Prevalence and Risk Factors of White Matter Lesions in Tibetan Patients Without Acute Stroke.

Background and Purpose- Studies on the prevalence and risk factors of white matter lesions (WMLs) in Tibetans living at high altitudes are scarce. We conducted this study to determine the prevalence and risks of WMLs in Tibetan patients without or with nonacute stroke. Methods- We undertook a retrospective analysis of medical records of patients treated at the People's Hospital of Tibetan Autonomous Region and identified a total of 301 Tibetan patients without acute stroke. WML severity was graded by the Fazekas Scale. We assessed the overall and age-specific prevalence of WMLs and analyzed associations between WMLs and related factors with univariate and multivariate methods. Results- Of the 301 patients, 87 (28.9%) had peripheral vertigo, 83 (27.3%) had primary headache, 52 (17.3%) had a history of stroke, 36 (12.0%) had an anxiety disorder, 29 (9.6%) had epilepsy, 12 (4.0%) had infections of the central nervous system, and 3 (1.0%) had undetermined diseases. WMLs were present in 245 (81.4%) patients, and 54 (17.9%) were younger than 40 years. Univariate analysis showed that age, history of cerebral infarction, hypertension, the thickness of the common carotid artery intima, and plaque within the intracarotid artery were related risks for WMLs. Ordered logistic analysis showed that age, history of cerebral ischemic stroke, hypertension, male sex, and atrial fibrillation were associated with WML severity. Conclusions- Risk factors for WMLs appear similar for Tibetans residing at high altitudes and individuals living in the plains. Further investigations are needed to determine whether Tibetans residing at high altitudes have a higher burden of WMLs than inhabitants of the plains.

A study on the impact of tourism destination image and local attachment on the revisit intention: The moderating effect of perceived risk.

The revisit intention of tourists is an important guarantee for the sustainable and healthy development of tourism destination, and has also received attention from the current academic community. However, there is still insufficient research on the antecedents of revisit intention from the perspectives of tourism destination, image and nostalgia emotion. This study takes China's ecological tourism scenic area (Guilin Lijiang Scenic Area) as a case study, and uses questionnaire survey method to obtain research data for empirical research. The results of this study confirm that tourism destination image has a positive impact on nostalgia emotions and local attachment, nostalgia emotion has a positive impact on local attachment, and local attachment has a positive impact on revisit intention. Perceived risk plays a negative moderating effect between local attachment and revisit intention. In addition, this study also examined the mediating effect of nostalgia emotion and local attachment. This study is beneficial for enriching the theory of the influence mechanism of revisit intention from the perspective of consumer psychology. It is an interdisciplinary research result of management and psychology, providing theoretical reference for improving revisit intention in tourism destinations and promoting their healthy development.

A New Estimation Method for the Biological Interaction Predicting Problems.

For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods.