54 resultados para Depressielijst (DL)


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Lifetimes of excited states in 128Ce were measured using the recoil distance Doppler-shift (RDDS) and the Doppler-shift attenuation (DSAM) methods. The experiments were performed at the Wright Nuclear Structure Laboratory of Yale University. Excited states of 128Ce were populated in the 100Mo(32Si,4n) reaction at 120 MeV and the nuclear γ decay was measured with an array of eight Clover detectors positioned at forward and backward angles. The deduced yrast transition strengths together with the energies of the levels within the ground-state (gs) band of 128Ce are in agreement with the predicted values for the X(5) critical point symmetry. Thus, we suggest 128Ce as a benchmark X(5) nucleus in the mass A ≈ 130 region. © World Scientific Publishing Company.

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We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.

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We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likely number of disease subtypes, given the data. We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas, for which there are gene expression, copy number variation, methylation and microRNA data. We identify 8 distinct consensus subtypes and study their prognostic value for death, new tumour events, progression and recurrence. The consensus subtypes are prognostic of tumour recurrence (log-rank p-value of $3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is driven principally by the methylation data (log-rank p-value of $2.0 \times 10^{-3}$) but the effect is strengthened by the other 3 data types, demonstrating the value of integrating multiple data types. Of particular note is a subtype of 47 patients characterised by very low levels of methylation. This subtype has very low rates of tumour recurrence and no new events in 10 years of follow up. We also identify a small gene expression subtype of 6 patients that shows particularly poor survival outcomes. Additionally, we note a consensus subtype that showly a highly distinctive data signature and suggest that it is therefore a biologically distinct subtype of glioblastoma. The code is available from https://sites.google.com/site/multipledatafusion/

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We numerically modeled the spatio-temporal dynamics of Dicke superradiance in GaN/InGaN heterostructure quantum wells in a ridge waveguide cavity. Model predictions envisage ultrashort pulses of intensities superior to what can be obtained in mode-locked lasers. ©2010 IEEE.

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Biodegradable polymers can be applied to a variety of implants for controlled and local drug delivery. The aim of this study is to develop a biodegradable and nanoporous polymeric platform for a wide spectrum of drug-eluting implants with special focus on stent-coating applications. It was synthesized by poly(DL-lactide-co-glycolide) (PLGA 65:35, PLGA 75:25) and polycaprolactone (PCL) in a multilayer configuration by means of a spin-coating technique. The antiplatelet drug dipyridamole was loaded into the surface nanopores of the platform. Surface characterization was made by atomic force microscopy (AFM) and spectroscopic ellipsometry (SE). Platelet adhesion and drug-release kinetic studies were then carried out. The study revealed that the multilayer films are highly nanoporous, whereas the single layers of PLGA are atomically smooth and spherulites are formed in PCL. Their nanoporosity (pore diameter, depth, density, surface roughness) can be tailored by tuning the growth parameters (eg, spinning speed, polymer concentration), essential for drug-delivery performance. The origin of pore formation may be attributed to the phase separation of polymer blends via the spinodal decomposition mechanism. SE studies revealed the structural characteristics, film thickness, and optical properties even of the single layers in the triple-layer construct, providing substantial information for drug loading and complement AFM findings. Platelet adhesion studies showed that the dipyridamole-loaded coatings inhibit platelet aggregation that is a prerequisite for clotting. Finally, the films exhibited sustained release profiles of dipyridamole over 70 days. These results indicate that the current multilayer phase therapeutic approach constitutes an effective drug-delivery platform for drug-eluting implants and especially for cardiovascular stent applications.

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We present the development of a drug-loaded triple-layer platform consisting of thin film biodegradable polymers, in a properly designed form for the desired gradual degradation. Poly(dl-lactide-co-glycolide) (PLGA (65:35), PLGA (75:25)) and polycaprolactone (PCL) were grown by spin coating technique, to synthesize the platforms with the order PCL/PLGA (75:25)/PLGA (65:35) that determine their degradation rates. The outer PLGA (65:35) layer was loaded with dipyridamole, an antiplatelet drug. Spectroscopic ellipsometry (SE) in the Vis-far UV range was used to determine the nanostructure, as well as the content of the incorporated drug in the as-grown platforms. In situ and real-time SE measurements were carried out using a liquid cell for the dynamic evaluation of the fibrinogen and albumin protein adsorption processes. Atomic force microscopy studies justified the SE results concerning the nanopores formation in the polymeric platforms, and the dominant adsorption mechanisms of the proteins, which were defined by the drug incorporation in the platforms. © 2013 Elsevier B.V. All rights reserved.