Antidepressants, but not antipsychotics, modulate GR function in human whole blood: An insight into molecular mechanisms

Clinical studies have demonstrated an impairment of glucocorticoid receptor (GR)-mediated negative feedback on the hypothalamic-pituitary-adrenal (HPA) axis in patients with major depression (GR resistance), and its resolution by antidepressant treatment. Recently, we showed that this impairment is indeed due to a dysfunction of GR in depressed patients (Carvalho et al., 2009), and that the ability of the antidepressant clomipramine to decrease GR function in peripheral blood cells is impaired in patients with major depression who are clinically resistant to treatment (Carvalho et al. 2008). To further investigate the effect of antidepressants on GR function in humans, we have compared the effect of the antidepressants clomipramine, amytriptiline, sertraline, paroxetine and venlafaxine, and of the antipsychotics, haloperidol and risperidone, on GR function in peripheral blood cells from healthy volunteers (n=33). GR function was measured by glucocorticoid inhibition of lypopolysaccharide (LPS)-stimulated interleukin-6 (IL-6) levels. Compared to vehicle-treated cells, all antidepressants inhibited dexamethasone (DEX, 10-100nM) inhibition of LPS-stimulated IL-6 levels (p values ranging from 0.007 to 0.1). This effect was specific to antidepressants, as antipsychotics had no effect on DEX-inhibition of LPS-stimulated IL-6 levels. The phosphodiesterase (PDE) type 4 inhibitor, rolipram, potentiated the effect of antidepressants on GR function, while the GR antagonist, RU-486, inhibited the effect of antidepressants on GR function. These findings indicate that the effect of antidepressants on GR function are specific for this class of psychotropic drugs, and involve second messenger pathways relevant to GR function and inflammation. Furthermore, it also points towards a possible mechanism by which one maybe able to overcome treatment-resistant depression. Research in this field will lead to new insights into the pathophysiology and treatment of affective disorders.

GPU accelerated algorithms for computing matrix function vector products with applications to exponential integrators and fractional diffusion

The efficient computation of matrix function vector products has become an important area of research in recent times, driven in particular by two important applications: the numerical solution of fractional partial differential equations and the integration of large systems of ordinary differential equations. In this work we consider a problem that combines these two applications, in the form of a numerical solution algorithm for fractional reaction diffusion equations that after spatial discretisation, is advanced in time using the exponential Euler method. We focus on the efficient implementation of the algorithm on Graphics Processing Units (GPU), as we wish to make use of the increased computational power available with this hardware. We compute the matrix function vector products using the contour integration method in [N. Hale, N. Higham, and L. Trefethen. Computing Aα, log(A), and related matrix functions by contour integrals. SIAM J. Numer. Anal., 46(5):2505–2523, 2008]. Multiple levels of preconditioning are applied to reduce the GPU memory footprint and to further accelerate convergence. We also derive an error bound for the convergence of the contour integral method that allows us to pre-determine the appropriate number of quadrature points. Results are presented that demonstrate the effectiveness of the method for large two-dimensional problems, showing a speedup of more than an order of magnitude compared to a CPU-only implementation.

WT1 interacts with MAD2 and regulates mitotic checkpoint function

Tumour suppressors safeguard the fidelity of the mitotic checkpoint by transcriptional regulation of genes that encode components of the mitotic checkpoint complex (MCC). Here we report a new role for the tumour suppressor and transcription factor, WT1, in the mitotic checkpoint. We show that WT1 regulates the MCC by directly interacting with the spindle assembly checkpoint protein, MAD2. WT1 colocalizes with MAD2 during mitosis and preferentially binds to the functionally active, closed-conformer, C-MAD2. Furthermore, WT1 associates with the MCC containing MAD2, BUBR1 and CDC20, resulting in prolonged inhibition of the anaphase-promoting complex/cyclosome (APC/C) and delayed degradation of its substrates SECURIN and CYCLIN B1. Strikingly, RNA interference-mediated depletion of WT1 leads to enhanced turnover of SECURIN, decreased lag time to anaphase and defects in chromosome segregation. Our findings identify WT1 as a regulator of the mitotic checkpoint and chromosomal stability.

