed that altitude significantly contributed to PAD (compared to Beijing, Lhasa coefficient = −0.57, P  <  .001; Nagqu coefficient = −1.55, P  <  .001). Conclusions High altitude might be an independent contributor to the delayed BA development of children. Advances in knowledge The impact of altitude on BA development was revealed for the first time, highlighting the necessity of considering the altitude of the area when evaluating BA development for children residing in high-altitude regions.ke [ABSTRACT FROM AUTHOR]