25,898 research outputs found
Predicting chemical environments of bacteria from receptor signaling
Sensory systems have evolved to respond to input stimuli of certain
statistical properties, and to reliably transmit this information through
biochemical pathways. Hence, for an experimentally well-characterized sensory
system, one ought to be able to extract valuable information about the
statistics of the stimuli. Based on dose-response curves from in vivo
fluorescence resonance energy transfer (FRET) experiments of the bacterial
chemotaxis sensory system, we predict the chemical gradients chemotactic
Escherichia coli cells typically encounter in their natural environment. To
predict average gradients cells experience, we revaluate the phenomenological
Weber's law and its generalizations to the Weber-Fechner law and fold-change
detection. To obtain full distributions of gradients we use information theory
and simulations, considering limitations of information transmission from both
cell-external and internal noise. We identify broad distributions of
exponential gradients, which lead to log-normal stimuli and maximal drift
velocity. Our results thus provide a first step towards deciphering the
chemical nature of complex, experimentally inaccessible cellular
microenvironments, such as the human intestine.Comment: DG and GM contributed equally to this wor
Stability and response of polygenic traits to stabilizing selection and mutation
When polygenic traits are under stabilizing selection, many different
combinations of alleles allow close adaptation to the optimum. If alleles have
equal effects, all combinations that result in the same deviation from the
optimum are equivalent. Furthermore, the genetic variance that is maintained by
mutation-selection balance is per locus, where is the mutation
rate and the strength of stabilizing selection. In reality, alleles vary in
their effects, making the fitness landscape asymmetric, and complicating
analysis of the equilibria. We show that that the resulting genetic variance
depends on the fraction of alleles near fixation, which contribute by , and on the total mutational effects of alleles that are at intermediate
frequency. The interplay between stabilizing selection and mutation leads to a
sharp transition: alleles with effects smaller than a threshold value of
remain polymorphic, whereas those with larger effects are
fixed. The genetic load in equilibrium is less than for traits of equal
effects, and the fitness equilibria are more similar. We find that if the
optimum is displaced, alleles with effects close to the threshold value sweep
first, and their rate of increase is bounded by . Long term
response leads in general to well-adapted traits, unlike the case of equal
effects that often end up at a sub-optimal fitness peak. However, the
particular peaks to which the populations converge are extremely sensitive to
the initial states, and to the speed of the shift of the optimum trait value.Comment: Accepted in Genetic
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Causal contribution and dynamical encoding in the striatum during evidence accumulation.
A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation
Asymmetric Actor Critic for Image-Based Robot Learning
Deep reinforcement learning (RL) has proven a powerful technique in many
sequential decision making domains. However, Robotics poses many challenges for
RL, most notably training on a physical system can be expensive and dangerous,
which has sparked significant interest in learning control policies using a
physics simulator. While several recent works have shown promising results in
transferring policies trained in simulation to the real world, they often do
not fully utilize the advantage of working with a simulator. In this work, we
exploit the full state observability in the simulator to train better policies
which take as input only partial observations (RGBD images). We do this by
employing an actor-critic training algorithm in which the critic is trained on
full states while the actor (or policy) gets rendered images as input. We show
experimentally on a range of simulated tasks that using these asymmetric inputs
significantly improves performance. Finally, we combine this method with domain
randomization and show real robot experiments for several tasks like picking,
pushing, and moving a block. We achieve this simulation to real world transfer
without training on any real world data.Comment: Videos of experiments can be found at http://www.goo.gl/b57WT
Panel estimation of the impact of exchange rate uncertainty on investment in the major industrial countries
We estimate the impact of exchange rate uncertainty on investment, using panel estimation
featuring a decomposition of exchange rate volatility derived from the components
GARCH model of Engle and Lee (1999). For a poolable subsample of EU countries, it is
the transitory and not the permanent component of volatility which adversely affects
investment, implying high frequency shocks of the type that may be generated by volatile
short term capital flows are most deleterious for investment. Results based on EGARCH
also suggest that the response of investment to exchange rate uncertainty may depend
partly on the sign of the initial shock. (100 words
Adaptive evolution of molecular phenotypes
Molecular phenotypes link genomic information with organismic functions,
fitness, and evolution. Quantitative traits are complex phenotypes that depend
on multiple genomic loci. In this paper, we study the adaptive evolution of a
quantitative trait under time-dependent selection, which arises from
environmental changes or through fitness interactions with other co-evolving
phenotypes. We analyze a model of trait evolution under mutations and genetic
drift in a single-peak fitness seascape. The fitness peak performs a
constrained random walk in the trait amplitude, which determines the
time-dependent trait optimum in a given population. We derive analytical
expressions for the distribution of the time-dependent trait divergence between
populations and of the trait diversity within populations. Based on this
solution, we develop a method to infer adaptive evolution of quantitative
traits. Specifically, we show that the ratio of the average trait divergence
and the diversity is a universal function of evolutionary time, which predicts
the stabilizing strength and the driving rate of the fitness seascape. From an
information-theoretic point of view, this function measures the
macro-evolutionary entropy in a population ensemble, which determines the
predictability of the evolutionary process. Our solution also quantifies two
key characteristics of adapting populations: the cumulative fitness flux, which
measures the total amount of adaptation, and the adaptive load, which is the
fitness cost due to a population's lag behind the fitness peak.Comment: Figures are not optimally displayed in Firefo
Valuing Volatility Spillovers
forecasting, adcc, volatility spillovers, valuing
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