1,620 research outputs found
Ratio control in a cascade model of cell differentiation
We propose a kind of reaction-diffusion equations for cell differentiation,
which exhibits the Turing instability. If the diffusivity of some variables is
set to be infinity, we get coupled competitive reaction-diffusion equations
with a global feedback term. The size ratio of each cell type is controlled by
a system parameter in the model. Finally, we extend the model to a cascade
model of cell differentiation. A hierarchical spatial structure appears as a
result of the cell differentiation. The size ratio of each cell type is also
controlled by the system parameter.Comment: 13 pages, 7 figure
Morphogen Transport in Epithelia
We present a general theoretical framework to discuss mechanisms of morphogen
transport and gradient formation in a cell layer. Trafficking events on the
cellular scale lead to transport on larger scales. We discuss in particular the
case of transcytosis where morphogens undergo repeated rounds of
internalization into cells and recycling. Based on a description on the
cellular scale, we derive effective nonlinear transport equations in one and
two dimensions which are valid on larger scales. We derive analytic expressions
for the concentration dependence of the effective diffusion coefficient and the
effective degradation rate. We discuss the effects of a directional bias on
morphogen transport and those of the coupling of the morphogen and receptor
kinetics. Furthermore, we discuss general properties of cellular transport
processes such as the robustness of gradients and relate our results to recent
experiments on the morphogen Decapentaplegic (Dpp) that acts in the fruit fly
Drosophila
HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
Modelling the mass distributions of strong gravitational lenses is often
necessary to use them as astrophysical and cosmological probes. With the high
number of lens systems () expected from upcoming surveys, it is timely
to explore efficient modeling approaches beyond traditional MCMC techniques
that are time consuming. We train a CNN on images of galaxy-scale lenses to
predict the parameters of the SIE mass model (, and ).
To train the network, we simulate images based on real observations from the
HSC Survey for the lens galaxies and from the HUDF as lensed galaxies. We
tested different network architectures, the effect of different data sets, and
using different input distributions of . We find that the CNN
performs well and obtain with the network trained with a uniform distribution
of the following median values with scatter:
, ,
,
and . The bias in is driven by
systems with small . Therefore, when we further predict the multiple
lensed image positions and time delays based on the network output, we apply
the network to the sample limited to . In this case, the offset
between the predicted and input lensed image positions is
and for and ,
respectively. For the fractional difference between the predicted and true time
delay, we obtain . Our CNN is able to predict the SIE
parameters in fractions of a second on a single CPU and with the output we can
predict the image positions and time delays in an automated way, such that we
are able to process efficiently the huge amount of expected lens detections in
the near future.Comment: 17 pages, 14 Figure
HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
Modeling of strong gravitational lenses is a necessity for further
applications in astrophysics and cosmology. Especially with the large number of
detections in current and upcoming surveys such as the Rubin Legacy Survey of
Space and Time (LSST), it is timely to investigate in automated and fast
analysis techniques beyond the traditional and time consuming Markov chain
Monte Carlo sampling methods. Building upon our convolutional neural network
(CNN) presented in Schuldt et al. (2021b), we present here another CNN,
specifically a residual neural network (ResNet), that predicts the five mass
parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center
and , ellipticity and , Einstein radius ) and the
external shear (, ) from ground-based imaging
data. In contrast to our CNN, this ResNet further predicts a 1
uncertainty for each parameter. To train our network, we use our improved
pipeline from Schuldt et al. (2021b) to simulate lens images using real images
of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra
Deep Field as lens galaxies and background sources, respectively. We find
overall very good recoveries for the SIE parameters, while differences remain
in predicting the external shear. From our tests, most likely the low image
resolution is the limiting factor for predicting the external shear. Given the
run time of milli-seconds per system, our network is perfectly suited to
predict the next appearing image and time delays of lensed transients in time.
Therefore, we also present the performance of the network on these quantities
in comparison to our simulations. Our ResNet is able to predict the SIE and
shear parameter values in fractions of a second on a single CPU such that we
are able to process efficiently the huge amount of expected galaxy-scale lenses
in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&
Mode transitions in a model reaction-diffusion system driven by domain growth and noise
Pattern formation in many biological systems takes place during growth of the underlying domain. We study a specific example of a reaction–diffusion (Turing) model in which peak splitting, driven by domain growth, generates a sequence of patterns. We have previously shown that the pattern sequences which are presented when the domain growth rate is sufficiently rapid exhibit a mode-doubling phenomenon. Such pattern sequences afford reliable selection of certain final patterns, thus addressing the robustness problem inherent of the Turing mechanism. At slower domain growth rates this regular mode doubling breaks down in the presence of small perturbations to the dynamics. In this paper we examine the breaking down of the mode doubling sequence and consider the implications of this behaviour in increasing the range of reliably selectable final patterns
Noise-induced inhibitory suppression of malfunction neural oscillators
Motivated by the aim to find new medical strategies to suppress undesirable
neural synchronization we study the control of oscillations in a system of
inhibitory coupled noisy oscillators. Using dynamical properties of inhibition,
we find regimes when the malfunction oscillations can be suppressed but the
information signal of a certain frequency can be transmitted through the
system. The mechanism of this phenomenon is a resonant interplay of noise and
the transmission signal provided by certain value of inhibitory coupling.
Analyzing a system of three or four oscillators representing neural clusters,
we show that this suppression can be effectively controlled by coupling and
noise amplitudes.Comment: 10 pages, 14 figure
HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
We present a systematic search for wide-separation (Einstein radius >1.5"),
galaxy-scale strong lenses in the 30 000 sq.deg of the Pan-STARRS 3pi survey on
the Northern sky. With long time delays of a few days to weeks, such systems
are particularly well suited for catching strongly lensed supernovae with
spatially-resolved multiple images and open new perspectives on early-phase
supernova spectroscopy and cosmography. We produce a set of realistic
simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of
lens luminous red galaxies with known redshift and velocity dispersion from
SDSS. First of all, we compute the photometry of mock lenses in gri bands and
apply a simple catalog-level neural network to identify a sample of 1050207
galaxies with similar colors and magnitudes as the mocks. Secondly, we train a
convolutional neural network (CNN) on Pan-STARRS gri image cutouts to classify
this sample and obtain sets of 105760 and 12382 lens candidates with scores
pCNN>0.5 and >0.9, respectively. Extensive tests show that CNN performances
rely heavily on the design of lens simulations and choice of negative examples
for training, but little on the network architecture. Finally, we visually
inspect all galaxies with pCNN>0.9 to assemble a final set of 330 high-quality
newly-discovered lens candidates while recovering 23 published systems. For a
subset, SDSS spectroscopy on the lens central regions proves our method
correctly identifies lens LRGs at z~0.1-0.7. Five spectra also show robust
signatures of high-redshift background sources and Pan-STARRS imaging confirms
one of them as a quadruply-imaged red source at z_s = 1.185 strongly lensed by
a foreground LRG at z_d = 0.3155. In the future, we expect that the efficient
and automated two-step classification method presented in this paper will be
applicable to the deeper gri stacks from the LSST with minor adjustments.Comment: 18 pages and 11 figures (plus appendix), submitted to A&
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