Molecular Mechanisms Underlying Memory Consolidation of Taste Information in the Cortex

The senses of taste and odor are both chemical senses. However, whereas an organism can detect an odor at a relatively long distance from its source, taste serves as the ultimate proximate gatekeeper of food intake: it helps in avoiding poisons and consuming beneficial substances. The automatic reaction to a given taste has been developed during evolution and is well adapted to conditions that may occur with high probability during the lifetime of an organism. However, in addition to this automatic reaction, animals can learn and remember tastes, together with their positive or negative values, with high precision and in light of minimal experience. This ability of mammalians to learn and remember tastes has been studied extensively in rodents through application of reasonably simple and well defined behavioral paradigms. The learning process follows a temporal continuum similar to those of other memories: acquisition, consolidation, retrieval, relearning, and reconsolidation. Moreover, inhibiting protein synthesis in the gustatory cortex (GC) specifically affects the consolidation phase of taste memory, i.e., the transformation of short- to long-term memory, in keeping with the general biochemical definition of memory consolidation. This review aims to present a general background of taste learning, and to focus on recent findings regarding the molecular mechanisms underlying taste–memory consolidation in the GC. Specifically, the roles of neurotransmitters, neuromodulators, immediate early genes, and translation regulation are addressed

Unifying annotated discourse hierarchies to create a gold standard

Human annotation of discourse corpora typically results in segmentation hierarchies that vary in their degree of agreement. This paper presents several techniques for unifying multiple discourse annotations into a single hierarchy, deemed a “gold standard ” — the segmentation that best captures the underlying linguistic structure of the discourse. It proposes and analyzes methods that consider the level of embeddedness of a segmentation as well as methods that do not. A corpus containing annotated hierarchical discourses, the Boston Directions Corpus, was used to evaluate the “goodness” of each technique, by comparing the similarity of the segmentation it derives to the original annotations in the corpus. Several metrics of similarity between hierarchical segmentations are computed: precision/recall of matching utterances, pairwise inter-reliability scores ( ¡), and non-crossing-brackets. A novel method for unification that minimizes conflicts among annotators outperforms methods that require consensus among a majority for the ¡ and recall metrics, while capturing much of the structure of the discourse. When higher recall is preferred, methods requiring a majority are preferable to those that demand full consensus among annotators.Engineering and Applied Science

Early prediction of student performance in a health data science MOOC

Funding: This work was supported by the Medical Research Council [grant number MR/N013166/1].Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students' low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners' final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE counterfactual method to automatically suggest simple changes to the learning behaviour of low- and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course.Publisher PD