Quantification of the effects of antibodies on the extra- and intracellular dynamics of Salmonella enterica.

Antibodies are known to be essential in controlling Salmonella infection, but their exact role remains elusive. We recently developed an in vitro model to investigate the relative efficiency of four different human immunoglobulin G (IgG) subclasses in modulating the interaction of the bacteria with human phagocytes. Our results indicated that different IgG subclasses affect the efficacy of Salmonella uptake by human phagocytes. In this study, we aim to quantify the effects of IgG on intracellular dynamics of infection by combining distributions of bacterial numbers per phagocyte observed by fluorescence microscopy with a mathematical model that simulates the in vitro dynamics. We then use maximum likelihood to estimate the model parameters and compare them across IgG subclasses. The analysis reveals heterogeneity in the division rates of the bacteria, strongly suggesting that a subpopulation of intracellular Salmonella, while visible under the microscope, is not dividing. Clear differences in the observed distributions among the four IgG subclasses are best explained by variations in phagocytosis and intracellular dynamics. We propose and compare potential factors affecting the replication and death of bacteria within phagocytes, and we discuss these results in the light of recent findings on dormancy of Salmonella.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Fluorine redistribution and incorporation during solid phase epitaxy of preamorphized Si

The redistribution of fluorine during solid phase epitaxial regrowth (SPER) of preamorphized Si has been experimentally investigated, explained, and simulated, for different F concentrations and temperatures. We demonstrate, by a detailed analysis and modeling of F secondary ion mass spectrometry chemical-concentration profiles, that F segregates in amorphous Si during SPER by splitting in three possible states: (i) a diffusive one that migrates in amorphous Si; (ii) an interface segregated state evidenced by the presence of a F accumulation peak at the amorphous-crystal interface; (iii) a clustered F state. The interplay among these states and their roles in the F incorporation into crystalline Si are fully described. It is shown that diffusive F migrates by a trap limited diffusion mechanism and also interacts with the advancing interface by a sticking-release dynamics that regulates the amount of F segregated at the interface. We demonstrate that this last quantity determines the regrowth rate through an exponential law. On the other hand we show that neither the diffusive F nor the one segregated at the interface can directly incorporate into the crystal but F has to cluster in the amorphous phase before being incorporated in the crystal, in agreement with recent experimental observations. The trends of the model parameters as a function of the temperature are shown and discussed obtaining a clear energetic scheme of the F redistribution and incorporation in preamorphized Si. The above physical understanding and the model could have a strong impact on the use of F as a tool for optimizing the doping profiles in the fabrication of ultrashallow junctions. © 2010 The American Physical Society.

Occlusive Components Analysis

We study unsupervised learning in a probabilistic generative model for occlusion. The model uses two types of latent variables: one indicates which objects are present in the image, and the other how they are ordered in depth. This depth order then determines how the positions and appearances of the objects present, specified in the model parameters, combine to form the image. We show that the object parameters can be learnt from an unlabelled set of images in which objects occlude one another. Exact maximum-likelihood learning is intractable. However, we show that tractable approximations to Expectation Maximization (EM) can be found if the training images each contain only a small number of objects on average. In numerical experiments it is shown that these approximations recover the correct set of object parameters. Experiments on a novel version of the bars test using colored bars, and experiments on more realistic data, show that the algorithm performs well in extracting the generating causes. Experiments based on the standard bars benchmark test for object learning show that the algorithm performs well in comparison to other recent component extraction approaches. The model and the learning algorithm thus connect research on occlusion with the research field of multiple-causes component extraction methods.

Reynolds number effects on scalar dissipation rate transport and its modeling in turbulent premixed combustion

The effects of turbulent Reynolds number, Ret, on the transport of scalar dissipation rate of reaction progress variable in the context of Reynolds averaged Navier-Stokes simulations have been analyzed using three-dimensional simplified chemistry-based direct numerical simulation (DNS) data of freely propagating turbulent premixed flames with different values of Ret. Scaling arguments have been used to explain the effects of Ret on the turbulent transport, scalar-turbulence interaction, and the combined reaction and molecular dissipation terms. Suitable modifications to the models for these terms have been proposed to account for Ret effects, and the model parameters include explicit Ret dependence. These expressions approach expected asymptotic limits for large values of Ret. However, turbulent Reynolds number Ret does not seem to have any major effects on the modeling of the term arising from density variation. Copyright © Taylor and Francis Group, LLC.

