Coralistas amadores: auto-imagem, dificuldades e sintomas na voz cantada

Purpose: to evaluate vocal self-perception, difficulties and presence of negative symptoms after singing of amateur choir singers of different vocal classifications, age and experience. Method: one hundred and twenty five singers answered a questionnaire containing identification data, information about self-perception of the singing voice, difficulties with singing and negative symptoms after singing. Results: the comparison considering vocal classification evidenced greater difficulties with high notes for altos and basses, greater difficulty regarding the transition to high notes for basses and greater vocal fatigue for altos. Comparing the singers by age, both adults and young adults referred more breathiness than the elderly. The adults referred better vocal intensity than the young adults. The young adults referred better timbre than adults. Regarding the experience, the less experienced singers reported self-perception of hoarseness and presence of hoarseness after singing in greater number than the experienced singers. Conclusion: the difficulties with singing are connected to the vocal classification and do not depend on age or experience. Vocal symptoms are related to the vocal classification and to the experience with singing. Negative self-perception is also related the vocal classification and to the experience with singing, and positive self-perception was more reported by experienced singers.

Impact of traumatic dental injuries and malocclusions on quality of life of young children

Abstract Background The presence of traumatic dental injuries and malocclusions can have a negative impact on quality of life of young children and their parents, affecting their oral health and well-being. The aim of this study was to assess the impact of traumatic dental injuries and anterior malocclusion traits on the Oral Health-Related Quality of Life (OHRQoL) of children between 2 and 5 years-old. Methods Parents of 260 children answered the six domains of the Early Childhood Oral Health Impact Scale (ECOHIS) on their perception of the OHRQoL (outcome). Two calibrated dentists assessed the types of traumatic dental injuries (Kappa = 0.9) and the presence of anterior malocclusion traits (Kappa = 1.0). OHRQoL was measured using the ECOHIS. Poisson regression was used to associate the type of traumatic dental injury and the presence of anterior malocclusion traits to the outcome. Results The presence of anterior malocclusion traits did not show a negative impact on the overall OHRQoL mean or in each domain. Only complicated traumatic dental injuries showed a negative impact on the symptoms (p = 0.005), psychological (p = 0.029), self image/social interaction (p = 0.004) and family function (p = 0.018) domains and on the overall OHRQoL mean score (p = 0.002). The presence of complicated traumatic dental injuries showed an increased negative impact on the children's quality of life (RR = 1.89; 95% CI = 1.36, 2.63; p < 0.001). Conclusions Complicated traumatic dental injuries have a negative impact on the OHRQoL of preschool children and their parents, but anterior malocclusion traits do not.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.