Beam Energy and Centrality Dependence of Direct-Photon Emission from Ultrarelativistic Heavy-Ion Collisions.

The PHENIX collaboration presents first measurements of low-momentum (0.41  GeV/c) direct-photon yield dN_{γ}^{dir}/dη is a smooth function of dN_{ch}/dη and can be well described as proportional to (dN_{ch}/dη)^{α} with α≈1.25. This scaling behavior holds for a wide range of beam energies at the Relativistic Heavy Ion Collider and the Large Hadron Collider, for centrality selected samples, as well as for different A+A collision systems. At a given beam energy, the scaling also holds for high p_{T} (>5  GeV/c), but when results from different collision energies are compared, an additional sqrt[s_{NN}]-dependent multiplicative factor is needed to describe the integrated-direct-photon yield

Directed acyclic graph kernels for structural RNA analysis

<p>Abstract</p> <p>Background</p> <p>Recent discoveries of a large variety of important roles for non-coding RNAs (ncRNAs) have been reported by numerous researchers. In order to analyze ncRNAs by kernel methods including support vector machines, we propose stem kernels as an extension of string kernels for measuring the similarities between two RNA sequences from the viewpoint of secondary structures. However, applying stem kernels directly to large data sets of ncRNAs is impractical due to their computational complexity.</p> <p>Results</p> <p>We have developed a new technique based on directed acyclic graphs (DAGs) derived from base-pairing probability matrices of RNA sequences that significantly increases the computation speed of stem kernels. Furthermore, we propose profile-profile stem kernels for multiple alignments of RNA sequences which utilize base-pairing probability matrices for multiple alignments instead of those for individual sequences. Our kernels outperformed the existing methods with respect to the detection of known ncRNAs and kernel hierarchical clustering.</p> <p>Conclusion</p> <p>Stem kernels can be utilized as a reliable similarity measure of structural RNAs, and can be used in various kernel-based applications.</p

Gravitational waves from single neutron stars: an advanced detector era survey

With the doors beginning to swing open on the new gravitational wave astronomy, this review provides an up-to-date survey of the most important physical mechanisms that could lead to emission of potentially detectable gravitational radiation from isolated and accreting neutron stars. In particular we discuss the gravitational wave-driven instability and asteroseismology formalism of the f- and r-modes, the different ways that a neutron star could form and sustain a non-axisymmetric quadrupolar "mountain" deformation, the excitation of oscillations during magnetar flares and the possible gravitational wave signature of pulsar glitches. We focus on progress made in the recent years in each topic, make a fresh assessment of the gravitational wave detectability of each mechanism and, finally, highlight key problems and desiderata for future work.Comment: 39 pages, 12 figures, 2 tables. Chapter of the book "Physics and Astrophysics of Neutron Stars", NewCompStar COST Action 1304. Minor corrections to match published versio

A Measurement of Rb using a Double Tagging Method

The fraction of Z to bbbar events in hadronic Z decays has been measured by the OPAL experiment using the data collected at LEP between 1992 and 1995. The Z to bbbar decays were tagged using displaced secondary vertices, and high momentum electrons and muons. Systematic uncertainties were reduced by measuring the b-tagging efficiency using a double tagging technique. Efficiency correlations between opposite hemispheres of an event are small, and are well understood through comparisons between real and simulated data samples. A value of Rb = 0.2178 +- 0.0011 +- 0.0013 was obtained, where the first error is statistical and the second systematic. The uncertainty on Rc, the fraction of Z to ccbar events in hadronic Z decays, is not included in the errors. The dependence on Rc is Delta(Rb)/Rb = -0.056*Delta(Rc)/Rc where Delta(Rc) is the deviation of Rc from the value 0.172 predicted by the Standard Model. The result for Rb agrees with the value of 0.2155 +- 0.0003 predicted by the Standard Model.Comment: 42 pages, LaTeX, 14 eps figures included, submitted to European Physical Journal

Measurement of the B+ and B-0 lifetimes and search for CP(T) violation using reconstructed secondary vertices

The lifetimes of the B+ and B-0 mesons, and their ratio, have been measured in the OPAL experiment using 2.4 million hadronic Z(0) decays recorded at LEP. Z(0) --> b (b) over bar decays were tagged using displaced secondary vertices and high momentum electrons and muons. The lifetimes were then measured using well-reconstructed charged and neutral secondary vertices selected in this tagged data sample. The results aretau(B+) = 1.643 +/- 0.037 +/- 0.025 pstau(Bo) = 1.523 +/- 0.057 +/- 0.053 pstau(B+)/tau(Bo) = 1.079 +/- 0.064 +/- 0.041,where in each case the first error is statistical and the second systematic.A larger data sample of 3.1 million hadronic Z(o) decays has been used to search for CP and CPT violating effects by comparison of inclusive b and (b) over bar hadron decays, No evidence fur such effects is seen. The CP violation parameter Re(epsilon(B)) is measured to be Re(epsilon(B)) = 0.001 +/- 0.014 +/- 0.003and the fractional difference between b and (b) over bar hadron lifetimes is measured to(Delta tau/tau)(b) = tau(b hadron) - tau((b) over bar hadron)/tau(average) = -0.001 +/- 0.012 +/- 0.008

Functional Deficits in nNOSμ-Deficient Skeletal Muscle: Myopathy in nNOS Knockout Mice

