The production of tea (Camellia sinensis (L.) Kuntze), the world’s second most consumed beverage, is susceptible to extreme weather events. However, our understanding about the impacts of extreme temperatures and climate change on tea yields remains fairly limited. Here we quantify the historical and predict future fluctuations in tea yield caused by extreme temperatures in China, the largest tea producing country. We found that both heat and cold extremes were associated with significantly reduced tea yields. In the present climate, dominating cold extremes influence more than half of China’s tea production, with a maximum of 56.3% reduced annual production. In the near future, we predict positive net impacts of climate change on tea yield in all study regions at both the 1.5 °C and 2.0 °C global warming levels. Climate warming may diminish the negative impacts of cold extremes to 14%, especially at the current most affected northern tea growing regions (>28° N). However, new areas of yield reduction by intensified heat extremes will emerge, up to 14%–26% yield losses estimated at the Yangtze River (∼30° N) and southern China (<∼25° N) regions. Although the Paris Agreement targets limiting global warming to 1.5 °C, we expect up to 11%–24% heat-induced yield loss in Chongqing, Hunan, Anhui, and Zhejiang. Increasing heat extremes pose the most challenging changes for tea production in China. Therefore, addressing the regional difference of extreme temperature shifts is urgent for adapting tea production to climate change.
The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous detachment (VD), and vitreous hemorrhage (VH) in ophthalmic ultrasound in real time. Methods:We used a deep convolutional neural network to develop a deep learning system to screen multiple abnormal findings in ophthalmic ultrasonography with 3580 images for classification and 941 images for segmentation. Sixty-two videos were used as the test dataset in real time. External data containing 598 images were also used for validation. Another 155 images were collected to compare the performance of the model to experts. In addition, a study was conducted to assess the effect of the model in improving lesions recognition of the trainees. Results:The model achieved 0.94, 0.90, 0.92, 0.94, and 0.91 accuracy in recognizing normal, VD, VH, RD, and other lesions. Compared with the ophthalmologists, the modal achieved a 0.73 accuracy in classifying RD, VD, and VH, which has a better performance than most experts (P < 0.05). In the videos, the model had a 0.81 accuracy. With the model assistant, the accuracy of the trainees improved from 0.84 to 0.94. Conclusions:The model could serve as a screening tool to rapidly identify patients with RD, VD, and VH. In addition, it also has potential to be a good tool to assist training.Translational Relevance: We developed a deep learning model to make the ultrasound work more accurately and efficiently.
ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.
Advances in transport technology have been shown to play a vital role in urban development over millennia. From the engineering and pavement innovations of the Roman road network to the aerospace breakthroughs that enabled jet aircraft, cities have been reshaped by the mobility changes resulting from new designs for moving people and goods. This article explores the urbanization impacts of High-Speed Rail’s introduction in China, which has built the world’s largest High-Speed Rail network in record time. Since High-Speed Rail was launched in Japan in 1964, this technology has worked to reshape intercity travel as a revolutionary transportation alternative. High-Speed Rail has developed steadily across Japan, France, Germany, Italy, Switzerland during the 1970s and 1980s. It expanded to Russia, Spain, the United Kingdom, and Sweden in the 1990s. In the 21st century, China began developing High-Speed Rail on an unprecedented scale, and now has a national network that is longer than the totality of the rest of the world’s High-Speed Rail operations combined. China’s High-Speed Rail operation is exerting a transformative influence on urban form and function. This article synthesizes secondary research results to analyse the impacts of HSR on urbanization. These effects include population redistribution, urban spatial expansion and industrial development. We offer a typol-ogy that considers the urban effects of High-Speed Rail at three spatial levels: the station area, the urban jurisdiction, and the regional agglomeration. When organized through our typology, research findings demonstrate that High-Speed Rail influences urban population size, urban spatial layout and industrial development by changing the acces-sibility of cities. We highlight the processes by which High-Speed Rail ultimately affects the urbanization process for people, land use, and industrial development. However, High-Speed Rail’s impacts on urbanization are not always positive. While leveraging the development opportunity enabled by High-Speed Rail, governments around the world should also avoid potential negative impacts by drawing lessons from the experience of High-Speed Rail’s rapid de-ployment in China.
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