Dynamic balance of segregation distortion and selection maintains normal allele sizes at the myotonic dystrophy locus

Myotonic dystrophy (DM), an autosomal dominant neurological disorder, is caused by CTG-repeat expansions at the DMPK locus, with affected individuals having > or = 50 repeats of this trinucleotide. Reduced reproductive fitness of affected individuals and decreased viability of congenital DM have been noted. Expanded CTG-repeat alleles are highly unstable, predominantly yielding even higher repeat sizes. Preferential transmission of longer alleles from heterozygous mothers within the normal size range of alleles also is observed. In view of these observations, it is worth examining how DM has been maintained in human populations for hundreds of generations. We present an analysis of the dynamic properties of a model of joint effects of segregation distortion and selection (intensity of which increases with allele sizes of an individual's genotype). Our mathematical formulation and numerical analyses demonstrate that a weak segregation distortion during female meiosis, together with selection of comparable intensity (within the normal allele size range), can maintain an equilibrium distribution of allele frequencies. Genetic drift, acting in conjunction with the occasional contraction of alleles by mutation, can contribute to the balance of segregation distortion and mutation, in the sense that even weaker selection can explain the observed allele frequencies. The model is applied to CTG-repeat size distributions at the DMPK locus, observed in normal individuals from world populations.     

Minimal surfaces based object segmentation

A geometric approach for 3D object segmentation and representation is presented. The segmentation is obtained by deformable surfaces moving towards the objects to be detected in the 3D image. The model is based on curvature motion and the computation of surfaces with minimal areas, better known as minimal surfaces. The space where the surfaces are computed is induced from the 3D image (volumetric data) in which the objects are to be detected. The model links between classical deformable surfaces obtained via energy minimization, and intrinsic ones derived from curvature based flows. The new approach is stable, robust, and automatically handles changes in the surface topology during the deformation     

Outcome of peroneal neuropathies in patients with systemic malignant disease

BACKGROUND: Peroneal neuropathies in patients with systemic cancer previously have been attributed to weight loss, but to the authors' knowledge other associated conditions have not been assessed, and the outcome of peroneal neuropathies in cancer patients has not been studied. METHODS: A retrospective chart review of patients evaluated at the Mayo Clinic between 1984 and 1993 with systemic malignant disease and a clinical diagnosis of peroneal neuropathy was performed to define factors associated with peroneal neuropathies and to assess outcome. All patients underwent neurologic examination and electromyography. RESULTS: Fifty-eight patients with systemic malignant disease were found to have a peroneal neuropathy. Peroneal neuropathies occurred more often in men (45 patients) than in women (13 patients). The median age of the patients was 70 years. The most common cancers were hematologic (12 patients) and pulmonary (11 patients), followed by tumors of the prostate (8 patients), gastrointestinal tract (7 patients), transitional cell (5 patients), breast (5 patients), and colon (5 patients), as well as sarcomas and melanoma (5 patients). The median time to the diagnosis of peroneal neuropathy after the diagnosis of cancer was 5 months. At the time of diagnosis, 34 patients had severe deficits, 19 had moderate deficits, and 5 had mild deficits. Associated factors included weight loss (occurring in 60% of patients), leg crossing (35% of patients), recent chemotherapy (16% of patients), cutaneous vasculitis (5% of patients), and local metastatic lesions (3% of patients). In nearly 50% of patients, peroneal neuropathy improved (25.9%) or resolved (22.4%). In 39.7% of patients, follow-up was inadequate because death occurred soon after diagnosis. Of the patients with adequate follow-up before death, 80% had either improvement (42.9%) or resolution (37.1%). CONCLUSIONS: For those patients with systemic malignant disease in whom peroneal neuropathy develops, the outcome of the neuropathy is good, with the majority of patients achieving partial or complete resolution.     

Finding shortest paths on surfaces using level sets propagation

We present a new algorithm for determining minimal length paths between two regions on a three dimensional surface. The numerical implementation is based on finding equal geodesic distance contours from a given area. These contours are calculated as zero sets of a bivariate function designed to evolve so as to track the equal distance curves on the given surface. The algorithm produces all paths of minimal length between the source and destination areas on the surface given as height values on a rectangular grid     

Shape from shading: Level set propagation and viscosity solutions

We present a new implementation of an algorithm aimed at recovering a 3D shape from its 2D gray-level picture. In order to reconstruct the shape of the object, an almost arbitrarily initialized 3D function is propagated on a rectangular grid, so that a level set of this function tracks the height contours of the shape. The method imports techniques from differential geometry, fluid dynamics, and numerical analysis and provides an accurate shape from shading algorithm. The method solves some topological problems and gracefully handles cases of non-smooth surfaces that give rise to shocks in the propagating contours. Real and synthetic images of 3D profiles were submitted to the algorithm and the reconstructed surfaces are presented, demonstrating the effectiveness of the proposed method.R. Kimmel's work was partly supported by the Ollendorf Fund.B.B. Kimia's work was supported by the Sun Microsystems Academic Equipment Grant.A.M. Bruckstein's work was supported in part by the Fund for the Promotion of Research at the Technion.     

Dispersoid growth in Ni-2ThO2 due to self-diffusion sintering of particle clusters

The growth rates and activation energy for growth of thoria particles in thoria dispersion-strengthened nickel were measured after elevated temperature exposure. Growth was determined by direct measurement of thoria particle size on transmission electron micrographs. The thoria particles were distributed spatially in clusters in the hot consolitated alloy. Presence of particle clusters was found to provide the proper environment for a self-diffusion sintering-type particle coarsening. The rapid particle growth observed in this alloy form had too high a rate constant and too low an activation energy to constitute an Ostwald ripening mechanism, and was considered to reflect the grain growth stage of sintering. Further studies showed that thermomechanical treatments of hot consolidated material retarded this type of particle growth, by mechanically dispersing the ThO₂ particle clusters. W. SCHEITHAUER, JR. is currently on leave of absence at Lehigh University, Department of Industrial Engineering.     

Partial Matching of Nonrigid Shapes by Learning Piecewise Smooth Functions

Learning functions defined on non-flat domains, such as outer surfaces of non-rigid shapes, is a central task in computer vision and geometry processing. Recent studies have explored the use of neural fields to represent functions like light reflections in volumetric domains and textures on curved surfaces by operating in the embedding space. Here, we choose a different line of thought and introduce a novel formulation of partial shape matching by learning a piecewise smooth function on a surface. Our method begins with pairing sparse landmarks defined on a full shape and its part, using feature similarity. Next, a neural representation is optimized to fit these landmarks, efficiently interpolating between the matched features that act as anchors. This process results in a function that accurately captures the partiality. Unlike previous methods, the proposed neural model of functions is intrinsically defined on the given curved surface, rather than the classical embedding Euclidean space. This representation is shown to be particularly well-suited for representing piecewise smooth functions. We further extend the proposed framework to the more challenging part-to-part setting, where both shapes exhibit missing parts. Comprehensive experiments highlight that the proposed method effectively addresses partiality in shape matching and significantly outperforms leading state-of-the-art methods in challenging benchmarks. Code is available at https://github.com/davidgip74/Learning-Partiality-with-Implicit-Intrinsic-Functions