The Sphaleron Rate in SU(N) Gauge Theory

The sphaleron rate is defined as the diffusion constant for topological number NCS = int g^2 F Fdual/32 pi^2. It establishes the rate of equilibration of axial light quark number in QCD and is of interest both in electroweak baryogenesis and possibly in heavy ion collisions. We calculate the weak-coupling behavior of the SU(3) sphaleron rate, as well as making the most sensible extrapolation towards intermediate coupling which we can. We also study the behavior of the sphaleron rate at weak coupling at large Nc.Comment: 18 pages with 3 figure

The transition between stochastic and deterministic behavior in an excitable gene circuit

We explore the connection between a stochastic simulation model and an ordinary differential equations (ODEs) model of the dynamics of an excitable gene circuit that exhibits noise-induced oscillations. Near a bifurcation point in the ODE model, the stochastic simulation model yields behavior dramatically different from that predicted by the ODE model. We analyze how that behavior depends on the gene copy number and find very slow convergence to the large number limit near the bifurcation point. The implications for understanding the dynamics of gene circuits and other birth-death dynamical systems with small numbers of constituents are discussed.Comment: PLoS ONE: Research Article, published 11 Apr 201

4-point correlators in finite-temperature AdS/CFT: jet quenching correlations

There has been recent progress on computing real-time equilibrium 3-point functions in finite-temperature strongly-coupled N=4 super Yang-Mills (SYM). In this paper, we show an example of how to carry out a similar analysis for a 4-point function. We look at the stopping of high-energy "jets" in such strongly-coupled plasmas and relate the question of whether, on an event-by-event basis, each jet deposits its net charge over a narrow (~ 1/T) or wide (>> 1/T) spatial region. We relate this question to the calculation of a 4-point equilibrium correlator.Comment: 41 pages, 20 figures [change from v2: just a handful of minor grammar corrections

Plasma photoemission from string theory

Leading 't Hooft coupling corrections to the photoemission rate of the planar limit of a strongly-coupled {\cal {N}}=4 SYM plasma are investigated using the gauge/string duality. We consider the full order \alpha'^3 type IIB string theory corrections to the supergravity action, including higher order terms with the Ramond-Ramond five-form field strength. We extend our previous results presented in arXiv:1110.0526. Photoemission rates depend on the 't Hooft coupling, and their curves suggest an interpolating behaviour from strong towards weak coupling regimes. Their slopes at zero light-like momentum give the electrical conductivity as a function of the 't Hooft coupling, in full agreement with our previous results of arXiv:1108.6306. Furthermore, we also study the effect of corrections beyond the large N limit.Comment: 36 pages, 5 figures, paragraph added in the conclusions, references added, typos correcte

Delayed treatment of basilar thrombosis in a patient with a basilar aneurysm: a case report

<p>Abstract</p> <p>Introduction</p> <p>Acute occlusion of the basilar artery is a neurological emergency that has a high risk of severe disability and mortality. Delayed thrombolysis or endovascular therapy has been performed with some success in patients who present after 3 hours of symptom onset. Here we present the first case of delayed intra-arterial thrombolysis of a basilar artery thrombosis associated with a large saccular aneurysm.</p> <p>Case presentation</p> <p>A 73-year-old Caucasian man with a history of smoking and alcohol abuse presented to the Emergency Department complaining of diplopia and mild slurred speech and who progressed over 12 hours to coma and quadriparesis. He was found to have a large basilar tip aneurysm putting him at high risk for hemorrhage with lytic treatment.</p> <p>Conclusion</p> <p>The treatment options for basilar thrombosis are discussed. Aggressive treatment options should be considered despite long durations of clinical symptoms in basilar thrombosis, even in extremely high risk patients.</p

Intermediate distance correlators in hot Yang-Mills theory

Lattice measurements of spatial correlation functions of the operators FF and FF-dual in thermal SU(3) gauge theory have revealed a clear difference between the two channels at "intermediate" distances, x ~ 1/(pi T). This is at odds with the AdS/CFT limit which predicts the results to coincide. On the other hand, an OPE analysis at short distances (x << 1/(pi T)) as well as effective theory methods at long distances (x >> 1/(pi T)) suggest differences. Here we study the situation at intermediate distances by determining the time-averaged spatial correlators through a 2-loop computation. We do find unequal results, however the numerical disparity is small. Apart from theoretical issues, a future comparison of our results with time-averaged lattice measurements might also be of phenomenological interest in that understanding the convergence of the weak-coupling series at intermediate distances may bear on studies of the thermal broadening of heavy quarkonium resonances.Comment: 31 page

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

OBJECTIVE: In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS). METHODS: We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening. RESULTS: We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05). CONCLUSIONS: The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

Numerical Investigation into Dynamic Loading of Rubber Compound

The present paper analyses the heat generation build(up in silicone rubber samples when subjected to dynamic cyclic loading. Material properties of the rubber were determined through thermal and mechanical experimental testing. These properties are necessary to set up the computational model. The model includes a fully coupled transient nonlinear thermo(mechanical finite element analysis. In order to validate this approach, numerical results are compared with those gathered experimentally. The numerical model developed and validated could be used to simulate various industrial applications, involving rubber parts, for efficient and sustainable desig

Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials