Power-gated MOS current mode logic (PG-MCML): a power aware DPA-resistant standard cell library

MOS Current Mode Logic (MCML) is one of the most promising logic style to counteract power analysis attacks. Unfortunately, the static power consumption of MCML standard cells is significantly higher compared to equivalent functions implemented using static CMOS logic. As a result, the use of such a logic style is very limited in portable devices. Paradoxically, these devices are the most sensitive to physical attacks, thus the ones which would benefit more from the adoption of MCML

An Investigation of the Ionic Conductivity and Species Crossover of Lithiated Nafion 117 in Nonaqueous Electrolytes

Nonaqueous redox flow batteries are a fast-growing area of research and development motivated by the need to develop low-cost energy storage systems. The identification of a highly conductive, yet selective membrane, is of paramount importance to enabling such a technology. Herein, we report the swelling behavior, ionic conductivity, and species crossover of lithiated Nafion 117 membranes immersed in three nonaqueous electrolytes (PC, PC : EC, and DMSO). Our results show that solvent volume fraction within the membrane has the greatest effect on both conductivity and crossover. An approximate linear relationship between diffusive crossover of neutral redox species (ferrocene) and the ionic conductivity of membrane was observed. As a secondary effect, the charge on redox species modifies crossover rates in accordance with Donnan exclusion. The selectivity of membrane is derived mathematically and compared to experimental results reported here. The relatively low selectivity for lithiated Nafion 117 in nonaqueous conditions suggests that new membranes are required for competitive nonaqueous redox flow batteries to be realized. Potential design rules are suggested for the future membrane engineering work.United States. Dept. of Energy. Office of Basic Energy Sciences. Joint Center for Energy Storage Researc

Characterization of Fabric-to-Fabric Friction: Application to Medical Compression Bandages

Fabric-to-fabric friction is involved in the action mechanism of medical compression devices such as compression bandages or lumbar belts. To better understand the action of such devices, it is essential to characterize, in their use conditions (mainly pressure and stretch), the frictional properties of the fabrics they are composed of. A characterization method of fabric-to-fabric friction was developed. This method was based on the customization of the fourth instrument of the Kawabata Evaluation System, initially designed for fabric roughness and friction characterization. A friction contactor was developed so that the stretch of the fabric and the applied load can vary to replicate the use conditions. This methodology was implemented to measure the friction coefficient of several medical compression bandages. In the ranges of pressure and bandage stretch investigated in the study, bandage-to-bandage friction coefficient showed very little variation. This simple and reliable method, which was tested for commercially available medical compression bandages, could be used for other medical compression fabrics

From Spiking Neuron Models to Linear-Nonlinear Models

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates

Modulation of defense signal transduction by flagellin-induced WRKY41 transcription factor in Arabidopsis thaliana

Flagellin, a component of the flagellar filament of Pseudomonas syringae pv. tabaci 6605 (Pta), induces hypersensitive reaction in its non-host Arabidopsis thaliana. We identified the WRKY41 gene, which belongs to a multigene family encoding WRKY plant-specific transcription factors, as one of the flagellin-inducible genes in A. thaliana. Expression of WRKY41 is induced by inoculation with the incompatible pathogen P. syringae pv. tomato DC3000 (Pto) possessing AvrRpt2 and the non-host pathogens Pta within 6-h after inoculation, but not by inoculation with the compatible Pto. Expression of WRKY41 was also induced by inoculation of A. thaliana with an hrp-type three secretion system (T3SS)-defective mutant of Pto, indicating that effectors produced by T3SS in the Pto wild-type suppress the activation of WRKY41. Arabidopsis overexpressing WRKY41 showed enhanced resistance to the Pto wild-type but increased susceptibility to Erwinia carotovora EC1. WRKY41-overexpressing Arabidopsis constitutively expresses the PR5 gene, but suppresses the methyl jasmonate-induced PDF1.2 gene expression. These results demonstrate that WRKY41 may be a key regulator in the cross talk of salicylic acid and jasmonic acid pathways.</p

The Plant Pathogen Pseudomonas syringae pv. tomato Is Genetically Monomorphic and under Strong Selection to Evade Tomato Immunity

