Fucana ativa via das Map quinases e inibe a proliferação de células de ovário de hamster chinês (CHO)

Fucan is a term used to denominate L-fucose rich sulfated polysaccharides. The fucans have been studied due their pharmacological activities like antithrombotic, antiproliferative and antioxidant. We have extracted three fucan fractions from the brown seaweed Spatoglossum schröederi. These fucans were denominated Fuc B 1, Fuc B 1.5 and Fuc B 2. The chemical analyzes show that the fucans have very similar composition as demonstrated by agarose electrophoresis gel, sugar and sulfate content. The antiproliferative effect was determined by MTT and BrdU methodologies in CHO cells. The inhibition of proliferation effect of the three fractions was about 40%. Therefore this we proceed just with the Fuc B 2 due the higher yield. There is no apoptosis indication using the anexin V/propidium iodide test. We found a cell cycle phase G1 arrest. The western blotting show that the PKC; pFAK; pERK 1/2 are activated when the cells were treated with fucans. The treatement with inhibitor of MAPK PD98059 extinguished the fucan effect. These results indicates that fucan act by the ERK pathway inducing the cell death.

Técnicas visuais de localização e mapeamento simultâneos sem extração de primitivas geométricas da imagem

In Simultaneous Localization and Mapping (SLAM - Simultaneous Localization and Mapping), a robot placed in an unknown location in any environment must be able to create a perspective of this environment (a map) and is situated in the same simultaneously, using only information captured by the robot s sensors and control signals known. Recently, driven by the advance of computing power, work in this area have proposed to use video camera as a sensor and it came so Visual SLAM. This has several approaches and the vast majority of them work basically extracting features of the environment, calculating the necessary correspondence and through these estimate the required parameters. This work presented a monocular visual SLAM system that uses direct image registration to calculate the image reprojection error and optimization methods that minimize this error and thus obtain the parameters for the robot pose and map of the environment directly from the pixels of the images. Thus the steps of extracting and matching features are not needed, enabling our system works well in environments where traditional approaches have difficulty. Moreover, when addressing the problem of SLAM as proposed in this work we avoid a very common problem in traditional approaches, known as error propagation. Worrying about the high computational cost of this approach have been tested several types of optimization methods in order to find a good balance between good estimates and processing time. The results presented in this work show the success of this system in different environments