Muscle atrophy occurs in many conditions, including use of glucocorticoids. N‐3 (omega‐3) is widely consumed due its healthy properties; however, concomitant use with glucocorticoids can increase its side effects. We evaluated the influences of N‐3 on glucocorticoid atrophy considering IGF‐1, Myostatin, MEK/ERK, AMPK pathways besides the ubiquitin‐proteasome system (UPS) and autophagic/lysosomal systems. Sixty animals constituted six groups: CT, N‐3 (EPA 100 mg/kg/day for 40 days), DEXA 1.25 (DEXA 1.25 mg/kg/day for 10 days), DEXA 1.25 + N3 (EPA for 40 days + DEXA 1.25 mg/kg/day for the last 10 days), DEXA 2.5 (DEXA 2.5 mg/kg/day for 10 days), and DEXA 2.5 + N3 (EPA for 40 days + DEXA 2.5 mg/kg/day for 10 days). Results: N‐3 associated with DEXA increases atrophy (fibers 1 and 2A), FOXO3a, P‐SMAD2/3, Atrogin‐1/MAFbx (mRNA) expression, and autophagic protein markers (LC3II, LC3II/LC3I, LAMP‐1 and acid phosphatase). Additionally, N‐3 supplementation alone decreased P‐FOXO3a, PGC1‐alpha, and type 1 muscle fiber area. Conclusion: N‐3 supplementation increases muscle atrophy caused by DEXA in an autophagic, AMPK and UPS process.

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Weightless Neural Networks (WNNs) are a powerful mechanism for pattern recognition. Aiming at enhancing their learning capabilities, Multivalued Probabilistic Logic Neurons (M-PLN) are used, instead of crisp neurons with a 0/1 output. An M-PLN bookkeeps a triggering probability for each input pattern to be recognized. The M-PLN model attempts to strengthen the discrepancies between distinct patterns used during the training process and those that have not yet been processed. In this paper, an efficient yet customizable hardware architecture for M-PLN based WNN is proposed. It implements the structure and learning process of a weightless pyramidal WNN, augmented by a probabilistic rewarding/punishing search algorithm. The training algorithm can adapt itself to the overall hit ratio so far achieved by the network. Using class-dedicated layers, the hardware is able to handle image classification in parallel and thus, very efficiently. Furthermore, the classification process is performed in a pipelined manner so its stages never stop working until all input images are classified. Nonetheless, only one of these layers is active during network training. Last but not least, the architecture is customizable as its structure can be tailored in accordance to the application characteristics. It was modeled and functionally tested. Estimated time requirements based on many simulations are reported. The architecture exhibits performance and reconfiguration capabilities that are very promising and encouraging towards the synthesis of a prototype.

Over the last decades, plastic production has increased exponentially, with estimated production to reach 33 billion tons in 2050. Simultaneously, only 9% of this material is recycled, generating a huge amount of waste and causing environmental pollution. Among these, polyethylene (PE) is one of the most important existing polymers due to the wide range of applications, being obtained mainly from a non-renewable source, such as petrochemical naphtha. However, a socioeconomic awareness towards sustainable development has given rise to the green PE, a bioplastic with physicochemical characteristics similar to those of fossil origin, but derived from renewable raw materials (MPs), such as sugarcane ethanol, and already commercially produced in Brazil by Braskem. Therefore, the present work provides an overview and assesses technological aspects of the bioplastics industry, emphasizing green PE. It was observed that there is a perspective that several works are moving towards the development of new routes to obtain the PE, as well as scientific relevance and commercial interest in this product. In addition, difficulties were noted due to the costs of implementing a plant in operation, and in the use of new MPs for its production, although in the short term, partnerships are still the best choice as well as investment in research and development. Nas últimas décadas, a produção de plásticos aumentou de forma exponencial, estima-se que a produção deste material chegue a 33 bilhões de toneladas até 2050. Entretanto, apenas 9 % é reciclado, gerando uma enorme quantidade de resíduos e causando poluição ambiental. Dentre estes, o polietileno (PE) é um dos mais importantes polímeros existentes devido à sua variada gama de aplicações, sendo obtido principalmente a partir de uma fonte não renovável, como a nafta petroquímica. Contudo, uma conscientização socioeconômica em relação ao desenvolvimento sustentável, fez surgir o PE verde. Um bioplástico com características físico-químicas semelhantes ao de origem fóssil, mas oriundo de matérias-primas renováveis (MPs), como o etanol de cana de açúcar, e já produzido comercialmente no Brasil pela Braskem. Posto isto, o presente trabalho traz um panorama geral e avalia aspectos tecnológicos da indústria de bioplásticos, dando ênfase ao PE verde. Observou-se que existe uma perspectiva de que diversos trabalhos estejam se encaminhando para o desenvolvimento de novas rotas de obtenção do PE, bem como uma relevância científica e interesse comercial no mesmo. Além disso, notam-se dificuldades devido aos custos da implementação de uma planta em operação, e na utilização de novas MPs para sua produção, embora que, em curto prazo, fazer parcerias ainda é a melhor escolha, bem como investimento em pesquisa e desenvolvimento.