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Evaluation of land-use suitability can prevent problems, such as environmental disruption, wastage of resources, and ecological disruption, when unsuitable tourism-based exploration is undertaken in an area. This study summarizes a novel concept and proposes the idea of wellness tourism, which constitutes health preservation, sports and recovery, medical healing, and aged nursing, integrated with Chinese culture. A spatial suitability evaluation system for wellness tourism was developed in a mountain area via the integration of the analytic network process-Delphi. As wellness tourism activities diversified, land suitability was assigned to four kinds of wellness tourism activities, while considering their unique requirements. Comparative analysis and five-degree suitable maps of four kinds of activities revealed that Yining City and its peripheral localities have the potential of functioning as a comprehensive and national wellness tourist destination. The counties of Horgos, Huocheng, Qapqal, Zhaosu, Tekes, Tokkuztara, and Narat should make full use of their strengths, as they have the advantage of catering to different wellness tourism activities. This paper discusses some conceptual aspects of wellness tourism, provides an example for the selection of potential areas for wellness tourism in the mountainous regions of China, and provides baseline information that can support the development of wellness tourism.

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Ecological risk assessment plays an important role in avoiding disasters and reducing losses. Natural world heritage site is the most precious natural assets on earth, yet few studies have assessed ecological risks from the perspective of world heritage conservation and management. A methodology for considering ecological threats and vulnerabilities and focusing on heritage value was introduced and discussed for the Bogda component of the Xinjiang Tianshan Natural World Heritage Site. Three important results are presented. (1) Criteria layers and ecological risk showed obvious spatial heterogeneity. Extremely high-risk and high-risk areas, accounting for 13.60% and 32.56%, respectively, were mainly gathered at Tianchi Lake and Bogda Glacier, whereas the extremely low-risk and low-risk areas, covering 1.33% and 17.51% of the site,were mainly distributed to the north and scattered around in the southwest montane region. (2) The level of risk was positively correlated with the type of risk, and as the level of risk increases, the types of risk increase. Only two risk types were observed in the extremely low-risk areas, whereas six risk types were observed in the high-risk areas and eight risk types were observed in the extremely high-risk areas. (3) From the perspective of risk probability and ecological damage, four risk management categories were proposed, and correlative strategies were proposed to reduce the possibility of ecological risk and to sustain or enhance heritage value.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein–protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision–recall curve of 0.63 and a Cohen’s Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network’s information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

6(A)-(2-Aminoethylamino)-6(A)-deoxy-beta-cyclodextrin (CDen) was synthesized and formed a binary complex with Cu(II) which was shown to be an effective chiral selector for separation of underivatized amino acid enantiomers in capillary electrophoresis (CE). Moreover, the chiral resolution was greatly enhanced by the presence of polyethyl glycol (PEG) and tert-butyl alcohol in the running buffer. The optimum experimental conditions were 20 mmol/L CDen, 20 mmol/L CuSO(4).5H(2)O, 5.0 mg/mL PEG20000 and 1.0% v/v tert-butyl alcohol, pH 5.80. With the proposed method, the four selected aromatic chiral amino acid pairs were separated in less than 15 min.

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