883 resultados para Equality Set Projection


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Purpose: To evaluate the diagnostic value and image quality of CT with filtered back projection (FBP) compared with adaptive statistical iterative reconstructed images (ASIR) in body stuffers with ingested cocaine-filled packets.Methods and Materials: Twenty-nine body stuffers (mean age 31.9 years, 3 women) suspected for ingestion of cocaine-filled packets underwent routine-dose 64-row multidetector CT with FBP (120kV, pitch 1.375, 100-300 mA and automatic tube current modulation (auto mA), rotation time 0.7sec, collimation 2.5mm), secondarily reconstructed with 30 % and 60 % ASIR. In 13 (44.83%) out of the body stuffers cocaine-filled packets were detected, confirmed by exact analysis of the faecal content including verification of the number (range 1-25). Three radiologists independently and blindly evaluated anonymous CT examinations (29 FBP-CT and 68 ASIR-CT) for the presence and number of cocaine-filled packets indicating observers' confidence, and graded them for diagnostic quality, image noise, and sharpness. Sensitivity, specificity, area under the receiver operating curve (ROC) Az and interobserver agreement between the 3 radiologists for FBP-CT and ASIR-CT were calculated.Results: The increase of the percentage of ASIR significantly diminished the objective image noise (p<0.001). Overall sensitivity and specificity for the detection of the cocaine-filled packets were 87.72% and 76.15%, respectively. The difference of ROC area Az between the different reconstruction techniques was significant (p= 0.0101), that is 0.938 for FBP-CT, 0.916 for 30 % ASIR-CT, and 0.894 for 60 % ASIR-CT.Conclusion: Despite the evident image noise reduction obtained by ASIR, the diagnostic value for detecting cocaine-filled packets decreases, depending on the applied ASIR percentage.

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Traditionally, compositional data has been identified with closed data, and the simplex has been considered as the natural sample space of this kind of data. In our opinion, the emphasis on the constrained nature ofcompositional data has contributed to mask its real nature. More crucial than the constraining property of compositional data is the scale-invariant property of this kind of data. Indeed, when we are considering only few parts of a full composition we are not working with constrained data but our data are still compositional. We believe that it is necessary to give a more precisedefinition of composition. This is the aim of this oral contribution

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The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

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There are no validated criteria for the diagnosis of sensory neuronopathy (SNN) yet. In a preliminary monocenter study a set of criteria relying on clinical and electrophysiological data showed good sensitivity and specificity for a diagnosis of probable SNN. The aim of this study was to test these criteria on a French multicenter study. 210 patients with sensory neuropathies from 15 francophone reference centers for neuromuscular diseases were included in the study with an expert diagnosis of non-SNN, SNN or suspected SNN according to the investigations performed in these centers. Diagnosis was obtained independently from the set of criteria to be tested. The expert diagnosis was taken as the reference against which the proposed SNN criteria were tested. The set relied on clinical and electrophysiological data easily obtainable with routine investigations. 9/61 (16.4 %) of non-SNN patients, 23/36 (63.9 %) of suspected SNN, and 102/113 (90.3 %) of SNN patients according to the expert diagnosis were classified as SNN by the criteria. The SNN criteria tested against the expert diagnosis in the SNN and non-SNN groups had 90.3 % (102/113) sensitivity, 85.2 % (52/61) specificity, 91.9 % (102/111) positive predictive value, and 82.5 % (52/63) negative predictive value. Discordance between the expert diagnosis and the SNN criteria occurred in 20 cases. After analysis of these cases, 11 could be reallocated to a correct diagnosis in accordance with the SNN criteria. The proposed criteria may be useful for the diagnosis of probable SNN in patients with sensory neuropathy. They can be reached with simple clinical and paraclinical investigations.

