50,248 research outputs found
Optimizing experimental parameters for tracking of diffusing particles
We describe how a single-particle tracking experiment should be designed in
order for its recorded trajectories to contain the most information about a
tracked particle's diffusion coefficient. The precision of estimators for the
diffusion coefficient is affected by motion blur, limited photon statistics,
and the length of recorded time-series. We demonstrate for a particle
undergoing free diffusion that precision is negligibly affected by motion blur
in typical experiments, while optimizing photon counts and the number of
recorded frames is the key to precision. Building on these results, we describe
for a wide range of experimental scenarios how to choose experimental
parameters in order to optimize the precision. Generally, one should choose
quantity over quality: experiments should be designed to maximize the number of
frames recorded in a time-series, even if this means lower information content
in individual frames
Estimating the reproduction number of Ebola virus (EBOV) during the 2014 outbreak in West Africa
The 2014 Ebola virus (EBOV) outbreak in West Africa is the largest outbreak
of the genus Ebolavirus to date. To better understand the spread of infection
in the affected countries, it is crucial to know the number of secondary cases
generated by an infected index case in the absence and presence of control
measures, i.e., the basic and effective reproduction number. In this study, I
describe the EBOV epidemic using an SEIR
(susceptible-exposed-infectious-recovered) model and fit the model to the most
recent reported data of infected cases and deaths in Guinea, Sierra Leone and
Liberia. The maximum likelihood estimates of the basic reproduction number are
1.51 (95% confidence interval [CI]: 1.50-1.52) for Guinea, 2.53 (95% CI:
2.41-2.67) for Sierra Leone and 1.59 (95% CI: 1.57-1.60) for Liberia. The model
indicates that in Guinea and Sierra Leone the effective reproduction number
might have dropped to around unity by the end of May and July 2014,
respectively. In Liberia, however, the model estimates no decline in the
effective reproduction number by end-August 2014. This suggests that control
efforts in Liberia need to be improved substantially in order to stop the
current outbreak.Comment: Published version, PLOS Currents Outbreaks. 2014 Sep
Dean and Hearlson\u27s How youth ministry can change theological education – If we let it (Book Review)
A review of Dean, K.C., & Hearlson, C.L. (Eds.). (2016). How youth ministry can change theological education – If we let it. Grand Rapids, MI: William B. Eerdmans Publishing. 331 pp. $30.00. ISBN 978080287193
Constraining gravity at large scales with the 2MASS Photometric Redshift catalogue and Planck lensing
We present a new measurement of structure growth at obtained
by correlating the cosmic microwave background (CMB) lensing potential map from
the \textit{Planck} satellite with the angular distribution of the 2MASS
Photometric Redshift galaxies. After testing for, and finding no evidence for
systematic effects, we calculate the angular auto- and cross-power spectra. We
combine these spectra to estimate the amplitude of structure growth using the
bias-independent estimator introduced by Giannantonio et al. 2016. We
find that the relative amplitude of with respect to the predictions based
on \textit{Planck} cosmology is , fully consistent
with the expectations for the standard cosmological model. Considering
statistical errors only, we forecast that a joint analysis between an LSST-like
photometric galaxy sample and lensing maps from upcoming ground-based CMB
surveys like the Simons Observatory and CMB-S4 can yield sub-percent
constraints on the growth history and differentiate between different models of
cosmic acceleration.Comment: 14 pages, 8 figures, 1 table, updated to match published version on
Ap
Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks
Stochastic simulations are one of the cornerstones of the analysis of
dynamical processes on complex networks, and are often the only accessible way
to explore their behavior. The development of fast algorithms is paramount to
allow large-scale simulations. The Gillespie algorithm can be used for fast
simulation of stochastic processes, and variants of it have been applied to
simulate dynamical processes on static networks. However, its adaptation to
temporal networks remains non-trivial. We here present a temporal Gillespie
algorithm that solves this problem. Our method is applicable to general Poisson
(constant-rate) processes on temporal networks, stochastically exact, and up to
multiple orders of magnitude faster than traditional simulation schemes based
on rejection sampling. We also show how it can be extended to simulate
non-Markovian processes. The algorithm is easily applicable in practice, and as
an illustration we detail how to simulate both Poissonian and non-Markovian
models of epidemic spreading. Namely, we provide pseudocode and its
implementation in C++ for simulating the paradigmatic
Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and
a Susceptible-Infected-Recovered model with non-constant recovery rates. For
empirical networks, the temporal Gillespie algorithm is here typically from 10
to 100 times faster than rejection sampling.Comment: Minor changes and updates to reference
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