Ereie (France)

Abstract Graph/network representation learning (or graph/network embedding) is a widely used machine learning technique in industry recommending systems and has recently been applied in computational biology. Popular network representation learning algorithms include random walk and matrix factorization methods, but they do not scale well to large networks. To accommodate the fast growth of real-world network datasets, especially biological datasets, we engineered and improved several network embedding algorithms via intensive computational optimization (e.g., randomized-generalized singular value decomposition/SVD, efficient sketching via ProbMinHash including edge weights) and parallelization to allow ultra-fast and accurate embedding of large- scale networks. We present GraphEmbed, a computer program for scalable, memory-efficient network embedding. GraphEmbed can perform embedding for large-scale networks with several billion nodes in less than 2 hours on a commodity computing cluster. We benchmark it against standard datasets and demonstrate consistent speed and accuracy advantages over state-of-the- art techniques. We also propose centric AUC, a new metric for evaluating link-prediction accuracy in network embedding. It corrects the bias in conventional AUC caused by the highly skewed node degree distributions, which are typically found in real-world networks, especially biological networks. Taken together, GraphEmbed solves a major challenge in large-scale network representation learning for networks in general and biological networks in particular.

Low water intake is associated with long-term adverse health outcomes. Despite this, few studies had examined hydration physiology in the general population, where adequate intake, and not excessive loss, is the primary determinant of hydration. This study sought to evaluate the responsiveness of hydration biomarkers in urine and blood to acute changes in water intake. Key findings were that markers of urine concentration and urine volume change rapidly in response to a change in water intake, whereas plasma osmolality is maintained. Moreover, urine concentration varies as a function of the time of day, with nighttime and morning urine samples being more concentrated than afternoon samples. These results suggest that short, well-timed urine samples, such as those taken in the afternoon, may provide values comparable with 24-hour collections, which are more time-consuming and less convenient.