IT consumerization and its effects on IT business value, IT capabilities, and the IT function

IT consumerization is both a major opportunity and significant challenge for organizations. However, IS research has hardly discussed the implications for IT management so far. In this paper we address this topic by empirically identifying organizational themes for IT consumerization and conceptually exploring the direct and indirect effects on the business value of IT, IT capabilities, and the IT function. More specifically, based on two case studies, we identify eight organizational themes: consumer IT strategy, policy development and responsibilities, consideration of private life of employees, user involvement into IT-related processes, individualization, updated IT infrastructure, end user support, and data and system security. The contributions of this paper are: (1) the identification of organizational themes for IT consumerization; (2) the proposed effects on the business value of IT, IT capabilities and the IT function, and; (3) combining empirical insights into IT consumerization with managerial theories in the IS discipline.

Understanding the function and mechanisms of intestinal cell kinase in the growth and survival of prostate cancer cells

This project was a step forward in discovering the potential role of intestinal cell kinase in prostate cancer development. Intestinal cell kinase was shown to be upregulated in prostate cancer cells and altered expression led to changes in key cell survival proteins. This study used in vitro experiments to monitor changes in cell growth, protein and RNA expression.

Cross-cultural adaptation and validation of the Foot Function Index to Spanish

Background The purpose of this study was to adapt and validate the Foot Function Index to the Spanish (FFI-Sp) following the guidelines of the American Academy of Orthopaedic Surgeons. Methods A cross-sectional study 80 participants with some foot pathology. A statistical analysis was made, including a correlation study with other questionnaires (the Foot Health Status Questionnaire, EuroQol 5-D, Visual Analogue Pain Scale, and the Short Form SF-12 Health Survey). Data analysis included reliability, construct and criterion-related validity and factor analyses. Results The principal components analysis with varimax rotation produced 3 principal factors that explained 80% of the variance. The confirmatory factor analysis showed an acceptable fit with a comparative fit index of 0.78. The FFI-Sp demonstrated excellent internal consistency on the three subscales: pain 0.95; disability 0.96; and activity limitation 0.69, the subscale that scored lowest. The correlation between the FFI-Sp and the other questionnaires was high to moderate. Conclusions The Spanish version of the Foot Function Index (FFI-Sp) is a tool that is a valid and reliable tool with a very good internal consistency for use in the assessment of pain, disability and limitation of the function of the foot, for use both in clinic and research.

A kinematic analysis of the hand function

Background The evaluation of the hand function is an essential element within the clinical practice. The usual assessments are focus on the ability to perform activities of daily life. The inclusion of instruments to measure kinematic variables provides a new approach to the assessment. Inertial sensors adapted to the hand could be used as a complementary instrument to the traditional assessment. Material: clinimetric assessment (Upper Limb Functional Index, Quick Dash), antrophometric variables (eight and weight), dynamometry (palm preasure) was taken. Functional analysis was made with Acceleglove system for the right hand and computer system. The glove has six acceleration sensor, one on each finger and another one on the reverse palm. Method Analytic, transversal approach. Ten healthy subject made six task on evaluation table (tripod pinch, lateral pinch and tip pinch, extension grip, spherical grip and power grip). Each task was made and measure three times, the second one was analyze for the results section. A Matlab script was created for the analysis of each movement and detection phase based on module vector. Results The module acceleration vector offers useful information of the hand function. The data analysis obtained during the performance of functional gestures allows to identify five different phases within the movement, three static phase and tow dynamic, each module vector was allied to one task. Conclusion Module vector variables could be used for the analysis of the different task made by the hand. Inertial sensor could be use as a complement for the traditional assessment system.

Probabilistic multi-tensor estimation using the tensor distribution function

Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. Recently, a number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the Tensor Distribution Function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.

