Single passage in mouse organs enhances the survival and spread of Salmonella enterica.

Intravenous inoculation of Salmonella enterica serovar Typhimurium into mice is a prime experimental model of invasive salmonellosis. The use of wild-type isogenic tagged strains (WITS) in this system has revealed that bacteria undergo independent bottlenecks in the liver and spleen before establishing a systemic infection. We recently showed that those bacteria that survived the bottleneck exhibited enhanced growth when transferred to naive mice. In this study, we set out to disentangle the components of this in vivo adaptation by inoculating mice with WITS grown either in vitro or in vivo. We developed an original method to estimate the replication and killing rates of bacteria from experimental data, which involved solving the probability-generating function of a non-homogeneous birth-death-immigration process. This revealed a low initial mortality in bacteria obtained from a donor animal. Next, an analysis of WITS distributions in the livers and spleens of recipient animals indicated that in vivo-passaged bacteria started spreading between organs earlier than in vitro-grown bacteria. These results further our understanding of the influence of passage in a host on the fitness and virulence of Salmonella enterica and represent an advance in the power of investigation on the patterns and mechanisms of host-pathogen interactions.This work was funded by a Medical Research Council (MRC) grant (G0801161) awarded to AJG, PM and DJM. RD was supported by BBSRC grant BB/I002189/1 awarded to PM. OR is supported by a University Research Fellowship from the Royal Society.This is the final version of the article. It was first available from Royal Society Publishing via http://dx.doi.org/10.1098/rsif.2015.070

Une approche spectrale de la métamodélisation multi-échelle appliquée à la propagation acoustique

There exists many numerical methods to simulate wave propagation through complex media with a very good precision. However, taking into account the fluctuations of the propagation medium necessitates a statistical approach implying a prohibitive numerical cost.To have those studies affordable, we propose the construction of a metamodel based on a polynomial chaos decomposition of normal modes. This approach presents the great advantage to give statistics of signals propagating in a random medium at an affordable numerical cost.Those results are illustrated with acoustic propagation in the atmosphere. In fact, meteorological fluctuations have a critical impact on the propagation, it is therefore essential to take them into account. The numerical cost of a simulation over thousands of kilometers fully justifies the use of a metamodel. An application to source localization is proposed to illustrate the joint use of a metamodel and a bayesian inversion. The bayesian framework allows a resolution of the inverse problem in a probabilistic context able to take into account the fluctuations of the medium and uncertainties due to unknown source localization.De nombreuses méthodes permettent de simuler numériquement la propagation d'une onde dans un milieu complexe avec une excellente précision. Cependant, la prise en compte des fluctuations du milieu de propagation requiert un traitement statistique nécessitant un grand nombre d'appel a des codes de calcul souvent coûteux.Afin de rendre accessible ces études nous proposons la construction d'un métamodèle basé sur une décomposition en polynômes de chaos des modes normaux. Cette approche permet de restituer les statistiques des signaux se propageant dans un milieu aléatoire avec un coût calcul moindre.Les applications proposées dans cette thèse concernent la propagation d'ondes acoustiques dans l'atmosphère terrestre. En effet, les fluctuations météorologiques modifiant considérablement les conditions de propagation, leur prise en compte est indispensable. Le coût numérique de la simulation sur un domaine de plusieurs centaines de milliers de kilomètres carrés justifie pleinement l'utilisation d'un métamodèle.Une application à la localisation de source couplant ces techniques de métamodèlisation avec une approche bayésienne est aussi proposée. En effet, le cadre bayésien permet une résolution du problème inverse dans un cadre probabiliste capable de prendre en compte les fluctuations du milieu et l'incertitude sur la localisation de la source

Acoustic propagation in random media using polynomial chaos expansions

International audienceSound propagation in the atmosphere is highly dependent on the information to specify the waveguide parameters. For real-world applications, there is considerable uncertainty regarding this information, and it is more realistic to consider the wind and temperature profiles as random functions, with associated probability distribution functions. Even though the numerical methods currently-in-use allow accurate results for a given atmosphere, high dimensionality of the random functions severely limits the ability to compute the random process representing the acoustic field, and some form of sampling reduction is necessary. In this work we use polynomial chaos (gPC)-based metamodels to represent the effect of large-scale features onto the acoustic normal modes. The impact of small-scale atmospheric structures is modelled using a perturbative approach of the coupling matrix. This two-level approach allows to estimate the statistical influence of each mode as the frequency varies. An excellent agreement is obtained with the gPC-based propagation model, with a few realizations of the random process, when compared with the Monte Carlo approach, with its thousands of realizations

