Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.     

Expression of marker genes during early ear development in medaka

Induction of the otic placode involves a number of regulatory interactions. Early studies revealed that the induction of this program is initiated by instructive signals from the mesendoderm as well as from the adjacent hindbrain. Further investigations on the molecular level identified in zebrafish Fgf3, Fgf8, Foxi1, Pax8, Dlx3b and Dlx4b genes as key players during the induction phase. Thereafter an increasing number of genes participates in the regulatory interactions finally resulting in a highly structured sensory organ. Based on data from zebrafish we selected medaka genes with presumptive functions during early ear development for an expression analysis. In addition we isolated Foxi1 and Dlx3b gene fragments from embryonic cDNA. Altogether we screened the spatio-temporal distribution of more than 20 representative marker genes for otic development in medaka embryos, with special emphasis on the early phases. Whereas the spatial distribution of these genes is largely conserved between medaka and zebrafish, our comparative analysis revealed several differences, in particular for the timing of expression.     

New Insight into the Interactions of Arbutin with Mushroom Tyrosinase

The Protein Journal - As a safe substitute for hydroquinone, β-arbutin, a natural plant substance, and its synthetic counterpart, α-arbutin, are used in depigmentation formulations....     

Association Between Hypertension in Healthy Participants and Zinc and Copper Status: a Population-Based Study

Biological Trace Element Research - The prevalence of hypertension (HTN) is increasing globally. It has been shown that there is an association between micronutrient deficiency and HTN. In the...     

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.     

Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.