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Determination of Total Galactose from Dried Blood Spots—Extensive Assay Evaluation of a CE-Marked Test-Kit  [PDF]
Ralph Fingerhut, Toni Torresani
Journal of Analytical Sciences, Methods and Instrumentation (JASMI) , 2013, DOI: 10.4236/jasmi.2013.33020
Abstract:

Most newborn screening laboratories use CE-marked or FDA-approved test-kits, like in routine clinical chemistry. National regulations require only minimal evaluation from the customer, if the test-kits are used as specified by the manufacturer. The microtiter-based kit-concept is often based on the perception, that the laboratory always processes whole microtiter plates. However, in the daily routine, this is rather a rare exception, which leads to much higher costs per newborn, compared to the costs per assay in the test-kits. In addition the amount of wasted resources is quite high. Performance of the Neonatal Total Galactose kit from Perkin Elmer was tested. We have determined specificity, limit of detection (LOD), limit of quantitation (LOQ), intra and inter assay variation, recovery, stability of measuring signal and reagents. Results were also compared with the Astoria Pacific Spot Check System. In addition, we had (by chance) the opportunity to test 2 kits, which were already expired for more than 3 years. LOD was 165 - 306 μmol/L and LOQ 475 - 703 μmol/L, depending on the definition of LOD/LOQ. Mean recovery was 112.8%, intra assay CVs were 11.3, 7.3, 4.0, and 3.0, and inter assay CVs 28.7, 15.9, 7.8, and 9.3, at 220, 590, 1200, and 2060 μmol/L respectively. Reconstituted and mixed reagents must be used within some hours, and were unstable even if stored at -20℃. However, if the reconstituted galactose substrate reagent and galactose oxidase reagent were only mixed according to the daily requirements, and the rest stored separately at -20℃, they were stable for at least 12 days. The performance of the expired test-kits did not differ from the others. The performance of the Total Galactose kit is comparable to other tests used for newborn screening. However, we could significantly reduce the costs per newborn and reduce unnecessary production of waste, by thorough validation and modification of the assay procedures.

Asociación entre riesgo cardiovascular y consumo de licopeno en mujeres pre y postmenopáusicas
Torresani,María Elena;
Archivos Latinoamericanos de Nutrición , 2009,
Abstract: this work aimed at assessing association between cardiovascular risk (cvr) and lycopene intake in pre- and post-menopausal women, as well as its correlation with ldl-c and hdl-c values and waist circumference (wc). a transversal design of comparison and correlation was carried out for independent samples. a 316 women (40-65 y) sample attending nutritional consultation at a research foundation for endocrino metabollic diseases in buenos aires city (2005-2007) was randomized according to biological stage (35.8% premenopausal and 64.2% postmenopausal women). cvr was obtained based on framingham score and lycopene intake (source food and all lycopene containing food) according to weekly consumption frequency (mg/d and weekly/servings). association between variables was calculated with the student test, fisher test and pearson correlation coefficient (alpha significance level: 0.05). at both biological stages and for each cvr category, an inverse relationship was observed with lycopene intake, but only in pre-menopausal women with low cvr (category iii), lycopene intake was significantly greater than in those women who had moderate cvr (category ii). there was a significant correlation in postmenopausal women between ldl-c values and lycopene intake supplied by source food. however, in both biological stages a significant correlation was found between ldl-c values and all lycopene containing food consumption. no significant correlation was found between lycopene intake, hdl-c values and wc. these findings point out the relevances of a preventive nutritional approach at woman?s different biological stages
A survey of uncertainty principles and some signal processing applications
Benjamin Ricaud,Bruno Torresani
Mathematics , 2012,
Abstract: The goal of this paper is to review the main trends in the domain of uncertainty principles and localization, emphasize their mutual connections and investigate practical consequences. The discussion is strongly oriented towards, and motivated by signal processing problems, from which significant advances have been made recently. Relations with sparse approximation and coding problems are emphasized.
Representation of operators in the time-frequency domain and generalized Gabor multipliers
Monika Dorfler,Bruno Torresani
Mathematics , 2008,
Abstract: Starting from a general operator representation in the time-frequency domain, this paper addresses the problem of approximating linear operators by operators that are diagonal or band-diagonal with respect to Gabor frames. A characterization of operators that can be realized as Gabor multipliers is given and necessary conditions for the existence of (Hilbert-Schmidt) optimal Gabor multiplier approximations are discussed and an efficient method for the calculation of an operator's best approximation by a Gabor multiplier is derived. The spreading function of Gabor multipliers yields new error estimates for these approximations. Generalizations (multiple Gabor multipliers) are introduced for better approximation of overspread operators. The Riesz property of the projection operators involved in generalized Gabor multipliers is characterized, and a method for obtaining an operator's best approximation by a multiple Gabor multiplier is suggested. Finally, it is shown that in certain situations, generalized Gabor multipliers reduce to a finite sum of regular Gabor multipliers with adapted windows.
EXMOVES: Classifier-based Features for Scalable Action Recognition
Du Tran,Lorenzo Torresani
Computer Science , 2013,
Abstract: This paper introduces EXMOVES, learned exemplar-based features for efficient recognition of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input sequence. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated positive space-time volume and a large number of unannotated videos. Our representation offers two main advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that simple linear classification models trained on our global video descriptor yield action recognition accuracy approaching the state-of-the-art but at orders of magnitude lower cost, since at test-time no sliding window is necessary and linear models are efficient to train and test. This enables scalable action recognition, i.e., efficient classification of a large number of different actions even in large video databases. We show the generality of our approach by building our mid-level descriptors from two different low-level feature representations. The accuracy and efficiency of the approach are demonstrated on several large-scale action recognition benchmarks.
