José M. Arteiro

An artificial intelligence approach to Bacillus amyloliquefaciens CCMI 1051 cultures: application to the production of anti-fungal compounds.

The combined effect of incubation time (IT) and aspartic acid concentration (AA) on the predicted biomass concentration (BC), Bacillus sporulation (BS) and anti-fungal activity of compounds (AFA) produced by Bacillus amyloliquefaciens CCMI 1051, was studied using Artificial Neural Networks (ANNs). The values predicted by ANN were in good agreement with experimental results, and were better than those obtained when using Response Surface Methodology. The database used to train and validate ANNs contains experimental data of B. amyloliquefaciens cultures (AFA, BS and BC) with different incubation times (1-9 days) using aspartic acid (3-42 mM) as nitrogen source. After the training and validation stages, the 2-7-6-3 neural network results showed that maximum AFA can be achieved with 19.5 mM AA on day 9; however, maximum AFA can also be obtained with an incubation time as short as 6 days with 36.6 mM AA. Furthermore, the model results showed two distinct behaviors for AFA, depending on IT.

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

Wild edible mushrooms Amanita ponderosa Malençon and Heim are very appreciated in gastronomy, with high export potential. This species grows in some microclimates, namely in the southwest of the Iberian Peninsula. The results obtained demonstrate that A. ponderosa mushrooms showed different inorganic composition according to their habitat and the molecular data, obtained by M13-PCR, allowed to distinguish the mushrooms at species level and to differentiate the A. ponderosa strains according to their location. Taking into account, on the one hand, that the characterisation of different strains is essential in further commercialisation and certification process and, on the other hand, the molecular studies are quite time consuming and an expensive process, the development of formal models to predict the molecular profile based on inorganic composition comes to be something essential. In the present work, Artificial Neural Networks (ANNs) were used to solve this problem. The ANN selected to predict molecular profile based on inorganic composition has a 6-7-14 topology. A good match between the observed and predicted values was observed. The present findings are wide potential application and both health and economical benefits arise from this study.

MSP-PCR and RAPD molecular biomarkers to characterizeAmanita ponderosa mushrooms

Amanita ponderosa is a specie of wild edible mushrooms growing spontaneously in some Mediterranean microclimates, namely in Alentejo and Andaluzia, in the Iberian Peninsula. The nutritional values of these fungi make them highly exportable. Due to the wide diversity of mushrooms in nature, it is essential to differentiate and to identify the various edible species. RAPD markers have been used as a valuable tool to distinguish the different genotypes, although this method has not yet been used toAmanita ponderosa. Two methods were used to establish different genetic fingerprinting patterns of edible mushrooms. Samples ofAmanita ponderosa were collected in six different regions of the southwest of the Iberian Peninsula and compared by RAPD-PCR and MSP-PCR. Additionally, to compare molecular profiles with others genera of edible mushrooms, three species of Basidiomycetes (Pleurotus ostreatus, Lactarius deliciosus andCoriolus versicolor) and an Ascomycete were used. Results showed that some molecular markers discriminate among an Ascomycete from Basidiomycetes (Amanita ponderosa, Pleurotus ostreatus, Lactarius deliciosus andCoriolus versicolor) and discriminate among the different genera within basidiomycetes, as it is expected. Moreover, OPF-6, OPG-2, OPG3 and M13 primes allowed to unravel a level of genetic polymorphism withinAmanita ponderosa mushrooms collected from different geographic origin.

Ecological mining: a case study on dam water quality

The automatic assessment of barrage water quality is very restricted due to the distances, the number of biochemical parameters to be considered and the financial resources spent to obtain their values. To this scenario should be added the latency times between the sampling moment and the outcome of the laboratory analyses. Although the idea of considering sensors for remote acquisition of data is not new, there are some constraints to be addressed, like the existence of sensors to measure the pertinent parameters and their efficiency, the costs involved and the possibility of remote sensing. The application of this alternative is highly dependent on the relevance of the candidate parameters. At this point, the Data Mining (DM) approach assumes an important role, in the sense that it can reveal the relative importance of the parameters, as well the prediction models to determine the water quality and finally the associated accuracies. This paper introduces a decision framework to support the selection of biochemical parameters to be considered in remote sensing of water contained in barrages. The framework enables the comparison of the efficiency of two kinds of models, using decision trees. The first one uses all the water quality indicators, including the time and cost consuming variables, while the second model is based only on remotely real-time acquired parameters. When comparing both strategies under several criteria (e.g., cost, time and confidence), the latter method was showed to be the best alternative.

Molecular evaluation of some Amanita ponderosa and the fungal strains living in association with these mushrooms in the southwestern Iberian Peninsula

Amanita ponderosa are wild edible mushrooms that grow only in some microclimates, particularly those in the southwestern part of the Iberian Peninsula. Due to the vast diversity of mushrooms in nature, as well as nutrient variability, which is highly dependent on soil type and environmental conditions, it is essential to be able to characterize fungal microbiota that lives in association with mushrooms and to differentiate A. ponderosa strains of different regions for certification purposes. In this study, we characterized the genetic profile of A. ponderosa mushrooms and the fungal strains that live in association with them in their natural habitat and compared the fingerprinting profiles obtained by M13-PCR amplification of the genomic DNA. We found that the predominant fungal isolates living in association with A. ponderosa were Aspergillus spp., Penicillium spp. and Mucor spp. M13-PCR molecular analysis showed that different fungal isolates had different genetic profiles. This approach allowed us to differentiate the different fungi strains isolated from fruiting bodies of A. ponderosa both rapidly and in a reproducible manner and to group them according to genus. Our fingerprinting analyses also distinguished different A. ponderosa mushrooms collected from different regions. Consequently, we conclude that this method is a very discriminatory approach for differentiating both A. ponderosa from different sites and the fungal microbiota that lives in association with these mushrooms.