Recently, genome sequencing of many isolates of genetically monomorphic bacterial human pathogens has given new insights into pathogen microevolution and phylogeography. Here, we report a genome-based micro-evolutionary study of a bacterial plant pathogen, Pseudomonas syringae pv. tomato. Only 267 mutations were identified between five sequenced isolates in 3,543,009 nt of analyzed genome sequence, which suggests a recent evolutionary origin of this pathogen. Further analysis with genome-derived markers of 89 world-wide isolates showed that several genotypes exist in North America and in Europe indicating frequent pathogen movement between these world regions. Genome-derived markers and molecular analyses of key pathogen loci important for virulence and motility both suggest ongoing adaptation to the tomato host. A mutational hotspot was found in the type III-secreted effector gene hopM1. These mutations abolish the cell death triggering activity of the full-length protein indicating strong selection for loss of function of this effector, which was previously considered a virulence factor. Two non-synonymous mutations in the flagellin-encoding gene fliC allowed identifying a new microbe associated molecular pattern (MAMP) in a region distinct from the known MAMP flg22. Interestingly, the ancestral allele of this MAMP induces a stronger tomato immune response than the derived alleles. The ancestral allele has largely disappeared from today's Pto populations suggesting that flagellin-triggered immunity limits pathogen fitness even in highly virulent pathogens. An additional non-synonymous mutation was identified in flg22 in South American isolates. Therefore, MAMPs are more variable than expected differing even between otherwise almost identical isolates of the same pathogen strain

Deletions in the Repertoire of Pseudomonas syringae pv. tomato DC3000 Type III Secretion Effector Genes Reveal Functional Overlap among Effectors

The γ-proteobacterial plant pathogen Pseudomonas syringae pv. tomato DC3000 uses the type III secretion system to inject ca. 28 Avr/Hop effector proteins into plants, which enables the bacterium to grow from low inoculum levels to produce bacterial speck symptoms in tomato, Arabidopsis thaliana, and (when lacking hopQ1-1) Nicotiana benthamiana. The effectors are collectively essential but individually dispensable for the ability of the bacteria to defeat defenses, grow, and produce symptoms in plants. Eighteen of the effector genes are clustered in six genomic islands/islets. Combinatorial deletions involving these clusters and two of the remaining effector genes revealed a redundancy-based structure in the effector repertoire, such that some deletions diminished growth in N. benthamiana only in combination with other deletions. Much of the ability of DC3000 to grow in N. benthamiana was found to be due to five effectors in two redundant-effector groups (REGs), which appear to separately target two high-level processes in plant defense: perception of external pathogen signals (AvrPto and AvrPtoB) and deployment of antimicrobial factors (AvrE, HopM1, HopR1). Further support for the membership of HopR1 in the same REG as AvrE was gained through bioinformatic analysis, revealing the existence of an AvrE/DspA/E/HopR effector superfamily, which has representatives in virtually all groups of proteobacterial plant pathogens that deploy type III effectors

Combined assessment of DYRK1A, BDNF and homocysteine levels as diagnostic marker for Alzheimer’s disease

Early identification of Alzheimer’s disease (AD) risk factors would aid development of interventions to delay the onset of dementia, but current biomarkers are invasive and/or costly to assess. Validated plasma biomarkers would circumvent these challenges. We previously identified the kinase DYRK1A in plasma. To validate DYRK1A as a biomarker for AD diagnosis, we assessed the levels of DYRK1A and the related markers brain-derived neurotrophic factor (BDNF) and homocysteine in two unrelated AD patient cohorts with age-matched controls. Receiver-operating characteristic curves and logistic regression analyses showed that combined assessment of DYRK1A, BDNF and homocysteine has a sensitivity of 0.952, a specificity of 0.889 and an accuracy of 0.933 in testing for AD. The blood levels of these markers provide a diagnosis assessment profile. Combined assessment of these three markers outperforms most of the previous markers and could become a useful substitute to the current panel of AD biomarkers. These results associate a decreased level of DYRK1A with AD and challenge the use of DYRK1A inhibitors in peripheral tissues as treatment. These measures will be useful for diagnosis purposes.This work was supported by the FEANS. We acknowledge the platform accommodation and animal testing of the animal facility at the Institute Jacques-Monod (University Paris Diderot) and the FlexStation3 facility of the Functional and Adaptive Biology (BFA) LaboratoryPeer reviewe

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein–Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein–Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential

Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity [... however,] even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. [...] Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation.Comment: 49 pages, 11 figures, 7 table