Generalized durative event detection on social media.

Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first real-world task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information.

Twitter-aided decision making: a review of recent developments.

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms.

Structural basis for selective inhibition of human serine hydroxymethyltransferase by secondary bile acid conjugate.

Bile acids are metabolites of cholesterol that facilitate lipid digestion and absorption in the small bowel. Bile acids work as agonists of receptors to regulate their own metabolism. Bile acids also regulate other biological systems such as sugar metabolism, intestinal multidrug resistance, and adaptive immunity. However, numerous physiological roles of bile acids remain undetermined. In this study, we solved the crystal structure of human serine hydroxymethyltransferase (hSHMT) in complex with an endogenous secondary bile acid glycine conjugate. The specific interaction between hSHMT and the ligand was demonstrated using mutational analyses, biophysical measurements, and structure-activity relationship studies, suggesting that secondary bile acid conjugates may act as modulators of SHMT activity.