Skeletal muscle nNOSμ (neuronal nitric oxide synthase mu) localizes to the sarcolemma through interaction with the dystrophin-associated glycoprotein (DAG) complex, where it synthesizes nitric oxide (NO). Disruption of the DAG complex occurs in dystrophinopathies and sarcoglycanopathies, two genetically distinct classes of muscular dystrophy characterized by progressive loss of muscle mass, muscle weakness and increased fatigability. DAG complex instability leads to mislocalization and downregulation of nNOSμ; but this is thought to play a minor role in disease pathogenesis. This view persists without knowledge of the role of nNOS in skeletal muscle contractile function in vivo and has influenced gene therapy approaches to dystrophinopathy, the majority of which do not restore sarcolemmal nNOSμ. We address this knowledge gap by evaluating skeletal muscle function in nNOS knockout (KN1) mice using an in situ approach, in which the muscle is maintained in its normal physiological environment. nNOS-deficiency caused reductions in skeletal muscle bulk and maximum tetanic force production in male mice only. Furthermore, nNOS-deficient muscles from both male and female mice exhibited increased susceptibility to contraction-induced fatigue. These data suggest that aberrant nNOSμ signaling can negatively impact three important clinical features of dystrophinopathies and sarcoglycanopathies: maintenance of muscle bulk, force generation and fatigability. Our study suggests that restoration of sarcolemmal nNOSμ expression in dystrophic muscles may be more important than previously appreciated and that it should be a feature of any fully effective gene therapy-based intervention

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy

<p>Abstract</p> <p>Background</p> <p>Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.</p> <p>Results</p> <p>Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the <it>pseudo</it>-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.</p> <p>Conclusions</p> <p>This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.</p

Sarcolemma-localized nNOS is required to maintain activity after mild exercise

Many neuromuscular conditions are characterized by an exaggerated exercise- induced fatigue response that is disproportionate to activity level. This fatigue is not necessarily correlated with greater central or peripheral fatigue in patients(1), and some patients experience severe fatigue without any demonstrable somatic disease(2). Except in myopathies that are due to specific metabolic defects, the mechanism underlying this type of fatigue remains unknown(2). With no treatment available, this form of inactivity is a major determinant of disability(3). Here we show, using mouse models, that this exaggerated fatigue response is distinct from a loss in specific force production by muscle, and that sarcolemma-localized signalling by neuronal nitric oxide synthase ( nNOS) in skeletal muscle is required to maintain activity after mild exercise. We show that nNOS- null mice do not have muscle pathology and have no loss of muscle- specific force after exercise but do display this exaggerated fatigue response to mild exercise. In mouse models of nNOS mislocalization from the sarcolemma, prolonged inactivity was only relieved by pharmacologically enhancing the cGMP signal that results from muscle nNOS activation during the nitric oxide signalling response to mild exercise. Our findings suggest that the mechanism underlying the exaggerated fatigue response to mild exercise is a lack of contraction- induced signalling from sarcolemma- localized nNOS, which decreases cGMP- mediated vasomodulation in the vessels that supply active muscle after mild exercise. Sarcolemmal nNOS staining was decreased in patient biopsies from a large number of distinct myopathies, suggesting a common mechanism of fatigue. Our results suggest that patients with an exaggerated fatigue response to mild exercise would show clinical improvement in response to treatment strategies aimed at improving exercise- induced signalling.Paul D. Wellstone Muscular Dystrophy Cooperative Research Center Grant ; University of Iowa Cardiovascular Interdisciplinary Research ; National Research Service Award ; National Institute of Arthritis and Musculoskeletal and Skin Diseases ; National Institutes of Health ; Senator Paul D. Wellstone Fellowship ; Muscular Dystrophy Association Development Grant ; Howard Hughes Medical InstituteWe thank M. Anderson and M. Henry for comments, and M. M. Kilburg, K. Uppal, B. J. Steinmann and S. Watkins and members of the Campbell laboratory for scientific contributions. This work was supported in part by a Paul D. Wellstone Muscular Dystrophy Cooperative Research Center Grant. Y.M.K. was supported by grants from the University of Iowa Cardiovascular Interdisciplinary Research/ National Research Service Award (NRSA) Fellowship, from an individual NRSA Fellowship from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, from the National Institutes of Health (NIH), and from a Senator Paul D. Wellstone Fellowship. E.P.R. was supported by a Muscular Dystrophy Association Development Grant. R.M.W. was supported by the NIH. K.P.C. is an investigator of the Howard Hughes Medical Institute.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62850/1/nature07414.pd

Genome-Scale Reconstruction of Escherichia coli's Transcriptional and Translational Machinery: A Knowledge Base, Its Mathematical Formulation, and Its Functional Characterization

Metabolic network reconstructions represent valuable scaffolds for ‘-omics’ data integration and are used to computationally interrogate network properties. However, they do not explicitly account for the synthesis of macromolecules (i.e., proteins and RNA). Here, we present the first genome-scale, fine-grained reconstruction of Escherichia coli's transcriptional and translational machinery, which produces 423 functional gene products in a sequence-specific manner and accounts for all necessary chemical transformations. Legacy data from over 500 publications and three databases were reviewed, and many pathways were considered, including stable RNA maturation and modification, protein complex formation, and iron–sulfur cluster biogenesis. This reconstruction represents the most comprehensive knowledge base for these important cellular functions in E. coli and is unique in its scope. Furthermore, it was converted into a mathematical model and used to: (1) quantitatively integrate gene expression data as reaction constraints and (2) compute functional network states, which were compared to reported experimental data. For example, the model predicted accurately the ribosome production, without any parameterization. Also, in silico rRNA operon deletion suggested that a high RNA polymerase density on the remaining rRNA operons is needed to reproduce the reported experimental ribosome numbers. Moreover, functional protein modules were determined, and many were found to contain gene products from multiple subsystems, highlighting the functional interaction of these proteins. This genome-scale reconstruction of E. coli's transcriptional and translational machinery presents a milestone in systems biology because it will enable quantitative integration of ‘-omics’ datasets and thus the study of the mechanistic principles underlying the genotype–phenotype relationship