Infrasound propagation in multiple-scale ran- dom media using generalized polynomial chaos

International audienceInfrasound propagation in realistic environments is highly dependent on the information to specify the waveguide parameters. For real-world applications, there is considerable uncertainty regarding this information, and it is more realistic to consider the wind and temperature profiles as random functions, with associated probability distribution functions reflecting phenomena that are filtered out in the available data. Even though the numerical methods currently-in-use allow accurate results for a given atmosphere, high dimensionality of the random functions severely limits the ability to compute the random process representing the acoustic field, and some form of sampling reduction is necessary. In this work, we use polynomial chaos (gPC)-based metamodels to represent the effect of large-scale atmospheric variability onto the acoustic normal modes. The impact of small-scale atmospheric structures is modelled using a perturbative approach of the coupling matrix. This multi-level approach allows to estimate the statistical influence of each mode as the frequency varies. An excellent agreement is obtained with the gPC-based propagation model, with a few realizations of the random process, when compared with the Monte Carlo approach, with its thousands of realizations. Furthermore, the gPC framework allows computing easily the Sobol indices without supplementary cost, which is essential for sensitivity studies

Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity

Abstract Spiking neural networks (SNNs) are gaining attention due to their energy-efficient computing ability, making them relevant for implementation on low-power neuromorphic hardware. Their biological plausibility has permitted them to benefit from unsupervised learning with bio-inspired plasticity rules, such as spike timing-dependent plasticity (STDP). However, standard STDP has some limitations that make it challenging to implement on hardware. In this paper, we propose a convolutional SNN (CSNN) integrating single-spike integrate-and-fire (SSIF) neurons and trained for the first time with voltage-dependent synaptic plasticity (VDSP), a novel unsupervised and local plasticity rule developed for the implementation of STDP on memristive-based neuromorphic hardware. We evaluated the CSNN on the TIDIGITS dataset, where, helped by our sound preprocessing pipeline, we obtained a performance better than the state of the art, with a mean accuracy of 99.43%. Moreover, the use of SSIF neurons, coupled with time-to-first-spike (TTFS) encoding, results in a sparsely activated model, as we recorded a mean of 5036 spikes per input over the 172 580 neurons of the network. This makes the proposed CSNN promising for the development of models that are extremely efficient in energy. We also demonstrate the efficiency of VDSP on the MNIST dataset, where we obtained results comparable to the state of the art, with an accuracy of 98.56%. Our adaptation of VDSP for SSIF neurons introduces a depression factor that has been very effective at reducing the number of training samples needed, and hence, training time, by a factor of two and more, with similar performance

Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity

International audienceAbstract Spiking neural networks (SNNs) are gaining attention due to their energy-efficient computing ability, making them relevant for implementation on low-power neuromorphic hardware. Their biological plausibility has permitted them to benefit from unsupervised learning with bio-inspired plasticity rules, such as spike timing-dependent plasticity (STDP). However, standard STDP has some limitations that make it challenging to implement on hardware. In this paper, we propose a convolutional SNN (CSNN) integrating single-spike integrate-and-fire (SSIF) neurons and trained for the first time with voltage-dependent synaptic plasticity (VDSP), a novel unsupervised and local plasticity rule developed for the implementation of STDP on memristive-based neuromorphic hardware. We evaluated the CSNN on the TIDIGITS dataset, where, helped by our sound preprocessing pipeline, we obtained a performance better than the state of the art, with a mean accuracy of 99.43%. Moreover, the use of SSIF neurons, coupled with time-to-first-spike (TTFS) encoding, results in a sparsely activated model, as we recorded a mean of 5036 spikes per input over the 172 580 neurons of the network. This makes the proposed CSNN promising for the development of models that are extremely efficient in energy. We also demonstrate the efficiency of VDSP on the MNIST dataset, where we obtained results comparable to the state of the art, with an accuracy of 98.56%. Our adaptation of VDSP for SSIF neurons introduces a depression factor that has been very effective at reducing the number of training samples needed, and hence, training time, by a factor of two and more, with similar performance

