Maternal and Postnatal High Linoleic Acid Diet Impacts Lipid Metabolism in Adult Rat Offspring in a Sex-Specific Manner

Linoleic acid (LA), an n-6 polyunsaturated fatty acid (PUFA), is essential for fetal growth and development. We aimed to investigate the effect of maternal and postnatal high LA (HLA) diet on plasma FA composition, plasma and hepatic lipids and genes involved in lipid metabolism in the liver of adult offspring. Female rats were fed with low LA (LLA; 1.44% LA) or HLA (6.21% LA) diets for 10 weeks before pregnancy, and during gestation/lactation. Offspring were weaned at postnatal day 25 (PN25), fed either LLA or HLA diets and sacrificed at PN180. Postnatal HLA diet decreased circulating total n-3 PUFA and alpha-linolenic acid (ALA), while increased total n-6 PUFA, LA and arachidonic acid (AA) in both male and female offspring. Maternal HLA diet increased circulating leptin in female offspring, but not in males. Maternal HLA diet decreased circulating adiponectin in males. Postnatal HLA diet significantly decreased aspartate transaminase (AST) in females and downregulated total cholesterol, HDL-cholesterol and triglycerides in the plasma of males. Maternal HLA diet downregulated the hepatic mRNA expression of Hmgcr in both male and female offspring and decreased the hepatic mRNA expression of Cpt1a and Acox1 in females. Both maternal and postnatal HLA diet decreased hepatic mRNA expression of Cyp27a1 in females. Postnatal diet significantly altered circulating fatty acid concentrations, with sex-specific differences in genes that control lipid metabolism in the adult offspring following exposure to high LA diet in utero.     

Invariant time-series factorization

Time-series analysis is an important domain of machine learning and a plethora of methods have been developed for the task. This paper proposes a new representation of time series, which in contrast to existing approaches, decomposes a time-series dataset into latent patterns and membership weights of local segments to those patterns. The process is formalized as a constrained objective function and a tailored stochastic coordinate descent optimization is applied. The time-series are projected to a new feature representation consisting of the sums of the membership weights, which captures frequencies of local patterns. Features from various sliding window sizes are concatenated in order to encapsulate the interaction of patterns from different sizes. The derived representation offers a set of features that boosts classification accuracy. Finally, a large-scale experimental comparison against 11 baselines over 43 real life datasets, indicates that the proposed method achieves state-of-the-art prediction accuracy results.     

Interactive 3D visualization of optical phenomena

Each year at colleges and universities nationwide, some 10,000 students take a course on optics, typically through a physics department. The method of instruction has changed very little in the past 40 years, and many of the textbook illustrations have remained essentially the same during this time. In particular, the illustrations rely heavily on line drawings and 2D graphs. Certain key concepts in optics possess fundamentally 3D aspects, which instructors typically demonstrate in a classroom laboratory. Currently, several computer-assisted instructional modules support teaching optics by visualizing optical phenomena. We created interactive 3D graphical modules for visualizing optical phenomena by applying 3D visualization techniques to instructional courseware