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

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets

Cell walls of the dimorphic fungal pathogens Sporothrix schenckii and Sporothrix brasiliensis exhibit bilaminate structures and sloughing of extensive and intact layers

This work was supported by the Fundação Carlos Chagas de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ), grants E-26/202.974/2015 and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), grants 229755/2013-5, Brazil. LMLB is a senior research fellow of CNPq and Faperj. NG acknowledged support from the Wellcome Trust (Trust (097377, 101873, 200208) and MRC Centre for Medical Mycology (MR/N006364/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

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

Identification of novel DNA repair proteins via primary sequence, secondary structure, and homology

<p>Abstract</p> <p>Background</p> <p>DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic detection of DNA repair proteins in genomes via informatics techniques is desirable; however, there are many forms of DNA repair and it is not a straightforward process to identify and classify repair proteins with a single optimal method. We perform a study of the ability of homology and machine learning-based methods to identify and classify DNA repair proteins, as well as scan vertebrate genomes for the presence of novel repair proteins. Combinations of primary sequence polypeptide frequency, secondary structure, and homology information are used as feature information for input to a Support Vector Machine (SVM).</p> <p>Results</p> <p>We identify that SVM techniques are capable of identifying portions of DNA repair protein datasets without admitting false positives; at low levels of false positive tolerance, homology can also identify and classify proteins with good performance. Secondary structure information provides improved performance compared to using primary structure alone. Furthermore, we observe that machine learning methods incorporating homology information perform best when data is filtered by some clustering technique. Analysis by applying these methodologies to the scanning of multiple vertebrate genomes confirms a positive correlation between the size of a genome and the number of DNA repair protein transcripts it is likely to contain, and simultaneously suggests that all organisms have a non-zero minimum number of repair genes. In addition, the scan result clusters several organisms' repair abilities in an evolutionarily consistent fashion. Analysis also identifies several functionally unconfirmed proteins that are highly likely to be involved in the repair process. A new web service, INTREPED, has been made available for the immediate search and annotation of DNA repair proteins in newly sequenced genomes.</p> <p>Conclusion</p> <p>Despite complexity due to a multitude of repair pathways, combinations of sequence, structure, and homology with Support Vector Machines offer good methods in addition to existing homology searches for DNA repair protein identification and functional annotation. Most importantly, this study has uncovered relationships between the size of a genome and a genome's available repair repetoire, and offers a number of new predictions as well as a prediction service, both which reduce the search time and cost for novel repair genes and proteins.</p

Effects of fishery protection on biometry and genetic structure of two target sea cucumber species from the Mediterranean Sea

Sea cucumber fisheries are now occurring in most of the tropical areas of the world, having expanded from its origin in the central Indo-Pacific. Due to the overexploitation of these resources and the increasing demand from Asian countries, new target species from Mediterranean Sea and northeastern Atlantic Ocean are being caught. The fishery effects on biometry and genetic structure of two target species (Holothuria polii and H. tubulosa) from Turkey, were assessed. The heaviest and largest individuals of H. polii were found into the non-fishery area of Kusadasi, also showing the highest genetic diversity. Similar pattern was detected in H. tubulosa, but only the weight was significantly higher in the protected area. However, the observed differences on the fishery effects between species, could be explained considering the different percentage of catches (80% for H. polii and 20% for H. tubulosa)

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

Genetic variability of the neogregarine apicystis bombi, an etiological agent of an emergent bumblebee disease

The worldwide spread of diseases is considered a major threat to biodiversity and a possible driver of the decline of pollinator populations, particularly when novel species or strains of parasites emerge. Previous studies have suggested that populations of introduced European honeybee (Apis mellifera) and bumblebee species (Bombus terrestris and Bombus ruderatus) in Argentina share the neogregarine parasite Apicystis bombi with the native bumblebee (Bombus dahlbomii). In this study we investigated whether A. bombi is acting as an emergent parasite in the non-native populations. Specifically, we asked whether A. bombi, recently identified in Argentina, was introduced by European, non-native bees. Using ITS1 and ITS2 to assess the parasite's intraspecific genetic variation in bees from Argentina and Europe, we found a largely unstructured parasite population, with only 15% of the genetic variation being explained by geographic location. The most abundant haplotype in Argentina (found in all 9 specimens of non-native species) was identical to the most abundant haplotype in Europe (found in 6 out of 8 specimens). Similarly, there was no evidence of structuring by host species, with this factor explaining only 17% of the genetic variation. Interestingly, parasites in native Bombus ephippiatus from Mexico were genetically distant from the Argentine and European samples, suggesting that sufficient variability does exist in the ITS region to identify continent-level genetic structure in the parasite. Thus, the data suggest that A. bombi from Argentina and Europe share a common, relatively recent origin. Although our data did not provide information on the direction of transfer, the absence of genetic structure across space and host species suggests that A. bombi may be acting as an emergent infectious disease across bee taxa and continents

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

Quantitative Trait Locus (QTL) Mapping Reveals a Role for Unstudied Genes in Aspergillus Virulence

Infections caused by the fungus Aspergillus are a major cause of morbidity and mortality in immunocompromised populations. To identify genes required for virulence that could be used as targets for novel treatments, we mapped quantitative trait loci (QTL) affecting virulence in the progeny of a cross between two strains of A. nidulans (FGSC strains A4 and A91). We genotyped 61 progeny at 739 single nucleotide polymorphisms (SNP) spread throughout the genome, and constructed a linkage map that was largely consistent with the genomic sequence, with the exception of one potential inversion of ∼527 kb on Chromosome V. The estimated genome size was 3705 cM and the average intermarker spacing was 5.0 cM. The average ratio of physical distance to genetic distance was 8.1 kb/cM, which is similar to previous estimates, and variation in recombination rate was significantly positively correlated with GC content, a pattern seen in other taxa. To map QTL affecting virulence, we measured the ability of each progeny strain to kill model hosts, larvae of the wax moth Galleria mellonella. We detected three QTL affecting in vivo virulence that were distinct from QTL affecting in vitro growth, and mapped the virulence QTL to regions containing 7–24 genes, excluding genes with no sequence variation between the parental strains and genes with only synonymous SNPs. None of the genes in our QTL target regions have been previously associated with virulence in Aspergillus, and almost half of these genes are currently annotated as “hypothetical”. This study is the first to map QTL affecting the virulence of a fungal pathogen in an animal host, and our results illustrate the power of this approach to identify a short list of unknown genes for further investigation