Track E Implementation Science, Health Systems and Economics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd

Shape Analysis of Planar Objects with Arbitrary Topologies using Conformal Geometry

The study of 2D shapes is a central problem in the field of computer vision. In 2D shape analysis, classification and recognition of objects from their observed silhouette are extremely crucial and yet difficult. It usually involves an efficient representation of 2D shape space with natural metric, so that its mathematical structure can be used for further analysis. Although significant progress has been made for the study of 2D simply-connected shapes, very few works have been done on the study of 2D objects with arbitrary topologies. In this work, we proposed a representation of general 2D domains with arbitrary topologies using conformal geometry. A natural metric can be defined on the proposed representation space, which gives a metric to measure dissimilarities between objects. The main idea is to map the exterior and interior of the domain conformally to unit disks and punctual disks (circle domains), using holomorphic 1-forms. A set of diffeomorphisms from the unit circle S1 to itself can be obtained, which together with the conformal modules are used to define the shape signature. We prove mathematically that our proposed signature uniquely represents shapes with arbitrary topologies. We also introduce a reconstruction algorithm to obtain shapes from their signatures. This completes our framework and allows us to go back and forth between shapes and signatures. Experimental results shows the efficacy of our proposed algorithm as a stable shape representation scheme

Diffusion-snakes using statistical shape knowledge

We present a novel extension of the Mumford-Shah functional that allows to incorporate statistical shape knowledge at the computational level of image segmentation. Our approach exhibits various favorable properties: non-local convergence, robustness against noise, and the ability to take into consideration both shape evidence in given image data and knowledge about learned shapes. In particular, the latter property distinguishes our approach from previous work on contour-evolution based image segmentation. Experimental results conrm these properties

Prolonged Drug Release from Gels, using Catanionic Mixtures

The use of catanionic drug-surfactant mixtures was proven to be an efficient novel method of obtaining prolonged drug release from gels. It was shown that various commonly used drug compounds are able to form catanionic mixtures together with oppositely charged surfactants. These mixtures exhibited interesting phase behaviour, where, among other structures, vesicles and large worm-like or branched micelles were found. The size of these aggregates makes them a potential means of prolonging the drug release from gels, as only monomer drugs in equilibrium with larger aggregates were readily able to diffuse through the gel. When the diffusion coefficient for drug release from the formulation based upon a catanionic mixture was compared to that obtained for the drug substance and gel alone, the coefficient was some 10 to 100 times smaller. The effects of changes in the pH and ionic strength on the catanionic aggregates was also investigated, and this method of prolonging the release was found to be quite resilient to variations in both. Although the phase behaviour was somewhat affected, large micelles and vesicles were still readily found. The drug release was significantly prolonged even under physiological conditions, that is, at a pH of 7.4 and an osmolality corresponding to 0.9% NaCl. Surfactants of low irritancy, capric and lauric acid, may successfully be used instead of the more traditional surfactants, such as sodium lauryl sulfate (SDS), and prolonged release can still be obtained with ease. Some attempts to deduce the release mechanism from the proposed systems have also been made using transient current measurements, dielectric spectroscopy, and modelling of the release using the regular solution theory. In these studies, the previous assumptions made concerning the mechanism responsible for the release were confirmed to a large extent. Only small amounts of the drug existed in monomer form, and most seemed to form large catanionic aggregates with the oppositely charged surfactant

Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2

We recently identified a novel susceptibility variant, rs865686, for estrogen-receptor positive breast cancer at 9q31.2. Here, we report a fine-mapping analysis of the 9q31.2 susceptibility locus using 43 160 cases and 42 600 controls of European ancestry ascertained from 52 studies and a further 5795 cases and 6624 controls of Asian ancestry from nine studies. Single nucleotide polymorphism (SNP) rs676256 was most strongly associated with risk in Europeans (odds ratios [OR] = 0.90 [0.88-0.92]; P-value = 1.58 x 10(-25)). This SNP is one of a cluster of highly correlated variants, including rs865686, that spans 14.5 kb. We identified two additional independent association signals demarcated by SNPs rs10816625 (OR = 1.12 [1.08-1.17]; P-value = 7.89 x 10(-09)) and rs13294895 (OR = 1.09 [1.06-1.12]; P-value = 2.97 x 10(-11)). SNP rs10816625, but not rs13294895, was also associated with risk of breast cancer in Asian individuals (OR = 1.12 [1.06-1.18]; P-value = 2.77 x 10(-05)). Functional genomic annotation using data derived from breast cancer cell-line models indicates that these SNPs localise to putative enhancer elements that bind known drivers of hormone-dependent breast cancer, including ER-alpha, FOXA1 and GATA-3. In vitro analyses indicate that rs10816625 and rs13294895 have allele-specific effects on enhancer activity and suggest chromatin interactions with the KLF4 gene locus. These results demonstrate the power of dense genotyping in large studies to identify independent susceptibility variants. Analysis of associations using subjects with different ancestry, combined with bioinformatic and genomic characterisation, can provide strong evidence for the likely causative alleles and their functional basis.Peer reviewe