Objectives: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the

Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract: China has a long history of eating staple plant foods which are mainly derived from food grains, especially rice and wheat. Food grain safety has been a worrying challenge on health and nutrition grounds in China, although evidence clearly suggests that expanding agricultural production is linked to reducing undernourishment. The focus of this study is to investigate consumers' anxieties about food grain safety in China. The nature and extent of consumer anxieties about grain safety, the cause of these anxieties, and possible ways to relieve anxiety are empirically analyzed. Data were collected using semi-structured interviews with 142 grain consumers in 29 provinces of China, in both rural and urban areas, during 2016. The results show that consumers are worried about the production and processing safety of food grains and genetically modified cereals and that the causes of anxiety are varied. Anxiety is amplified by social media reports of food scandals, polluted ecological environments, the high incidence of food-related chronic diseases and cancer, concerns about food system governance and lack of knowledge and ability to identify grain quality. Consumers seek to relieve their anxiety by identifying grain quality themselves, choosing foreign grains and paying close attention to reports about unsafe food. These findings have important implications for future programs aimed at improving consumer confidence about grain safety.

With the development of deep learning, fingerprints recognition based on neural networks is a widely used method in indoor localization. In this paper, we build a long short-term memory (LSTM) recurrent neuron network to make regression between fingerprints and locations in order to track the moving target. Simulations are in a BLE5.0 based environment and we use received signal strength indication (RSSI) as the element of fingerprints. Since the preparation of fingerprints is an inevitable and time-consuming process in the testing phase of LSTM, we propose two methods to improve the real-time performance of the localization without changing the structure of LSTM. A decentralized sorting algorithm is proposed to divide the received RSSI signals into multiple parts based on the MAC address of BLE5.0 equipment and use GPUs to sort each part. A complete fingerprint is a combination of these parts. Then, an optimization model aimed at maximum localization accuracy and minimal time used in the testing process of LSTM is proposed by changing the length of fingerprints. Many experiments simulated in different trajectories show that LSTM is more accurate in localization than many other neural networks. Further results demonstrate that using decentralized fingerprints preparation and finding an optimal fingerprint length can keep balance between the localization accuracy and real-time performance.

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The lncRNA HOXA-AS3 has been reported as a potential oncogene in tumors. Nevertheless, the molecular mechanism of HOXA-AS3 in pancreatic cancer (PC) progression remains unknown. We performed quantitative real-time (qRT) PCR assay to detect the expression levels of HOXA-AS3, miR-29c in PC specimens. Then, we transfected sgRNA-HOXA-AS3, miR-29c mimics, miR-29c inhibitors, or vector-CDK6 plasmids into PC cell lines to regulate the expression levels of HOXA-AS3, miR-29c or CDK6. Luciferase reporter assay was performed to identify the correlations among miR-29c, HOXA-AS3 and 3' UTR of CDK6.The ability of cell proliferation was assessed by cell counting and subcutaneous tumor growth assay. HOXA-AS3 level was upregulated in PC, and its knockdown suppressed PC cells proliferation, whereas miR-29c antagonized the regulatory effect of HOXA-AS3 knockdown by directly binding to HOXA-AS3.Moreover, CDK6 was a target of miR-29c and miR-29c exerted anti-proliferation effects through inhibiting CDK6. HOXA-AS3 could accelerate the growth of PC cells partially by regulating the miR-29c/CDK6 axis, which could be used as a potential therapeutic target in CRISPR-mediated PC treatment.

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The changes in utilization of agricultural land have gradually grown into one of the major factors impacting grain output in China. This study explores the various components of agricultural production in China from the land utilization perspective, involving changes in grain production per unit area, multi-cropping index, and adjustment of agricultural structure. Compared with the record values, different research methodologies are used to analyze the potential of above three components. The results indicate that grain production potential of 65.68×10 9 kg was unexploited in 2006, in which 45.8×10 9 kg came from the restructuring in agriculture. So we can infer that the reduction of grain production in China could be primarily attributed to agricultural restructuring in recent years. So the productive potential can be fully restored by increasing agricultural investment, or recovering agricultural structure in favorable conditions. So we can say that China's current condition of food security is good.

Soil arsenic (AS) contamination has attracted a great deal of attention because of its detrimental effects on environments and humans. AS and inorganic AS compounds have been classified as a class of carcinogens by the World Health Organization. In order to select a high-precision method for predicting the soil AS content using hyperspectral techniques, we collected 90 soil samples from six different land use types to obtain the soil AS content by chemical analysis and hyperspectral data based on an indoor hyperspectral experiment. A partial least squares regression (PLSR), a support vector regression (SVR), and a back propagation neural network (BPNN) were used to establish a relationship between the hyperspectral and the soil AS content to predict the soil AS content. In addition, the feasibility and modeling accuracy of different interval spectral resampling, different spectral pretreatment methods, feature bands, and full-band were compared and discussed to explore the best inversion method for estimating soil AS content by hyperspectral. The results show that 10 nm + second derivative (SD) + BPNN is the optimum method to predict soil AS content estimation; R 2 v is 0.846 and residual predictive deviation (RPD) is 2.536. These results can expand the representativeness and practicability of the model to a certain extent and provide a scientific basis and technical reference for soil pollution monitoring. Sustainability 2020, 12, x FOR PEER REVIEW 3 of 19 polluting enterprises in the suburb, such as chemical plants, funeral homes, medical waste treatment 95 plants, and so on. These polluting enterprises are also direct discharge areas of wastewater, waste 96 gas, and waste slag in Weinan City. In recent years, heavy metal pollution caused by city expansion 97 has drastically affects the soil ecological environment and human health. 98 2.2. Acquisition and Processing of Soil Data 99 The results of previous field investigations indicated that the soil AS content in the mining area 100 is mainly affected by human activities and shows a large difference, and the suburban soil AS 101 content is mainly affected by land use types and shows a small difference. Therefore, from 102 November to December, 2018, 90 soil samples were collected in accordance with the uniform grid 103 method with a higher sampling density in the mining area (50 m) and a lower density in the suburb 104 (200 m). The 50 soil samples were collected from the mining area and the rest form the suburb. There105 were six members in the research group with unified training on the sampling method. First, sample 106 points ware selected on the satellite map, and then the six members were divided into two groups to 107 finish the sampling job in each area. A real-time kinematic (RTK) was used to precisely locate every 108 sampling location. Figure 1 shows the position of the sampling points. The sampling was conducted 109 at a depth of 0-20 cm. Stones, plant residues, and other large debris were removed from each fresh 110 sample, which were then mixed tho...