Proposta de política pública para fortalecimento dos Núcleos de Inovação Tecnológica no Amapá.

No cenário brasileiro, a Lei de Inovação, promulgada em 2004 e reiterada pelo Marco Legal de Inovação em 2016, estabeleceu que as Instituições de Ciência e Tecnologia (ICTs) dispusessem de Núcleos de Inovação Tecnológica (NITs) para gerir suas respectivas políticas de inovação. No estado do Amapá, atualmente, existem apenas dois NITs, criados há pouco tempo e que enfrentam dificuldades para poder cumprir seu papel institucional. Por isso a necessidade de realizar políticas públicas voltadas ao fortalecimento dos NITs no Estado. Portanto, o objetivo deste artigo é apresentar uma proposta de política pública que impulsione o fortalecimento da profissionalização dos NITs do Amapá. Os dados foram extraídos de uma entrevista semiestruturada e dos sites dos NITs. Como resultado, espera-se o melhor desempenho dos NITs, com maior agilidade e segurança quanto à proteção da propriedade intelectual e melhor capacidade de análise e avaliação pelos NITs das invenções desenvolvidas no Estado

Parasitic infections in pirarucu fry, Arapaima gigas Schinz, 1822 (Arapaimatidae) kept in a semi-intensive fish farm in Central Amazon, Brazil.

Studies regarding parasite fauna in farmed fish are of great relevance to lhe knowledge of the parasites species. allowing interference in their proliferation in order to avoid epizooties and consequently. economical losses, This study was designed to investigate the prevalence and intensity of parasites in fry Arapaima gigas maintained in ponds of a semi-intensive fish farm in Amazonas State, Brazil. On necropsy, 96,0% of A. gigas were found parasitized by Dawestrema cycloancistrioides. Dawestrema cycloancistrioides (Monogenoidea). Trichodina sp., Ichthyobodo sp. (Protozoa). Camallamus tridentatus, Terranova serrata, Goezia spinulosa (Nematoda) and Argulidae. However, D. cycloancistrium. D, cycloancistrioides and Trichodina fariai were the parasites of' greatest intensity. This study is the first to report parasitic infections in farmed A. gigas and the results indicated a high rate of infection that might lead to important changes in the health of the hosts

Cardiac gene expression and systemic cytokine profile are complementary in a murine model of post-ischemic heart failure

After myocardial infarction (MI), activation of the immune system and inflammatory mechanisms, among others, can lead to ventricular remodeling and heart failure (HF). The interaction between these systemic alterations and corresponding changes in the heart has not been extensively examined in the setting of chronic ischemia. The main purpose of this study was to investigate alterations in cardiac gene and systemic cytokine profile in mice with post-ischemic HF. Plasma was tested for IgM and IgG anti-heart reactive repertoire and inflammatory cytokines. Heart samples were assayed for gene expression by analyzing hybridization to AECOM 32k mouse microarrays. Ischemic HF significantly increased the levels of total serum IgM (by 5.2-fold) and total IgG (by 3.6-fold) associated with a relatively high content of anti-heart specificity. A comparable increase was observed in the levels of circulating pro-inflammatory cytokines such as IL-1β (3.8X) and TNF-α (6.0X). IFN-γ was also increased by 3.1-fold in the MI group. However, IL-4 and IL-10 were not significantly different between the MI and sham-operated groups. Chemokines such as MCP-1 and IL-8 were 1.4- and 13-fold increased, respectively, in the plasma of infarcted mice. We identified 2079 well annotated unigenes that were significantly regulated by post-ischemic HF. Complement activation and immune response were among the most up-regulated processes. Interestingly, 21 of the 101 quantified unigenes involved in the inflammatory response were significantly up-regulated and none were down-regulated. These data indicate that post-ischemic heart remodeling is accompanied by immune-mediated mechanisms that act both systemically and locally

Spatio-Temporal Deep Learning-Based Methods for Defect Detection: An Industrial Application Study Case

Data-driven methods—particularly machine learning techniques—are expected to play a key role in the headway of Industry 4.0. One increasingly popular application in this context is when anomaly detection is employed to test manufactured goods in assembly lines. In this work, we compare supervised, semi/weakly-supervised, and unsupervised strategies to detect anomalous sequences in video samples which may be indicative of defective televisions assembled in a factory. We compare 3D autoencoders, convolutional neural networks, and generative adversarial networks (GANs) with data collected in a laboratory. Our methodology to simulate anomalies commonly found in TV devices is discussed in this paper. We also propose an approach to generate anomalous sequences similar to those produced by a defective device as part of our GAN approach. Our results show that autoencoders perform poorly when trained with only non-anomalous data—which is important because class imbalance in industrial applications is typically skewed towards the non-anomalous class. However, we show that fine-tuning the GAN is a feasible approach to overcome this problem, achieving results comparable to those of supervised methods