Closure of the Monte Carlo dynamical equation in the spherical Sherrington-Kirkpatrick model

We study the analytical solution of the Monte Carlo dynamics in the spherical Sherrington-Kirkpatrick model using the technique of the generating function. Explicit solutions for one-time observables (like the energy) and two-time observables (like the correlation and response function) are obtained. We show that the crucial quantity which governs the dynamics is the acceptance rate. At zero temperature, an adiabatic approximation reveals that the relaxational behavior of the model corresponds to that of a single harmonic oscillator with an effective renormalized mass.

Stochastic semiclassical cosmological models

We consider the classical stochastic fluctuations of spacetime geometry induced by quantum fluctuations of massless nonconformal matter fields in the early Universe. To this end, we supplement the stress-energy tensor of these fields with a stochastic part, which is computed along the lines of the Feynman-Vernon and Schwinger-Keldysh techniques; the Einstein equation is therefore upgraded to a so-called Einstein-Langevin equation. We consider in some detail the conformal fluctuations of flat spacetime and the fluctuations of the scale factor in a simple cosmological model introduced by Hartle, which consists of a spatially flat isotropic cosmology driven by radiation and dust.

Dark energy equation of state and anthropic selection

We explore the possibility that the dark energy is due to a potential of a scalar field and that the magnitude and the slope of this potential in our part of the Universe are largely determined by anthropic selection effects. We find that, in some models, the most probable values of the slope are very small, implying that the dark energy density stays constant to very high accuracy throughout cosmological evolution. In other models, however, the most probable values of the slope are such that the slow roll condition is only marginally satisfied, leading to a recollapse of the local universe on a time scale comparable to the lifetime of the Sun. In the latter case, the effective equation of state varies appreciably with the redshift, leading to a number of testable predictions.

Metastable liquid-liquid phase transition in a single-component system with only one crystal phase and no density anomaly

We investigate the phase behavior of a single-component system in three dimensions with spherically-symmetric, pairwise-additive, soft-core interactions with an attractive well at a long distance, a repulsive soft-core shoulder at an intermediate distance, and a hard-core repulsion at a short distance, similar to potentials used to describe liquid systems such as colloids, protein solutions, or liquid metals. We showed [Nature (London) 409, 692 (2001)] that, even with no evidence of the density anomaly, the phase diagram has two first-order fluid-fluid phase transitions, one ending in a gas¿low-density-liquid (LDL) critical point, and the other in a gas¿high-density-liquid (HDL) critical point, with a LDL-HDL phase transition at low temperatures. Here we use integral equation calculations to explore the three-parameter space of the soft-core potential and perform molecular dynamics simulations in the interesting region of parameters. For the equilibrium phase diagram, we analyze the structure of the crystal phase and find that, within the considered range of densities, the structure is independent of the density. Then, we analyze in detail the fluid metastable phases and, by explicit thermodynamic calculation in the supercooled phase, we show the absence of the density anomaly. We suggest that this absence is related to the presence of only one stable crystal structure.

A single point mutation in the pore region of the epithelial Na+ channel changes ion selectivity by modifying molecular sieving.

The epithelial Na+ channel (ENaC) belongs to a new class of channel proteins called the ENaC/DEG superfamily involved in epithelial Na+ transport, mechanotransduction, and neurotransmission. The role of ENaC in Na+ homeostasis and in the control of blood pressure has been demonstrated recently by the identification of mutations in ENaC beta and gamma subunits causing hypertension. The function of ENaC in Na+ reabsorption depends critically on its ability to discriminate between Na+ and other ions like K+ or Ca2+. ENaC is virtually impermeant to K+ ions, and the molecular basis for its high ionic selectivity is largely unknown. We have identified a conserved Ser residue in the second transmembrane domain of the ENaC alpha subunit (alphaS589), which when mutated allows larger ions such as K+, Rb+, Cs+, and divalent cations to pass through the channel. The relative ion permeability of each of the alphaS589 mutants is related inversely to the ionic radius of the permeant ion, indicating that alphaS589 mutations increase the molecular cutoff of the channel by modifying the pore geometry at the selectivity filter. Proper geometry of the pore is required to tightly accommodate Na+ and Li+ ions and to exclude larger cations. We provide evidence that ENaC discriminates between cations mainly on the basis of their size and the energy of dehydration.

