Triangle Singularity as the Origin of the a_{1}(1420).

The COMPASS Collaboration experiment recently discovered a new isovector resonancelike signal with axial-vector quantum numbers, the a_{1}(1420), decaying to f_{0}(980)π. With a mass too close to and a width smaller than the axial-vector ground state a_{1}(1260), it was immediately interpreted as a new light exotic meson, similar to the X, Y, Z states in the hidden-charm sector. We show that a resonancelike signal fully matching the experimental data is produced by the decay of the a_{1}(1260) resonance into K^{*}(→Kπ)K[over ¯] and subsequent rescattering through a triangle singularity into the coupled f_{0}(980)π channel. The amplitude for this process is calculated using a new approach based on dispersion relations. The triangle-singularity model is fitted to the partial-wave data of the COMPASS experiment. Despite having fewer parameters, this fit shows a slightly better quality than the one using a resonance hypothesis and thus eliminates the need for an additional resonance in order to describe the data. We thereby demonstrate for the first time in the light-meson sector that a resonancelike structure in the experimental data can be described by rescattering through a triangle singularity, providing evidence for a genuine three-body effect.

Transcriptomic and biochemical investigations support the role of rootstock-scion interaction in grapevine berry quality.

BACKGROUND: In viticulture, rootstock genotype plays a critical role to improve scion physiology, berry quality and to adapt grapevine (Vitis vinifera L.) to different environmental conditions. This study aimed at investigating the effect of two different rootstocks (1103 Paulsen - P - and Mgt 101-14 - M) in comparison with not grafted plants - NGC - on transcriptome (RNA-seq and small RNA-seq) and chemical composition of berry skin in Pinot noir, and exploring the influence of rootstock-scion interaction on grape quality. Berry samples, collected at veraison and maturity, were investigated at transcriptional and biochemical levels to depict the impact of rootstock on berry maturation. RESULTS: RNA- and miRNA-seq analyses highlighted that, at veraison, the transcriptomes of the berry skin are extremely similar, while variations associated with the different rootstocks become evident at maturity, suggesting a greater diversification at transcriptional level towards the end of the ripening process. In the experimental design, resembling standard agronomic growth conditions, the vines grafted on the two different rootstocks do not show a high degree of diversity. In general, the few genes differentially expressed at veraison were linked to photosynthesis, putatively because of a ripening delay in not grafted vines, while at maturity the differentially expressed genes were mainly involved in the synthesis and transport of phenylpropanoids (e.g. flavonoids), cell wall loosening, and stress response. These results were supported by some differences in berry phenolic composition detected between grafted and not grafted plants, in particular in resveratrol derivatives accumulation. CONCLUSIONS: Transcriptomic and biochemical data demonstrate a stronger impact of 1103 Paulsen rootstock than Mgt 101-14 or not grafted plants on ripening processes related to the secondary metabolite accumulations in berry skin tissue. Interestingly, the MYB14 gene, involved in the feedback regulation of resveratrol biosynthesis was up-regulated in 1103 Paulsen thus supporting a putative greater accumulation of stilbenes in mature berries.

Transition path theory from biased simulations.

Transition Path Theory (TPT) provides a rigorous framework to investigate the dynamics of rare thermally activated transitions. In this theory, a central role is played by the forward committor function q+(x), which provides the ideal reaction coordinate. Furthermore, the reactive dynamics and kinetics are fully characterized in terms of two time-independent scalar and vector distributions. In this work, we develop a scheme which enables all these ingredients of TPT to be efficiently computed using the short non-equilibrium trajectories generated by means of a specific combination of enhanced path sampling techniques. In particular, first we further extend the recently introduced self-consistent path sampling algorithm in order to compute the committor q+(x). Next, we show how this result can be exploited in order to define efficient algorithms which enable us to directly sample the transition path ensemble.

All-atom calculation of protein free-energy profiles.

