Cannabinoid receptor trafficking in peripheral cells is dynamically regulated by a binary biochemical switch

The cannabinoid G protein-coupled receptors (GPCRs) CB₁ and CB₂ are expressed in different peripheral cells. Localization of GPCRs in the cell membrane determines signaling via G protein pathways. Here we show that unlike in transfected cells, CB receptors in cell lines and primary human cells are not internalized upon agonist interaction, but move between cytoplasm and cell membranes by ligand-independent trafficking mechanisms. Even though CB receptors are expressed in many cells of peripheral origin they are not always localized in the cell membrane and in most cancer cell lines the ratios between CB₁ and CB₂ receptor gene and surface expression vary significantly. In contrast, CB receptor cell surface expression in HL60 cells is subject to significant oscillations and CB₂ receptors form oligomers and heterodimers with CB₁ receptors, showing synchronized surface expression, localization and trafficking. We show that hydrogen peroxide and other nonspecific protein tyrosine phosphatase inhibitors (TPIs) such as phenylarsine oxide trigger both CB₂ receptor internalization and externalization, depending on receptor localization. Phorbol ester-mediated internalization of CB receptors can be inhibited via this switch. In primary human immune cells hydrogen peroxide and other TPIs lead to a robust internalization of CB receptors in monocytes and an externalization in T cells. This study describes, for the first time, the dynamic nature of CB receptor trafficking in the context of a biochemical switch, which may have implications for studies on the cell-type specific effects of cannabinoids and our understanding of the regulation of CB receptor cell surface expression.

K(L) --> pi0 e+ e- and K(L) --> pi0 mu+ mu-: A binary star on the stage of flavor physics

A systematic analysis of New Physics impacts on the rare decays KL→π0ell+ell- is performed. Thanks to their different sensitivities to flavor-changing local effective interactions, these two modes could provide valuable information on the nature of the possible New Physics at play. In particular, a combined measurement of both modes could disentangle scalar/pseudoscalar from vector or axial-vector contributions. For the latter, model-independent bounds are derived. Finally, the KL→π0μ+μ- forward-backward CP-asymmetry is considered, and shown to give interesting complementary information.

Intracavity Generation of Radially Polarized CO2 Laser Beams Based on a Simple Binary Dielectric Diffraction Grating

We present experimental results on the intracavity generation of radially polarized light by incorporation of a polarization-selective mirror in a CO2 -laser resonator. The selectivity is achieved with a simple binary dielectric diffraction grating etched in the backsurface of the mirror substrate. Very high polarization selectivity was achieved, and good agreement of simulation and experimental results is shown. The overall radial polarization purity of the generated laser beam was found to be higher than 90% .

A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints

Publication bias and related bias in meta-analysis is often examined by visually checking for asymmetry in funnel plots of treatment effect against its standard error. Formal statistical tests of funnel plot asymmetry have been proposed, but when applied to binary outcome data these can give false-positive rates that are higher than the nominal level in some situations (large treatment effects, or few events per trial, or all trials of similar sizes). We develop a modified linear regression test for funnel plot asymmetry based on the efficient score and its variance, Fisher's information. The performance of this test is compared to the other proposed tests in simulation analyses based on the characteristics of published controlled trials. When there is little or no between-trial heterogeneity, this modified test has a false-positive rate close to the nominal level while maintaining similar power to the original linear regression test ('Egger' test). When the degree of between-trial heterogeneity is large, none of the tests that have been proposed has uniformly good properties.

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

OBJECTIVES: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. METHODS: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. RESULTS: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. CONCLUSION: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.