Caracterização Inorgânica de Cogumelos Amanita Ponderosa: Abordagem em Data Mining

A espécie silvestre de cogumelos Amanita ponderosa é característica de microclimas da Península Ibérica. Gastronomicamente é muito relevante, devido não só ao consumo tradicional das populações rurais, mas também devido ao seu valor comercial nos mercados gourmet. Desta forma a caracterização mineral de cogumelos comestíveis torna-se extremamente importante para os processos de certificação e comercialização. O objetivo deste estudo focou a análise da composição inorgânica de corpos de frutificação de A. ponderosa (Ca, K, Mg, Na, P, Ag, Al, Ba, Cd, Cr, Cu, Fe, Mn, Pb e Zn) e seus respetivos substratos de solo de 24 pontos de amostragem diferentes do sudoeste da Península Ibérica (nomeadamente Alentejo, Andaluzia e Extremadura). A análise da composição mineral revelou alto conteúdo em macroelementos, tais como: potássio, fósforo e magnésio, presença de oligoelementos importantes e baixos teores de metais pesados nos limites da Dose Diária Recomendada (DDR). O fenómeno de bioconcentração foi observado para alguns macro e microelementos, tais como K, Cu, Zn, Mg, P, Ag e Cd. Por outro lado, observou-se que os corpos de frutificação de Amanita ponderosa apresentam diferentes perfis inorgânicos de acordo com a sua localização. Metodologias de Data Mining foram aplicadas de forma a estudar a composição mineral dos corpos de frutificação de A. ponderosa, tendo sido utilizado o método de agrupamento "k-means" recorrendo a Árvores de Decisão (DTs) de forma a explicar o modelo de segmentação. Os resultados apontaram que é possível gerar um modelo explicativo de segmentação, realizado com dados baseados na composição inorgânica de cogumelos e conteúdo mineral do solo, mostrando a possibilidade de relacionar esses dois tipos de dados

A Data Mining Approach to Characterize Amanita ponderosa Mushrooms Using Inorganic Profile and M13-PCR Molecular Data

Wild eatable mushrooms Amanita ponderosa are very appreciated in gastronomy, showing high export potential. This specie grows spontaneously in some microclimates, namely in the southwest of the Iberian Peninsula. The aim of this study is to find inorganic and molecular markers that allow to characterize the wild A. ponderosa strains collected from different geographical locations in the Iberian Peninsula. Molecular approach using the microsatellite primer M13-PCR allowed to distinguish the mushrooms at specie level and to differentiate the A. ponderosa strains according to their location. Data mining tools were used in order to correlate inorganic and molecular results. A. ponderosa strains showed different inorganic composition according to their habitat. It was developed a segmentation model based on the molecular analysis, which allow relating the clusters obtained with the geographical site of sampling. There were also developed explanatory models of the segmentation, using decision trees, by following two different strategies. One of them based on the bands of DNA and, the other one, based on the mineral composition. The results show that it may be possible to relate the molecular and inorganic data. The present findings are wide potential application and both health and economical benefits arise from this study

Modelling molecular and inorganic data of Amanita ponderosa mushrooms using artificial neural networks

Amanita ponderosa are wild mushroom eatable, growing spontaneously in some Mediterranean microclimates, namely in Alentejo and Andaluzia, in the Iberian Peninsula, due to its Mediterranean characteristics. The aim of this study was to evaluate inorganic composition of mycorrhizal Amanita ponderosa collected from different regions of the southwest of the Iberian Peninsula and to access molecular biomarkers using artificial neural networks. Fruiting bodies of the A. ponderosa mushrooms were collected in Spring from different locations area, in the southwest of the Iberian Peninsula. Three individuals were sampled per location. The inorganic analyses showed that mineral composition of these mushrooms depends on the ecosystem where they grow. Levels of trace metals are considerably lower, acceptable to human consumption at nutritional and low toxic levels. Molecular approach using the microsatellite primer M13-PCR allowed to distinguish the mushrooms at specie level and to differentiate the A. ponderosa strains according to their location. Data mining tools were used in order to correlate inorganic and molecular results. In order to obtain the best prediction of the M13 PCR DNA band profile, different network structures and architectures were elaborated and evaluated. In the present work the error metric used was the mean squared error. The neural network selected for modelling the data has a 6-7-14 topology, i.e. an input layer with six nodes, a hidden layer with seven nodes and a fourteen nodes output layer. A good match between the experimental and predicted values can be observed