The Bias Functional (BF) approach is a variational method which enables one to efficiently generate ensembles of reactive trajectories for complex biomolecular transitions, using ordinary computer clusters. For example, this scheme was applied to simulate in atomistic detail the folding of proteins consisting of several hundreds of amino acids and with experimental folding time of several minutes. A drawback of the BF approach is that it produces trajectories which do not satisfy microscopic reversibility. Consequently, this method cannot be used to directly compute equilibrium observables, such as free energy landscapes or equilibrium constants. In this work, we develop a statistical analysis which permits us to compute the potential of mean-force (PMF) along an arbitrary collective coordinate, by exploiting the information contained in the reactive trajectories calculated with the BF approach. We assess the accuracy and computational efficiency of this scheme by comparing its results with the PMF obtained for a small protein by means of plain molecular dynamics.

Self-consistent calculation of protein folding pathways.

We introduce an iterative algorithm to efficiently simulate protein folding and other conformational transitions, using state-of-the-art all-atom force fields. Starting from the Langevin equation, we obtain a self-consistent stochastic equation of motion, which directly yields the reaction pathways. From the solution of this set of equations we derive a stochastic estimate of the reaction coordinate. We validate this approach against the results of plain MD simulations of the folding of a small protein, which were performed on the Anton supercomputer. In order to explore the computational efficiency of this algorithm, we apply it to generate a folding pathway of a protein that consists of 130 amino acids and has a folding rate of the order of s-1.

Dimensional reduction of Markov state models from renormalization group theory.

Renormalization Group (RG) theory provides the theoretical framework to define rigorous effective theories, i.e., systematic low-resolution approximations of arbitrary microscopic models. Markov state models are shown to be rigorous effective theories for Molecular Dynamics (MD). Based on this fact, we use real space RG to vary the resolution of the stochastic model and define an algorithm for clustering microstates into macrostates. The result is a lower dimensional stochastic model which, by construction, provides the optimal coarse-grained Markovian representation of the system's relaxation kinetics. To illustrate and validate our theory, we analyze a number of test systems of increasing complexity, ranging from synthetic toy models to two realistic applications, built form all-atom MD simulations. The computational cost of computing the low-dimensional model remains affordable on a desktop computer even for thousands of microstates.

Folding Mechanism of Proteins Im7 and Im9: Insight from All-Atom Simulations in Implicit and Explicit Solvent.

Im7 and Im9 are evolutionary related proteins with almost identical native structures. In spite of their structural similarity, experiments show that Im7 folds through a long-lived on-pathway intermediate, while Im9 folds according to two-state kinetics. In this work, we use a recently developed enhanced path sampling method to generate many folding trajectories for these proteins, using realistic atomistic force fields, in both implicit and explicit solvent. Overall, our results are in good agreement with the experimental ϕ values and with the result of ϕ-value-restrained molecular dynamics (MD) simulations. However, our implicit solvent simulations fail to predict a qualitative difference in the folding pathways of Im7 and Im9. In contrast, our simulations in explicit solvent correctly reproduce the fact that only protein Im7 folds through a on-pathway intermediate. By analyzing our atomistic trajectories, we provide a physical picture which explains the observed difference in the folding kinetics of these chains.

Computing Reaction Pathways of Rare Biomolecular Transitions using Atomistic Force-Fields.

The Dominant Reaction Pathway (DRP) method is an approximate variational scheme which can be used to compute reaction pathways in conformational transitions undergone by large biomolecules (up to ~10(3) amino-acids) using realistic all-atom force fields. We first review the status of development of this method. Next, we discuss its validation against the results of plain MD protein folding simulations performed by the DE-Shaw group using the Anton supercomputer. Finally, we review a few representative applications of the DRP approach to study reactions which are far too complex and rare to be investigated by plain MD, even on the Anton machine.

Variational scheme to compute protein reaction pathways using atomistic force fields with explicit solvent.

We introduce a variational approximation to the microscopic dynamics of rare conformational transitions of macromolecules. Within this framework it is possible to simulate on a small computer cluster reactions as complex as protein folding, using state of the art all-atom force fields in explicit solvent. We test this method against MD simulations of the folding of an α and a ß protein performed with the same all-atom force field on the Anton supercomputer. We find that our approach yields results consistent with those of MD simulations, at a computational cost orders of magnitude smaller.

Quantum charge transport and conformational dynamics of macromolecules.

