Bioelectricity generation using two chamber microbial fuel cell treating wastewater from food processing

Electricity generation from microbial fuel cells which treat food processing wastewater was investigated in this study. Anaerobic anode and aerobic cathode chambers were separated by a proton exchange membrane in a two-compartment MFC reactor. Buffer solutions and food industry wastewater were used as electrolytes in the anode and cathode chambers, respectively. The produced voltage and current intensity were measured using a digital multimeter. Effluents from the anode compartment were tested for COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity. The maximum current density and power production were measured 527 mA/m² and 230 mW/m² in the anode area, respectively, at operation organic loading (OLR) of 0.364 g COD/l.d. At OLR of 0.182 g COD/l.d, maximum voltage and columbic efficiency production were recorded 0.475 V and 21%, respectively. Maximum removal efficiency of COD, BOD₅, NH₃, P, TSS, VSS, SO₄ and alkalinity were 86, 79, 73, 18, 68, 62, 30 and 58%, respectively. The results indicated that catalysts and mediator-less microbial fuel cells (CAML-MFC) can be considered as a better choice for simple and complete energy conversion from the wastewater of such industries and also this could be considered as a new method to offset wastewater treatment plant operating costs.     

BMI1, Stem Cell Factor Acting as Novel Serum-biomarker for Caucasian and African-American Prostate Cancer

Background

Lack of reliable predictive biomarkers is a stumbling block in the management of prostate cancer (CaP). Prostate-specific antigen (PSA) widely used in clinics has several caveats as a CaP biomarker. African-American CaP patients have poor prognosis than Caucasians, and notably the serum-PSA does not perform well in this group. Further, some men with low serum-PSA remain unnoticed for CaP until they develop disease. Thus, there is a need to identify a reliable diagnostic and predictive biomarker of CaP. Here, we show that BMI1 stem-cell protein is secretory and could be explored for biomarker use in CaP patients.

Methodology/Principal Findings

Semi-quantitative analysis of BMI1 was performed in prostatic tissues of TRAMP (autochthonous transgenic mouse model), human CaP patients, and in cell-based models representing normal and different CaP phenotypes in African-American and Caucasian men, by employing immunohistochemistry, immunoblotting and Slot-blotting. Quantitative analysis of BMI1 and PSA were performed in blood and culture-media of siRNA-transfected and non-transfected cells by employing ELISA. BMI1 protein is (i) secreted by CaP cells, (ii) increased in the apical region of epithelial cells and stromal region in prostatic tumors, and (iii) detected in human blood. BMI1 is detectable in blood of CaP patients in an order of increasing tumor stage, exhibit a positive correlation with serum-PSA and importantly is detectable in patients which exhibit low serum-PSA. The clinical significance of BMI1 as a biomarker could be ascertained from observation that CaP cells secrete this protein in higher levels than cells representative of benign prostatic hyperplasia (BPH).

Conclusions/Significance

BMI1 could be developed as a dual bio-marker (serum and biopsy) for the diagnosis and prognosis of CaP in Caucasian and African-American men. Though compelling these data warrant further investigation in a cohort of African-American patients.     

Modeling and analysis of soybean (Glycine max. L) Cu/Zn,Mn and Fe superoxide dismutases

Superoxide dismutase (SOD, EC 1.15.1.1) is an important metal-containing antioxidant enzyme that provides the first line of defense against toxic superoxide radicals by catalyzing their dismutation to oxygen and hydrogen peroxide. SOD is classified into four metalloprotein isoforms, namely, Cu/Zn SOD, Mn SOD, Ni SOD and Fe SOD. The structural models of soybean SOD isoforms have not yet been solved. In this study, we describe structural models for soybean Cu/Zn SOD, Mn SOD and Fe SOD and provide insights into the molecular function of this metal-binding enzyme in improving tolerance to oxidative stress in plants.     

