Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

Detecting Spatiotemporal Structure Boundaries: Beyond Motion Discontinuities

Abstract. The detection of motion boundaries has been and remains a longstanding challenge in computer vision. In this paper, the recovery of motion boundaries is recast in a broader scope, as focus is placed on the more general problem of detecting spacetime structure boundaries, where motion boundaries constitute a special case. This recasting allows uniform consideration of boundaries between a wider class of spacetime patterns than previously considered in the literature, both coherent motion as well as additional dynamic patterns. Examples of dynamic patterns beyond standard motion that are encompassed by the proposed approach include, flicker, transparency and various dynamic textures (e.g., scintillation). Toward this end, a novel representation and method for detecting these boundaries in raw image sequence data are presented. Central to the representation is the description of oriented spacetime structure in a distributed manner. Empirical evaluation of the proposed boundary detector on challenging natural imagery suggests its efficacy.

Image Segmentation by Flexible Models Based on Robust Regularized Networks

The object of this paper is to present aform ulation for thesegmF tation and restorationproblem using flexiblem dels with a robust regularized network (RRN). A two-steps iterativealgorithm is presented. In the first step an approximoh-- of the classification iscomJq-( by using a localmh)q)F)hfl-5 algorithm and in the second step the param-5hfl of the RRN are updated. The use of robust potentials ismh)N ated by (a) classification errors that can resultfrom the use of localmlh))FfiE algorithm in theimh-fiF) tation, and (b) the need to adapt the RN using localimhgradientinformfl -fi) to imfiE ve fidelity of them odel to the data