Intermittent electrical stimuli for guidance of human mesenchymal stem cell lineage commitment towards neural-like cells on electroconductive substrates

In the context of the role of multiple physical factors in dictating stem cell fate, the present paper demonstrates the effectiveness of the intermittently delivered external electric field stimulation towards switching the stem cell fate to specific lineage, when cultured in the absence of biochemical growth factors. In particular, our findings present the ability of human mesenchymal stem cells (hMSCs) to respond to the electric stimuli by adopting extended neural-like morphology on conducting polymeric substrates. Polyaniline (PANI) is selected as the model system to demonstrate this effect, as the electrical conductivity of the polymeric substrates can be systematically tailored over a broad range (10(-9) to 10 S/cm) from highly insulating to conducting by doping with varying concentrations (10(-5) to 1 M) of HCl. On the basis of the culture protocol involving the systematic delivery of intermittent electric field (dc) stimulation, the parametric window of substrate conductivity and electric field strength was established to promote significant morphological extensions, with minimal cellular damage. A time dependent morphological change in hMSCs with significant filopodial elongation was observed after 7 days of electrically stimulated culture. Concomitant with morphological changes, a commensurate increase in the expression of neural lineage commitment markers such as nestin and PI tubulin was recorded from hMSCs grown on highly conducting substrates, as revealed from the mRNA expression analysis using Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) as well as by immune-fluorescence imaging. Therefore, the present work establishes the key role of intermittent and systematic delivery of electric stimuli as guidance cues in promoting neural-like differentiation of hMSCs, when grown on electroconductive substrates. (C) 2014 Elsevier Ltd. All rights reserved.

Heterogeneity in Dye-TiO2 Interactions Dictate Charge Transfer Efficiencies for Diketopyrrolopyrrole-Based Polymer Sensitized Solar Cells

Power conversion efficiency of a solar cell is a complex parameter which usually hides the molecular details of the charge generation process. For rationally tailoring the overall device efficiency of the dye-sensitized solar cell, detailed molecular understanding of photoinduced reactions at the dye-TiO2 interface has to be achieved. Recently, near-IR absorbing diketopyrrolopyrrole-based (DPP) low bandgap polymeric dyes with enhanced photostabilities have been used for TiO2 sensitization with moderate efficiencies. To improve the reported device performances, a critical analysis of the polymerTiO(2) interaction and electron transfer dynamics is imperative. Employing a combination of time-resolved optical measurements complemented by low temperature EPR and steady-state Raman spectroscopy on polymerTiO(2) conjugates, we provide direct evidence for photoinduced electron injection from the TDPP-BBT polymer singlet state into TiO2 through the C-O group of the DPP-core. A detailed excited state description of the electron transfer process in films reveals instrument response function (IRF) limited (<110 fs) charge injection from a minor polymer fraction followed by a picosecond recombination. The major fraction of photoexcited polymers, however, does not show injection indicating pronounced ground state heterogeneity induced due to nonspecific polymerTiO(2) interactions. Our work therefore underscores the importance of gathering molecular-level insight into the competitive pathways of ultrafast charge generation along with probing the chemical heterogeneity at the nanoscale within the polymerTiO2 films for optimizing photovoltaic device efficiencies.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of C-M alpha. Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.

Spatio-temporal correlations in aqueous systems: computational studies of static and dynamic heterogeneity by 2D-IR spectroscopy

Local heterogeneity is ubiquitous in natural aqueous systems. It can be caused locally by external biomolecular subsystems like proteins, DNA, micelles and reverse micelles, nanoscopic materials etc., but can also be intrinsic to the thermodynamic nature of the aqueous solution itself (like binary mixtures or at the gas-liquid interface). The altered dynamics of water in the presence of such diverse surfaces has attracted considerable attention in recent years. As these interfaces are quite narrow, only a few molecular layers thick, they are hard to study by conventional methods. The recent development of two dimensional infra-red (2D-IR) spectroscopy allows us to estimate length and time scales of such dynamics fairly accurately. In this work, we present a series of interesting studies employing two dimensional infra-red spectroscopy (2D-IR) to investigate (i) the heterogeneous dynamics of water inside reverse micelles of varying sizes, (ii) supercritical water near the Widom line that is known to exhibit pronounced density fluctuations and also study (iii) the collective and local polarization fluctuation of water molecules in the presence of several different proteins. The spatio-temporal correlation of confined water molecules inside reverse micelles of varying sizes is well captured through the spectral diffusion of corresponding 2D-IR spectra. In the case of supercritical water also, we observe a strong signature of dynamic heterogeneity from the elongated nature of the 2D-IR spectra. In this case the relaxation is ultrafast. We find remarkable agreement between the different tools employed to study the relaxation of density heterogeneity. For aqueous protein solutions, we find that the calculated dielectric constant of the respective systems unanimously shows a noticeable increment compared to that of neat water. However, the `effective' dielectric constant for successive layers shows significant variation, with the layer adjacent to the protein having a much lower value. Relaxation is also slowest at the surface. We find that the dielectric constant achieves the bulk value at distances more than 3 nm from the surface of the protein.

Role of deformation temperature on the evolution and heterogeneity of texture during equal channel angular pressing of magnesium

Investigations on texture evolution and through-thickness texture heterogeneity during equal channel angular pressing (ECAP) of pure magnesium at 200 degrees C, 150 degrees C and room temperature (RT) was carried out by neutron, high energy synchrotron X-ray and electron back-scatter diffraction. Irrespective of the ECAP temperature, a distinctive basal (B) and pyramidal (C-2) II type of fibers forms. The texture differs in the bottom 1 mm portion, where the B-fiber is shifted similar to 55 degrees due to negative shear attributed to friction. (C) 2015 Elsevier Inc. All rights reserved.

Genetic diversity and proviral DNA load in different neural compartments of HIV-1 subtype C infection

In India, the low prevalence of HIV-associated dementia (HAD) in the Human immunodeficiency virus type 1 (HIV-1) subtype C infection is quite paradoxical given the high-rate of macrophage infiltration into the brain. Whether the direct viral burden in individual brain compartments could be associated with the variability of the neurologic manifestations is controversial. To understand this paradox, we examined the proviral DNA load in nine different brain regions and three different peripheral tissues derived from ten human subjects at autopsy. Using a highly sensitive TaqMan probe-based real-time PCR, we determined the proviral load in multiple samples processed in parallel from each site. Unlike previously published reports, the present analysis identified uniform proviral distribution among the brain compartments examined without preferential accumulation of the DNA in any one of them. The overall viral DNA burden in the brain tissues was very low, approximately 1 viral integration per 1000 cells or less. In a subset of the tissue samples tested, the HIV DNA mostly existed in a free unintegrated form. The V3-V5 envelope sequences, demonstrated a brain-specific compartmentalization in four of the ten subjects and a phylogenetic overlap between the neural and non-neural compartments in three other subjects. The envelope sequences phylogenetically belonged to subtype C and the majority of them were R5 tropic. To the best of our knowledge, the present study represents the first analysis of the proviral burden in subtype C postmortem human brain tissues. Future studies should determine the presence of the viral antigens, the viral transcripts, and the proviral DNA, in parallel, in different brain compartments to shed more light on the significance of the viral burden on neurologic consequences of HIV infection.

Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators

There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.