Immune evasion of the CD1d/NKT cell axis

Many reviews on the CD1d/NKT cell axis focus on the ability of CD1d-restricted NKT cells to serve as effector cells in a variety of disorders, be they infectious diseases, cancer or autoimmunity. In contrast, here, we discuss the ways that viruses, bacteria and tumor cells can evade the CD1d/NKT cell axis. As a result, these disease states have a better chance to establish a foothold and potentially cause problems for the subsequent adaptive immune response, as the host tries to rid itself of infections or tumors

Regularities

The neoclassical q-theory is a good start to understand the cross section of returns. Under constant return to scale, stock returns equal levered investment returns that are tied directly with characteristics. This equation generates the relations of average returns with book-to-market, investment, and earnings surprises. We estimate the model by minimizing the differences between average stock returns and average levered investment returns via GMM. Our model captures well the average returns of portfolios sorted on capital investment and on size and book-to-market, including the small-stock value premium. Our model is also partially successful in capturing the post-earnings-announcement drift and its higher magnitude in small firms.

Point And Density Forecasts In Panel Data Models

This dissertation develops econometric methods that facilitate estimation and improve forecasting performance in panel data models. The panel considered in this paper features large cross-sectional dimension (N) but short time series (T). It is modeled by a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. Due to short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, the methods developed in this dissertation assume that there is an underlying distribution of the heterogeneous parameters and pool the information from the whole cross-section together via this distribution. Chapter 2, coauthored with Hyungsik Roger Moon and Frank Schorfheide, constructs point forecasts using an empirical Bayes method that builds on Tweedie\u27s formula to obtain the posterior mean of the heterogeneous coefficients under a correlated random effects distribution. We show that the risk of a predictor based on a non-parametric estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions. In Chapter 3, I focus on density forecasts and use a full Bayes approach, where the distribution of the heterogeneous coefficients is modeled nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors. I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. I prove that both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Monte Carlo simulations and an application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches

Leucine supplementation differentially enhances pancreatic cancer growth in lean and overweight mice

Kristyn A Liu1†, Laura M Lashinger1†, Audrey J Rasmussen1† and Stephen D Hursting12* Author Affiliations 1 Department of Nutritional Sciences, University of Texas at Austin, Austin, TX 78723, USA 2 Department of Molecular Carcinogenesis, University of Texas M.D. Anderson Cancer Center, 1808 Park Road 1c, Smithville, TX 78957, USABackground: The risk of pancreatic cancer, the 4th deadliest cancer for both men and women in the United States, is increased by obesity. Calorie restriction (CR) is a well-known dietary regimen that prevents or reverses obesity and suppresses tumorigenesis in a variety of animal models, at least in part via inhibition of mammalian target of rapamycin (mTOR) signaling. Branched-chain amino acids (BCAA), especially leucine, activate mTOR and enhance growth and proliferation of myocytes and epithelial cells, which is why leucine is a popular supplement among athletes. Leucine is also increasingly being used as a treatment for pancreatic cancer cachexia, but the effects of leucine supplementation on pancreatic tumor growth have not been elucidated. Results: Supplementation with leucine increased pancreatic tumor growth in both lean (104 ± 17 mm3 versus 46 ± 13 mm3; P <0.05) and overweight (367 ± 45 mm3 versus 230 ± 39 mm3; P <0.01) mice, but tumor enhancement was associated with different biological outcomes depending on the diet. In the lean mice, leucine increased phosphorylation of mTOR and downstream effector S6 ribosomal protein, but in the overweight mice, leucine reduced glucose clearance and thus increased the amount of circulating glucose available to the tumor. Conclusion: These findings show that leucine supplementation enhances tumor growth in both lean and overweight mice through diet-dependent effects in a murine model of pancreatic cancer, suggesting caution against the clinical use of leucine supplementation for the purposes of skeletal muscle enhancement in cachectic patients.Nutritional Science

Computational illumination for high-speed in vitro Fourier ptychographic microscopy

We demonstrate a new computational illumination technique that achieves large space-bandwidth-time product, for quantitative phase imaging of unstained live samples in vitro. Microscope lenses can have either large field of view (FOV) or high resolution, not both. Fourier ptychographic microscopy (FPM) is a new computational imaging technique that circumvents this limit by fusing information from multiple images taken with different illumination angles. The result is a gigapixel-scale image having both wide FOV and high resolution, i.e. large space-bandwidth product (SBP). FPM has enormous potential for revolutionizing microscopy and has already found application in digital pathology. However, it suffers from long acquisition times (on the order of minutes), limiting throughput. Faster capture times would not only improve imaging speed, but also allow studies of live samples, where motion artifacts degrade results. In contrast to fixed (e.g. pathology) slides, live samples are continuously evolving at various spatial and temporal scales. Here, we present a new source coding scheme, along with real-time hardware control, to achieve 0.8 NA resolution across a 4x FOV with sub-second capture times. We propose an improved algorithm and new initialization scheme, which allow robust phase reconstruction over long time-lapse experiments. We present the first FPM results for both growing and confluent in vitro cell cultures, capturing videos of subcellular dynamical phenomena in popular cell lines undergoing division and migration. Our method opens up FPM to applications with live samples, for observing rare events in both space and time

The Success Story of Gold-Based Catalysts for Gas- and Liquid-Phase Reactions: A Brief Perspective and Beyond

Gold has long held the fascination of mankind. For millennia it has found use in art, cosmetic metallurgy and architecture; this element is seen as the ultimate statement of prosperity and beauty. This myriad of uses is made possible by the characteristic inertness of bulk gold; allowing it to appear long lasting and above the tarnishing experienced by other metals, in part providing its status as the most noble meta