48 resultados para microcosmic optical parameter

em Université de Lausanne, Switzerland


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In the investigation of thin films of transition metal nitrides, an essential role is played by the accurate determination of their chemical composition. Actually the chemical composition depends on the deposition parameters and influences the optical properties. These relations are illustrated in thin films of TiNx and (Ti1-yVy)N-x deposited by reactive magnetron sputtering from composite targets of the elements. By variation of the nitrogen partial pressure and the target composition, different samples have been obtained. The chemical composition has been measured by electron probe microanalysis at low irradiation voltages. The optical properties are evaluated by ex-situ ellipsometry. Using the screened Drude model, they are correlated with the differences in composition. Adding vanadium or nitrogen in Ti-N is shown to have the same effect on the optical properties.

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OBJECTIVE: The purpose of this study was to compare the use of different variables to measure the clinical wear of two denture tooth materials in two analysis centers. METHODS: Twelve edentulous patients were provided with full dentures. Two different denture tooth materials (experimental material and control) were placed randomly in accordance with the split-mouth design. For wear measurements, impressions were made after an adjustment phase of 1-2 weeks and after 6, 12, 18, and 24 months. The occlusal wear of the posterior denture teeth of 11 subjects was assessed in two study centers by use of plaster replicas and 3D laser-scanning methods. In both centers sequential scans of the occlusal surfaces were digitized and superimposed. Wear was described by use of four different variables. Statistical analysis was performed after log-transformation of the wear data by use of the Pearson and Lin correlation and by use of a mixed linear model. RESULTS: Mean occlusal vertical wear of the denture teeth after 24 months was between 120μm and 212μm, depending on wear variable and material. For three of the four variables, wear of the experimental material was statistically significantly less than that of the control. Comparison of the two study centers, however, revealed correlation of the wear variables was only moderate whereas strong correlation was observed among the different wear variables evaluated by each center. SIGNIFICANCE: Moderate correlation was observed for clinical wear measurements by optical 3D laser scanning in two different study centers. For the two denture tooth materials, wear measurements limited to the attrition zones led to the same qualitative assessment.

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An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

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Digital holography microscopy (DHM) is an optical microscopy technique which allows recording non-invasively the phase shift induced by living cells with nanometric sensitivity. Here, we exploit the phase signal as an indicator of dry mass (related to the protein concentration). This parameter allows monitoring the protein production rate and its evolution during the cell cycle. ©2008 COPYRIGHT SPIE

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Recently, atomic force microscope (AFM) manufacturers have begun producing instruments specifically designed to image biological specimens. In most instances, they are integrated with an inverted optical microscope, which permits concurrent optical and AFM imaging. An important component of the set-up is the imaging chamber, whose design determines the nature of the experiments that can be conducted. Many different imaging chamber designs are available, usually designed to optimize a single parameter, such as the dimensions of the substrate or the volume of fluid that can be used throughout the experiment. In this report, we present a universal fluid cell, which simultaneously optimizes all of the parameters that are important for the imaging of biological specimens in the AFM. This novel imaging chamber has been successfully tested using mammalian, plant, and microbial cells.

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PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

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X-ray is a technology that is used for numerous applications in the medical field. The process of X-ray projection gives a 2-dimension (2D) grey-level texture from a 3- dimension (3D) object. Until now no clear demonstration or correlation has positioned the 2D texture analysis as a valid indirect evaluation of the 3D microarchitecture. TBS is a new texture parameter based on the measure of the experimental variogram. TBS evaluates the variation between 2D image grey-levels. The aim of this study was to evaluate existing correlations between 3D bone microarchitecture parameters - evaluated from μCT reconstructions - and the TBS value, calculated on 2D projected images. 30 dried human cadaveric vertebrae were acquired on a micro-scanner (eXplorer Locus, GE) at isotropic resolution of 93 μm. 3D vertebral body models were used. The following 3D microarchitecture parameters were used: Bone volume fraction (BV/TV), Trabecular thickness (TbTh), trabecular space (TbSp), trabecular number (TbN) and connectivity density (ConnD). 3D/2D projections has been done by taking into account the Beer-Lambert Law at X-ray energy of 50, 100, 150 KeV. TBS was assessed on 2D projected images. Correlations between TBS and the 3D microarchitecture parameters were evaluated using a linear regression analysis. Paired T-test is used to assess the X-ray energy effects on TBS. Multiple linear regressions (backward) were used to evaluate relationships between TBS and 3D microarchitecture parameters using a bootstrap process. BV/TV of the sample ranged from 18.5 to 37.6% with an average value at 28.8%. Correlations' analysis showedthat TBSwere strongly correlatedwith ConnD(0.856≤r≤0.862; p<0.001),with TbN (0.805≤r≤0.810; p<0.001) and negatively with TbSp (−0.714≤r≤−0.726; p<0.001), regardless X-ray energy. Results show that lower TBS values are related to "degraded" microarchitecture, with low ConnD, low TbN and a high TbSp. The opposite is also true. X-ray energy has no effect onTBS neither on the correlations betweenTBS and the 3Dmicroarchitecture parameters. In this study, we demonstrated that TBS was significantly correlated with 3D microarchitecture parameters ConnD and TbN, and negatively with TbSp, no matter what X-ray energy has been used. This article is part of a Special Issue entitled ECTS 2011. Disclosure of interest: None declared.

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Fluorescence cystoscopy enhances detection of early bladder cancer. Water used to inflate the bladder during the procedure rapidly contains urine, which may contain fluorochromes. This frequently degradesfluorescence images. Samples of bladder washout fluid (BWF) or urine were collected (15 subjects). We studiedtheir fluorescence properties and assessed changes induced by pH (4 to 9) and temperature (15°C to 41°C).A typical fluorescence spectrum of BWF features a main peak (excitation/emission: 320∕420 nm, FWHM =50∕100 nm) and a weaker (5% to 20% of main peak intensity), secondary peak (excitation/emission: 455∕525 nm, FWHM = 80∕50 nm). Interpatient fluctuations of fluorescence intensity are observed. Fluorescence intensity decreases when temperature increases (max 30%) or pH values vary (max 25%). Neither approach is compatible with clinical settings. Fluorescence lifetime measurements suggest that 4-pyridoxic acid/riboflavin is the most likely molecule responsible for urine's main/secondary fluorescence peak. Our measurements give an insight into the spectroscopy of the detrimental background fluorescence. This should be included in the optical design of fluorescence cystoscopes. We estimate that restricting the excitation range from 370-430 nm to 395-415 nm would reduce the BWF background by a factor 2.

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Nonlinear optical nanocrystals have been recently introduced as a promising alternative to fluorescent probes for multiphoton microscopy. We present for the first time a complete survey of the properties of five nanomaterials (KNbO(3), LiNbO(3), BaTiO(3), KTP, and ZnO), describing their preparation and stabilization and providing quantitative estimations of their nonlinear optical response. In the light of their prospective use as biological and clinical markers, we assess their biocompatibility on human healthy and cancerous cell lines. Finally, we demonstrate the great potential for cell imaging of these inherently nonlinear probes in terms of optical contrast, wavelength flexibility, and signal photostability.

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In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

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Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models - in the form of Bayesian networks - address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).

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Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.