Coupled Depth Learning
Mohammad Haris Baig,Lorenzo Torresani
Computer Science , 2015,
Abstract: In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map is computed. We express the global depth map of an image as a linear combination of a depth basis learned from examples. The depth basis captures spatial and statistical regularities and reduces the problem of coarse depth estimation to the task of predicting the input-specific coefficients in the linear combination, which are much fewer than the number of pixels. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are {\bf coupled} and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. This global estimation is then used as a guidance by a local refinement method that introduces depth details that could not be captured at the coarse level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.
Semantic Segmentation with Boundary Neural Fields
Gedas Bertasius,Jianbo Shi,Lorenzo Torresani
Computer Science , 2015,
Abstract: The state-of-the-art in semantic segmentation is currently represented by fully convolutional networks (FCNs). However, FCNs use large receptive fields and many pooling layers, both of which cause blurring and low spatial resolution in the deep layers. As a result FCNs tend to produce segmentations that are poorly localized around object boundaries. Prior work has attempted to address this issue in post-processing steps, for example using a color-based CRF on top of the FCN predictions. However, these approaches require additional parameters and low-level features that are difficult to tune and integrate into the original network architecture. Additionally, most CRFs use color-based pixel affinities, which are not well suited for semantic segmentation and lead to spatially disjoint predictions. To overcome these problems, we introduce a Boundary Neural Field (BNF), which is a global energy model integrating FCN predictions with boundary cues. The boundary information is used to enhance semantic segment coherence and to improve object localization. Specifically, we first show that the convolutional filters of semantic FCNs provide good features for boundary detection. We then employ the predicted boundaries to define pairwise potentials in our energy. Finally, we show that our energy decomposes semantic segmentation into multiple binary problems, which can be relaxed for efficient global optimization. We report extensive experiments demonstrating that minimization of our global boundary-based energy yields results superior to prior globalization methods, both quantitatively as well as qualitatively.
DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection
Gedas Bertasius,Jianbo Shi,Lorenzo Torresani
Computer Science , 2014,
Abstract: Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection. We achieve this goal by means of a multi-scale deep network that consists of five convolutional layers and a bifurcated fully-connected sub-network. The section from the input layer to the fifth convolutional layer is fixed and directly lifted from a pre-trained network optimized over a large-scale object classification task. This section of the network is applied to four different scales of the image input. These four parallel and identical streams are then attached to a bifurcated sub-network consisting of two independently-trained branches. One branch learns to predict the contour likelihood (with a classification objective) whereas the other branch is trained to learn the fraction of human labelers agreeing about the contour presence at a given point (with a regression criterion). We show that without any feature engineering our multi-scale deep learning approach achieves state-of-the-art results in contour detection.
High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision
Gedas Bertasius,Jianbo Shi,Lorenzo Torresani
Computer Science , 2015,
Abstract: Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High" scheme, where low-level boundaries aid high-level vision tasks. Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks.
Variables relacionadas con la calidad de atención de la consulta nutricional y percepción del éxito en el tratamiento para el control del peso corporal Variables related to the quality of care nutritional consulting and perception of success in treatments for body weight control
ME Torresani,SJ Urrutia,MJ Vainer,MM Vallote
Diaeta , 2011,
Abstract: Objetivo: Determinar la asociación entre variables relacionadas con la calidad de la consulta nutricional y la percepción del paciente en el éxito del tratamiento para el control del peso corporal en un grupo de mujeres mayores de 20 a os, habitantes de la Ciudad Autónoma de Buenos Aires o del Gran Buenos Aires. Metodología: Dise o observacional, transversal de correlación. Muestreo aleatorio simple de 97 mujeres que concurrieron por lo menos una vez a una consulta nutricional llevada a cabo por un Licenciado en Nutrición. Se realizó encuesta estructurada y voluntaria analizando como variable dependiente la percepción del éxito del tratamiento nutricional y como variables independientes tres variables relacionadas con la calidad de la atención como la escucha del profesional (buena, regular o mala), indicaciones adecuadas a gustos, hábitos y tolerancias digestivas y tipo de material entregado en la consulta. El análisis estadístico se realizó con SPSS 15,0 aplicando diferencia de proporciones y OR con valor p<0,05. Resultados: Del total de la muestra estudiada el 61,9% percibió como exitoso a su último tratamiento para el control del peso corporal. Para la mayoría el ámbito físico donde se desarrolló la consulta fue adecuado (95,9%), siendo suficiente el tiempo destinado a la misma (89,7%), con buena escucha llevada a cabo por el profesional (75,3%). La percepción del éxito fue asociada significativamente con una buena escucha del profesional en la consulta (p: 0,0001) y con el manejo de indicaciones adecuadas a los gustos y hábitos de las pacientes (p: 0,002). No se observo asociación entre las indicaciones adecuadas a la tolerancia digestiva y el tipo de material empleado con la percepción de éxito en la consulta nutricional. Conclusiones: La percepción del éxito en el tratamiento nutricional fue asociada significativamente con una buena escucha por parte del profesional en la consulta e indicaciones adecuadas en cuanto a sus gustos y hábitos alimentarios. Objective: to determine the association between variables related to the quality of care nutritional consulting and the patients′ perception of success in treatments for body weight control in a group of women over 20 years old, residents of the Autonomous City of Buenos Aires or Greater Buenos Aires. Methodology: Observational, cross-correlation design. Simple random sample of 97 women who attended, at least once, for nutritional consulting led by a registered dietitian. A voluntary and structured survey was carried out analyzing as dependent variable the perception of success of the nutritional tre
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