Community based trial of home blood pressure monitoring with nurse-led telephone support in patients with stroke or transient ischaemic attack recently discharged from hospital.

BACKGROUND: High blood pressure in patients with stroke increases the risk of recurrence but management in the community is often inadequate. Home blood pressure monitoring may increase patients' involvement in their care, increase compliance, and reduce the need for patients to attend their General Practitioner if blood pressure is adequately controlled. However the value of home monitoring to improve blood pressure control is unclear. In particular its use has not been evaluated in stroke patients in whom neurological and cognitive ability may present unique challenges. DESIGN: Community based randomised trial with follow up after 12 months. PARTICIPANTS: 360 patients admitted to three South London Stroke units with stroke or transient ischaemic attack within the past 9 months will be recruited from the wards or outpatients and randomly allocated into two groups. All patients will be visited by the specialist nurse at home at baseline when she will measure their blood pressure and administer a questionnaire. These procedures will be repeated at 12 months follow up by another researcher blind as to whether the patient is in intervention or control group. INTERVENTION: INTERVENTION patients will be given a validated home blood pressure monitor and support from the specialist nurse. Control patients will continue with usual care (blood pressure monitoring by their practice). Main outcome measures in both groups after 12 months: 1. Change in systolic blood pressure.2. Cost effectiveness: Incremental cost of the intervention to the National Health Service and incremental cost per quality adjusted life year gained

Control of the Spanwise Distribution of Circulation on NACA 0012 and Flat Plate Wings

Open-loop active flow control is used to modify the spanwise distribution of circulation around an NACA 0012 and flat plate wing. The leading edge on both airfoils and tip regions of the NACA airfoil contain spatially localized actuators that can be independently controlled in terms of amplitude and frequency, allowing the spanwise distribution of circulation to be modified. Different orientations of the pulsed-blowing actuators were used to provide upstream, downstream, in-line with the flow, and outward span components of actuation. The actuation effectiveness was documented using force balance measurements of the lift and drag, smoke-wire visualization, surface pressure measurements and PIV velocity field measurements. Actuation with an upstream component is shown to be far more effective in reducing the separated region than actuation in the streamwise direction. Initial measurements of the change in circulation on the suction surface of the airfoil indicate that spatially localized forcing produces global changes over the wing, primarily associated with the reduction in size of the separated flow region

Control-Group Feature Normalization for Multivariate Pattern Analysis Using the Support Vector Machine

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We also show that control-based normalization provides better interpretation with respect to the estimated multivariate disease pattern and improves the classifier performance in many cases

Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

A combined interpolation - 3D Level Set Method (I+3DLSM) based segmentation process is presented. The performance in terms of accuracy of the 3-dimensional (3D) level set method (LSM) in the segmentation of throat regions from highly anisotropic magnetic resonance imaging (MRI) volumes, with and without an interpolation step is evaluated. Qualitative and quantitative results from real MRI data suggest that performing interpolation, to reconstruct isotropic MRI volumes, prior to 3D LSM improves the accuracy of the segmentation results, compared to interpolation post 3D LSM and no interpolation at all

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease effects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201

Palladium nanoparticles by electrospinning from poly(acrylonitrile-co-acrylic acid)-PdCl2 solutions. Relations between preparation conditions, particle size, and catalytic activity

Catalytic palladium (Pd) nanoparticles on electrospun copolymers of acrylonitrile and acrylic acid (PAN-AA) mats were produced via reduction of PdCl2 with hydrazine. Fiber mats were electrospun from homogeneous solutions of PAN-AA and PdCl2 in dimethylformamide (DMF). Pd cations were reduced to Pd metals when fiber mats were treated in an aqueous hydrazine solution at room temperature. Pd atoms nucleate and form small crystallites whose sizes were estimated from the peak broadening of X-ray diffraction peaks. Two to four crystallites adhere together and form agglomerates. Agglomerate sizes and fiber diameters were determined by scanning and transmission electron microscopy. Spherical Pd nanoparticles were dispersed homogeneously on the electrospun nanofibers. The effects of copolymer composition and amount of PdCl2 on particle size were investigated. Pd particle size mainly depends on the amount of acrylic acid functional groups and PdCl2 concentration in the spinning solution. Increasing acrylic acid concentration on polymer chains leads to larger Pd nanoparticles. In addition, Pd particle size becomes larger with increasing PdCl2 concentration in the spinning solution. Hence, it is possible to tune the number density and the size of metal nanoparticles. The catalytic activity of the Pd nanoparticles in electrospun mats was determined by selective hydrogenation of dehydrolinalool (3,7-dimethyloct-6- ene-1-yne-3-ol, DHL) in toluene at 90 °C. Electrospun fibers with Pd particles have 4.5 times higher catalytic activity than the current Pd/Al2O3 catalyst

Value at Risk models with long memory features and their economic performance

We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms

The effect of size ratio on the sphere structure factor in colloidal sphere-plate mixtures

The following article appeared in Journal of Chemical Physics 137.20 (2012): 204909 and may be found at http://scitation.aip.org/content/aip/journal/jcp/137/20/10.1063/1.4767722Binary mixtures of colloidal particles of sufficiently different sizes or shapes tend to demix at high concentration. Already at low concentration, excluded volume interactions between the two species give rise to structuring effects. Here, a new theoretical description is proposed of the structure of colloidal sphere-plate mixtures, based on a density expansion of the work needed to insert a pair of spheres and a single sphere in a sea of them, in the presence or not of plates. The theory is first validated using computer simulations. The predictions are then compared to experimental observations using silica spheres and gibbsite platelets. Small-angle neutron scattering was used to determine the change of the structure factor of spheres on addition of platelets, under solvent contrast conditions where the platelets were invisible. Theory and experiment agreed very well for a platelet/sphere diameter ratio Dd 2.2 and reasonably well for Dd 5. The sphere structure factor increases at low scattering vector Q in the presence of platelets; a weak reduction of the sphere structure factor was predicted at larger Q, and for the system with Dd 2.2 was indeed observed experimentally. At fixed particle volume fraction, an increase in diameter ratio leads to a large change in structure factor. Systems with a larger diameter ratio also phase separate at lower concentrationsG. Cinacchi was supported by the EU through a Marie Curie Research Fellowship PIEF-GA-2008-220557 and now by the Ministry of Research of Spain through the Ramón y Cajal contract (Contract. No. RYC-2010-07475). N. Doshi was jointly supported by Imerys and EPSRC DTA. Experiments at ILL were supported by beamtime allocations 9-12- 216 and 9-10-1044. Materials were kindly donated by AZ Electronics (Klebosol) and Lubrizol (Solsperse 41000