Altered brainstem responses to modafinil in schizophrenia: implications for adjunctive treatment of cognition.

Candidate pro-cognitive drugs for schizophrenia targeting several neurochemical systems have consistently failed to demonstrate robust efficacy. It remains untested whether concurrent antipsychotic medications exert pharmacodynamic interactions that mitigate pro-cognitive action in patients. We used functional MRI (fMRI) in a randomized, double-blind, placebo-controlled within-subject crossover test of single-dose modafinil effects in 27 medicated schizophrenia patients, interrogating brainstem regions where catecholamine systems arise to innervate the cortex, to link cellular and systems-level models of cognitive control. Modafinil effects were evaluated both within this patient group and compared to a healthy subject group. Modafinil modulated activity in the locus coeruleus (LC) and ventral tegmental area (VTA) in the patient group. However, compared to the healthy comparison group, these effects were altered as a function of task demands: the control-independent drug effect on deactivation was relatively attenuated (shallower) in the LC and exaggerated (deeper) in the VTA; in contrast, again compared to the comparison group, the control-related drug effects on positive activation were attenuated in LC, VTA and the cortical cognitive control network. These altered effects in the LC and VTA were significantly and specifically associated with the degree of antagonism of alpha-2 adrenergic and dopamine-2 receptors, respectively, by concurrently prescribed antipsychotics. These sources of evidence suggest interacting effects on catecholamine neurons of chronic antipsychotic treatment, which respectively increase and decrease sustained neuronal activity in LC and VTA. This is the first direct evidence in a clinical population to suggest that antipsychotic medications alter catecholamine neuronal activity to mitigate pro-cognitive drug action on cortical circuits

The Potential for a GPU-Like Overlay Architecture for FPGAs

We propose a soft processor programming model and architecture inspired by graphics processing units (GPUs) that are well-matched to the strengths of FPGAs, namely, highly parallel and pipelinable computation. In particular, our soft processor architecture exploits multithreading, vector operations, and predication to supply a floating-point pipeline of 64 stages via hardware support for up to 256 concurrent thread contexts. The key new contributions of our architecture are mechanisms for managing threads and register files that maximize data-level and instruction-level parallelism while overcoming the challenges of port limitations of FPGA block memories as well as memory and pipeline latency. Through simulation of a system that (i) is programmable via NVIDIA's high-level Cg language, (ii) supports AMD's CTM r5xx GPU ISA, and (iii) is realizable on an XtremeData XD1000 FPGA-based accelerator system, we demonstrate the potential for such a system to achieve 100% utilization of a deeply pipelined floating-point datapath

Wissensmanagement - quo vadis? Case Positions zur Umsetzung in den Unternehmen ; eine selektive Bestandsaufnahme

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Sketch-To-Solution: An Exploration of Viscous CFD with Automatic Grids

Numerical simulation of the Reynolds-averaged NavierStokes (RANS) equations has become a critical tool for the design of aerospace vehicles. However, the issues that affect the grid convergence of three dimensional RANS solutions are not completely understood, as documented in the AIAA Drag Prediction Workshop series. Grid adaption methods have the potential for increasing the automation and discretization error control of RANS solutions to impact the aerospace design and certification process. The realization of the CFD Vision 2030 Study includes automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification by using analysis without demonstration in flight test (i.e., certification or qualification by analysis). For example, the Cart3D inviscid analysis package has automated Cartesian cut-cell gridding with output-based error control. Fueled by recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a similar work flow is explored for viscous CFD simulations; where a CFD application engineer provides geometry, boundary conditions, and flow parameters, and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation

Targeting autophagy: a novel anticancer strategy with therapeutic implications for imatinib resistance

