BACKGROUND AND PURPOSE:Although microcephaly is the most prominent feature of congenital Zika syndrome, a spectrum with less severe cases is starting to be recognized. Our aim was to review neuroimaging of infants to detect cases without microcephaly and compare them with those with microcephaly.

Arachidonic acid (AA) seems to play an important role in testicular steroidogenesis, although controversial data exist in the literature. In the present study AA induced a dose related increase of testosterone (T) formation and, at the highest dose, stimulated the production of prostaglandin E2 (PGE2), leukotrienes B4 (LTB4) and C4 (LTC4) by purified rat Leydig cells. The contemporary addition of the prostaglandin synthesis blocker, indomethacin (IND), and AA further increased T formation, decreased PGE2 levels and did not modify LTB4 and LTC4 concentrations. The addition of a lipoxygenase inhibitor, nordihydroguaiaretic acid (NDGA, 5 microM), did not influence the stimulatory effect of AA on T and PGE2 formation while it decreased the output of LTB4 and LTC4. When 20 microM NDGA was used in addition to AA the expected reduction of leukotrienes release was observed together with a surprising impairment of T and PGE2 secretion. PGE2 and PGF2 alpha did not modify basal T production but reduced HCG-stimulated T secretion at the 10 nM dose. When 5-12- and 15-HETE were tested an enhancement of basal T formation was observed at the 10nM dose. 5-HETE (10nM) stimulated HCG-induced T production. LTA4, LTB4 and LTE4 did not influence basal T output while LTC4 and LTD4 inhibited it. LTC4 (10nM) induced a decrease of HCG-stimulated T production. These findings suggest that: 1) exogenous AA stimulates T secretion; 2) conversion of AA to cycloxygenated and lipoxygenated metabolites is not required for its steroidogenic effect; 3) cycloxygenated and lipoxygenated compounds play a diverse modulatory role on testicular steroidogenesis.

BACKGROUND AND PURPOSE:The differentiation of pilocytic astrocytomas and high-grade astrocytomas is sometimes difficult. There are limited comparisons in the literature of the advanced MR imaging findings of pilocytic astrocytomas versus high-grade astrocytomas. The purpose of this study was to assess the MR imaging, PWI, DWI, and MR spectroscopy characteristics of pilocytic astrocytomas compared with high-grade astrocytomas.

Purpose In late 2019, the SARS-CoV-2 virus spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using COVID-19 diagnostic X-rays: low cost, fast, and widely available. Methods We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Results Support vector machines stood out, reaching an average accuracy of 89.78%, average sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963, respectively. Conclusion Using features based on textures and shapes combined with classical classifiers, the developed system was able to differentiate COVID-19 from viral and bacterial pneumonia with low computational cost.

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In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

Um dos principais insumos na definição da geração das usinas hidrelétricas é a previsão de vazões. Na elaboração dessas previsões, diversos modelos podem ser utilizados. Pode-se citar como exemplo os modelos físicos, estatísticos e os baseados na técnica de redes neurais. O uso da técnica de redes neurais tem se intensificado cada vez mais, uma vez que, modelos baseados nessa técnica são de fácil aplicação e têm proporcionado resultados satisfatórios. A análise prévia das informações que serão usadas na calibração e utilização da rede neural pode trazer ganhos significativos no desempenho da mesma. Assim, esse trabalho apresenta a análise dos dados pluviométricos e fluviométricos da área a montante do reservatório de Três Marias, no rio São Francisco, bem como a calibração de um modelo baseado na técnica de redes neurais para a previsão de vazões naturais afluentes. São apresentados o processo de preenchimento de falhas históricas, análise de consistência e análise geoestatística, como ferramenta para seleção de postos pluviométricos e análise de precipitação média da área. Os resultados obtidos mostraram que o modelo calibrado com a técnica de redes neurais teve um desempenho superior ao modelo estocástico PREVIVAZ e, entre as redes neurais analisadas, a NSRBN teve um desempenho um pouco superior a MLP.