A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

Exact exchange-correlation potential of a ionic Hubbard model with a free surface

We use Lanczos exact diagonalization to compute the exact exchange-correlation (xc) potential of a Hubbard chain with large binding energy ("the bulk") followed by a chain with zero binding energy ("the vacuum"). Several results of density functional theory in the continuum (sometimes controversial) are verified in the lattice. In particular we show explicitly that the fundamental gap is given by the gap in the Kohn-Sham spectrum plus a contribution due to the jump of the xc-potential when a particle is added. The presence of a staggered potential and a nearest-neighbor interaction V allows to simulate a ionic solid. We show that in the ionic regime in the small hopping amplitude limit the xc-contribution to the gap equals V, while in the Mott regime it is determined by the Hubbard U interaction. In addition we show that correlations generates a new potential barrier at the surface

Compensatory T Cell Responses in IRG-Deficient Mice Prevent Sustained Chlamydia trachomatis Infections

The obligate intracellular pathogen Chlamydia trachomatis is the most common cause of bacterial sexually transmitted diseases in the United States. In women C. trachomatis can establish persistent genital infections that lead to pelvic inflammatory disease and sterility. In contrast to natural infections in humans, experimentally induced infections with C. trachomatis in mice are rapidly cleared. The cytokine interferon-γ (IFNγ) plays a critical role in the clearance of C. trachomatis infections in mice. Because IFNγ induces an antimicrobial defense system in mice but not in humans that is composed of a large family of Immunity Related GTPases (IRGs), we questioned whether mice deficient in IRG immunity would develop persistent infections with C. trachomatis as observed in human patients. We found that IRG-deficient Irgm1/m3(-/-) mice transiently develop high bacterial burden post intrauterine infection, but subsequently clear the infection more efficiently than wildtype mice. We show that the delayed but highly effective clearance of intrauterine C. trachomatis infections in Irgm1/m3(-/-) mice is dependent on an exacerbated CD4+ T cell response. These findings indicate that the absence of the predominant murine innate effector mechanism restricting C. trachomatis growth inside epithelial cells results in a compensatory adaptive immune response, which is at least in part driven by CD4+ T cells and prevents the establishment of a persistent infection in mice

Scanning the horizon: towards transparent and reproducible neuroimaging research

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions