835 research outputs found
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
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