miR-7 and miR-214 are specifically expressed during neuroblastoma differentiation, cortical development and embryonic stem cells differentiation, and control neurite outgrowth in vitro

The mammalian nervous system exerts essential control on many physiological processes in the organism and is itself controlled extensively by a variety of genetic regulatory mechanisms. microRNA (miR), an abundant class of small non-coding RNA, are emerging as important post-transcriptional regulators of gene expression in the brain. Increasing evidence indicates that miR regulate both the development and function of the nervous system. Moreover, deficiency in miR function has also been implicated in a number of neurological disorders. Expression profile analysis of miR is necessary to understand their complex role in the regulation of gene expression during the development and differentiation of cells. Here we present a comparative study of miR expression profiles in neuroblastoma, in cortical development, and in neuronal differentiation of embryonic stem (ES) cells. By microarray profiling in combination with real time PCR we show that miR-7 and miR-214 are modulated in neuronal differentiation (as compared to miR-1, -16 and -133a), and control neurite outgrowth in vitro. These findings provide an important step toward further elucidation of miR function and miR-related gene regulatory networks in the mammalian central nervous system. (C) 2010 Elsevier Inc. All rights reserved.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: Optimisation by an artificial neural network

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3 x 3 x 2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (XI), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24 h (Q(24)), mucoadhesive force (F-max) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different (p > 0.05, two-sided paired t-test). For gels, Q(24) values were higher than their corresponding freeze-dried tablets. F-max values for freeze-dried tablets were significantly different (2-4 times greater, p > 0.05, two-sided paired t-test) compared to equivalent gets. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved. (C) 2009 Elsevier B.V. All rights reserved.

Rapid sympatric ecological differentiation of crater lake cichlid fishes within historic times

Background: After a volcano erupts, a lake may form in the cooled crater and become an isolated aquatic ecosystem. This makes fishes in crater lakes informative for understanding sympatric evolution and ecological diversification in barren environments. From a geological and limnological perspective, such research offers insight about the process of crater lake ecosystem establishment and speciation. In the present study we use genetic and coalescence approaches to infer the colonization history of Midas cichlid fishes (Amphilophus cf. citrinellus) that inhabit a very young crater lake in Nicaragua-the ca. 1800 year-old Lake Apoyeque. This lake holds two sympatric, endemic morphs of Midas cichlid: one with large, hypertrophied lips (~20% of the total population) and another with thin lips. Here we test the associated ecological, morphological and genetic diversification of these two morphs and their potential to represent incipient speciation.
Results: Gene coalescence analyses [11 microsatellite loci and mitochondrial DNA (mtDNA) sequences] suggest that crater lake Apoyeque was colonized in a single event from the large neighbouring great lake Managua only about 100 years ago. This founding in historic times is also reflected in the extremely low nuclear and mitochondrial genetic diversity in Apoyeque. We found that sympatric adult thin- and thick-lipped fishes occupy distinct ecological trophic niches. Diet, body shape, head width, pharyngeal jaw size and shape and stable isotope values all differ significantly between the two lip-morphs. The eco-morphological features pharyngeal jaw shape, body shape, stomach contents and stable isotopes (d15N) all show a bimodal distribution of traits, which is compatible with the expectations of an initial stage of ecological speciation under disruptive selection. Genetic differentiation between the thin- and thick-lipped population is weak at mtDNA sequence (FST = 0.018) and absent at nuclear microsatellite loci (FST < 0.001).
Conclusions: This study provides empirical evidence of eco-morphological differentiation occurring very quickly after the colonization of a new and vacant habitat. Exceptionally low levels of neutral genetic diversity and inference from coalescence indicates that the Midas cichlid population in Apoyeque is much younger (ca. 100 years or generations old) than the crater itself (ca. 1 800 years old). This suggests either that the crater remained empty for many hundreds of years after its formation or that remnant volcanic activity prevented the establishment of a stable fish population during the early life of the crater lake. Based on our findings of eco-morphological variation in the Apoyeque Midas cichlids, and known patterns of adaptation in Midas cichlids in general, we suggest that this population may be in a very early stage of speciation (incipient species), promoted by disruptive selection and ecological diversification.

