Among the possibilities for simulating the immune system, the multiagent systems approach has proved to be attractive, since only the behavior of the types of agents is specified. The global behavior emerges from the interactions among agents. This feature is similar to the behavior of the immune system, consisting of large amounts of cell types that interact to maintain the body health. The simulation of the immune system requires modeling various types of cells and substances. This paper presents the modeling of a software agent that simulates the behavior of the mast cells. Some simulations were performed to validate the model.

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A sepse representa um evento mórbido de extrema importância do ponto de vista clínico e de saúde pública, atingindo milhares de pessoas anualmente, no mundo. De uma perspectiva imunológica, os macrófagos são células extremamente importantes na interação Homo sapiens sapiens/ bactérias, ainda que muitos aspectos da atuação dessas células permaneçam aguardando elucidação. O presente artigo apresenta os requerimentos para a simulação computacional do macrófago no sistema AutoSimmune, permitindo o desenvolvimento de estudos sobre o papel dessa célula na sepse. Artificial macrophages and the human immune system computational modeling for the investigation of sepsis pathophysiology: Perspectives Abstract Sepsis represents a morbid event of fundamental importance both from clinical and public health point of view, affecting thousands of people worldwide annually. From the immunological perspective, macrophages are extremely important cells in the interaction Homo sapiens sapiens / bacteria. However, several aspects concerning the action of these cells await elucidation. This paper presents the requirements for computational simulation of macrophage in the AutoSimmune system, allowing the development of studies on the role of this cell in sepsis. Palavras

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Advancements in neural network architectures have improved the quality of several tasks in computational linguistics. Among the tasks benefited we can mention question and answer systems, dialogue systems, opinion mining and the automatic generation of texts, just to mention a few. Despite the advances, there is still room for contributions, since there are still open problems. In the case of text generation, especially in the musical genre, there are challenges for the production of texts that involve poetry and idioms. In particular, some of these challenges are linked to the treatment of metaphors and metonymy and the generation of paraphrases. This paper presents an analysis of the generation of excerpts of lyrics based on a pre-trained GPT-2 neural network model, after fine-tuning with two lyrics corpora, one in English and one in Portuguese. An analysis of the spelling, syntax and semantics of the generated texts are presented, as well as the discussion about the attempt to find a pattern in the sections generated by the implemented tool. Research demonstrates the potential for using such models in the generation of poetic texts.