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 $w$ 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 $d_{L}$ with redshift $z$ by comparing (normalizing) it with the $\Lambda$CDM model. Since the equation of state parameter is $z$-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

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

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