Autophagy is an ancient, intracellular degradative system which plays important roles in regulating protein homeostasis and which is essential for survival when cells are faced with metabolic stress. Increasing evidence suggests that autophagy also functions as a tumor suppressor mechanism that harnesses the growth and/or survival of cells as they transition towards a rapidly dividing malignant state. However, the impact of autophagy on cancer progression and on the efficacy of cancer therapeutics is controversial. In particular, although the induction of autophagy has been reported after treatment with a number of therapeutic agents, including imatinib, this response has variously been suggested to either impair or contribute to the effects of anticancer agents. More recent studies support the notion that autophagy compromises the efficacy of anticancer agents, where agents such as chloroquine (CQ) that impair autophagy augment the anticancer activity of histone deacetylase (HDAC) inhibitors and alkylating agents. Inhibition of autophagy is a particularly attractive strategy for the treatment of imatinib-refractory chronic myelogenous leukemia (CML) since a combination of CQ with the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) compromises the survival of even BCR-ABL-T315I+ imatinib-resistant CML. Additional studies are clearly needed to establish the clinical utility of autophagy inhibitors and to identify patients most likely to benefit from this novel therapeutic approach

Quantum Effects in Neural Networks

We develop the statistical mechanics of the Hopfield model in a transverse field to investigate how quantum fluctuations affect the macroscopic behavior of neural networks. When the number of embedded patterns is finite, the Trotter decomposition reduces the problem to that of a random Ising model. It turns out that the effects of quantum fluctuations on macroscopic variables play the same roles as those of thermal fluctuations. For an extensive number of embedded patterns, we apply the replica method to the Trotter-decomposed system. The result is summarized as a ground-state phase diagram drawn in terms of the number of patterns per site, $\alpha$, and the strength of the transverse field, $\Delta$. The phase diagram coincides very accurately with that of the conventional classical Hopfield model if we replace the temperature T in the latter model by $\Delta$. Quantum fluctuations are thus concluded to be quite similar to thermal fluctuations in determination of the macroscopic behavior of the present model.Comment: 34 pages, LaTeX, 9 PS figures, uses jpsj.st

Using the ionospheric response to the solar eclipse on 20th March 2015 to detect spatial structure in the solar corona

Long-term variability has previously been observed in the relative magnitude of annual and semi-annual variations in the critical frequency (related to the peak electron concentration) of the ionospheric F2 layer (foF2). In this paper we investigate the global patterns in such variability by calculating the time varying power ratio of semi-annual to annual components seen in ionospheric foF2 data sequences from 77 ionospheric monitoring stations around the world. The temporal variation in power ratios observed at each station was then correlated with the same parameter calculated from similar epochs for the Slough/Chilton data set (for which there exists the longest continuous sequence of ionospheric data). This technique reveals strong regional variation in the data, which bears a striking similarity to the regional variation observed in long-term changes to the height of the ionospheric F2 layer. We argue that since both the height and peak density of the ionospheric F2 region are influenced by changes to thermospheric circulation and composition, the observed long-term and regional variability can be explained by such changes. In the absence of long-term measurements of thermospheric composition, detailed modelling work is required to investigate these processes

IKK phosphorylates Huntingtin and targets it for degradation by the proteasome and lysosome

Expansion of the polyglutamine repeat within the protein Huntingtin (Htt) causes Huntington's disease, a neurodegenerative disease associated with aging and the accumulation of mutant Htt in diseased neurons. Understanding the mechanisms that influence Htt cellular degradation may target treatments designed to activate mutant Htt clearance pathways. We find that Htt is phosphorylated by the inflammatory kinase IKK, enhancing its normal clearance by the proteasome and lysosome. Phosphorylation of Htt regulates additional post-translational modifications, including Htt ubiquitination, SUMOylation, and acetylation, and increases Htt nuclear localization, cleavage, and clearance mediated by lysosomal-associated membrane protein 2A and Hsc70. We propose that IKK activates mutant Htt clearance until an age-related loss of proteasome/lysosome function promotes accumulation of toxic post-translationally modified mutant Htt. Thus, IKK activation may modulate mutant Htt neurotoxicity depending on the cell's ability to degrade the modified species