Fibroblasts are a highly heterogeneous population of cells, being found in a large number of different tissues. These cells produce the extracellular matrix, which is essential to preserve structural integrity of connective tissues. Fibroblasts are frequently engaged in migration and remodeling, exerting traction forces in the extracellular matrix, which is crucial for matrix deposition and wound healing. In addition, previous studies performed on primary myoblasts suggest that the E3 ligase MuRF2 might function as a cytoskeleton adaptor. Here, we hypothesized that MuRF2 also plays a functional role in skeletal muscle fibroblasts. We found that skeletal muscle fibroblasts express MuRF2 and its siRNA knock-down promoted decreased fibroblast migration, cell border accumulation of polymerized actin, and down-regulation of the phospho-Akt expression. Our results indicated that MuRF2 was necessary to maintain the actin cytoskeleton functionality in skeletal muscle fibroblasts via Akt activity and exerted an important role in extracellular matrix remodeling in the skeletal muscle tissue.

In the present work, we have analyzed the electrophilic and nucleophilic chemical reactivity of the neutral and anionic chlorophenoxyacetic acid herbicides, 4-CPA, 24D-CPA, 34-CPA and 245T-CPA at the X/6-311++G(2d,2p) level of theory (where X= wB97XD, MPW91B1K and MP2). Chemical reactivity was analyzed in the aqueous phase and employing global and local DFT reactivity descriptors. The structural parameters derived from DFT calculations are equivalent to those obtained at the MP2 level. The Fukui Function values suggest that nucleophilic attacks to the neutral and anionic forms would cause dechlorination on 24D-CPA, 34-CPA and 245T-CPA and hydrogen abstraction in 4-CPA. At pH values lower than 2.7, electrophilic attacks would cause the cleavage of the ether bond in 4-CPA, 24D-CPA and 34-CPA and dechlorination in 245T-CPA. But, at pH > 3.6, electrophiles may cleave the ether bond to start the degradation of the four CPAs.

RESUMO O uso do algorítmo de Mapas Auto-Organizáveis (SOM) para extração de características e redução de dimensionalidade aplicado à detecção de objetos subaquáticos em regiões marinhas foi apresentado. Simulações em Elementos Finitos foram utilizadas para a geração de um modelo direto da região de estudo e, a partir destes dados, um Mapa Auto-Organizável foi utilizado para ajustar os dados e retornar um modelo similar, com di men sio na li da de menor e mesmas características. Sensores Virtuais foram criados automaticamente pelo algoritmo SOM com resultados consistentes, completando os vazios de resolução dos dados simulados. Estes resultados são úteis para acelerar algoritmos de auxílio à tomada de decisão reduzindo o número de entradas destes algoritmos e em outros assuntos, como bioeletromagnética.

ABSTRACT The use of Self-Organizing Map (SOM) algorithm for feature extraction and dimensionality reduction applied to underwater object detection with Low Frequency Electromagnetic Waves is presented. Computer simulation is used to generate a direct model for the study region, and a Self Organizing Map Algorithm is used to fit the data and return a similar model, with smaller dimensionality and same characteristics. Results show that virtual sensors are created by the SOM algorithm with consistent predictions, filling the resolution gap of the input data. These results are useful for fastening decision making algorithms by reducing the number of inputs to a group of significant data.