Cancer has always been an enormous threat to human health and survival. Surgery, radiotherapy, and chemotherapy could improve the survival of cancer patients, but most patients with advanced cancer usually have a poor survival or could not afford the high cost of chemotherapy. The emergence of oncolytic viruses provided a new strategy for us to alleviate or even cure malignant tumors. An oncolytic virus can be described as a genetically engineered or naturally existing virus that can selectively replicate in cancer cells and then kill them without damaging the healthy cells. There have been many kinds of oncolytic viruses, such as herpes simplex virus, adenovirus, and Coxsackievirus. Moreover, they have different clinical applications in cancer treatment. This review focused on the clinical application of oncolytic virus and predicted the prospect by analyzing the advantages and disadvantages of oncolytic virotherapy.
After the BOT road operation contract expires, generally, the road will be transferred to the government, and then the government operates the road independently without charging costs from its users. Facing the huge amount of the operation cost, Chinese government tends to continue to charge the road users to guarantee the high quality of road operation. Then, the government will have to decide whether a private firm or government itself would be suitable to operate the road. A model is presented for decision-making through balancing interests between the government and the private firm with an introduction of an intermediate variable, i.e., bidding price. Three scenarios are investigated in the model, including the optimization of government operation, the optimization of private firm operation, and government operation with an improper decision of the intermediate variable. Improper intermediate variable will result in a higher toll charged by the government than by a private firm. The method of avoiding an improper decision is investigated. The result shows that the intermediate variable should be determined to be the government operation cost, based on which the private operator could be chosen, if available. With consideration of the private operator’s profit to be guaranteed by the government, the maximum subsidy should be equal to the minimum private operator’s profit to be disclosed when the contract is signed.
Background Since the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network. Objective The aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic. Methods We first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power. Results Only a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred. Conclusions Increasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.
How to purchase social capital effectively concerns the success or failure of PPP project. In the bidding process, due to the fuzziness of project information in PPP project, it is difficult for experts to quantify the scores of various indicators of social capital. However, traditional project information is relatively clear. If traditional project evaluation method is simply applied to the PPP project procurement, it is likely to be difficult to accept. Therefore, based on triangular fuzzy multi-attributes decision-making theory, this paper takes the situation that decision makers are unable to give accurate measurement due to the fuzziness of project information into account and selects suitable bidding evaluation indicators to establish corresponding evaluation model. By means of TOPSIS, this model could carry out a comprehensive evaluation of social capital and obtain the ranking quickly by which we could obtain a more reasonable bidding evaluation results of PPP project. The case analysis proves practicality and effectiveness of this model 1 .
UNSTRUCTURED Frequent interregional contacts and the high rate of infection spread catalyzed the formation of 2019-nCoV epidemic network. Identifying influential nodes and highlighting the hidden structural properties of the network is central for epidemic prevention and control. In this paper, we first construct the 2019-nCoV epidemic network among provinces in mainland China, after using the degree distribution to reveal some basic characteristics, the k-core decomposition method is employed to provide some static and dynamic evidence of figuring out the influential nodes and hierarchical structure, and then we exhibit the influence power of the above nodes and its evolution. Results yield unexpected information on which are influential nodes and how important they are, as well as their geographic distribution and dynamic modes. Such a better understanding of how epidemic network form and function may help reduce the damaging effects of 2019-nCoV.
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