Evaluación de las condiciones experimentales básicas para la producción de biomasa a partir de la microalga Chlorella vulgaris

La utilización de las microalgas como fuente de energía para la obtención de aceite y biodiesel presenta muchas ventajas respecto a otras materias primas. Las microalgas tienen un alto rendimiento por superficie cultivada, diversifica el tipo de combustible a producir y no compite con la producción de alimentos, siendo clasificado como un biocombustible de tercera generación. En el presente trabajo se evalúa la influencia del rendimiento de biomasa microalgal bajo diferentes condiciones de cultivo. La microalga Chlorella vulgaris fue cultivada a escala de laboratorio utilizando 6 elermeyers de 250 mL siguiendo la metodología descrita en el medio decultivo Watanabe modificado. Las condiciones de cultivo fueron 28°C ± 1°C con un pH aproximado entre 6 y 7. A partir del recuento celular mediante microscopía fue posible determinar la velocidad de crecimiento de la microalga en estudio con una duración de diez días; alcanzándose la máxima concentración de células en el cultivo con vitaminas y suministro de aire de 450.5×104 células/mL. A los cinco días los cultivos con vitaminas mostraron 8 veces mayor rendimiento, mientras que sin estas 7 veces. El mayor incremento se registró para el cultivo con vitaminas y suministro permanentede aire el cual incrementó su producción de biomasa 20 veces. Los resultados obtenidos en esta investigación demuestran el potencial que tiene la microalga Chlorella vulgaris para su uso con fines energéticos, encaminados fundamentalmente a la obtención de biodiesel

ANN multiscale model of anti-HIV Drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks

[Abstract] This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.Ministerio de Educación, Cultura y Deportes; AGL2011-30563-C03-0

Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks

AbstractThis work deals with obtaining models for predicting the cetane number and ignition delay using artificial neural networks. Models for the estimation of the cetane number of biodiesel from their methyl ester composition and ignition delay of palm oil and rapeseed biodiesel using artificial neural networks were obtained. For the prediction of the cetane number model, 38 biodiesel fuels and 10 pure fatty acid methyl esters from the available literature were given as inputs. The best neural network for predicting the cetane number was a conjugate gradient descend (11:4:1) showing 96.3% of correlation for the validation data and a mean absolute error of 1.6. The proposed network is useful for prediction of the cetane number of biodiesel in a wide range of composition but keeping the percent of total unsaturations lower than 80%. The model for prediction of the ignition delay was developed from 5 inputs: cetane number, engine speed, equivalence ratio, mean pressure and temperature. The results showed that the neural network corresponding to a topology (5:2:1) with a back propagation algorithm gave the best prediction of the ignition delay. The correlation coefficient and the mean absolute error were 97.2% and 0.03 respectively. The models developed to predict cetane number and ignition delay using artificial neural networks showed higher accuracy than 95%. Hence, the ANN models developed can be used for the prediction of cetane number and ignition delay of biodiesel

Evaluación de las condiciones experimentales básicas para la producción de biomasa a partir de la microalga Chlorella vulgaris

La utilización de las microalgas como fuente de energía para la obtención de aceite y biodiesel presenta muchas ventajas respecto a otras materias primas. Las microalgas tienen un alto rendimiento por superficie cultivada, diversifica el tipo de combustible a producir y no compite con la producción de alimentos, siendo clasificado como un biocombustible de tercera generación. En el presente trabajo seevalúa la influencia del rendimiento de biomasa microalgal bajo diferentes condiciones de cultivo. La microalga Chlorella vulgaris fue cultivada a escala de laboratorio utilizando 6 elermeyers de 250 mL siguiendo la metodología descrita en el medio de cultivo Watanabe modificado. Las condiciones de cultivo fueron 28°C ± 1°C con un pH aproximado entre 6 y 7. A partir del recuento celular mediante microscopía fue posible determinar la velocidad de crecimiento de la microalga en estudio con una duración de diez días; alcanzándose la máxima concentración de células en el cultivo con vitaminas y suministro de aire de 450.5×104 células/mL. A los cinco días los cultivos con vitaminas mostraron 8 veces mayor rendimiento, mientras que sin estas 7 veces. El mayor incremento se registró para el cultivo con vitaminas y suministro permanente de aire el cual incrementó su producción de biomasa 20 veces. Los resultados obtenidos en esta investigación de muestran el potencial que tiene la microalga Chlorella vulgaris para su uso con fines energéticos, encaminados fundamentalmente a la obtención de biodiesel

Globally important islands where eradicating invasive mammals will benefit highly threatened vertebrates

Invasive alien species are a major threat to native insular species. Eradicating invasive mammals from islands is a feasible and proven approach to prevent biodiversity loss. We developed a conceptual framework to identify globally important islands for invasive mammal eradications to prevent imminent extinctions of highly threatened species using biogeographic and technical factors, plus a novel approach to consider socio-political feasibility. We applied this framework using a comprehensive dataset describing the distribution of 1,184 highly threatened native vertebrate species (i.e. those listed as Critically Endangered or Endangered on the IUCN Red List) and 184 non-native mammals on 1,279 islands worldwide. Based on extinction risk, irreplaceability, severity of impact from invasive species, and technical feasibility of eradication, we identified and ranked 292 of the most important islands where eradicating invasive mammals would benefit highly threatened vertebrates. When socio-political feasibility was considered, we identified 169 of these islands where eradication planning or operation could be initiated by 2020 or 2030 and would improve the survival prospects of 9.4% of the Earth’s most highly threatened terrestrial insular vertebrates (111 of 1,184 species). Of these, 107 islands were in 34 countries and territories and could have eradication projects initiated by 2020. Concentrating efforts to eradicate invasive mammals on these 107 islands would benefit 151 populations of 80 highly threatened vertebrates and make a major contribution towards achieving global conservation targets adopted by the world’s nations.This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication