831 research outputs found
An equation of state for purely kinetic k-essence inspired by cosmic topological defects
We investigate the physical properties of a purely kinetic k-essence model
with an equation of state motivated in superconducting membranes. We compute
the equation of state parameter and discuss its physical evolution via a
nonlinear equation of state. Using the adiabatic speed of sound and energy
density, we restrict the range of parameters of the model in order to have an
acceptable physical behavior. Furthermore, we analyze the evolution of the
luminosity distance with redshift by comparing (normalizing) it
with the CDM model. Since the equation of state parameter is
-dependent the evolution of the luminosity distance is also analyzed using
the Alcock-Paczy\'{n}ski test.Comment: 19 pages, 13 figures, typos corrected and references adde
The Ribosome Uses Cooperative Conformational Changes to Maximize the Efficiency of Protein Synthesis
Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation
Determining the mechanism by which transfer RNAs (tRNAs) rapidly and
precisely transit through the ribosomal A, P and E sites during translation
remains a major goal in the study of protein synthesis. Here, we report the
real-time dynamics of the L1 stalk, a structural element of the large ribosomal
subunit that is implicated in directing tRNA movements during translation.
Within pre-translocation ribosomal complexes, the L1 stalk exists in a dynamic
equilibrium between open and closed conformations. Binding of elongation factor
G (EF-G) shifts this equilibrium towards the closed conformation through one of
at least two distinct kinetic mechanisms, where the identity of the P-site tRNA
dictates the kinetic route that is taken. Within post-translocation complexes,
L1 stalk dynamics are dependent on the presence and identity of the E-site
tRNA. Collectively, our data demonstrate that EF-G and the L1 stalk
allosterically collaborate to direct tRNA translocation from the P to the E
sites, and suggest a model for the release of E-site tRNA
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
We address the problem of analyzing sets of noisy time-varying signals that
all report on the same process but confound straightforward analyses due to
complex inter-signal heterogeneities and measurement artifacts. In particular
we consider single-molecule experiments which indirectly measure the distinct
steps in a biomolecular process via observations of noisy time-dependent
signals such as a fluorescence intensity or bead position. Straightforward
hidden Markov model (HMM) analyses attempt to characterize such processes in
terms of a set of conformational states, the transitions that can occur between
these states, and the associated rates at which those transitions occur; but
require ad-hoc post-processing steps to combine multiple signals. Here we
develop a hierarchically coupled HMM that allows experimentalists to deal with
inter-signal variability in a principled and automatic way. Our approach is a
generalized expectation maximization hyperparameter point estimation procedure
with variational Bayes at the level of individual time series that learns an
single interpretable representation of the overall data generating process.Comment: 9 pages, 5 figure
Comparison of X-Ray Crystal Structure of the 30S Subunit-Antibiotic Complex with NMR Structure of Decoding Site Oligonucleotide-Paromomycin Complex
AbstractAminoglycoside antibiotics that bind to 16S ribosomal RNA in the aminoacyl-tRNA site (A site) cause misreading of the genetic code and inhibit translocation. Structures of an A site RNA oligonucleotide free in solution and bound to the aminoglycosides paromomycin or gentamicin C1a have been determined by NMR. Recently, the X-ray crystal structure of the entire 30S subunit has been determined, free and bound to paromomycin. Distinct differences were observed in the crystal structure, particularly at A1493. Here, the NMR structure of the oligonucleotide-paromomycin complex was determined with higher precision and is compared with the X-ray crystal structure of the 30S subunit complex. The comparison shows the validity of both structures in identifying critical interactions that affect ribosome function
A silicon-based single-electron interferometer coupled to a fermionic sea
We study Landau-Zener-Stueckelberg-Majorana (LZSM) interferometry under the
influence of projective readout using a charge qubit tunnel-coupled to a
fermionic sea. This allows us to characterise the coherent charge qubit
dynamics in the strong-driving regime. The device is realised within a silicon
complementary metal-oxide-semiconductor (CMOS) transistor. We first read out
the charge state of the system in a continuous non-demolition manner by
measuring the dispersive response of a high-frequency electrical resonator
coupled to the quantum system via the gate. By performing multiple fast
passages around the qubit avoided crossing, we observe a multi-passage LZSM
interferometry pattern. At larger driving amplitudes, a projective measurement
to an even-parity charge state is realised, showing a strong enhancement of the
dispersive readout signal. At even larger driving amplitudes, two projective
measurements are realised within the coherent evolution resulting in the
disappearance of the interference pattern. Our results demonstrate a way to
increase the state readout signal of coherent quantum systems and replicate
single-electron analogues of optical interferometry within a CMOS transistor
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Graphical models for inferring single molecule dynamics
The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.
The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.
The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics
High thermal tolerance in high-elevation species and laboratory-reared colonies of tropical bumble bees
Bumble bees are key pollinators with some species reared in captivity at a commercial scale, but with significant evidence of population declines and with alarming predictions of substantial impacts under climate change scenarios. While studies on the thermal biology of temperate bumble bees are still limited, they are entirely absent from the tropics where the effects of climate change are expected to be greater. Herein, we test whether bees' thermal tolerance decreases with elevation and whether the stable optimal conditions used in laboratory-reared colonies reduces their thermal tolerance. We assessed changes in the lower (CTMin) and upper (CTMax) critical thermal limits of four species at two elevations (2600 and 3600 m) in the Colombian Andes, examined the effect of body size, and evaluated the thermal tolerance of wild-caught and laboratory-reared individuals of Bombus pauloensis. We also compiled information on bumble bees' thermal limits and assessed potential predictors for broadscale patterns of variation. We found that CTMin decreased with increasing elevation, while CTMax was similar between elevations. CTMax was slightly higher (0.84°C) in laboratory-reared than in wild-caught bees while CTMin was similar, and CTMin decreased with increasing body size while CTMax did not. Latitude is a good predictor for CTMin while annual mean temperature, maximum and minimum temperatures of the warmest and coldest months are good predictors for both CTMin and CTMax. The stronger response in CTMin with increasing elevation, and similar CTMax, supports Brett's heat-invariant hypothesis, which has been documented in other taxa. Andean bumble bees appear to be about as heat tolerant as those from temperate areas, suggesting that other aspects besides temperature (e.g., water balance) might be more determinant environmental factors for these species. Laboratory-reared colonies are adequate surrogates for addressing questions on thermal tolerance and global warming impacts
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