Abstract
The present study aimed to investigate the relationship between achievement goals, anxiety, selfconfidence and subjective well-being (positive and negative affect, and satisfaction with life) through path analysis. The sample consisted of 169 Brazilian athletes (140 males and 29 females), aged between 17 and 59 years, of different sports and competitive levels. The questionnaires TEOSQ, CSAI-2, EBES and SWLS were applied the day before the competition, on-site training of athletes. Main results partially demonstrated that anxiety and self-confidence mediated the relationship between motivational orientations and subjective well-being. Based on modification indices of the structural model, regression coefficients were specified between ego orientation and negative affect, and between task orientation and the positive dimensions of well-being (positive affect and satisfaction). Results were discussed taking into account the theoretical and practical implications of these relationships. © FTCD/FIP-MOC.

Abstract

Abstract
The purpose of the study was to examine the reliability, factorial validity evidence, invariance (by gender, type of sport and competitive level) and evidence of convergent validity of the CSAI-2. The total sample consisted of 375 athletes (284 males and 91 females). For evidence of convergent validity, the sample consisted of 163 athletes (115 males and 48 females). The athletes responded to the instruments (CSAI-2 and reduced version of the State Trait Anxiety Inventory - STAI) an hour before starting competitions. The results showed reliability (alpha > .70) and good indices of fit (CFI = .959, GFI = .942 and RMSEA = .044) for the reduced model of 17 items (CSAI-2R). The invariance and the evidence of convergent validity were supported. The Brazilian reduced version of CSAI-2 showed good psychometric properties, supporting its use in Brazilian athletes.

Abstract

Abstract
In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a process often used as disease progression readout and to develop therapeutic strategies. This work presents an image processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode Caenorhabditis Elegans. A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations were subsequently segmented based on an iso-value and blended with the resulting volume mesh. The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative diseases treatment planning and interventions prevention.

Abstract
In Computed Tomography (CT), bone segmentation is considered an important step to extract bone parameters, which are frequently useful for computer-aided diagnosis, surgery and treatment of many diseases such as osteoporosis. Consequently, the development of accurate and reliable segmentation techniques is essential, since it often provides a great impact on quantitative image analysis and diagnosis outcome. This chapter presents an automated multistep approach for bone segmentation in volumetric CT datasets. It starts with a three-dimensional (3D) watershed operation on an image gradient magnitude. The outcome of the watershed algorithm is an over-partioning image of many 3D regions that can be merged, yielding a meaningful image partitioning. In order to reduce the number of regions, a merging procedure was performed that merges neighbouring regions presenting a mean intensity distribution difference of ±15%. Finally, once all bones have been distinguished in high contrast, the final 3D bone segmentation was achieved by selecting all regions with bone fragments, using the information retrieved by a threshold mask. The bones contours were accurately defined according to the watershed regions outlines instead of considering the thresholding segmentation result. This new method was tested to segment the rib cage on 185 CT images, acquired at the São João Hospital of Porto (Portugal) and evaluated using the dice similarity coefficient as a statistical validation metric, leading to a coefficient mean score of 0.89. This could represent a step forward towards accurate and automatic quantitative analysis in clinical environments and decreasing time-consumption, user dependence and subjectivity.