Differences in Standing and Sitting Postures of Youth with Idiopathic Scoliosis from Quantitative Analysis of Digital Photographs

The objective of this study was to explore whether differences in standing and sitting postures of youth with idiopathic scoliosis could be detected from quantitative analysis of digital photographs. Standing and sitting postures of 50 participants aged 10–20-years-old with idiopathic scoliosis (Cobb angle: 15° to 60°) were assessed from digital photographs using a posture evaluation software program. Based on the XY coordinates of markers, 13 angular and linear posture indices were calculated in both positions. Paired t-tests were used to compare values of standing and sitting posture indices. Significant differences between standing and sitting positions (p < 0.05) were found for head protraction, shoulder elevation, scapula asymmetry, trunk list, scoliosis angle, waist angles, and frontal and sagittal plane pelvic tilt. Quantitative analysis of digital photographs is a clinically feasible method to measure standing and sitting postures among youth with scoliosis and to assist in decisions on therapeutic interventions.

Validity of a Quantitative Clinical Measurement Tool of Trunk Posture in Idiopathic Scoliosis

STUDY DESIGN: Concurrent validity between postural indices obtained from digital photographs (two-dimensional [2D]), surface topography imaging (three-dimensional [3D]), and radiographs. OBJECTIVE: To assess the validity of a quantitative clinical postural assessment tool of the trunk based on photographs (2D) as compared to a surface topography system (3D) as well as indices calculated from radiographs. SUMMARY OF BACKGROUND DATA: To monitor progression of scoliosis or change in posture over time in young persons with idiopathic scoliosis (IS), noninvasive and nonionizing methods are recommended. In a clinical setting, posture can be quite easily assessed by calculating key postural indices from photographs. METHODS: Quantitative postural indices of 70 subjects aged 10 to 20 years old with IS (Cobb angle, 15 degrees -60 degrees) were measured from photographs and from 3D trunk surface images taken in the standing position. Shoulder, scapula, trunk list, pelvis, scoliosis, and waist angles indices were calculated with specially designed software. Frontal and sagittal Cobb angles and trunk list were also calculated on radiographs. The Pearson correlation coefficients (r) was used to estimate concurrent validity of the 2D clinical postural tool of the trunk with indices extracted from the 3D system and with those obtained from radiographs. RESULTS: The correlation between 2D and 3D indices was good to excellent for shoulder, pelvis, trunk list, and thoracic scoliosis (0.81>r<0.97; P<0.01) but fair to moderate for thoracic kyphosis, lumbar lordosis, and thoracolumbar or lumbar scoliosis (0.30>r<0.56; P<0.05). The correlation between 2D and radiograph spinal indices was fair to good (-0.33 to -0.80 with Cobb angles and 0.76 for trunk list; P<0.05). CONCLUSION: This tool will facilitate clinical practice by monitoring trunk posture among persons with IS. Further, it may contribute to a reduction in the use of radiographs to monitor scoliosis progression.

Le rôle de la méthylation de l’ADN dans les fonctions cérébrales fronto-limbiques et la réactivité au stress quotidien

