A Computational Approach for Identifying Plant-Based Foods for Addressing Vitamin Deficiency Diseases

Vitamins are nutrients that are essential to human health, and deficiencies have been shown to cause severe diseases. In this study, a computational approach was used to identify vitamin deficiency diseases and plant-based foods with vitamin content. Data from the United States Department of Agriculture Standard Reference (SR27), National Library of Medicine\u27s Medical Subject Headings and MEDLINE, and Wikipedia were combined to identify vitamin deficiency diseases and vitamin content of plant-based foods. A total of 41,584 vitamin-disease associations were identified from MEDLINE-indexed articles as well as from entries in Wikipedia. The SR27 identified 1912 foods that contained at least one vitamin, with an average of 1276 foods per vitamin. Vitamin B12 and D contained the fewest number of foods (n=135 and 70, respectively). The results of this study establish the foundation for developing a process to link vitamin deficiency diseases to vitamin-rich foods

Understanding Hong Kong Business Teachers in Action: The Case of Formulation of Teaching Strategies

This article examines four categories of teaching strategy used in business classes by a group of 26 secondary school business teachers in Hong Kong, using grounded theoretical coding techniques in the analysis. Each of the teaching categories is illustrated with typical extracts from interviews and is discussed in relation to its effectiveness and the formulation of teaching strategies. The study found that the teachers used varied teaching approaches to develop students’ competence, with diverse considerations of influential factors in formulating their teaching strategies. It is recommended that teachers increase their awareness of their teaching approaches in classroom practice, formulate the most effective teaching strategies for their business classes, and develop an open learning space to promote their professional judgment on the formulation of teaching strategies

Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation

Consider the task of tensor estimation, i.e. estimating a low-rank 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a generalization of the collaborative filtering algorithm for sparse tensor estimation and argue that it achieves sample complexity that nearly matches the conjectured computationally efficient lower bound on the sample complexity. Our algorithm uses the matrix obtained from the flattened tensor to compute similarity, and estimates the tensor entries using a nearest neighbor estimator. We prove that the algorithm recovers the tensor with maximum entry-wise error and mean-squared-error (MSE) decaying to $0$ as long as each entry is observed independently with probability $p = \Omega(n^{-3/2 + \kappa})$ for any arbitrarily small $\kappa> 0$. Our analysis sheds insight into the conjectured sample complexity lower bound, showing that it matches the connectivity threshold of the graph used by our algorithm for estimating similarity between coordinates