1,521 research outputs found
Clustering Coefficients of Protein-Protein Interaction Networks
The properties of certain networks are determined by hidden variables that
are not explicitly measured. The conditional probability (propagator) that a
vertex with a given value of the hidden variable is connected to k of other
vertices determines all measurable properties. We study hidden variable models
and find an averaging approximation that enables us to obtain a general
analytical result for the propagator. Analytic results showing the validity of
the approximation are obtained. We apply hidden variable models to
protein-protein interaction networks (PINs) in which the hidden variable is the
association free-energy, determined by distributions that depend on
biochemistry and evolution. We compute degree distributions as well as
clustering coefficients of several PINs of different species; good agreement
with measured data is obtained. For the human interactome two different
parameter sets give the same degree distributions, but the computed clustering
coefficients differ by a factor of about two. This shows that degree
distributions are not sufficient to determine the properties of PINs.Comment: 16 pages, 3 figures, in Press PRE uses pdflate
Exposure of prepubertal beef bulls to cycling females does not enhance sexual development
The objective of this study was to determine whether continuous, long-term, fenceline
exposure of prepubertal beef bulls to cycling beef females reduced age at puberty and influenced the percentage of bulls that passed an initial breeding soundness examination (BSE). Bulls (Angus, N = 37; Simmental, N = 22; Hereford, N = 10; Simmental x Angus, N = 8) averaging 202 ± 21.5 d of age were given either continuous fenceline and visual exposure to cycling females (Exposed: N= 41) or no exposure (Control: N=36). Estrus was induced in cycling beef females so at least 3 females were in standing estrus each week during the 182 d of exposure to bulls. Scrotal circumference (SC), body weight, and blood samples were collected every 28 d. When bulls had SC ≥ 26 cm, semen samples were obtained monthly via electroejaculation until puberty was achieved (≥ 50 x 106 sperm/mL with at least 10% progressive motility). Behavioral
observations were conducted twice monthly, once when females were in estrus and once during diestrus. Homosexual mounting, flehmen responses, and number of times near penned females were recorded for each observation period. Breeding soundness examinations were conducted when bulls averaged 364 ± 21.5 d of age. Normal sperm morphology of at least 70% and sperm motility of at least 30% were required to pass the BSE. Age, body weight, and SC at puberty did not differ between Exposed and Control bulls (320 ± 28 d and 311 ± 29 d; 466.2 ± 12.2 and 437.7 ± 13.5 kg; and 34.4 ± 2.5 cm and 34.9 ± 2.5 cm, respectively). Percentage of bulls passing their initial BSE did not differ between treatments (Exposed: 87.8%, Control: 75.0%). Treatment, month, and female estrous stage interacted (P = 0.05) to affect the number of mount attempts and flehmen responses. Exposed bulls entered the cow area more times (P < 0.001)
during estrus than diestrus in months one, two and three. We concluded that bulls given
3 continuous, long-term, fenceline exposure to cycling beef females do not have enhanced sexual development
IMPACT BEHAVIOUR OF THE BASEBALL WITH IMPLICATIONS FOR PLAYER SAFETY
Fatalities amongst baseball pitchers occur primarily as a result of impact by the balled ball (Adler & Monticone, 1996). It was hypothesized the material properties of the baseball would affect ball exit velocity after bat-ball impact, and thereby the available time for the pitcher to take evasive action. Material behaviour of the baseball under conditions representative of those during bat-ball impact have not been previously investigated. The results of quasistatic compression testing were used to develop a mathematical model of the nonlinear viscoelastic behaviour of the baseball as the basis for dynamic analysis of the bat-ball impact
Random boundaries: quantifying segmentation uncertainty in solutions to boundary-value problems
Engineering simulations using boundary-value partial differential equations
often implicitly assume that the uncertainty in the location of the boundary
has a negligible impact on the output of the simulation. In this work, we
develop a novel method for describing the geometric uncertainty in
image-derived models and use a naive method for subsequently quantifying a
simulation's sensitivity to that uncertainty. A Gaussian random field is
constructed to represent the space of possible geometries, based on
image-derived quantities such as pixel size, which can then be used to probe
the simulation's output space. The algorithm is demonstrated with examples from
biomechanics where patient-specific geometries are often segmented from
low-resolution, three-dimensional images. These examples show the method's wide
applicability with examples using linear elasticity and fluid dynamics. We show
that important biomechanical outputs of these example simulations, namely
maximum principal stress and wall shear stress, can be highly sensitive to
realistic uncertainties in geometry
A Better Definition of the Kilogram
This article reviews several recent proposed redefinitions of the kilogram,
and compares them with respect to practical realizations, uncertainties
(estimated standard deviations), and educational aspects.Comment: 10 pages, no figure
Improving Indoor Environmental Quality for Public Health: Impediments and Policy Recommendations
Automated modeling of brain bioelectric activity within the 3D Slicer environment
Electrocorticography (ECoG) or intracranial electroencephalography (iEEG)
monitors electric potential directly on the surface of the brain and can be
used to inform treatment planning for epilepsy surgery when paired with
numerical modeling. For solving the inverse problem in epilepsy seizure onset
localization, accurate solution of the iEEG forward problem is critical which
requires accurate representation of the patient's brain geometry and tissue
electrical conductivity. In this study, we present an automatic framework for
constructing the brain volume conductor model for solving the iEEG forward
problem and visualizing the brain bioelectric field on a deformed
patient-specific brain model within the 3D Slicer environment. We solve the
iEEG forward problem on the predicted postoperative geometry using the finite
element method (FEM) which accounts for patient-specific inhomogeneity and
anisotropy of tissue conductivity. We use an epilepsy case study to illustrate
the workflow of our framework developed and integrated within 3D Slicer
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