Deep-sequencing identification of the genomic targets of the cytidine deaminase AID and its cofactor RPA in B lymphocytes

The cytidine deaminase AID hypermutates immunoglobulin genes but can also target oncogenes, leading to tumorigenesis. The extent of AID's promiscuity and its predilection for immunoglobulin genes are unknown. We report here that AID interacted broadly with promoter-proximal sequences associated with stalled polymerases and chromatin-activating marks. In contrast, genomic occupancy of replication protein A (RPA), an AID cofactor, was restricted to immunoglobulin genes. The recruitment of RPA to the immunoglobulin loci was facilitated by phosphorylation of AID at Ser38 and Thr140. We propose that stalled polymerases recruit AID, thereby resulting in low frequencies of hypermutation across the B cell genome. Efficient hypermutation and switch recombination required AID phosphorylation and correlated with recruitment of RPA. Our findings provide a rationale for the oncogenic role of AID in B cell malignancy.

Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

Impact factor statistics and publication practice: What can we learn? (Editorial)

Peer review procedures and citation statistics are important yet often neglected components of the scientific publication process. Here I discuss fundamental consequences of such quality measures for the scientific community and propose three remedial actions: (1) use of a ''Combined Impact Estimate'' as a measure of citation statistics, (2) adoption of an open reviewing policy and (3) acceleration of the publication process in order to raise the reputation of the entire discipline (in our case: behavioural science). Authors, reviewers and editors are invited to contribute to the improvement of publication practice.

Heart failure events, and case fatalities in Switzerland based on hospital statistics and cause of death statistics

In Switzerland there is a shortage of population-based information on heart failure (HF) incidence and case fatalities (CF). The aim of this study was to estimate HF event rates and both in- and out-of-hospital CF rates.