Carotenoids: Experimental Ionization Energies and Capacity at Inhibiting Lipid Peroxidation in a Chemical Model of Dietary Oxidative Stress

Carotenoids are important natural pigments and micronutrients contributing to health prevention by several mechanisms, including their electron-donating (antioxidant) activity. In this work, a large series of carotenoids, including 11 carotenes and 14 xanthophylls, have been investigated by wavelength-resolved atmospheric pressure photoionization mass spectrometry (DISCO line of SOLEIL synchrotron), thus allowing the experimental determination of their ionization energy (IE) for the first time. On the other hand, the carotenoids have been also investigated for their ability to inhibit the heme iron-induced peroxidation of linoleic acid in mildly acidic micelles, a simple but relevant chemical model of oxidative stress in the gastric compartment. Thus, the carotenoids can be easily classified from IC₅₀ concentrations deduced from the time dependence of the lipid hydroperoxide concentration. With a selection of two carotenes and three xanthophylls a quantitative analysis is also provided to extract stoichio-kinetic parameters. The influence of the carotenoid structure (number of conjugated carbon–carbon double bonds, presence of terminal six-membered rings, hydroxyl, keto, and/or epoxy groups) on the IE, IC₅₀, and stoichio-kinetic parameters is discussed in details. The data show that the antioxidant activity of carotenes is well correlated to their electron-donating capacity, which itself largely depends on the length of the conjugated polyene chain. By contrast, the IE of xanthophylls is poorly correlated to the polyene chain length because of the strong, and sometimes unexpected, electronic effects of the O-atoms. Although IE remains an approximate predictor of the antioxidant activity of xanthophylls, other factors (interaction with the aqueous phase, competing radical-scavenging mechanisms, the residual activity of the antioxidant’s oxidation products) probably play a significant role

Contact-less phonon detection with massive cryogenic absorbers

International audienceWe have developed a contactless technique for the real time measurement of athermal (Cooper-pair breaking) phonons in an absorber held at subkelvin temperatures. In particular, a thin-film aluminum superconducting resonator was realized on a 30 g high-resistivity silicon crystal. The lumped-element resonator is inductively excited/readout by a radio frequency microstrip feed-line deposited on another wafer; the sensor, a kinetic inductance detector, is readout without any physical contact or wiring to the absorber. The resonator demonstrates excellent electrical properties, particularly in terms of its internal quality factor. The detection of alphas and gammas in the massive absorber is achieved, with an RMS energy resolution of about 1.4 keV, which is already interesting for particle physics applications. The resolution of this prototype detector is mainly limited by the low ( ≈ 0.3 %) conversion efficiency of deposited energy to superconducting excitations (quasiparticles). The demonstrated technique can be further optimized and used to produce large arrays of athermal phonon detectors, for use in rare event searches such as dark matter direct detection, neutrinoless double beta decay, or coherent elastic neutrino-nucleus scattering

CONCERTO : Digital processing for finding and tuning LEKIDs

We describe the on-line algorithms developed to probe Lumped Element Kinetic Inductance Detectors (LEKID) in this paper. LEKIDs are millimeter wavelength detectors for astronomy. LEKID arrays are currently operated in different instruments as: NIKA2 at the IRAM telescope in Spain, KISS at the Teide Observatory telescope in Tenerife, and CONCERTO at the APEX 12-meter telescope in Chile. LEKIDs are superconducting microwave resonators able to detect the incoming light at millimeter wavelengths and they are well adapted for frequency multiplexing (currently up to 360 pixels on a single microwave guide). Nevertheless, their use for astronomical observations requires specific readout and acquisition systems both to deal with the instrumental and multiplexing complexity, and to adapt to the observational requirements (e.g. fast sampling rate, background variations, on-line calibration, photometric accuracy, etc). This paper presents the different steps of treatment from identifying the resonance frequency of each LEKID to the continuous automatic control of drifting LEKID resonance frequencies induced by background variations