Linear versus nonlinear methods of sire evaluation for categorical traits: a simulation study

Linear (BLUP) and nonlinear (GFCAT) methods of sire evaluation for categorical data were compared using Monte Carlo techniques. Binary and ordered tetrachotomous responses were generated from an underlying normal distribution via fixed thresholds, so as to model incidences in the population as a whole. Sires were sampled from a normal distribution and family structure consisted of half-sib groups of equal or unequal size; simulations were done at several levels of heritability (h2). When a one-way model was tenable or when responses were tetrachotomous, the differences between the 2 methods were negligible. However, when responses were binary, the layout was highly unbalanced and a mixed model was appropriate to describe the underlying variate, GFCAT elicited significantly larger responses to truncation selection than BLUP at h2.20 or.50 and when the incidence in the = population was below 25 p. 100. The largest observed difference in selection efficiency between the 2 methods was 12 p. 100

New insight into cataract formation -- enhanced stability through mutual attraction

Small-angle neutron scattering experiments and molecular dynamics simulations combined with an application of concepts from soft matter physics to complex protein mixtures provide new insight into the stability of eye lens protein mixtures. Exploring this colloid-protein analogy we demonstrate that weak attractions between unlike proteins help to maintain lens transparency in an extremely sensitive and non-monotonic manner. These results not only represent an important step towards a better understanding of protein condensation diseases such as cataract formation, but provide general guidelines for tuning the stability of colloid mixtures, a topic relevant for soft matter physics and industrial applications.Comment: 4 pages, 4 figures. Accepted for publication on Phys. Rev. Let

Point Source Extraction with MOPEX

MOPEX (MOsaicking and Point source EXtraction) is a package developed at the Spitzer Science Center for astronomical image processing. We report on the point source extraction capabilities of MOPEX. Point source extraction is implemented as a two step process: point source detection and profile fitting. Non-linear matched filtering of input images can be performed optionally to increase the signal-to-noise ratio and improve detection of faint point sources. Point Response Function (PRF) fitting of point sources produces the final point source list which includes the fluxes and improved positions of the point sources, along with other parameters characterizing the fit. Passive and active deblending allows for successful fitting of confused point sources. Aperture photometry can also be computed for every extracted point source for an unlimited number of aperture sizes. PRF is estimated directly from the input images. Implementation of efficient methods of background and noise estimation, and modified Simplex algorithm contribute to the computational efficiency of MOPEX. The package is implemented as a loosely connected set of perl scripts, where each script runs a number of modules written in C/C++. Input parameter setting is done through namelists, ASCII configuration files. We present applications of point source extraction to the mosaic images taken at 24 and 70 micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer extragalactic First Look Survey and to a Digital Sky Survey image. Completeness and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS

Ontdek het verborgen rendement in uw bedrijf

Het zit verborgen in de minder sterke punten in uw bedrijfsvoering, onvolkomenheden die bij verbetering al snel tot een flinke rendementsverbetering kunnen leiden

Cell size distribution in a random tessellation of space governed by the Kolmogorov-Johnson-Mehl-Avrami model: Grain size distribution in crystallization

The space subdivision in cells resulting from a process of random nucleation and growth is a subject of interest in many scientific fields. In this paper, we deduce the expected value and variance of these distributions while assuming that the space subdivision process is in accordance with the premises of the Kolmogorov-Johnson-Mehl-Avrami model. We have not imposed restrictions on the time dependency of nucleation and growth rates. We have also developed an approximate analytical cell size probability density function. Finally, we have applied our approach to the distributions resulting from solid phase crystallization under isochronal heating conditions

New Monte Carlo method for planar Poisson-Voronoi cells

By a new Monte Carlo algorithm we evaluate the sidedness probability p_n of a planar Poisson-Voronoi cell in the range 3 \leq n \leq 1600. The algorithm is developed on the basis of earlier theoretical work; it exploits, in particular, the known asymptotic behavior of p_n as n\to\infty. Our p_n values all have between four and six significant digits. Accurate n dependent averages, second moments, and variances are obtained for the cell area and the cell perimeter. The numerical large n behavior of these quantities is analyzed in terms of asymptotic power series in 1/n. Snapshots are shown of typical occurrences of extremely rare events implicating cells of up to n=1600 sides embedded in an ordinary Poisson-Voronoi diagram. We reveal and discuss the characteristic features of such many-sided cells and their immediate environment. Their relevance for observable properties is stressed.Comment: 35 pages including 10 figures and 4 table

hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

Gene expression can be used to subtype breast cancer with improved prediction of risk of recurrence and treatment responsiveness over that obtained using routine immunohistochemistry (IHC). However, in the clinic, molecular profiling is primarily used for ER+ cancer and is costly and tissue destructive, requires specialized platforms and takes several weeks to obtain a result. Deep learning algorithms can effectively extract morphological patterns in digital histopathology images to predict molecular phenotypes quickly and cost-effectively. We propose a new, computationally efficient approach called hist2RNA inspired by bulk RNA-sequencing techniques to predict the expression of 138 genes (incorporated from six commercially available molecular profiling tests), including luminal PAM50 subtype, from hematoxylin and eosin (H&E) stained whole slide images (WSIs). The training phase involves the aggregation of extracted features for each patient from a pretrained model to predict gene expression at the patient level using annotated H&E images from The Cancer Genome Atlas (TCGA, n=335). We demonstrate successful gene prediction on a held-out test set (n=160, corr=0.82 across patients, corr=0.29 across genes) and perform exploratory analysis on an external tissue microarray (TMA) dataset (n=498) with known IHC and survival information. Our model is able to predict gene expression and luminal PAM50 subtype (Luminal A versus Luminal B) on the TMA dataset with prognostic significance for overall survival in univariate analysis (c-index=0.56, hazard ratio=2.16, p<0.005), and independent significance in multivariate analysis incorporating standard clinicopathological variables (c-index=0.65, hazard ratio=1.85, p<0.005).Comment: 15 pages, 10 figures, 2 table