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The dynamical structure of genetic networks determines the occurrence of various biological mechanisms, such as cellular differentiation. However, the question of how cellular diversity evolves in relation to the inherent stochasticity and intercellular communication remains still to be understood. Here, we define a concept of stochastic bifurcations suitable to investigate the dynamical structure of genetic networks, and show that under stochastic influence, the expression of given proteins of interest is defined via the probability distribution of the phase variable, representing one of the genes constituting the system. Moreover, we show that under changing stochastic conditions, the probabilities of expressing certain concentration values are different, leading to different functionality of the cells, and thus to differentiation of the cells in the various types.
Background: The elderly need strength training more and more as they grow older to stay mobile for their everyday activities. The goal of training is to reduce the loss of muscle mass and the resulting loss of motor function. The dose-response relationship of training intensity to training effect has not yet been fully elucidated.
Methods: PubMed was selectively searched for articles that appeared in the past 5 years about the effects and dose-response relationship of strength training in the elderly.
Results: Strength training in the elderly (> 60 years) increases muscle strength by increasing muscle mass, and by improving the recruitment of motor units, and increasing their firing rate. Muscle mass can be increased through training at an intensity corresponding to 60% to 85% of the individual maximum voluntary strength. Improving the rate of force development requires training at a higher intensity (above 85%), in the elderly just as in younger persons. It is now recommended that healthy old people should train 3 or 4 times weekly for the best results; persons with poor performance at the outset can achieve improvement even with less frequent training. Side effects are rare.
Conclusion: Progressive strength training in the elderly is efficient, even with higher intensities, to reduce sarcopenia, and to retain motor function.
Quantum theory (QT) is usually formulated in terms of abstract mathematical postulates involving Hilbert spaces, state vectors and unitary operators. In this paper, we show that the full formalism of QT can instead be derived from five simple physical requirements, based on elementary assumptions regarding preparations, transformations and measurements. This is very similar to the usual formulation of special relativity, where two simple physical requirements-the principles of relativity and light speed invariance-are used to derive the mathematical structure of Minkowski space-time. Our derivation provides insights into the physical origin of the structure of quantum state spaces (including a group-theoretic explanation of the Bloch ball and its three dimensionality) and suggests several natural possibilities to construct consistent modifications of QT.
We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and on completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.
The outcomes of measurements on entangled quantum systems can be nonlocally correlated. However, while it is easy to write down toy theories allowing arbitrary nonlocal correlations, those allowed in quantum mechanics are limited. Quantum correlations cannot, for example, violate a principle known as macroscopic locality, which implies that they cannot violate Tsirelson's bound. This paper shows that there is a connection between the strength of nonlocal correlations in a physical theory and the structure of the state spaces of individual systems. This is illustrated by a family of models in which local state spaces are regular polygons, where a natural analogue of a maximally entangled state of two systems exists. We characterize the nonlocal correlations obtainable from such states. The family allows us to study the transition between classical, quantum and super-quantum correlations by varying only the local state space. We show that the strength of nonlocal correlations-in particular whether the maximally entangled state violates Tsirelson's bound or not-depends crucially on a simple geometric property of the local state space, known as strong self-duality. This result is seen to be a special case of a general theorem, which states that a broad class of entangled states in probabilistic theories-including, by extension, all bipartite classical and quantum states-cannot violate macroscopic locality. Finally, our results show that models exist that are locally almost indistinguishable from quantum mechanics, but can nevertheless generate maximally nonlocal correlations.
Background: Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature.
Results: We introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods.
Conclusions: We present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.
Background: In many species males face a higher predation risk than females because males display elaborate traits that evolved under sexual selection, which may attract not only females but also predators. Females are, therefore, predicted to avoid such conspicuous males under predation risk. The present study was designed to investigate predator-induced changes of female mating preferences in Atlantic mollies (Poecilia mexicana). Males of this species show a pronounced polymorphism in body size and coloration, and females prefer large, colorful males in the absence of predators.
Results: In dichotomous choice tests predator-naive (lab-reared) females altered their initial preference for larger males in the presence of the cichlid Cichlasoma salvini, a natural predator of P. mexicana, and preferred small males instead. This effect was considerably weaker when females were confronted visually with the non-piscivorous cichlid Vieja bifasciata or the introduced non-piscivorous Nile tilapia (Oreochromis niloticus). In contrast, predator experienced (wild-caught) females did not respond to the same extent to the presence of a predator, most likely due to a learned ability to evaluate their predators' motivation to prey.
Conclusions: Our study highlights that (a) predatory fish can have a profound influence on the expression of mating preferences of their prey (thus potentially affecting the strength of sexual selection), and females may alter their mate choice behavior strategically to reduce their own exposure to predators. (b) Prey species can evolve visual predator recognition mechanisms and alter their mate choice only when a natural predator is present. (c) Finally, experiential effects can play an important role, and prey species may learn to evaluate the motivational state of their predators.
