Growth of Chlorella vulgaris and Nannochloris oculata in effluents of Tilapia farming for the production of fatty acids with potential in biofuels

The use of microalgae in wastewater treatment and its biotechnological exploitation for the production of biofuels is a potential environmental application. Some species of microalgae are notable due to their lipid composition and fatty acid profile suitable for biofuel production. During the present study, a factorial 23 experimental design was conducted, which assessed three factors: i) two species of microalgae (Chlorella vulgaris and Nannochloris oculata), ii) two types of culture media [wastewater of tilapia farming (WTF) and bold’s basal medium (BB)], and iii) two types of lighting (multi-LED lamps and white light). Microalgae were inoculated in photobioreactors in 6 L of medium (WTF or BBM) at an initial concentration of 1.0 × 106 cells ml-1 at 20 ± 2°C. The highest average cell density as well as the highest productivity of biomass observed in the treatments was C. vulgaris treatment in BBM and multi-LED lighting (8.83 × 107 cells ml-1 and 0.0854 g l-1 d-1, respectively). Although the majority of lipid productivity was obtained in the exponential phase of N. oculata cultivated in multi-LEDs in both treatments (BBM with 58% and WTF with 52%), cultivation of both species was generally maintained in WTF and were those that presented the major lipid productivity (2-18 mg l-1 d-1) in comparison with those cultivated in BBM. Palmitic, stearic, oleic, linoleic, linolenic and eicosanoic (C16–C20) fatty acids were present in both species of microalgae in concentrations between 26 and 74%. Based on the results of the present study, we conclude that cultivation of N. oculata and/or C. vulgaris in WTF illuminated with multi-LEDs is an economic and sustainable alternative for biodiesel production because it can represent up to 58% of lipids with a fatty acid profile optimal up to 74% of the total fatty acids.Key words: Chlorella vulgaris, Nannochloris oculata, production of fatty acids, wastewater of tilapia farming, production of biofuels

Serological detection of antibodies against Paracoccidioides brasiliensis in dogs with leishmaniasis

The aim of this study was to detect antibodies against Paracoccidioides brasiliensis in dogs seropositive and seronegative for leishmaniasis. Sera from 836 dogs (449 positive and 387 negative to leishmaniasis) were analysed by ELISA and the immunodiffusion test using gp43 and exoantigen, respectively. The analysis of the 836 serum samples by ELISA and the immunodiffusion test showed a positivity of 67.8 % and 7.3%, respectively, for P. brasiliensis infection. The dogs positive to leishmaniasis showed a higher reactivity to gp43 (79.9%) and exoantigen (12.7%) than the negative ones (54.0% and 1.0%, respectively). The higher reactivity to P. brasiliensis antigens may be due to cross-reactivity or a co-infection of dogs by Leishmania and P. brasiliensis. The lower correlation (0.187) observed between reactivity to gp43 and Leishmania antigen reinforces the latter hypothesis

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

<p>Abstract</p> <p>Background</p> <p>The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.</p> <p>Results</p> <p>We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.</p> <p>Conclusion</p> <p>We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.</p

L1pred: A Sequence-Based Prediction Tool for Catalytic Residues in Enzymes with the L1-logreg Classifier

To understand enzyme functions, identifying the catalytic residues is a usual first step. Moreover, knowledge about catalytic residues is also useful for protein engineering and drug-design. However, to experimentally identify catalytic residues remains challenging for reasons of time and cost. Therefore, computational methods have been explored to predict catalytic residues. Here, we developed a new algorithm, L1pred, for catalytic residue prediction, by using the L1-logreg classifier to integrate eight sequence-based scoring functions. We tested L1pred and compared it against several existing sequence-based methods on carefully designed datasets Data604 and Data63. With ten-fold cross-validation, L1pred showed the area under precision-recall curve (AUPR) and the area under ROC curve (AUC) of 0.2198 and 0.9494 on the training dataset, Data604, respectively. In addition, on the independent test dataset, Data63, it showed the AUPR and AUC values of 0.2636 and 0.9375, respectively. Compared with other sequence-based methods, L1pred showed the best performance on both datasets. We also analyzed the importance of each attribute in the algorithm, and found that all the scores contributed more or less equally to the L1pred performance

Fast and accurate protein substructure searching with simulated annealing and GPUs

<p>Abstract</p> <p>Background</p> <p>Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.</p> <p>Results</p> <p>We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).</p> <p>Conclusions</p> <p>The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.</p

Barriers to antiretroviral therapy adherence in rural Mozambique

<p>Abstract</p> <p>Background</p> <p>HIV is treated as a chronic disease, but high lost-to-follow-up rates and poor adherence to medication result in higher mortality, morbidity, and viral mutation. Within 18 clinical sites in rural Zambézia Province, Mozambique, patient adherence to antiretroviral therapy has been sub-optimal.</p> <p>Methods</p> <p>To better understand barriers to adherence, we conducted 18 community and clinic focus groups in six rural districts. We interviewed 76 women and 88 men, of whom 124 were community participants (CP; 60 women, 64 men) and 40 were health care workers (HCW; 16 women, 24 men) who provide care for those living with HIV.</p> <p>Results</p> <p>While there was some consensus, both CP and HCW provided complementary insights. CP focus groups noted a lack of confidentiality and poor treatment by hospital staff (42% CP vs. 0% HCW), doubt as to the benefits of antiretroviral therapy (75% CP vs. 0% HCW), and sharing medications with family members (66% CP vs. 0%HCW). Men expressed a greater concern about poor treatment by HCW than women (83% men vs. 0% women). Health care workers blamed patient preference for traditional medicine (42% CP vs. 100% HCW) and the side effects of medication for poor adherence (8% CP vs. 83% CHW).</p> <p>Conclusions</p> <p>Perspectives of CP and HCW likely reflect differing sociocultural and educational backgrounds. Health care workers must understand community perspectives on causes of suboptimal adherence as a first step toward effective intervention.</p

A Folding Pathway-Dependent Score to Recognize Membrane Proteins

While various approaches exist to study protein localization, it is still a challenge to predict where proteins localize. Here, we consider a mechanistic viewpoint for membrane localization. Taking into account the steps for the folding pathway of α-helical membrane proteins and relating biophysical parameters to each of these steps, we create a score capable of predicting the propensity for membrane localization and call it FP3mem. This score is driven from the principal component analysis (PCA) of the biophysical parameters related to membrane localization. FP3mem allows us to rationalize the colocalization of a number of channel proteins with the Cav1.2 channel by their fewer propensities for membrane localization