How single-trial electrical neuroimaging contributes to multisensory research.

This study details a method to statistically determine, on a millisecond scale and for individual subjects, those brain areas whose activity differs between experimental conditions, using single-trial scalp-recorded EEG data. To do this, we non-invasively estimated local field potentials (LFPs) using the ELECTRA distributed inverse solution and applied non-parametric statistical tests at each brain voxel and for each time point. This yields a spatio-temporal activation pattern of differential brain responses. The method is illustrated here in the analysis of auditory-somatosensory (AS) multisensory interactions in four subjects. Differential multisensory responses were temporally and spatially consistent across individuals, with onset at approximately 50 ms and superposition within areas of the posterior superior temporal cortex that have traditionally been considered auditory in their function. The close agreement of these results with previous investigations of AS multisensory interactions suggests that the present approach constitutes a reliable method for studying multisensory processing with the temporal and spatial resolution required to elucidate several existing questions in this field. In particular, the present analyses permit a more direct comparison between human and animal studies of multisensory interactions and can be extended to examine correlation between electrophysiological phenomena and behavior.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Structural assessment of single amino acid mutations: application to TP53 function.

Single amino acid substitution is the type of protein alteration most related to human diseases. Current studies seek primarily to distinguish neutral mutations from harmful ones. Very few methods offer an explanation of the final prediction result in terms of the probable structural or functional effect on the protein. In this study, we describe the use of three novel parameters to identify experimentally-verified critical residues of the TP53 protein (p53). The first two parameters make use of a surface clustering method to calculate the protein surface area of highly conserved regions or regions with high nonlocal atomic interaction energy (ANOLEA) score. These parameters help identify important functional regions on the surface of a protein. The last parameter involves the use of a new method for pseudobinding free-energy estimation to specifically probe the importance of residue side-chains to the stability of protein fold. A decision tree was designed to optimally combine these three parameters. The result was compared to the functional data stored in the International Agency for Research on Cancer (IARC) TP53 mutation database. The final prediction achieved a prediction accuracy of 70% and a Matthews correlation coefficient of 0.45. It also showed a high specificity of 91.8%. Mutations in the 85 correctly identified important residues represented 81.7% of the total mutations recorded in the database. In addition, the method was able to correctly assign a probable functional or structural role to the residues. Such information could be critical for the interpretation and prediction of the effect of missense mutations, as it not only provided the fundamental explanation of the observed effect, but also helped design the most appropriate laboratory experiment to verify the prediction results.

Critical single proximal left arterial descending coronary artery stenosis to mimic chronic myocardial ischemia: a new model induced by minimal invasive technology.

BACKGROUND/AIMS: The present report examines a new pig model for progressive induction of high-grade stenosis, for the study of chronic myocardial ischemia and the dynamics of collateral vessel growth. METHODS: Thirty-nine Landrace pigs were instrumented with a novel experimental stent (GVD stent) in the left anterior descending coronary artery. Eight animals underwent transthoracic echocardiography at rest and under low-dose dobutamine. Seven animals were examined by nuclear PET and SPECT analysis. Epi-, mid- and endocardial fibrosis and the numbers of arterial vessels were examined by histology. RESULTS: Functional analysis showed a significant decrease in global left ventricular ejection fraction (24.5 +/- 1.6%) 3 weeks after implantation. There was a trend to increased left ventricular ejection fraction after low-dose dobutamine stress (36.0 +/- 6.6%) and a significant improvement of the impaired regional anterior wall motion. PET and SPECT imaging documented chronic hibernation. Myocardial fibrosis increased significantly in the ischemic area with a gradient from epi- to endocardial. The number of arterial vessels in the ischemic area increased and coronary angiography showed abundant collateral vessels of Rentrop class 1. CONCLUSION: The presented experimental model mimics the clinical situation of chronic myocardial ischemia secondary to 1-vessel coronary disease.

