Cannabis-Associated Pneumothorax: A Case Report.

The use of cannabis for therapeutic and recreational purposes has been on the rise in recent years. This has increased the prevalence of cannabis use disorder across various demographic subgroups. A recent medical literature review describes a few cases demonstrating the association of spontaneous pneumothorax and bullous lung disease in cannabis users without concomitant tobacco use. We herein present a case report of a young male with chronic cannabis use who presented with right-sided spontaneous pneumothorax and bilateral apical blebs.

Predicting Positive and Negative Relationships in Large Social Networks.

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.