Multimodal Pressure-Flow Method to Assess Dynamics of Cerebral Autoregulation in Stroke and Hypertension

Background: This study evaluated the effects of stroke on regulation of cerebral blood flow in response to fluctuations in systemic blood pressure (BP). The autoregulatory dynamics are difficult to assess because of the nonstationarity and nonlinearity of the component signals. Methods: We studied 15 normotensive, 20 hypertensive and 15 minor stroke subjects (48.0 ± 1.3 years). BP and blood flow velocities (BFV) from middle cerebral arteries (MCA) were measured during the Valsalva maneuver (VM) using transcranial Doppler ultrasound. Results: A new technique, multimodal pressure-flow analysis (MMPF), was implemented to analyze these short, nonstationary signals. MMPF analysis decomposes complex BP and BFV signals into multiple empirical modes, representing their instantaneous frequency-amplitude modulation. The empirical mode corresponding to the VM BP profile was used to construct the continuous phase diagram and to identify the minimum and maximum values from the residual BP (BPR) and BFV (BFVR) signals. The BP-BFV phase shift was calculated as the difference between the phase corresponding to the BPR and BFVR minimum (maximum) values. BP-BFV phase shifts were significantly different between groups. In the normotensive group, the BFVR minimum and maximum preceded the BPR minimum and maximum, respectively, leading to large positive values of BP-BFV shifts. Conclusion: In the stroke and hypertensive groups, the resulting BP-BFV phase shift was significantly smaller compared to the normotensive group. A standard autoregulation index did not differentiate the groups. The MMPF method enables evaluation of autoregulatory dynamics based on instantaneous BP-BFV phase analysis. Regulation of BP-BFV dynamics is altered with hypertension and after stroke, rendering blood flow dependent on blood pressure

Trioctylphosphine as Both Solvent and Stabilizer to Synthesize CdS Nanorods

High quality CdS nanorods are synthesized reproducibly with cadmium acetate and sulfur as precursors in trioctylphosphine solution. The morphology, crystalline form and phase composition of CdS nanorods are characterized by transmission electron microscopy (TEM), high-resolution TEM and X-ray diffraction (XRD). CdS nanorods obtained are uniform with an aspect ratio of about 5:1 and in a wurtzite structure. The influence of reaction conditions on the growth of CdS nanorods demonstrates that low precursor concentration and high reaction temperature (260 °C) are favorable for the formation of uniform CdS nanorods with 85.3% of product yield

Forward-time simulation of realistic samples for genome-wide association studies

<p>Abstract</p> <p>Background</p> <p>Forward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods.</p> <p>Results</p> <p>Using a general-purpose forward-time population genetics simulation environment, we developed a forward-time simulation method that can be used to simulate realistic samples for genome-wide association studies. We examined the properties of this simulation method by comparing simulated samples with real data and demonstrated its wide applicability using four examples, including a simulation of case-control samples with a disease caused by multiple interacting genetic and environmental factors, a simulation of trio families affected by a disease-predisposing allele that had been subjected to either slow or rapid selective sweep, and a simulation of a structured population resulting from recent population admixture.</p> <p>Conclusions</p> <p>Our algorithm simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. Because of its flexibility to generate different types of samples with arbitrary disease or quantitative trait models, this simulation method can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for genome-wide association studies.</p

Breakdown of Fermi-liquid theory in a cuprate superconductor

The behaviour of electrons in solids is remarkably well described by Landau's Fermi-liquid theory, which says that even though electrons in a metal interact they can still be treated as well-defined fermions, called ``quasiparticles''. At low temperature, the ability of quasiparticles to transport heat is strictly given by their ability to transport charge, via a universal relation known as the Wiedemann-Franz law, which no material in nature has been known to violate. High-temperature superconductors have long been thought to fall outside the realm of Fermi-liquid theory, as suggested by several anomalous properties, but this has yet to be shown conclusively. Here we report on the first experimental test of the Wiedemann-Franz law in a cuprate superconductor, (Pr,Ce)$_2$CuO$_4$. Our study reveals a clear departure from the universal law and provides compelling evidence for the breakdown of Fermi-liquid theory in high-temperature superconductors.Comment: 7 pages, 3 figure

Feature selection for chemical sensor arrays using mutual information

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays

Anticancer Gene Transfer for Cancer Gene Therapy

Gene therapy vectors are among the treatments currently used to treat malignant tumors. Gene therapy vectors use a specific therapeutic transgene that causes death in cancer cells. In early attempts at gene therapy, therapeutic transgenes were driven by non-specific vectors which induced toxicity to normal cells in addition to the cancer cells. Recently, novel cancer specific viral vectors have been developed that target cancer cells leaving normal cells unharmed. Here we review such cancer specific gene therapy systems currently used in the treatment of cancer and discuss the major challenges and future directions in this field

Image Feature Extraction Acceleration

Image feature extraction is instrumental for most of the best-performing algorithms in computer vision. However, it is also expensive in terms of computational and memory resources for embedded systems due to the need of dealing with individual pixels at the earliest processing levels. In this regard, conventional system architectures do not take advantage of potential exploitation of parallelism and distributed memory from the very beginning of the processing chain. Raw pixel values provided by the front-end image sensor are squeezed into a high-speed interface with the rest of system components. Only then, after deserializing this massive dataflow, parallelism, if any, is exploited. This chapter introduces a rather different approach from an architectural point of view. We present two Application-Specific Integrated Circuits (ASICs) where the 2-D array of photo-sensitive devices featured by regular imagers is combined with distributed memory supporting concurrent processing. Custom circuitry is added per pixel in order to accelerate image feature extraction right at the focal plane. Specifically, the proposed sensing-processing chips aim at the acceleration of two flagships algorithms within the computer vision community: the Viola-Jones face detection algorithm and the Scale Invariant Feature Transform (SIFT). Experimental results prove the feasibility and benefits of this architectural solution.Ministerio de Economía y Competitividad TEC2012-38921-C02, IPT-2011- 1625-430000, IPC-20111009Junta de Andalucía TIC 2338-2013Xunta de Galicia EM2013/038Office of NavalResearch (USA) N00014141035