1,422 research outputs found
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Phosphoenolpyruvate Carboxykinase as the Sole Anaplerotic Enzyme in Saccharomyces cerevisiae
Pyruvate carboxylase is the sole anaplerotic enzyme in glucose-grown cultures of wild-type Saccharomyces cerevisiae. Pyruvate carboxylase-negative (Pyc–) S. cerevisiae strains cannot grow on glucose unless media are supplemented with C4 compounds, such as aspartic acid. In several succinate-producing prokaryotes, phosphoenolpyruvate carboxykinase (PEPCK) fulfills this anaplerotic role. However, the S. cerevisiae PEPCK encoded by PCK1 is repressed by glucose and is considered to have a purely decarboxylating and gluconeogenic function. This study investigates whether and under which conditions PEPCK can replace the anaplerotic function of pyruvate carboxylase in S. cerevisiae. Pyc– S. cerevisiae strains constitutively overexpressing the PEPCK either from S. cerevisiae or from Actinobacillus succinogenes did not grow on glucose as the sole carbon source. However, evolutionary engineering yielded mutants able to grow on glucose as the sole carbon source at a maximum specific growth rate of ca. 0.14 h–1, one-half that of the (pyruvate carboxylase-positive) reference strain grown under the same conditions. Growth was dependent on high carbon dioxide concentrations, indicating that the reaction catalyzed by PEPCK operates near thermodynamic equilibrium. Analysis and reverse engineering of two independently evolved strains showed that single point mutations in pyruvate kinase, which competes with PEPCK for phosphoenolpyruvate, were sufficient to enable the use of PEPCK as the sole anaplerotic enzyme. The PEPCK reaction produces one ATP per carboxylation event, whereas the original route through pyruvate kinase and pyruvate carboxylase is ATP neutral. This increased ATP yield may prove crucial for engineering of efficient and low-cost anaerobic production of C4 dicarboxylic acids in S. cerevisiae
Quantum rejection sampling
Rejection sampling is a well-known method to sample from a target
distribution, given the ability to sample from a given distribution. The method
has been first formalized by von Neumann (1951) and has many applications in
classical computing. We define a quantum analogue of rejection sampling: given
a black box producing a coherent superposition of (possibly unknown) quantum
states with some amplitudes, the problem is to prepare a coherent superposition
of the same states, albeit with different target amplitudes. The main result of
this paper is a tight characterization of the query complexity of this quantum
state generation problem. We exhibit an algorithm, which we call quantum
rejection sampling, and analyze its cost using semidefinite programming. Our
proof of a matching lower bound is based on the automorphism principle which
allows to symmetrize any algorithm over the automorphism group of the problem.
Our main technical innovation is an extension of the automorphism principle to
continuous groups that arise for quantum state generation problems where the
oracle encodes unknown quantum states, instead of just classical data.
Furthermore, we illustrate how quantum rejection sampling may be used as a
primitive in designing quantum algorithms, by providing three different
applications. We first show that it was implicitly used in the quantum
algorithm for linear systems of equations by Harrow, Hassidim and Lloyd.
Secondly, we show that it can be used to speed up the main step in the quantum
Metropolis sampling algorithm by Temme et al.. Finally, we derive a new quantum
algorithm for the hidden shift problem of an arbitrary Boolean function and
relate its query complexity to "water-filling" of the Fourier spectrum.Comment: 19 pages, 5 figures, minor changes and a more compact style (to
appear in proceedings of ITCS 2012
Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of
approximate Bayesian inference based on optimal transport theory. Wasserstein
variational inference uses a new family of divergences that includes both
f-divergences and the Wasserstein distance as special cases. The gradients of
the Wasserstein variational loss are obtained by backpropagating through the
Sinkhorn iterations. This technique results in a very stable likelihood-free
training method that can be used with implicit distributions and probabilistic
programs. Using the Wasserstein variational inference framework, we introduce
several new forms of autoencoders and test their robustness and performance
against existing variational autoencoding techniques.Comment: 8 pages, 1 figur
Weak Acid Permeation in Synthetic Lipid Vesicles and Across the Yeast Plasma Membrane
We present a fluorescence-based approach for determination of the permeability of small molecules across the membranes of lipid vesicles and living cells. With properly designed experiments, the method allows us to assess the membrane physical properties both in vitro and in vivo. We find that the permeability of weak acids increases in the order of benzoic > acetic > formic > lactic, both in synthetic lipid vesicles and the plasma membrane of Saccharomyces cerevisiae, but the permeability is much lower in yeast (one to two orders of magnitude). We observe a relation between the molecule permeability and the saturation of the lipid acyl chain (i.e., lipid packing) in the synthetic lipid vesicles. By analyzing wild-type yeast and a manifold knockout strain lacking all putative lactic acid transporters, we conclude that the yeast plasma membrane is impermeable to lactic acid on timescales up to ∼2.5 h.BT/Industrial Microbiolog
Forward Amortized Inference for Likelihood-Free Variational Marginalization
In this paper, we introduce a new form of amortized variational inference by
using the forward KL divergence in a joint-contrastive variational loss. The
resulting forward amortized variational inference is a likelihood-free method
as its gradient can be sampled without bias and without requiring any
evaluation of either the model joint distribution or its derivatives. We prove
that our new variational loss is optimized by the exact posterior marginals in
the fully factorized mean-field approximation, a property that is not shared
with the more conventional reverse KL inference. Furthermore, we show that
forward amortized inference can be easily marginalized over large families of
latent variables in order to obtain a marginalized variational posterior. We
consider two examples of variational marginalization. In our first example we
train a Bayesian forecaster for predicting a simplified chaotic model of
atmospheric convection. In the second example we train an amortized variational
approximation of a Bayesian optimal classifier by marginalizing over the model
space. The result is a powerful meta-classification network that can solve
arbitrary classification problems without further training.Comment: 9 pages, 3 figure
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