CD137 (4-1BB) is a costimulatory molecule that can be manipulated for the treatment of cancer and autoimmune disease. Although it is known that agonistic antibodies (mAbs) against CD137 enhance the rejection of murine tumors in a natural killer (NK) cell-and T celldependent fashion, the mechanism for NK dependence is poorly understood. In this study, we evaluated the ability of 2 different glycoforms of a chimerized antihuman CD137 mAb, an aglycosylated (GA) and a low fucose form (GG), to react with human NK cells. Both mAbs bound similarly to CD137 and partially blocked the interaction between CD137 and CD137 ligand. However, unlike GA mAb, immobilized GG mAb activated NK cells and enhanced CD137 expression. These effects were seemingly dependent on

These data have both prognostic and therapeutic relevance and support the design of a prospective trial to determine the influence of FcgammaRIIIa polymorphisms on the clinical outcome of patients with SCCHN treated with alpha-EGFR mAbs.

The role of human bone marrow (BM) CD8+ T cells in the immune response to viral Ags is poorly defined. We report here the identification and characterization of a functionally enhanced effector memory CD8+ T cell population (TEM) in the BM of patients undergoing total joint replacement for osteoarthritis. These BM-derived TEM differ strikingly from correlate cells in peripheral blood (PB), expressing elevated levels of CD27, HLA-DR, CD38, CD69, and unique patterns of chemokine receptors. Interestingly, while BM TEM have low levels of resting perforin and granzyme B, these molecules evidence profound up-regulation in response to TCR stimulation resulting in enhanced cytotoxic potential. Moreover, compared with the TEM subset in PB, BM CD8+ TEM cells demonstrate a more vigorous recall response to pooled viral Ags. Our results reveal that human BM serves as a repository for viral Ag-specific TEM with great therapeutic potential in vaccine development.

In this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author.

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We construct several explicit asymptotic two-sided confidence intervals (CIs) for the difference between two correlated proportions using the method of variance of estimates recovery (MOVER). The basic idea is to recover variance estimates required for the proportion difference from the confidence limits for single proportions. The CI estimators for a single proportion, which are incorporated with the MOVER, include the Agresti-Coull, the Wilson, and the Jeffreys CIs. Our simulation results show that the MOVER-type CIs based on the continuity corrected Phi coefficient and the Tango score CI perform satisfactory in small sample designs and spare data structures. We illustrate the proposed CIs with several real examples.

Although the item count technique is useful in surveys with sensitive questions, privacy of those respondents who possess the sensitive characteristic of interest may not be well protected due to a defect in its original design. In this article, we propose two new survey designs (namely the Poisson item count technique and negative binomial item count technique) which replace several independent Bernoulli random variables required by the original item count technique with a single Poisson or negative binomial random variable, respectively. The proposed models not only provide closed form variance estimate and confidence interval within [0, 1] for the sensitive proportion, but also simplify the survey design of the original item count technique. Most importantly, the new designs do not leak respondents' privacy. Empirical results show that the proposed techniques perform satisfactorily in the sense that it yields accurate parameter estimate and confidence interval.