We study the dynamics of quantum excitations inside macromolecules which can undergo conformational transitions. In the first part of the paper, we use the path integral formalism to rigorously derive a set of coupled equations of motion which simultaneously describe the molecular and quantum transport dynamics, and obey the fluctuation/dissipation relationship. We also introduce an algorithm which yields the most probable molecular and quantum transport pathways in rare, thermally activated reactions. In the second part of the paper, we apply this formalism to simulate the propagation of a quantum charge during the collapse of a polymer from an initial stretched conformation to a final globular state. We find that the charge dynamics is quenched when the chain reaches a molten globule state. Using random matrix theory we show that this transition is due to an increase of quantum localization driven by dynamical disorder. We argue that collapsing conducting polymers may represent a physical realization of quantum small-world networks with dynamical rewiring probability.

Microscopically computing free-energy profiles and transition path time of rare macromolecular transitions.

We introduce a rigorous method to microscopically compute the observables which characterize the thermodynamics and kinetics of rare macromolecular transitions for which it is possible to identify a priori a slow reaction coordinate. In order to sample the ensemble of statistically significant reaction pathways, we define a biased molecular dynamics (MD) in which barrier-crossing transitions are accelerated without introducing any unphysical external force. In contrast to other biased MD methods, in the present approach the systematic errors which are generated in order to accelerate the transition can be analytically calculated and therefore can be corrected for. This allows for a computationally efficient reconstruction of the free-energy profile as a function of the reaction coordinate and for the calculation of the corresponding diffusion coefficient. The transition path time can then be readily evaluated within the dominant reaction pathways approach. We illustrate and test this method by characterizing a thermally activated transition on a two-dimensional energy surface and the folding of a small protein fragment within a coarse-grained model.

Quantum diffusive dynamics of macromolecular transitions.

We study the role of quantum fluctuations of atomic nuclei in the real-time dynamics of non-equilibrium macro-molecular transitions. To this goal we introduce an extension of the dominant reaction pathways formalism, in which the quantum corrections to the classical overdamped Langevin dynamics are rigorously taken into account to order h(2). We first illustrate our approach in simple cases, and compare with the results of the instanton theory. Then we apply our method to study the C7(eq) → C7(ax) transition of alanine dipeptide. We find that the inclusion of quantum fluctuations can significantly modify the reaction mechanism for peptides. For example, the energy difference which is overcome along the most probable pathway is reduced by as much as 50%.

Fluctuations in the ensemble of reaction pathways.

The dominant reaction pathway is a rigorous framework to microscopically compute the most probable trajectories, in nonequilibrium transitions. In the low-temperature regime, such dominant pathways encode the information about the reaction mechanism and can be used to estimate nonequilibrium averages of arbitrary observables. On the other hand, at sufficiently high temperatures, the stochastic fluctuations around the dominant paths become important and have to be taken into account. In this work, we develop a technique to systematically include the effects of such stochastic fluctuations, to order k(B)T. This method is used to compute the probability for a transition to take place through a specific reaction channel and to evaluate the reaction rate.

Molecular dynamics at low time resolution.

The internal dynamics of macromolecular systems is characterized by widely separated time scales, ranging from fraction of picoseconds to nanoseconds. In ordinary molecular dynamics simulations, the elementary time step Δt used to integrate the equation of motion needs to be chosen much smaller of the shortest time scale in order not to cut-off physical effects. We show that in systems obeying the overdamped Langevin equation, it is possible to systematically correct for such discretization errors. This is done by analytically averaging out the fast molecular dynamics which occurs at time scales smaller than Δt, using a renormalization group based technique. Such a procedure gives raise to a time-dependent calculable correction to the diffusion coefficient. The resulting effective Langevin equation describes by construction the same long-time dynamics, but has a lower time resolution power, hence it can be integrated using larger time steps Δt. We illustrate and validate this method by studying the diffusion of a point-particle in a one-dimensional toy model and the denaturation of a protein.

Communications: Ab initio dynamics of rare thermally activated reactions.

We introduce a framework to investigate ab initio the dynamics of rare thermally activated reactions, which cannot be studied using the existing techniques. The electronic degrees of freedom are described at the quantum-mechanical level in the Born-Oppenheimer approximation, while the nuclear degrees of freedom are coupled to a thermal bath, through a classical Langevin equation. This method is based on the path integral representation for the stochastic dynamics and yields the time evolution of both nuclear and electronic degrees of freedom, along the most probable reaction pathways, without spending computational time to explore metastable states. As a first illustrative application, we characterize the dominant pathway in the cyclobutene-->butadiene reaction, using the semiempirical Parametrized Model 3 (PM3) approach.

