Renewable Composite Quantile Method and Algorithm for Nonparametric Models with Streaming Data

We are interested in renewable estimations and algorithms for nonparametric models with streaming data. In our method, the nonparametric function of interest is expressed through a functional depending on a weight function and a conditional distribution function (CDF). The CDF is estimated by renewable kernel estimations combined with function interpolations, based on which we propose the method of renewable weighted composite quantile regression (WCQR). Then we fully use the model structure and obtain new selectors for the weight function, such that the WCQR can achieve asymptotic unbiasness when estimating specific functions in the model. We also propose practical bandwidth selectors for streaming data and find the optimal weight function minimizing the asymptotic variance. The asymptotical results show that our estimator is almost equivalent to the oracle estimator obtained from the entire data together. Besides, our method also enjoys adaptiveness to error distributions, robustness to outliers, and efficiency in both estimation and computation. Simulation studies and real data analyses further confirm our theoretical findings.Comment: 24 pages, 0 figure

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Endocytic recycling and vesicular transport systems mediate transcytosis of Leptospira interrogans across cell monolayer.

Many bacterial pathogens can cause septicemia and spread from the bloodstream into internal organs. During leptospirosis, individuals are infected by contact with Leptospira-containing animal urine-contaminated water. The spirochetes invade internal organs after septicemia to cause disease aggravation, but the mechanism of leptospiral excretion and spreading remains unknown. Here, we demonstrated that Leptospira interrogans entered human/mouse endothelial and epithelial cells and fibroblasts by caveolae/integrin-β1-PI3K/FAK-mediated microfilament-dependent endocytosis to form Leptospira (Lep)-vesicles that did not fuse with lysosomes. Lep-vesicles recruited Rab5/Rab11 and Sec/Exo-SNARE proteins in endocytic recycling and vesicular transport systems for intracellular transport and release by SNARE-complex/FAK-mediated microfilament/microtubule-dependent exocytosis. Both intracellular leptospires and infected cells maintained their viability. Leptospiral propagation was only observed in mouse fibroblasts. Our study revealed that L. interrogans utilizes endocytic recycling and vesicular transport systems for transcytosis across endothelial or epithelial barrier in blood vessels or renal tubules, which contributes to spreading in vivo and transmission of leptospirosis