Aroma Compounds Prevision using Artificial Neural Networks Influence of Newly Indigenous Saccharomyces SPP in White Wine Produced with Vitis Vinifera Cv Siria

Commercial yeasts strains of Saccharomyces cerevisae are frequently used in white wine production as starters in fermentation process, however, these strains can affect the wine characteristics. The aim of this study was to evaluate the effect of three strains of Saccharomyces spp. (var. 1, 2 and 3) on wine aroma compounds produced in microvinification assays. Microvinification assays were carried out with Vitis vinifera cv Síria grapes using the strains in study as starters. Aroma compounds were identified and quantified by GC-FID and GC-MS. At the end of fermentation process and during the first three months of maturation some aroma compounds were detected, namely propanol, isobutanol, isoamyl acetate, isoamylic alcohol, ethyl hexanoate, ethyl lactate, hexanol, ethyl octanoate, 3-ethylhydroxibutirate, benzaldehyde, 3-methyl-2-butanol, 2,3-butanediol, g-butyrolactone, ethyl decanoate, diethyl succinate, methionol, 4-hydroxi-2-butyrolactone, heptanoic acid, phenylethyl acetate, ethyl dodecanoate, phenylethanol, octanoic acid, 2-methoxy-4- vinylphenol and decanoic acid. Artificial Neural Networks (ANNs) were used to predict the concentration of twelve wine aroma compounds from the phenyl ethanol, propanol, isobutanol, hexanol, heptanoic acid, octanoic acid and decanoic acid concentrations. Results showed that, either, maturation time and Saccharomyces strain used as starter influence the aroma compounds produced. Wines produced with S. cerevisae var. 1 and S. cerevisae var. 2 showed a similar composition in aroma compounds, relatively to the wines produced with the strain S. cerevisae var. 3. However, for S. cerevisae var. 1 and S. cerevisae var. 2 the time of maturation influence the aroma composition of wines. From a technological approach, the choice of yeast strain and maturation time has decisive influence on the aroma compounds produced

Prediction of bioactive compounds activity against wood contaminant fungi using artificial neural networks

Biopesticides based on natural endophytic bacteria to control plant diseases are an ecological alternative to the chemical treatments. Bacillus species produce a wide variety of metabolites with biological activity like iturinic lipopeptides. This work addresses the production of biopesticides based on natural endophytic bacteria, isolated from Quercus suber. Artificial Neural Networks were used to maximize the percentage of inhibition triggered by antifungal activity of bioactive compounds produced by Bacillus amyloliquefaciens. The active compounds, produced in liquid cultures, inhibited the growth of fifteen fungi and exhibited a broader spectrum of antifungal activity against surface contaminant fungi, blue stain fungi and phytopathogenic fungi. A 19-7-6-1 neural network was selected to predict the percentage of inhibition produced by antifungal bioactive compounds. A good match among the observed and predicted values was obtained with the R2 values varying between 0.9965 – 0.9971 and 0.9974 – 0.9989 for training and test sets. The 19-7-6-1 neural network was used to establish the dilution rates that maximize the production of antifungal bioactive compounds, namely 0.25 h-1 for surface contaminant fungi, 0.45 h-1 for blue stain fungi and between 0.30 and 0.40 h-1 for phytopathogenic fungi. Artificial neural networks show great potential in the modelling and optimization of these bioprocesses.Les biopesticides à base de bactéries endophytes naturelles pour lutter contre les maladies des plantes constituent une alternative écologique aux traitements chimiques. Les espèces de Bacillus produisent une grande variété de métabolites biologiquement actifs tels que les lipopeptides ituriniques. Cette étude porte sur la production de biopesticides par des bactéries endophytes naturelles isolées du Quercus suber L. Des réseaux neuronaux artificiels ont été utilisés pour maximiser le pourcentage d’inhibition provoquée par l’activité antifongique des composés bioactifs produits par Bacillus amyloliquefaciens. Les composés actifs, produits en culture liquide, ont inhibé la croissance de 15 champignons et avaient un spectre d’activé antifongique plus large contre les contaminants fongiques de surface, les champignons de bleuissement et les champignons phytopathogènes. Un réseau neuronal 19-7-6-1 a été choisi pour prédire le pourcentage d’inhibition produit par les composés bioactifs antifongiques. Une bonne concordance entre les valeurs observées et prédites a été obtenue; les valeurs de R2 variaient de 0,9965 a` 0,9971 et de 0,9974 a` 0,9989 pour les bases d’apprentissage et de test. Le réseau neuronal 19-7-6-1 a été utilisé pour établir les taux de dilution qui maximisent la production des composés bioactifs antifongiques, nommément 0,25 h−1 pour les contaminants fongiques de surface, 0,45 h−1 pour les champignons de bleuissement et entre 0,30 et 0,40 h−1 pour les champignons phytopathogènes. Les réseaux neuronaux artificiels ont un potentiel élevé pour modéliser et optimiser ces processus biologiques