A model of the evolution of dichogamy incorporating sex-ratio selection, anther-stigma interference, and inbreeding depression

Historically, explanations for the evolution of floral traits that reduce self-fertilization have tended to focus on selection to avoid inbreeding depression. However, there is growing support for the hypothesis that such traits also play a role in promoting efficient pollen dispersal by reducing anther-stigma interference. The relative importance of these two selective pressures is currently a popular topic of investigation. To date, there has been no theoretical exploration of the relative contributions of selection to avoid the genetic costs of self-fertilization and selection to promote efficient pollen dispersal on the evolution of floral traits. We developed a population genetic model to examine the influence of these factors on the evolution of dichogamy: the temporal separation of anther maturation and stigma receptivity. Our analysis indicates that anther-stigma interference can favor dichogamy even in the absence of in-breeding depression. Although anther-stigma interference and inbreeding depression are the key forces driving the initial evolution of dichogamy, selection to match the timing of pollen dispersal to the availability of ovules at the population level becomes a more potent force opposing the further evolution of dichogamy as the extent of temporal separation increases. This result may help to explain otherwise puzzling phenomena such as why dichogamy is rarely complete in nature and why dichogamy tends to be associated with asynchronous flower presentation.     

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.     

Phylogenetic Analysis of Selected Menthol-Producing Species Belonging to the Lamiaceae Family

Menthol is an organic compound with diverse medicinal and commercial applications, and is made either synthetically or through extraction from mint oils. The aim of the present study was to investigate menthol levels in selected menthol-producing species belonging to the Lamiaceae family, and to determine phylogenetic relationships of menthol dehydrogenase gene sequence among these species. Three genus of Lamiaceae, namely Mentha, Salvia, and Micromeria, were selected for phytochemical and phylogenetic analyses. After identification of each species based on menthol dehydrogenase gene in NCBI, BLAST software was used for the sequence alignment. MEGA4 software was used to draw phylogenetic tree for various species. Phytochemical analysis revealed that the highest and lowest amounts of both essential oil and menthol belonged to Mentha spicata and Micromeria hyssopifolia, respectively. The species Mentha spicata and Mentha piperita, which were assigned to one cluster in the dendrogram, contained the highest amounts of essential oil and menthol while Micromeria species, which was in the distinct cluster and placed in the farther evolutionary distance, contained the lowest amount of essential oil and menthol. Phylogenetic and phytochemistry analyses showed that essential oil and menthol contents of menthol-producing species are associated with menthol dehydrogenase gene sequence.     

Effect of chronic morphine exposure on response properties of rat barrel cortex neurons.

Chronic exposure to morphine can impair performance in tasks which need sensory processing. Using single unit recordings we investigate the effect of chronic morphine exposure on the firing properties of neurons in layers IV and V of the whisker-related area of rat primary somatosensory cortex. In urethane-anesthetized animals, neuronal activity was recorded in response to principal and adjacent whisker deflections either stimulated independently or in a conditioning test paradigm. A condition test ratio (CTR) was calculated for assessing the inhibitory receptive field. In layer IV, chronic morphine treatment did not change the spontaneous discharge activity. On responses to principal and adjacent whisker deflections did not show any significant changes following chronic morphine exposure. The magnitude Off responses to adjacent whisker deflection decreased while its response latency increased. In addition, there was a significant increase in the latency of Off responses to principal whisker deflection. CTR did not change significantly following morphine exposure. Layer V neurons, on the other hand, did not show any significant changes in their spontaneous activity or their evoked responses following morphine exposure. Our results suggest that chronic morphine exposure has a subtle modulatory effect on response properties of neurons in barrel cortex.     

A hidden aggregation‐prone structure in the heart of hypoxia inducible factor prolyl hydroxylase

Prolyl hydroxylase domain‐containing protein 2 (PHD2), as one of the most important regulators of angiogenesis and metastasis of cancer cells, is a promising target for cancer therapy drug design. Progressive studies imply that abnormality in PHD2 function may be due to misfolding. Therefore, study of the PHD2 unfolding pathway paves the way for a better understanding of the influence of PHD2 mutations and cancer cell metabolites on the protein folding pathway. We study the unfolding of the PHD2 catalytic domain using differential scanning calorimetry (DSC), fluorescence spectroscopy, and discrete molecular dynamics simulations (DMD). Using computational and experimental techniques, we find that PHD2 undergoes four transitions along the thermal unfolding pathway. To illustrate PHD2 unfolding events in atomic detail, we utilize DMD simulations. Analysis of computational results indicates an intermediate species in the PHD2 unfolding pathway that may enhance aggregation propensity, explaining mutation‐independent PHD2 malfunction. Proteins 2016; 84:611–623. © 2016 Wiley Periodicals, Inc.