Evaluation of the antioxidant activity and antitumor activity of marine invertebrates extracts

Dissertação de Mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologias, Universidade do Algarve, 2014

A proteomic study of the effects of nutrient restriction in utero on postnatal metabolism

It is widely recognized that protein restriction in utero may cause metabolic and endocrine adaptations, which may be of benefit to the neonate on a short-term basis but may cause adverse long-term conditions such as obesity, Type 2 diabetes, metabolic syndrome, hypertension and cardiovascular diseases. Adequate foetal and early post natal nutrient and energy supply is therefore essential for adult animal health, performance and life span. In this project it was investigated the progressive adaptations of the hepatic proteome in male mink offspring exposed to either a low protein (FL) or an adequate protein (FA) diet in utero fed either on a low protein (LP) or on an adequate (AP) diet from weaning until sexual maturity. Specifically, the aim was to determine the metabolic adaptations at selected phases of the animal’s first annual cycle and establish the metabolic priorities occurring during those phases. The three different morphological stages studied during the first year of development included, end of bone growth at 4 months of age, maximal fat accretion at 6 months of age and sexual maturity at 12 months of age. A reference proteome of mink liver coming from these different animal groups were generated using 2D electrophoresis coupled to MALDI-TOF analysis and the way in which dietary treatment affect their proteome was established. Approximately 330 proteins were detected in the mink liver proteome. A total of 27 comparisons were carried out between all different animal groups which resulted in 20 differentially expressed proteins. An extensive survey was conducted towards the characterization of these proteins including their subcellular localization, the biological processes in which they are involved and their molecular functions. This characterization allowed the identification of proteins in various processes including the glycolysis and fatty acid metabolism. The detailed analysis of the different dietary treatment animal groups was indicative of differences in metabolism and also to changes associated with development in mink.

Face segregation and recognition by cortical multi-scale line and edge coding

Models of visual perception are based on image representations in cortical area V1 and higher areas which contain many cell layers for feature extraction. Basic simple, complex and end-stopped cells provide input for line, edge and keypoint detection. In this paper we present an improved method for multi-scale line/edge detection based on simple and complex cells. We illustrate the line/edge representation for object reconstruction, and we present models for multi-scale face (object) segregation and recognition that can be embedded into feedforward dorsal and ventral data streams (the “what” and “where” subsystems) with feedback streams from higher areas for obtaining translation, rotation and scale invariance.

Multi-scale cortical keypoint representation for attention and object detection

Keypoints (junctions) provide important information for focus-of-attention (FoA) and object categorization/recognition. In this paper we analyze the multi-scale keypoint representation, obtained by applying a linear and quasi-continuous scaling to an optimized model of cortical end-stopped cells, in order to study its importance and possibilities for developing a visual, cortical architecture.We show that keypoints, especially those which are stable over larger scale intervals, can provide a hierarchically structured saliency map for FoA and object recognition. In addition, the application of non-classical receptive field inhibition to keypoint detection allows to distinguish contour keypoints from texture (surface) keypoints.

Face recognition by cortical multi-scale line and edge representations

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

Cortical object segregation and categorization by multi-scale line and edge coding

In this paper we present an improved scheme for line and edge detection in cortical area V1, based on responses of simple and complex cells, truly multi-scale with no free parameters. We illustrate the multi-scale representation for visual reconstruction, and show how object segregation can be achieved with coarse-to-finescale groupings. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only, and final categorization on coarse plus fine scales. Processing schemes are discussed in the framework of a complete cortical architecture.

Recognition of facial expressions by cortical multi-scale line and edge coding

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extraction. Simple, complex and end-stopped cells provide input for line, edge and keypoint detection. Detected events provide a rich, multi-scale object representation, and this representation can be stored in memory in order to identify objects. In this paper, the above context is applied to face recognition. The multi-scale line/edge representation is explored in conjunction with keypoint-based saliency maps for Focus-of-Attention. Recognition rates of up to 96% were achieved by combining frontal and 3/4 views, and recognition was quite robust against partial occlusions.

Cortical 3D face and object recognition using 2D projections

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. In cortical area V1 exist double-opponent colour blobs, also simple, complex and end-stopped cells which provide input for a multiscale line/edge representation, keypoints for dynamic feature routine, and saliency maps for Focus-of-Attention.

Multi-scale cortical keypoints for realtime hand tracking and gesture recognition

Human-robot interaction is an interdisciplinary research area which aims at integrating human factors, cognitive psychology and robot technology. The ultimate goal is the development of social robots. These robots are expected to work in human environments, and to understand behavior of persons through gestures and body movements. In this paper we present a biological and realtime framework for detecting and tracking hands. This framework is based on keypoints extracted from cortical V1 end-stopped cells. Detected keypoints and the cells’ responses are used to classify the junction type. By combining annotated keypoints in a hierarchical, multi-scale tree structure, moving and deformable hands can be segregated, their movements can be obtained, and they can be tracked over time. By using hand templates with keypoints at only two scales, a hand’s gestures can be recognized.