Acute pancreatitis (AP) is one of the most common acute abdominal diseases. The digestive disease committee, Chinese Association of Integrative Medicine, released Integrated traditional Chinese and Western medicine for diagnosis and treatment of acute pancreatitis in 2010.1 Since then, further studies and great progress have been made by domestic and foreign counterparts from the perspective of both Chinese and Western medicine in AP, including the classification, fluid resuscitation, organ function maintenance, surgery intervention, enteral nutrition (EN), and syndrome differentiation and treatment. It is necessary to update the consensus on diagnosis and treatment of integrated Chinese and Western medicine to meet clinical needs. Therefore, the 2012 Revision of the Atlanta Classification Standard (RAC) by the International AP Consensus,2 the 2013 the Management of Acute Pancreatitis by the American College of Gastroenterology,3, 4 the 2014 Guidelines for diagnosis and treatment of the acute pancreatitis guide (2014) by the Chinese medical association branch,5 the 2014 Guidelines on Integrative Medicine for Severe Acute Pancreatitis by the General Surgery Committee of the Chinese Society of Integrated Traditional Chinese and Western Medicine,6 and Traditional Chinese Medicine Consensus on the Diagnosis and Treatment for Acute Pancreatitis by the Spleen and Stomach committee of China Association of Traditional Chinese Medicine7, 8 were taken into account for the revision of the consensus published in 2010. The digestive specialists in Chinese and Western medicine had a discussion on traditional Chinese medicine (TCM) types, syndrome differentiation, the main points of integrative medicine, and so on. According to the Delphi method, Consensus of Integrative Diagnosis and Treatment of Acute Pancreatitis (the 2017 revision) has been passed after three rounds votes. (The voting options are as follows: (a) totally agree; (b) agree, but with some reservations; (c) agree, but with larger reservations; (d) disagree, but reserved; and (e) absolutely disagree. If more than two out of three choose (a), or over 85% choose (a) + (b), the consensus will be passed.) The final validation was carried out by the core expert group in Taizhou, Jiangsu on June 9, 2017. The full text is as follows.

Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

Compared with polymer-based biochips, such as polydimethylsiloxane (PDMS), glass based chips have drawn much attention due to their high transparency, chemical stability, and good biocompatibility. This paper investigated the glass molding process (GMP) for fabricating microstructures of microfluidic chips. The glass material was D-ZK3. Firstly, a mold with protrusion microstructure was prepared and used to fabricate grooves to evaluate the GMP performance in terms of roughness and height. Next, the molds for fabricating three typical microfluidic chips, for example, diffusion mixer chip, flow focusing chip, and cell counting chip, were prepared and used to mold microfluidic chips. The analysis of mold wear was then conducted by the comparison of mold morphology, before and after the GMP, which indicated that the mold was suitable for GMP. Finally, in order to verify the performance of the molded chips by the GMP, a mixed microfluidic chip was chosen to conduct an actual liquid filling experiment. The study indicated that the fabricating microstructure of glass microfluidic chip could be finished in 12 min with good surface quality, thus, providing a promising method for achieving mass production of glass microfluidic chips in the future.

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Precision agriculture (PA) technologies have great potential for promoting sustainable intensification of food production, ensuring targeted delivery of agricultural inputs, and hence food security and environmental protection. The benefits of PA technologies are applicable across a broad range of agronomic, environmental and rural socioeconomic contexts globally. However, farmer and land-manager adoption in low to middle income countries has typically been slower than that observed in more affluent countries. China is currently engaged in the process of agricultural modernisation to ensure food security for its 1.4 billion population and has developed a portfolio of policies designed to improve food security, while simultaneously promoting environmental protection. Particular attention has been paid to the reduction of agricultural inputs such as fertilisers and pesticides. The widespread adoption of PA technologies across the Chinese agricultural landscape is central to the success of these policies. However, socioeconomic and cultural barriers, farm scale, (in particular the prevalence of smaller family farms) and demographic changes in the rural population, (for example, the movement of younger people to the cities) represent barriers to PA adoption across China. A framework for ensuring an acceptable and accelerated PA technology trajectory is proposed which combines systematic understanding of farmer and end-user priorities and preferences for technology design throughout the technology development process, and subsequent end-user requirements for implementation (including demonstration of economic and agronomic benefits, and knowledge transfer). Future research will validate the framework against qualitative and quantitative socioeconomic, cultural and agronomic indicators of successful, or otherwise, PA implementation. The results will provide the evidence upon which to develop further policies regarding how to secure sustainable food production and how best to implement PA in China, as well as practical recommendations for optimising end-user uptake.