58 resultados para Input delayed
Resumo:
To use protein kinase C (PKC) d-knockout mice to investigate the role of PKCd in lesion development and to understand the underlying mechanism of the vascular disease.
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Burkholderia cenocepacia infects patients with cystic fibrosis. We have previously shown that B. cenocepacia can survive in macrophages within membrane vacuoles (BcCVs) that preclude fusion with the lysosome. The bacterial factors involved in B. cenocepacia intracellular survival are not fully elucidated. We report here that deletion of BCAM0628, encoding a predicted low-molecular weight protein tyrosine phosphatase (LMW-PTP) that is restricted to B. cenocepacia strains of the transmissible ET-12 clone, accelerates the maturation of the BcCVs. Compared to parental strain and deletion mutants in other LMW-PTPs that are widely conserved in Burkholderia species, a greater proportion of BcCVs containing the BCAM0628 mutant were targeted to the lysosome. Accelerated BcCV maturation was not due to reduced intracellular viability since BCAM0628 survived and replicated in macrophages similarly to the parental strain. Therefore, BCAM0628 was referred to as dpm (delayed phagosome maturation). We provide evidence that the Dpm protein is secreted during growth in vitro and upon macrophage infection. Dpm secretion requires an N-terminal signal peptide. Heterologous expression of Dpm in B. multivorans confers to this bacterium a similar phagosomal maturation delay as found with B. cenocepacia. We demonstrate that Dpm is an inactive phosphatase, suggesting that its contribution to phagosomal maturation arrest must be unrelated to tyrosine phosphatase activity.
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In a companion paper, Seitenzahl et al. have presented a set of three-dimensional delayed detonation models for thermonuclear explosions of near-Chandrasekhar-mass white dwarfs (WDs). Here,we present multidimensional radiative transfer simulations that provide synthetic light curves and spectra for those models. The model sequence explores both changes in the strength of the deflagration phase (which is controlled by the ignition configuration in our models) and the WD central density. In agreement with previous studies, we find that the strength of the deflagration significantly affects the explosion and the observables. Variations in the central density also have an influence on both brightness and colour, but overall it is a secondary parameter in our set of models. In many respects, the models yield a good match to the observed properties of normal Type Ia supernovae (SNe Ia): peak brightness, rise/decline time-scales and synthetic spectra are all in reasonable agreement. There are, however, several differences. In particular, the models are systematically too red around maximum light, manifest spectral line velocities that are a little too high and yield I-band light curves that do not match observations. Although some of these discrepancies may simply relate to approximations made in the modelling, some pose real challenges to the models. If viewed as a complete sequence, our models do not reproduce the observed light-curve width- luminosity relation (WLR) of SNe Ia: all our models show rather similar B-band decline rates, irrespective of peak brightness. This suggests that simple variations in the strength of the deflagration phase in Chandrasekhar-mass deflagration-to-detonation models do not readily explain the observed diversity of normal SNe Ia. This may imply that some other parameter within the Chandrasekhar-mass paradigm is key to the WLR, or that a substantial fraction of normal SNe Ia arise from an alternative explosion scenario.
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In this paper we propose a design methodology for low-power high-performance, process-variation tolerant architecture for arithmetic units. The novelty of our approach lies in the fact that possible delay failures due to process variations and/or voltage scaling are predicted in advance and addressed by employing an elastic clocking technique. The prediction mechanism exploits the dependence of delay of arithmetic units upon input data patterns and identifies specific inputs that activate the critical path. Under iso-yield conditions, the proposed design operates at a lower scaled down Vdd without any performance degradation, while it ensures a superlative yield under a design style employing nominal supply and transistor threshold voltage. Simulation results show power savings of upto 29%, energy per computation savings of upto 25.5% and yield enhancement of upto 11.1% compared to the conventional adders and multipliers implemented in the 70nm BPTM technology. We incorporated the proposed modules in the execution unit of a five stage DLX pipeline to measure performance using SPEC2000 benchmarks [9]. Maximum area and throughput penalty obtained were 10% and 3% respectively.
Resumo:
Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.
Resumo:
Purpose: To examine whether the levels of micronuclei induction, as a marker for genomic instability in the progeny of X-irradiated cells, correlates with DNA repair function.
Materials and methods: Two repair deficient cell lines (X-ray repair cross-complementing 1 [XRCC1] deficient cell line [EM9] and X-ray repair cross complementing 5 [XRCC5; Ku80] deficient X-ray sensitive Chinese hamster ovary [CHO] cell line [xrs5]) were used in addition to wild-type CHO cells. These cells were irradiated with low doses of X-rays (up to 1 Gy). Seven days after irradiation, micronuclei formed in binucleated cells were counted. To assess the contribution of the bystander effect micronuclei induction was measured in progeny of non-irradiated cells co-cultured with cells that had been irradiated with 1Gy.
Results: The delayed induction of micronuclei in 1 Gy-irradiated cells was observed in normal CHO and EM9 but not in xrs5. In the clone analysis, progenies of xrs5 under bystander conditions showed significantly higher levels of micronuclei, while CHO and EM9 did not.
Conclusion: Genomic instability induced by X-irradiation is associated with DSB (double-strand break) repair, even at low doses. It is also suggested that bystander signals, which lead to genomic instability, may be enhanced when DSB repair is compromised.
Forward Stepwise Ridge Regression (FSRR) based variable selection for highly correlated input spaces
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Discrimination of different species in various target scopes within a single sensing platform can provide many advantages such as simplicity, rapidness, and cost effectiveness. Here we design a three-input colorimetric logic gate based on the aggregation and anti-aggregation of gold nanoparticles (Au NPs) for the sensing of melamine, cysteine, and Hg2+. The concept takes advantages of the highly specific coordination and ligand replacement reactions between melamine, cysteine, Hg2+, and Au NPs. Different outputs are obtained with the combinational inputs in the logic gates, which can serve as a reference to discriminate different analytes within a single sensing platform. Furthermore, besides the intrinsic sensitivity and selectivity of Au NPs to melamine-like compounds, the “INH” gates of melamine/cysteine and melamine/Hg2+ in this logic system can be employed for sensitive and selective detections of cysteine and Hg2+, respectively.
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This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov–Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach.
Resumo:
The non-covalent incorporation of responsive luminescent lanthanide, Ln(iii), complexes with orthogonal outputs from Eu(iii) and Tb(iii) in a gel matrix allows for in situ logic operation with colorimetric outputs. Herein, we report an exemplar system with two inputs ([H(+)] and [F(-)]) within a p(HEMA-co-MMA) polymer organogel acting as a dual-responsive device and identify future potential for such systems.