Evaluation of Electro-Flocculation for Biomass Production of Marine Microalgae Phaodactylum tricornutum

Year: 2017 Volume: 11 Issue: 6 391 - 394 Pages

• Authors:
• Luciana C. Ramos
• Leandro J. Sousa
• Antônio Ferreira da Silva
• Valéria Gomes Oliveira Falcão
• Suzana T. Cunha Lima

Abstract: The commercial production of biodiesel using microalgae demands a high-energy input for harvesting biomass, making production economically unfeasible. Methods currently used involve mechanical, chemical, and biological procedures. In this work, a flocculation system is presented as a cost and energy effective process to increase biomass production of Phaeodactylum tricornutum. This diatom is the only species of the genus that present fast growth and lipid accumulation ability that are of great interest for biofuel production. The algae, selected from the Bank of Microalgae, Institute of Biology, Federal University of Bahia (Brazil), have been bred in tubular reactor with photoperiod of 12 h (clear/dark), providing luminance of about 35 μmol photons m-2s-1, and temperature of 22 °C. The medium used for growing cells was the Conway medium, with addition of silica. The seaweed growth curve was accompanied by cell count in Neubauer camera and by optical density in spectrophotometer, at 680 nm. The precipitation occurred at the end of the stationary phase of growth, 21 days after inoculation, using two methods: centrifugation at 5000 rpm for 5 min, and electro-flocculation at 19 EPD and 95 W. After precipitation, cells were frozen at -20 °C and, subsequently, lyophilized. Biomass obtained by electro-flocculation was approximately four times greater than the one achieved by centrifugation. The benefits of this method are that no addition of chemical flocculants is necessary and similar cultivation conditions can be used for the biodiesel production and pharmacological purposes. The results may contribute to improve biodiesel production costs using marine microalgae.

• Keywords:
• Biomass
• diatom
• flocculation
• microalgae.

Patient-Specific Modeling Algorithm for Medical Data Based on AUC

Year: 2016 Volume: 10 Issue: 1 105 - 110 Pages

• Authors:
• Guilherme Ribeiro
• Alexandre Oliveira
• Antonio Ferreira
• Shyam Visweswaran
• Gregory Cooper

Abstract: Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

• Keywords:
• Approach instance-based
• area Under the ROC Curve
• Patient-specific Decision Path
• clinical predictions.