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Obtaining automatic 3D profile of objects is one of the most important issues in computer vision. With this information, a large number of applications become feasible: from visual inspection of industrial parts to 3D reconstruction of the environment for mobile robots. In order to achieve 3D data, range finders can be used. Coded structured light approach is one of the most widely used techniques to retrieve 3D information of an unknown surface. An overview of the existing techniques as well as a new classification of patterns for structured light sensors is presented. This kind of systems belong to the group of active triangulation method, which are based on projecting a light pattern and imaging the illuminated scene from one or more points of view. Since the patterns are coded, correspondences between points of the image(s) and points of the projected pattern can be easily found. Once correspondences are found, a classical triangulation strategy between camera(s) and projector device leads to the reconstruction of the surface. Advantages and constraints of the different patterns are discussed

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In a search for new sensor systems and new methods for underwater vehicle positioning based on visual observation, this paper presents a computer vision system based on coded light projection. 3D information is taken from an underwater scene. This information is used to test obstacle avoidance behaviour. In addition, the main ideas for achieving stabilisation of the vehicle in front of an object are presented

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This paper describes a new reliable method, based on modal interval analysis (MIA) and set inversion (SI) techniques, for the characterization of solution sets defined by quantified constraints satisfaction problems (QCSP) over continuous domains. The presented methodology, called quantified set inversion (QSI), can be used over a wide range of engineering problems involving uncertain nonlinear models. Finally, an application on parameter identification is presented

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Our purpose is to provide a set-theoretical frame to clustering fuzzy relational data basically based on cardinality of the fuzzy subsets that represent objects and their complementaries, without applying any crisp property. From this perspective we define a family of fuzzy similarity indexes which includes a set of fuzzy indexes introduced by Tolias et al, and we analyze under which conditions it is defined a fuzzy proximity relation. Following an original idea due to S. Miyamoto we evaluate the similarity between objects and features by means the same mathematical procedure. Joining these concepts and methods we establish an algorithm to clustering fuzzy relational data. Finally, we present an example to make clear all the process

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The aim of this work was to establish a modified pre-diagnostic polymerase chain reaction (PCR) protocol using a single primer set that enables successful amplification of a highly conserved mammalian sequence in order to determine overall sample DNA quality for multiple mammalian species that inhabit areas endemic for leishmaniasis. The gene encoding interphotoreceptor retinoid-binding protein (IRBP), but not other conserved genes, was efficiently amplified in DNA samples from tail skin, ear skin, bone marrow, liver and spleen from all of the species tested. In tissue samples that were PCR-positive for Leishmania, we found that DNA from 100%, 55% and 22% of the samples tested resulted in a positive PCR reaction for the IRBP, beta-actin and beta-globin genes, respectively. Nucleotide sequencing of an IRBP amplicon resolved any questions regarding the taxonomical classification of a rodent, which was previously based simply on the morphological features of the animal. Therefore, PCR amplification and analysis of the IRBP amplicon are suitable for pre-diagnostically assessing DNA quality and identifying mammalian species living in areas endemic to leishmaniasis and other diseases.

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The aim of this study was to determine the benefits of a psy-chological treatment in women victims of mistreatments in psychological health and in the immune system. The participants in this study were 60 women users of the Equality Area of the City Council of Malaga. We set two groups of women up in relation of whether the women attended or not to the given therapy. Psychological variables (self-esteem, depression and anxiety) and levels of Inmunoglobulin A were evaluated before and after the treatment. The results showed differences between all the vari-ables before and after the treatment, with better valuation after the treat-ment. These differences were not shown in women that did not assist to the therapeutic sessions, and even, the values of depression and immu-noglobulin A levels were worse. We found also differences in the values of these variables when the two groups were compared. Women that re-ceived the treatment showed fewer indicators of psychological alterations and higher levels of immunoglobulin A than the women that did not assist to the sessions; in the pre-treatment these differences were not shown. This study enhances the significance of the psychological treatment for psychological and physic health in women victims of

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Trans-apical aortic valve replacement (AVR) is a new and rapidly growing therapy. However, there are only few training opportunities. The objective of our work is to build an appropriate artificial model of the heart that can replace the use of animals for surgical training in trans-apical AVR procedures. To reduce the necessity for fluoroscopy, we pursued the goal of building a translucent model of the heart that has nature-like dimensions. A simplified 3D model of a human heart with its aortic root was created in silico using the SolidWorks Computer-Aided Design (CAD) program. This heart model was printed using a rapid prototyping system developed by the Fab@Home project and dip-coated two times with dispersion silicone. The translucency of the heart model allows the perception of the deployment area of the valved-stent without using heavy imaging support. The final model was then placed in a human manikin for surgical training on trans-apical AVR procedure. Trans-apical AVR with all the necessary steps (puncture, wiring, catheterization, ballooning etc.) can be realized repeatedly in this setting.

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HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.