A novel measure of fractional anisotropy based on the tensor distribution function

Fractional anisotropy (FA), a very widely used measure of fiber integrity based on diffusion tensor imaging (DTI), is a problematic concept as it is influenced by several quantities including the number of dominant fiber directions within each voxel, each fiber's anisotropy, and partial volume effects from neighboring gray matter. With High-angular resolution diffusion imaging (HARDI) and the tensor distribution function (TDF), one can reconstruct multiple underlying fibers per voxel and their individual anisotropy measures by representing the diffusion profile as a probabilistic mixture of tensors. We found that FA, when compared with TDF-derived anisotropy measures, correlates poorly with individual fiber anisotropy, and may sub-optimally detect disease processes that affect myelination. By contrast, mean diffusivity (MD) as defined in standard DTI appears to be more accurate. Overall, we argue that novel measures derived from the TDF approach may yield more sensitive and accurate information than DTI-derived measures.

Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function

High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the tradeoff between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusionsensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.

Numerical computation of an Evans function for travelling waves

We demonstrate a geometrically inspired technique for computing Evans functions for the linearised operators about travelling waves. Using the examples of the F-KPP equation and a Keller–Segel model of bacterial chemotaxis, we produce an Evans function which is computable through several orders of magnitude in the spectral parameter and show how such a function can naturally be extended into the continuous spectrum. In both examples, we use this function to numerically verify the absence of eigenvalues in a large region of the right half of the spectral plane. We also include a new proof of spectral stability in the appropriate weighted space of travelling waves of speed c≥sqrt(2δ) in the F-KPP equation.

The tensor distribution function

Diffusion weighted magnetic resonance imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of six directions, second-order tensors (represented by three-by-three positive definite matrices) can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve more complicated white matter configurations, e.g., crossing fiber tracts. Recently, a number of high-angular resolution schemes with more than six gradient directions have been employed to address this issue. In this article, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Using the calculus of variations, we solve the TDF that optimally describes the observed data. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function. Moreover, a tensor orientation distribution function (TOD) may also be derived from the TDF, allowing for the estimation of principal fiber directions and their corresponding eigenvalues.

Single layer lead iodide: Computational exploration of structural, electronic and optical properties, strain induced band modulation and the role of spin-orbital-coupling

Graphitic like layered materials exhibit intriguing electronic structures and thus the search for new types of two-dimensional (2D) monolayer materials is of great interest for developing novel nano-devices. By using density functional theory (DFT) method, here we for the first time investigate the structure, stability, electronic and optical properties of monolayer lead iodide (PbI2). The stability of PbI2 monolayer is first confirmed by phonon dispersion calculation. Compared to the calculation using generalized gradient approximation, screened hybrid functional and spin–orbit coupling effects can not only predicts an accurate bandgap (2.63 eV), but also the correct position of valence and conduction band edges. The biaxial strain can tune its bandgap size in a wide range from 1 eV to 3 eV, which can be understood by the strain induced uniformly change of electric field between Pb and I atomic layer. The calculated imaginary part of the dielectric function of 2D graphene/PbI2 van der Waals type hetero-structure shows significant red shift of absorption edge compared to that of a pure monolayer PbI2. Our findings highlight a new interesting 2D material with potential applications in nanoelectronics and optoelectronics.

Conserved gene structure and function of interleukin-10 in teleost fish

Interleukin-10 (IL-10) is an important immunoregulatory cytokine produced by various types of cells. Researchers describe here the isolation and characterization of olive flounder IL-10 (ofIL-10) cDNA and genomic organization. The ofIL-10 gene encodes a 187 amino acid protein and is composed of a five exon/four intron structure, similar to other known IL-10 genes. The ofIL-10 promoter sequence analysis shows a high level of homology in putative binding sites for transcription factors which are sufficient for transcriptional regulation ofIL-10. Important structural residues are maintained in the ofIL-10 protein including the four cysteines responsible for the two intra-chain disulfide bridges reported for human IL-10 and two extra cysteine residues that exist only in fish species. The phylogenetic analysis clustered ofIL-10 with other fish IL-10s and apart from mammalian IL-10 molecules. Quantitative real-time Polymerase Chain Reaction (PCR) analysis demonstrated ubiquitous ofIL-10 gene expression in the 13 tissues examined. Additionally, the induction of ofIL-10 gene expression was observed in the kidney tissue from olive flounder infected with bacteria (Edawardsiella tarda) or virus (Viral Hemorrhagic Septicemia Virus; VHSV). These data indicate that IL-10 is an important immune regulator that is conserved strictly genomic organization and function during the evolution of vertebrate immunity.