La sérotonine (5-HT) joue un rôle crucial dans l'étiologie des troubles mentaux comme la dépression majeure, les troubles de comportement et les troubles anxieux. Des études ont montré que des altérations précoces du système 5-HT peuvent potentiellement influencer le développement du cerveau et le fonctionnement du système fronto-limbique, engendrant des conséquences pour la régulation émotionnelle. Il existe aussi des évidences que le stress précoce peut affecter la méthylation de l'ADN résultant d'une altération de l'expression génique. Toutefois, le lien entre la méthylation de l'ADN et la réactivité comportementale à des facteurs de stress de la vie quotidienne est inconnu. La méthylation du gène transporteur 5-HT (SLC6A4) est d'un intérêt particulier, étant donné le rôle de SLC6A4 dans le développement du cerveau, les troubles mentaux et la régulation du stress. L'objectif de cette thèse est d'étudier l'association entre (1) les niveaux périphériques de méthylation de l'ADN dans le gène SLC6A4 et les réponses neurales aux stimuli émotionnels dans les circuits fronto-limbiques du cerveau, ainsi qu’entre (2) la méthylation périphérique de SLC6A4 et la réactivité comportementale au stress de la vie quotidienne. Nous explorons également l'association entre les réponses neuronales fronto-limbique à des stimuli émotionnels et la réactivité comportementale au stress de la vie quotidienne (3). À cette fin, vingt-deux personnes (11 femmes) d’âge moyen de 34,0 ans (SD : 1,5) avec différents niveaux de méthylation au gène SLC6A4 ont été recrutés à partir de deux études longitudinales. Les participants ont subi une analyse IRMf qui comprenait une tâche de traitement émotionnel. Un questionnaire en ligne sur la réactivité au stress quotidien de la vie a été réalisé pendant 5 jours consécutifs. Des analyses corrélationnelles et de régression ont été effectuées pour examiner les associations entre les variables primaires. Les résultats préliminaires de cette étude ont montré que la méthylation de l'ADN est associée à la désactivation significative du gyrus précentral et gyrus fusiforme respectivement face à des stimuli de peur et de tristesse. Aucune association significative n'a été observée entre les niveaux de méthylation et l'activation de l'amygdale. En outre, les scores obtenus aux variables de stress de la vie quotidienne tels que la détresse chronique ont été associées à la désactivation du précuneus et du cortex cingulaire postérieur face à la tristesse. Ces résultats suggèrent l'implication potentielle des processus épigénétiques dans l'activation cérébrale spécifique et la sensibilité au stress de la vie courante.

A new species of Pseudostomella (Gastrotricha: Macrodasyida: Thaumastodermatidae) from a sandy beach of Kerala, India

During an interstitial faunal survey along the south-west coast of Kerala, India, gastrotrich fauna were found in abundance. Together with species of the genera Xenotrichula, Halichaetonotus and Tetranchyroderma, were present several undescribed thaumastodermatid gastrotrichs belonging to the buccal palp bearing genus Pseudostomella. Adults of the new species are characterized by the following traits: total body length of about 300 μm; cuticular armature made up of medium sized pentancres covering the entire dorsolateral surface; pre-buccal, grasping palps bearing five, large papillae dorsally and 4-6 smaller papillae ventrally; adhesive apparatus made up of six anterior, 22-24 ventrolateral, two dorsolateral and six posterior adhesive tubes; caudal organ pear-shaped; frontal organ spherical. Pseudostomella cheraensis sp. nov. is the fourth taxon of the genus known from India; however, all the previous species reported hitherto from India have tetrancres instead of pentancres.

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

Conditions for Viewpoint Dependent Face Recognition

Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view--which always has a symmetric view--should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laser-scanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry hypothesis: Learning a side view allowed better generalization performances than learning the frontal view.

Face Detection in Still Gray Images

We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.

Object Detection in Images by Components

In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.

Impact of the proliferation of information and technology in Higher Education

Resumen tomado de la publicaci??n

Disseny i implementació d'un dispositiu de localització GPS

L'objectiu final d'aquest projecte ha estat el de fer un localitzador GPS. Inicialment es parteix d'un mòdul LCD amb retroil•luminació Optrex DMF-5005N i un mòdul GPS Connexant TU30. De la unió d'aquests dos, més la circuiteria dissenyada, en sorgeix un sistema capaç de proporcionar dades fiables i útils per a l'usuari, com són les coordenades, la velocitat, l'alçada, la data i l'hora, entre d'altres. El resultat final del projecte està contingut en una carcassa al frontal de la qual, hi podem veure el panell LCD, un pulsador per canvi de pantalla i un altre per variar la velocitat de quilòmetres per hora a nusos i a la inversa

Funciones ejecutivas y rendimiento escolar en educaci??n primaria : un estudio exploratorio

Resumen de los autores. Res??menes en espa??ol e ingl??s