Chronic Treatment with Ivabradine Does Not Affect Cardiovascular Autonomic Control in Rats

A low resting heart rate (HR) would be of great benefit in cardiovascular diseases. Ivabradine a novel selective inhibitor of hyperpolarization-activated cyclic nucleotide gated (HCN) channels- has emerged as a promising HR lowering drug. Its effects on the autonomic HR control are little known. This study assessed the effects of chronic treatment with ivabradine on the modulatory, reflex and tonic cardiovascular autonomic control and on the renal sympathetic nerve activity (RSNA). Male Wistar rats were divided in 2 groups, receiving intraperitoneal injections of vehicle (VEH) or ivabradine (IVA) during 7 or 8 consecutive days. Rats were submitted to vessels cannulation to perform arterial blood pressure (AP) and HR recordings in freely moving rats. Time series of resting pulse interval and systolic AP were used to measure cardiovascular variability parameters. We also assessed the baroreflex, chemoreflex and the Bezold-Jarish reflex sensitivities. To better evaluate the effects of ivabradine on the autonomic control of the heart, we performed sympathetic and vagal autonomic blockade. As expected, ivabradine treated rats showed a lower resting (VEH: 362 +/- 16 bpm vs. IVA: 260 +/- 14 bpm, p = 0.0005) and intrinsic HR (VEH: 369 +/- 9 bpm vs. IVA: 326 +/- 11 bpm, p = 0.0146). However, the chronic treatment with ivabradine did not change normalized HR spectral parameters LF (nu) (VEH: 24.2 +/- 4.6 vs. IVA: 29.8 +/- 6.4p > 0.05)HF (nu) (VEH: 75.1 +/- 3.7 vs. IVA: 69.2 +/- 5.8p > 0.05), any cardiovascular reflexes, neither the tonic autonomic control of the HR (tonic sympathovagal indexVEH: 0.91 +/- 0.02 vs. IVA: 0.88 +/- 0.03, p = 0.3494). We performed the AP, HR and RSNA recordings in urethane-anesthetized rats. The chronic treatment with ivabradine reduced the resting HR (VEH: 364 +/- 12 bpm vs. IVA: 207 +/- 11 bpm, p < 0.0001), without affecting RSNA (VEH: 117 +/- 16 vs. IVA: 120 +/- 9 spikes/s, p = 0.9100) and mean arterial pressure (VEH: 70 +/- 4 vs. IVA: 77 +/- 6 mmHg, p = 0.3293). Our results suggest that, in health rats, the long-term treatment with ivabradine directly reduces the HR without changing the RSNA modulation and the reflex and tonic autonomic control of the heart.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)Fundacao de Amparo a Pesquisa de Minas Gerais (FAPEMIG)Universidade Federal de Ouro Preto (UFOP)Universidade Federal do Triangulo Mineiro (UFTM), BrazilUniv Fed Ouro Preto, Inst Exact & Biol Sci, Dept Biol Sci, Lab Cardiovasc Physiol, Ouro Preto, BrazilUniv Fed Ouro Preto, CBIOL NUPEB, Grad Program Biol Sci, Ouro Preto, BrazilUniv Fed Minas Gerais, Inst Biol Sci, Dept Physiol & Biophys, Lab Hypertens, Belo Horizonte, MG, BrazilUniv Fed Sao Paulo, Inst Sci & Technol, Biomed Engn Lab, Sao Jose Dos Campos, BrazilUniv Uberaba, Dept Physiol, Uberaba, BrazilUniv Milan, Osped Maggiore Policlin, IRCCS Ca Granda Fdn, Dept Clin Sci & Community Hlth, Milan, ItalyFed Univ Trianaulo Pvlineiro, Inst Biol & Nat Sci, Dept Physiol, Uberaba, BrazilUniv Fed Sao Paulo, Inst Sci & Technol, Biomed Engn Lab, Sao Jose Dos Campos, BrazilCNPq: 400851/2014-8Web of Scienc