Background: A direct pharmacological stimulation of soluble guanylate cyclase (sGC) is an emerging therapeutic approach to the management of various cardiovascular disorders associated with endothelial dysfunction. Novel sGC stimulators, including riociguat (BAY 63-2521), have a dual mode of action: They sensitize sGC to endogenously produced nitric oxide (NO) and also directly stimulate sGC independently of NO. Little is known about their effects on tissue remodeling and degeneration and survival in experimental malignant hypertension.
Methods and Results: Mortality, hemodynamics and biomarkers of tissue remodeling and degeneration were assessed in Dahl salt-sensitive rats maintained on a high salt diet and treated with riociguat (3 or 10 mg/kg/d) for 14 weeks. Riociguat markedly attenuated systemic hypertension, improved systolic heart function and increased survival from 33% to 85%. Histological examination of the heart and kidneys revealed that riociguat significantly ameliorated fibrotic tissue remodeling and degeneration. Correspondingly, mRNA expression of the pro-fibrotic biomarkers osteopontin (OPN), tissue inhibitor of matrix metalloproteinase-1 (TIMP-1) and plasminogen activator inhibitor-1 (PAI-1) in the myocardium and the renal cortex was attenuated by riociguat. In addition, riociguat reduced plasma and urinary levels of OPN, TIMP-1, and PAI-1.
Conclusions: Stimulation of sGC by riociguat markedly improves survival and attenuates systemic hypertension and systolic dysfunction, as well as fibrotic tissue remodeling in the myocardium and the renal cortex in a rodent model of pressure and volume overload. These findings suggest a therapeutic potential of sGC stimulators in diseases associated with impaired cardiovascular and renal functions.
Background: Inferring regulatory interactions between genes from transcriptomics time-resolved data, yielding reverse engineered gene regulatory networks, is of paramount importance to systems biology and bioinformatics studies. Accurate methods to address this problem can ultimately provide a deeper insight into the complexity, behavior, and functions of the underlying biological systems. However, the large number of interacting genes coupled with short and often noisy time-resolved read-outs of the system renders the reverse engineering a challenging task. Therefore, the development and assessment of methods which are computationally efficient, robust against noise, applicable to short time series data, and preferably capable of reconstructing the directionality of the regulatory interactions remains a pressing research problem with valuable applications.
Results: Here we perform the largest systematic analysis of a set of similarity measures and scoring schemes within the scope of the relevance network approach which are commonly used for gene regulatory network reconstruction from time series data. In addition, we define and analyze several novel measures and schemes which are particularly suitable for short transcriptomics time series. We also compare the considered 21 measures and 6 scoring schemes according to their ability to correctly reconstruct such networks from short time series data by calculating summary statistics based on the corresponding specificity and sensitivity. Our results demonstrate that rank and symbol based measures have the highest performance in inferring regulatory interactions. In addition, the proposed scoring scheme by asymmetric weighting has shown to be valuable in reducing the number of false positive interactions. On the other hand, Granger causality as well as information-theoretic measures, frequently used in inference of regulatory networks, show low performance on the short time series analyzed in this study.
Conclusions: Our study is intended to serve as a guide for choosing a particular combination of similarity measures and scoring schemes suitable for reconstruction of gene regulatory networks from short time series data. We show that further improvement of algorithms for reverse engineering can be obtained if one considers measures that are rooted in the study of symbolic dynamics or ranks, in contrast to the application of common similarity measures which do not consider the temporal character of the employed data. Moreover, we establish that the asymmetric weighting scoring scheme together with symbol based measures (for low noise level) and rank based measures (for high noise level) are the most suitable choices.
The meadow grasshopper, Chorthippus parallelus (Zetterstedt), is common and widespread in Central Europe, with a low dispersal range per generation. A population study in Central Germany (Frankenwald and Thuringer Schiefergebirge) showed strong interpopulation differences in abundance and individual fitness. We examined genetic variability using microsatellite markers within and between 22 populations in a short-to long-distance sampling (19 populations, Frankenwald, Schiefergebirge, as well as a southern transect), and in the Erzgebirge region (three populations), with the latter aiming to check for effects as a result of historical forest cover. Of the 671 C. parallelus captured, none was macropterous (functionally winged). All populations showed a high level of expected and observed heterozygosity (mean 0.80-0.90 and 0.60-0.75, respectively), whereas there was evidence of inbreeding (F(IS) values all positive). Allelic richness for all locus-population combinations was high (mean 9.3-11.2), whereas alleles per locus ranged from 15-62. At a local level, genic and genotypic differences were significant. Pairwise F(ST) values were in the range 0.00-0.04, indicating little interpopulation genetic differentiation. Similarly, the calculated gene flow was very high, based on the respective F(ST) (19.5) and using private alleles (7.7). A Neighbour-joining tree using Nei's D(A) and principal coordinate analysis separated two populations that were collected in the Erzgebirge region. Populations from this region may have escaped the effects of the historical forest cover. The visualization of the spatial arrangement of genotypes revealed one geographical barrier to gene flow in the short-distance sampling.