More than 30 years of Iberian geochemistry meetings: Contribuition from the XII Iberian congress of geochemistry and the XX geochemistry week

Os autores agradecem a participa??o de todos os intervenientes no encontro, nomeadamente oradores convidados, congressistas, patrocinadores, comiss?o cient?fica e colaboradores na parte log?stica. Agradece-se ainda ? Sociedade Geol?gica de Portugal, ? Universidade de ?vora e centros de investiga??o associados pelo apoio log?stico ao encontro. Os autores agradecem o financiado concedido pela Uni?o Europeia atrav?s do Fundo Europeu de Desenvolvimento Regional (Programa ALENTEJO 2020), atrav?s do projeto "Modelos metalog?nicos 3D da Zona de Ossa Morena: valoriza??o dos recursos minerais do Alentejo" (REF: ALT20-03-0145-FEDER-000028).The Iberian Geochemistry Congress is currently a reference congress in the aims of Iberian Geology, particularly in the Geochemistry domain. The first edition of this meeting was held in 1977, in which the first Geochemistry Week took place, at Instituto Superior Técnico (Lisboa). Ever since the Iberian Geochemistry Congresspublishersversionpublishe

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

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

Article history: Received 2 February 2010 Received in revised form 15 July 2010 Accepted 19 July 2010 Available online 27 July 2010 Keywords: Bacillus amiloliquefaciens Spore formation Anti-fungal activity Neural networks 1. Introduction Biopesticides based on natural endophytic bacteria to control plant diseases are a promising ecological alternative to chemical treatments. Bacillus species produce a wide variety of metabolites with interesting biological activities, among them iturinic lipopep- tides antibiotics (Bottone and Peluso, 2003; Cho et al., 2003; Moyne et al., 2001). The antimicrobial activity exhibited by Bacillus sp. is dependent on the culture medium composition, and different nitro- gen sources can result in the production of different antibiotics (Besson et al., 1987; Chevanet et al., 1986; Davis et al., 1999; Volpon et al., 2000). Aspartic acid is the preferred nitrogen source for the production of iturinic compounds by Bacillus subtilis (Besson et al., 1987) and Bacillus amyloliquefaciens (Caldeira et al., 2006, 2007, 2008). With increasing culture time, the nutrient content changes and adverse environmental conditions appear. Thus, incubation time is another factor influencing antibiotic production (Caldeira et al., 2008; Feio et al., 2004; Moyne et al., 2001), as the response to adverse environmental conditions can lead to activation of differ- ent mechanisms for the production of antibiotics giving a compet- itive advantage to the producer microorganism (Dieckmann et al., 2001). The link between antibiotic production and Bacillus sporulation is not fully understood. During production of lipopeptides in sub- ⇑ Corresponding author. Tel.: +351 266 745 315; fax: +351 266 745 303. E-mail address: [email protected] (H. Vicente). 0960-8524/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biortech.2010.07.080 abstract The combined effect of incubation time (IT) and aspartic acid concentration (AA) on the predicted bio- mass 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 con- tains 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

New Insights on Carotenoid Production by <em>Gordonia alkanivorans</em> Strain 1B

Gordonia alkanivorans strain 1B is a desulfurizing bacterium and a hyper-pigment producer. Most carotenoid optimization studies have been performed with light, but little is still known on how carbon/sulfur-source concentrations influence carotenoid production under darkness. In this work, a surface response methodology based on a two-factor Doehlert distribution (% glucose in a glucose/fructose 10 g/L mixture; sulfate concentration) was used to study carotenoid and biomass production without light. These responses were then compared to those previously obtained under light. Moreover, carbon consumption was also monitored, and different metabolic parameters were further calculated. The results indicate that both light and glucose promote slower growth rates, but stimulate carotenoid production and carbon conversion to carotenoids and biomass. Fructose induces higher growth rates, and greater biomass production at 72 h; however, its presence seems to inhibit carotenoid production. Moreover, although at a much lower yield than under light, results demonstrate that under darkness the highest carotenoid production can be achieved with 100% glucose (10 g/L), ≥27 mg/L sulfate, and high growth time (>216 h). These results give a novel insight into the metabolism of strain 1B, highlighting the importance of culture conditions optimization to increase the process efficiency for carotenoid and/or biomass production

Bioproducts from forest biomass: Essential oils and hydrolates from wastes of Cupressus lusitanica Mill. and Cistus ladanifer L.

Unattended forest wastes are, among others, a potential source of wildfires, as well as a growth media for forest pests. As a way of lowering the detrimental effect of these wastes, it is important to convert these under-valued resources into a value-generating market forest wastes use. Essential oils (EOs) and hydrolates (Hs) from Cupressus lusitanica and Cistus ladanifer waste products, resulting from forest landscaping in Portugal, were evaluated for chemical composition and biological activity. Essential oils and Hs were obtained by steam-distillation (SD) and hydrodistillation (HD). Essential oils and Hs volatiles were analysed by gas chromatography (GC) and gas chromatography-mass spectrometry (GC–MS). The antimicrobial activity of EOs was studied by disk agar diffusion method against Escherichia coli, Staphylococcus aureus and Candida albicans. Antioxidant activity of EOs and Hs was evaluated by 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) free radical, superoxide anion radical formation, xanthine oxidase and chelating metal ions assays. Antiinflammatory activity of Hs was assessed by albumin denaturation assay. Monoterpene hydrocarbons and oxygen-containing monoterpenes dominated C. lusitanica EO (SD, 82–86 %, HD, 80–85 %) and Hs volatiles (SD, 93–94 %; HD 64–81 %), respectively. α-Pinene (14–36 %), limonene (8–21 %), δ-3-carene (8–19 %) and sabinene (6–18 %) were the main EO constituents. Hydrolates volatiles were dominated by cis-3-hexen-1-ol (0.1–13 %), camphor (1–11 %), umbellulone (t-48 %), p-cymene-8-ol (11–16 %) and terpinen-4-ol (21–31 %). C. ladanifer EOs were dominated by monoterpene hydrocarbons (SD, 48–80 % and HD, 29 %) and Hs by oxygencontaining monoterpenes (SD, 38–43 %, HD, 39 %). The EO major constituents were α-pinene (13–28 %) and camphene (5–25 %), whereas 2,6,6-trimethyl cyclohexanone (2–12 %) and trans-pinocarveol (5–13 %) dominated the Hs volatiles. This study reports for the first time the chemical composition of the hydrolate volatiles of these two species and their anti-inflammatory properties. Among the studied biological activities, the EOs showed the best antioxidant properties while Hs demonstrated higher anti-inflammatory activity.info:eu-repo/semantics/publishedVersio