Reduced Nitro-oxidative Stress and Neural Cell Death Suggests a Protective Role for Microglial Cells in TNFα−/− Mice in Ischemic Retinopathy

Purpose. Neovascularization occurs in response to tissue ischemia and growth factor stimulation. In ischemic retinopathies, however, new vessels fail to restore the hypoxic tissue; instead, they infiltrate the transparent vitreous. In a model of oxygen-induced retinopathy (OIR), TNFa and iNOS, upregulated in response to tissue ischemia, are cytotoxic and inhibit vascular repair. The aim of this study was to investigate the mechanism for this effect.

Methods. Wild-type C57/BL6 (WT) and TNFa-/- mice were subjected to OIR by exposure to 75% oxygen (postnatal days 7–12). The retinas were removed during the hypoxic phase of the model. Retinal cell death was determined by TUNEL staining, and the microglial cells were quantified after Z-series capture with a confocal microscope. In situ peroxynitrite and superoxide were measured by using the fluorescent dyes DCF and DHE. iNOS, nitrotyrosine, and arginase were analyzed by real-time PCR, Western blot analysis, and activity determined by radiolabeled arginine conversion. Astrocyte coverage was examined after GFAP immunostaining.

Results. The TNFa-/- animals displayed a significant reduction in TUNEL-positive apoptotic cells in the inner nuclear layer of the avascular retina compared with that in the WT control mice. The reduction coincided with enhanced astrocytic survival and an increase in microglial cells actively engaged in phagocytosing apoptotic debris that displayed low ROS, RNS, and NO production and high arginase activity.

Conclusions. Collectively, the results suggest that improved vascular recovery in the absence of TNFa is associated with enhanced astrocyte survival and that both phenomena are dependent on preservation of microglial cells that display an anti-inflammatory phenotype during the early ischemic phase of OIR.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Two-stage mixed discrete-continuous identification of radial basis function (RBF) neural models for nonlinear systems

The identification of nonlinear dynamic systems using radial basis function (RBF) neural models is studied in this paper. Given a model selection criterion, the main objective is to effectively and efficiently build a parsimonious compact neural model that generalizes well over unseen data. This is achieved by simultaneous model structure selection and optimization of the parameters over the continuous parameter space. It is a mixed-integer hard problem, and a unified analytic framework is proposed to enable an effective and efficient two-stage mixed discrete-continuous; identification procedure. This novel framework combines the advantages of an iterative discrete two-stage subset selection technique for model structure determination and the calculus-based continuous optimization of the model parameters. Computational complexity analysis and simulation studies confirm the efficacy of the proposed algorithm.

Neural Network Modeling of Rheological Parameters of Grouts Containing Viscosity-Modifying Agent

The development of artificial neural network (ANN) models to predict the rheological behavior of grouts is described is this paper and the sensitivity of such parameters to the variation in mixture ingredients is also evaluated. The input parameters of the neural network were the mixture ingredients influencing the rheological behavior of grouts, namely the cement content, fly ash, ground-granulated blast-furnace slag, limestone powder, silica fume, water-binder ratio (w/b), high-range water-reducing admixture, and viscosity-modifying agent (welan gum). The six outputs of the ANN models were the mini-slump, the apparent viscosity at low shear, and the yield stress and plastic viscosity values of the Bingham and modified Bingham models, respectively. The model is based on a multi-layer feed-forward neural network. The details of the proposed ANN with its architecture, training, and validation are presented in this paper. A database of 186 mixtures from eight different studies was developed to train and test the ANN model. The effectiveness of the trained ANN model is evaluated by comparing its responses with the experimental data that were used in the training process. The results show that the ANN model can accurately predict the mini-slump, the apparent viscosity at low shear, the yield stress, and the plastic viscosity values of the Bingham and modified Bingham models of the pseudo-plastic grouts used in the training process. The results can also predict these properties of new mixtures within the practical range of the input variables used in the training with an absolute error of 2%, 0.5%, 8%, 4%, 2%, and 1.6%, respectively. The sensitivity of the ANN model showed that the trend data obtained by the models were in good agreement with the actual experimental results, demonstrating the effect of mixture ingredients on fluidity and the rheological parameters with both the Bingham and modified Bingham models.