Optimized recentered confidence spheres for the multivariate normal mean

Casella and Hwang, 1983, JASA, introduced a broad class of recentered confidence spheres for the mean $\boldsymbol{\theta}$ of a multivariate normal distribution with covariance matrix $\sigma^2 \boldsymbol{I}$, for $\sigma^2$ known. Both the center and radius functions of these confidence spheres are flexible functions of the data. For the particular case of confidence spheres centered on the positive-part James-Stein estimator and with radius determined by empirical Bayes considerations, they show numerically that these confidence spheres have the desired minimum coverage probability $1-\alpha$ and dominate the usual confidence sphere in terms of scaled volume. We shift the focus from the scaled volume to the scaled expected volume of the recentered confidence sphere. Since both the coverage probability and the scaled expected volume are functions of the Euclidean norm of $\boldsymbol{\theta}$, it is feasible to optimize the performance of the recentered confidence sphere by numerically computing both the center and radius functions so as to optimize some clearly specified criterion. We suppose that we have uncertain prior information that $\boldsymbol{\theta}= \boldsymbol{0}$. This motivates us to determine the center and radius functions of the confidence sphere by numerical minimization of the scaled expected volume of the confidence sphere at $\boldsymbol{\theta}= \boldsymbol{0}$, subject to the constraints that (a) the coverage probability never falls below $1-\alpha$ and (b) the radius never exceeds the radius of the standard $1-\alpha$ confidence sphere. Our results show that, by focusing on this clearly specified criterion, significant gains in performance (in terms of this criterion) can be achieved. We also present analogous results for the much more difficult case that $\sigma^2$ is unknown.Comment: arXiv admin note: text overlap with arXiv:1306.241

Fletcher-Turek Model Averaged Profile Likelihood Confidence Intervals

We evaluate the model averaged profile likelihood confidence intervals proposed by Fletcher and Turek (2011) in a simple situation in which there are two linear regression models over which we average. We obtain exact expressions for the coverage and the scaled expected length of the intervals and use these to compute these quantities in particular situations. We show that the Fletcher-Turek confidence intervals can have coverage well below the nominal coverage and expected length greater than that of the standard confidence interval with coverage equal to the same minimum coverage. In these situations, the Fletcher-Turek confidence intervals are unfortunately not better than the standard confidence interval used after model selection but ignoring the model selection process

Transcriptional memory-like imprints and enhanced functional activity in gamma delta T cells following resolution of malaria infection

Gamma delta T cells play an essential role in the immune response to many pathogens, including Plasmodium. However, long-lasting effects of infection on the gamma delta T cell population still remain inadequately understood. This study focused on assessing molecular and functional changes that persist in the gamma delta T cell population following resolution of malaria infection. We investigated transcriptional changes and memory-like functional capacity of malaria pre-exposed gamma delta T cells using a Plasmodium chabaudi infection model. We show that multiple genes associated with effector function (chemokines, cytokines and cytotoxicity) and antigen-presentation were upregulated in P. chabaudi-exposed gamma delta T cells compared to gamma delta T cells from naive mice. This transcriptional profile was positively correlated with profiles observed in conventional memory CD8(+) T cells and was accompanied by enhanced reactivation upon secondary encounter with Plasmodium-infected red blood cells in vitro. Collectively our data demonstrate that Plasmodium exposure result in "memory-like imprints" in the gamma delta T cell population and also promotes gamma delta T cells that can support antigen-presentation during subsequent infections

Dual roles for LUBAC signaling in thymic epithelial cell development and survival

Thymic epithelial cells (TECs) form a unique microenvironment that orchestrates T cell differentiation and immunological tolerance. Despite the importance of TECs for adaptive immunity, there is an incomplete understanding of the signalling networks that support their differentiation and survival. We report that the linear ubiquitin chain assembly complex (LUBAC) is essential for medullary TEC (mTEC) differentiation, cortical TEC survival and prevention of premature thymic atrophy. TEC-specific loss of LUBAC proteins, HOIL-1 or HOIP, severely impaired expansion of the thymic medulla and AIRE-expressing cells. Furthermore, HOIL-1-deficiency caused early thymic atrophy due to Caspase-8/MLKL-dependent apoptosis/necroptosis of cortical TECs. By contrast, deficiency in the LUBAC component, SHARPIN, caused relatively mild defects only in mTECs. These distinct roles for LUBAC components in TECs correlate with their function in linear ubiquitination, NF kappa B activation and cell survival. Thus, our findings reveal dual roles for LUBAC signaling in TEC differentiation and survival

T cell factor-1 is a Critical Factor in Determining Natural Killer and Group 1 Innate Lymphoid Cell Fate Decisions

5th Annual Meeting of the International-Cytokine-and-Interferon-Society (ICIS), Int Cytokine & Interferon Soc, Kanazawa, JAPAN, OCT 29-NOV 02, 2017International audienc