Impact of high-speed rail on tourism in China.

The "time-space compression" effect of high-speed rail (HSR) has effectively improved the accessibility of the cities and has had a profound impact on tourism. This study explores the impact of HSR on tourism development in cities along HSR lines from the perspective of transfer of transport advantages, then evaluates the impact of HSR on tourism development using panel data of 286 cities in China from 2005 to 2013 by the difference-in-differences (DID) method. The empirical results show that the opening of HSR has significantly increased the tourism revenue and tourist arrivals. These results are still holds after considering endogenous HSR lines placement, and by various robustness checks. Further analysis of nodal effect shows that node cities experienced greater growth in tourism revenue than non-node cities. The analysis of mechanism found that tourism development in node cities relied on hotel industry, while tourism development in non-node cities relied on scenic spots industry. The findings of this study validate the role of HSR as a catalyst for urban tourism development, and reveal the comparative advantages of tourism in different cities under the influence of HSR. This study has important reference value for the development of tourism industry policies in cities along and around HSR lines.

Protective effects of Tibetan kefir in mice with ochratoxin A-induced cecal injury.

Ochratoxin A (OTA) is reported to cause intestinal damage following ingestion of contaminated foods. Tibetan kefir (TK) is a fermented dairy product that possesses antioxidant, anti-inflammatory, and gut microbiota-regulating properties. However, it is not clear if TK can alleviate OTA-associated intestinal toxicity. Here, we investigated whether TK can prevent OTA-induced intestinal barrier disruption in mice. To this end, OTA-fed mice were treated with sterile water (control) or TK by oral gavage once daily, for 3 weeks. The histological changes of ceca, the expression of tight junction proteins and mucins, and the levels of oxidative stress, inflammatory response, and gut microbiota were then assessed. Results revealed that treatment with TK reversed OTA-driven histopathological changes in the ceca, and was associated with increased cecal mucin levels. TK administration to OTA-treated mice significantly elevated the expression levels of tight junction proteins (claudin-1, zonula occludens-1, and occludin). Additionally, TK supplementation suppressed OTA-induced oxidative stress and reduced inflammation via the NF-κB signaling pathway in the ceca. Moreover, TK supplementation depleted harmful bacteria (e.g., Turicibacter and Desulfovibrio), while supporting short-chain fatty acids (SCFAs)-producing bacteria (e.g., Lachnospiraceae, Blautia, and Ruminococcus), which maintained the SCFAs levels. Taken together, our findings indicate that TK may emerge as a viable dietary strategy to prevent intestinal toxicity-based injuries.

Prior Distribution Estimation of Monitored Information in the Intensive Care Unit with the Hidden Markov Model and Decision Tree Methods.

In the intensive care unit, the monitored variables collected from sensors may have different behaviors among patients with different clinical basic information. Giving prior information of the monitored variables based on their specific basic information as soon as the patient is admitted will support the clinicians with better decisions during the surgery. Instead of black box models, the explainable hidden Markov model is proposed, which can estimate the possible distribution parameters of the monitored variables under different clinical basic information. A Student's t-test or correlation test is conducted further to test whether the parameters have a significant relationship with the basic variables. The specific relationship is explored by using a conditional inference tree, which is an explainable model giving deciding rules. Instead of point estimation, interval forecast is chosen as the performance metrics including coverage rate and relative interval width, which provide more reliable results. By applying the methods to an intensive care unit data set with more than 20 thousand patients, the model has good performance with an area under the ROC Curve value of 0.75, which means the hidden states can generally be correctly labelled. The significant test shows that only a few combinations of the basic and monitored variables are not significant under the 0.01 significant level. The tree model based on different quantile intervals provides different coverage and width combination choices. A coverage rate around 0.8 is suggested, which has a relative interval width of 0.77.