Interstellar Turbulence II: Implications and Effects

Interstellar turbulence has implications for the dispersal and mixing of the elements, cloud chemistry, cosmic ray scattering, and radio wave propagation through the ionized medium. This review discusses the observations and theory of these effects. Metallicity fluctuations are summarized, and the theory of turbulent transport of passive tracers is reviewed. Modeling methods, turbulent concentration of dust grains, and the turbulent washout of radial abundance gradients are discussed. Interstellar chemistry is affected by turbulent transport of various species between environments with different physical properties and by turbulent heating in shocks, vortical dissipation regions, and local regions of enhanced ambipolar diffusion. Cosmic rays are scattered and accelerated in turbulent magnetic waves and shocks, and they generate turbulence on the scale of their gyroradii. Radio wave scintillation is an important diagnostic for small scale turbulence in the ionized medium, giving information about the power spectrum and amplitude of fluctuations. The theory of diffraction and refraction is reviewed, as are the main observations and scintillation regions.Comment: 46 pages, 2 figures, submitted to Annual Reviews of Astronomy and Astrophysic

Parasympathetic nervous system dysfunction, as identified by pupil light reflex, and its possible connection to hearing impairment

Context Although the pupil light reflex has been widely used as a clinical diagnostic tool for autonomic nervous system dysfunction, there is no systematic review available to summarize the evidence that the pupil light reflex is a sensitive method to detect parasympathetic dysfunction. Meanwhile, the relationship between parasympathetic functioning and hearing impairment is relatively unknown. Objectives To 1) review the evidence for the pupil light reflex being a sensitive method to evaluate parasympathetic dysfunction, 2) review the evidence relating hearing impairment and parasympathetic activity and 3) seek evidence of possible connections between hearing impairment and the pupil light reflex. Methods Literature searches were performed in five electronic databases. All selected articles were categorized into three sections: pupil light reflex and parasympathetic dysfunction, hearing impairment and parasympathetic activity, pupil light reflex and hearing impairment. Results Thirty-eight articles were included in this review. Among them, 36 articles addressed the pupil light reflex and parasympathetic dysfunction. We summarized the information in these data according to different types of parasympathetic-related diseases. Most of the studies showed a difference on at least one pupil light reflex parameter between patients and healthy controls. Two articles discussed the relationship between hearing impairment and parasympathetic activity. Both studies reported a reduced parasympathetic activity in the hearing impaired groups. The searches identified no results for pupil light reflex and hearing impairment. Discussion and Conclusions As the first systematic review of the evidence, our findings suggest that the pupil light reflex is a sensitive tool to assess the presence of parasympathetic dysfunction. Maximum constriction velocity and relative constriction amplitude appear to be the most sensitive parameters. There are only two studies investigating the relationship between parasympathetic activity and hearing impairment, hence further research is needed. The pupil light reflex could be a candidate measurement tool to achieve this goal

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Remote control of γc expression by arginine methylation

Arginine methylation is a post-translational modification that controls the abundance of gamma c cytokine receptor on mature T cells by a post-transcriptional mechanism.N