Identification and validation of reference genes for gene expression studies in water buffalo.

In gene expression analysis, a key step to obtain informative data from reverse transcription quantitative PCR (RT qPCR) assay is normalization, that is usually achieved by ratio to correct the abundance of the gene of interest against that of an endogenous reference gene. The finding of such reference genes, ideally expressed in a stable way in multiple tissue samples and in different experimental conditions, is a non-trivial problem. In this work, a set of genes potentially useful as reference for gene expression studies in water buffalo has been identified and evaluated. In the first step, a publicly available Bos taurus expressed sequence tags database has been downloaded from the TIGR Gene Index and mined by some simple frequency algorithms to find out which tentative consensuses are present in a remarkable number of different cDNA libraries and, consequently, are more suitable to be included in a starter set of candidate reference genes. To validate the potential of such candidates for their use as normalizers in buffalo gene expression analysis, an RT qPCR analysis has been carried out, in which the expression stability of these genes has been evaluated on a panel of buffalo tissues and organs. Our results indicate that ribosomal proteins L4 and L5 and Gek protein encoding genes can be useful as normalizers to compare gene expression levels across tissues and organs in buffalo.

Dominant reaction pathways in high-dimensional systems.

This paper is devoted to the development of a theoretical and computational framework denominated dominant reaction pathways (DRPs) to efficiently sample the statistically significant thermally activated reaction pathways, in multidimensional systems. The DRP approach is consistently derived from the Langevin equation through a systematic expansion in the thermal energy, k(B)T. Its main advantage with respect to existing simulation techniques is that it provides a natural and rigorous framework to perform the path sampling using constant displacement steps, rather than constant time steps. In our previous work, we have shown how to obtain the set of most probable reaction pathways, i.e., the lowest order in the k(B)T expansion. In this work, we show how to compute the corrections to the leading order due to stochastic fluctuations around the most probable trajectories. We also discuss how to obtain predictions for the evolution of arbitrary observables and how to generate conformations, which are representative of the transition state ensemble. We illustrate how our method works in practice by studying the diffusion of a point particle in a two-dimensional funneled external potential.

Simulating stochastic dynamics using large time steps.

We present an approach to investigate the long-time stochastic dynamics of multidimensional classical systems, in contact with a heat bath. When the potential energy landscape is rugged, the kinetics displays a decoupling of short- and long-time scales and both molecular dynamics or Monte Carlo (MC) simulations are generally inefficient. Using a field theoretic approach, we perform analytically the average over the short-time stochastic fluctuations. This way, we obtain an effective theory, which generates the same long-time dynamics of the original theory, but has a lower time-resolution power. Such an approach is used to develop an improved version of the MC algorithm, which is particularly suitable to investigate the dynamics of rare conformational transitions. In the specific case of molecular systems at room temperature, we show that elementary integration time steps used to simulate the effective theory can be chosen a factor approximately 100 larger than those used in the original theory. Our results are illustrated and tested on a simple system, characterized by a rugged energy landscape.

Environmental risk factors for canine toxoplasmosis in a deprived district of Botucatu, SP, Brazil

Toxoplasmosis is a worldwide zoonosis caused by Toxoplasma gondii that can infect a large variety of animals, including humans. The present study aimed to evaluate the frequency of anti-T.gondii antibodies in dogs from a peripheral district of Botucatu and to establish the association among some epidemiological variables in order to evaluate risk factors for toxoplasmosis infection. Serum samples from dogs were screened using an indirect fluorescent antibody (IFA) test. Anti-T.gondii antibody prevalence was 56 percent. The highest titer was 1024 (1.79 percent) and the most frequent titers were 16 (57.14 percent) and 64 (33.93 percent). The chi-square (X²) test revealed significant association among variables such as dog access to street, ingestion of raw meat and presence of synantropic animals in the domestic environment. These results demonstrate that toxoplasmosis is present in dogs from Jardim Santa Elisa district.