Roles of Lipids in Cancer

The term ‘lipids’ refers to a class of biological molecules primarily composed of hydrocarbons such as fatty acids, glycerolipids, sphingolipids and sterol lipids. Lipids take part in a variety of physiological functions and have specific roles depending on their chemical structure and localisation within or outside cells. For example, glycerolipids (e.g. triglycerides) are often used as energy stores, sterol lipids (e.g. cholesterol) and glycerophospholipids as structural components of cell membranes (e.g. the lipid bilayer), and sphingolipids as part of a signalling cascade. Since lipids are a source of energy and basic building block of all living cells, it is not surprising that development of cancer (i.e. uncontrolled proliferation of cells) is closely tied to the metabolism of lipids. This notion is supported by studies into the reprogrammed metabolic machinery in cancer cells, and also cell and animal model experiments showing that cancer growth and metastasis can be induced or inhibited by the exogenous addition of lipids. Here, we review how cancer cells can alter their lipid metabolism to meet their metabolic requirements, and the potential tumorigenic and tumour-suppressive mechanisms in which lipids are involved

Teaching and Learning in an ODL University: Bridging the Gap between the Learning Environment, Learners and Instructors

Many researchers have commented that educators need to use different techniques and methodologies in an online, distance learning (ODL) environment. The constructivist perspective is said to be especially relevant to the ODL environment as it emphasizes independent learning and the active construction of knowledge, which suits ODL learners as they are usually self-motivated and more mature. However, two questions come to mind – do instructors and learners in the ODL environment really embrace constructivist principles in teaching and learning, or is there a gap between the learning environment, learners and instructors? This paper attempts to answer these questions by determining the preferred teaching and learning styles of instructors and learners in an ODL university, to gauge if they are more behaviourist or constructivist in their teaching beliefs and practices. It also attempts to identify the constructivist elements present in an ODL university and come up with conceptual framework for nurturing constructivist practices.(Authors' abstract

POIBERT: A Transformer-based Model for the Tour Recommendation Problem

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.Comment: Accepted to the 2022 IEEE International Conference on Big Data (BigData2022