Weakly linked binary mixtures of F = 1 87Rb Bose-Einstein condensates

We present a study of binary mixtures of Bose-Einstein condensates confined in a double-well potential within the framework of the mean field Gross-Pitaevskii (GP) equation. We re-examine both the single component and the binary mixture cases for such a potential, and we investigate what are the situations in which a simpler two-mode approach leads to an accurate description of their dynamics. We also estimate the validity of the most usual dimensionality reductions used to solve the GP equations. To this end, we compare both the semi-analytical two-mode approaches and the numerical simulations of the one-dimensional (1D) reductions with the full 3D numerical solutions of the GP equation. Our analysis provides a guide to clarify the validity of several simplified models that describe mean-field nonlinear dynamics, using an experimentally feasible binary mixture of an F = 1 spinor condensate with two of its Zeeman manifolds populated, m = ±1.

Weakly linked binary mixtures of F = 1 87Rb Bose-Einstein condensates

We present a study of binary mixtures of Bose-Einstein condensates confined in a double-well potential within the framework of the mean field Gross-Pitaevskii (GP) equation. We re-examine both the single component and the binary mixture cases for such a potential, and we investigate what are the situations in which a simpler two-mode approach leads to an accurate description of their dynamics. We also estimate the validity of the most usual dimensionality reductions used to solve the GP equations. To this end, we compare both the semi-analytical two-mode approaches and the numerical simulations of the one-dimensional (1D) reductions with the full 3D numerical solutions of the GP equation. Our analysis provides a guide to clarify the validity of several simplified models that describe mean-field nonlinear dynamics, using an experimentally feasible binary mixture of an F = 1 spinor condensate with two of its Zeeman manifolds populated, m = ±1.

In vivo brain macromolecule signals in healthy and glioblastoma mouse models: (1) H magnetic resonance spectroscopy, post-processing and metabolite quantification at 14.1 T.

In (1) H magnetic resonance spectroscopy, macromolecule signals underlay metabolite signals, and knowing their contribution is necessary for reliable metabolite quantification. When macromolecule signals are measured using an inversion-recovery pulse sequence, special care needs to be taken to correctly remove residual metabolite signals to obtain a pure macromolecule spectrum. Furthermore, since a single spectrum is commonly used for quantification in multiple experiments, the impact of potential macromolecule signal variability, because of regional differences or pathologies, on metabolite quantification has to be assessed. In this study, we introduced a novel method to post-process measured macromolecule signals that offers a flexible and robust way of removing residual metabolite signals. This method was applied to investigate regional differences in the mouse brain macromolecule signals that may affect metabolite quantification when not taken into account. However, since no significant differences in metabolite quantification were detected, it was concluded that a single macromolecule spectrum can be generally used for the quantification of healthy mouse brain spectra. Alternatively, the study of a mouse model of human glioma showed several alterations of the macromolecule spectrum, including, but not limited to, increased mobile lipid signals, which had to be taken into account to avoid significant metabolite quantification errors.

Multidelayed random walks: Theory and application to the neolithic transition in Europe

We present a model in which particles (or individuals of a biological population) disperse with a rest time between consecutive motions (or migrations) which may take several possible values from a discrete set. Particles (or individuals) may also react (or reproduce). We derive a new equation for the effective rest time T˜ of the random walk. Application to the neolithic transition in Europe makes it possible to derive more realistic theoretical values for its wavefront speed than those following from the single-delayed framework presented previously [J. Fort and V. Méndez, Phys. Rev. Lett. 82, 867 (1999)]. The new results are consistent with the archaeological observations of this important historical process

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Elastic properties and secondary structure formation of single-stranded DNA at monovalent and divalent salt conditions

Single-stranded DNA (ssDNA) plays a major role in several biological processes. It is therefore of fundamental interest to understand how the elastic response and the formation of secondary structures are modulated by the interplay between base pairing and electrostatic interactions. Here we measure force-extension curves (FECs) of ssDNA molecules in optical tweezers set up over two orders of magnitude of monovalent and divalent salt conditions, and obtain its elastic parameters by fitting the FECs to semiflexible models of polymers. For both monovalent and divalent salts, we find that the electrostatic contribution to the persistence length is proportional to the Debye screening length, varying as the inverse of the square root of cation concentration. The intrinsic persistence length is equal to 0.7 nm for both types of salts, and the effectivity of divalent cations in screening electrostatic interactions appears to be 100-fold as compared with monovalent salt, in line with what has been recently reported for single-stranded RNA. Finally, we propose an analysis of the FECs using a model that accounts for the effective thickness of the filament at low salt condition and a simple phenomenological description that quantifies the formation of non-specific secondary structure at low forces.