Estimation of soil properties and deformations in staged constructions based on mcmc method

This paper presents a Bayesian probabilistic framework to assess soil properties and model uncertainty to better predict excavation-induced deformations using field deformation data. The potential correlations between deformations at different depths are accounted for in the likelihood function needed in the Bayesian approach. The proposed approach also accounts for inclinometer measurement errors. The posterior statistics of the unknown soil properties and the model parameters are computed using the Delayed Rejection (DR) method and the Adaptive Metropolis (AM) method. As an application, the proposed framework is used to assess the unknown soil properties of multiple soil layers using deformation data at different locations and for incremental excavation stages. The developed approach can be used for the design of optimal revisions for supported excavation systems. © 2010 ASCE.

Low voltage ride-through of DFIG and brushless DFIG: Similarities and differences

The brushless doubly fed induction generator (BDFIG) has been proposed as a viable alternative in wind turbines to the commonly used doubly fed induction generator (DFIG). The BDFIG retains the benefits of the DFIG, i.e. variable speed operation with a partially rated converter, but without the use of brush gear and slip rings, thereby conferring enhanced reliability. As low voltage ride-through (LVRT) performance of the DFIG-based wind turbine is well understood, this paper aims to analyze LVRT behavior of the BDFIG-based wind turbine in a similar way. In order to achieve this goal, the equivalence between their two-axis model parameters is investigated. The variation of flux linkages, back-EMFs and currents of both types of generator are elaborated during three phase voltage dips. Moreover, the structural differences between the two generators, which lead to different equivalent parameters and hence different LVRT capabilities, are investigated. The analytical results are verified via time-domain simulations for medium size wind turbine generators as well as experimental results of a voltage dip on a prototype 250 kVA BDFIG. © 2014 Elsevier B.V.

Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.

Modelling of submarine landslides and generated water waves

The slide of unstable sedimentary bodies and their hydraulic effects are studied by numerical means. A two-dimensional fluid mechanics model based on Navier-Stokes equations has been developed considering the sediments and water as a mixture. Viscoplastic and diffusion laws for the sediments have been introduced into the model. The numerical model is validated with an analytical solution for a Bingham flow. Laboratory experiments consisting in the slide of gravel mass have been carried out. The results of these experiments have shown the importance of the sediment rheology and the diffusion. The model parameters are adjusted by trial and error to match the observed “sandflow”.

并行计算模型参数动态分析软件包设计

并行计算模型的发展引入越来越多的模型参数。对并行计算模型参数动态采集分析软件包DEMPAT的整体框架进行研究,实现基于硬件性能计数器的存储层次参数采集模块。实验表明,该模块能够准确快速地获取存储层次参数且具有较好的可移植性。

并行计算模型参数动态采集分析软件工具包DEMPAT的设计初探

根据目标平台体系结构尤其是存储系统组织结构的差异，并行计算模型可以分成三代：共享存储并行计算模型、分布存储并行计算模型和层次存储并行计算模型。并行计算模型从第一代发展到第三代的过程中引入了越来越多的模型参数，而且随着处理器与存储系统之间速度差的不断增大，把存储层次引入并行计算模型成为一个新的发展趋势。这在提高模型分析精确度的同时也导致了模型的分析工作越来越复杂，因此有必要设计一个能够对并行计算模型参数进行半自动的获取和分析工具包，以便辅助模型分析工作。 本文首先介绍并行计算模型的发展过程和每个模型涉及到的模型参数，在此基础上提出了我们的并行计算模型参数动态采集分析软件工具包（DEMPAT）及其整体框架，并对现有的基准测试工具进行了简要介绍。我们着重分析了存储层次参数采集模块的设计理论和方法，实现了基于PAPI的高速缓存和TLB参数采集工具，并在主流的平台上进行了相关实验。另外我们采集了两种三角矩阵求逆算法的动态访存行为和浮点运算次数，揭示了存储访问复杂性对算法性能的影响。从实验结果可以看出我们的采集工具具有较好的可用性和精确性，可以作为并行计算模型参数动态采集分析工具中一个重要的组成部分。

Quasi-Elastic Scattering of C-16 from C-12 at 47.5 MeV/Nucleon

Differential cross sections for the quasi-elastic scattering of C-16 at 47.5 MeV/nucleon from C-12 target are measured. Coupled-channels calculations are carried out and the optical potential parameters are obtained by fitting the experimental angular distribution.

Neutron spectroscopic factors of Ar-34 and Ar-46 from (p,d) transfer reactions

Single-neutron-transfer measurements using (p,d) reactions have been performed at 33 MeV per nucleon with proton-rich Ar-34 and neutron-rich Ar-46 beams in inverse kinematics. The extracted spectroscopic factors are compared to the large-basis shell-model calculations. Relatively weak quenching of the spectroscopic factors is observed between Ar-34 and Ar-46. The experimental results suggest that neutron correlations have a weak dependence on the asymmetry of the nucleus over this isotopic region. The present results are consistent with the systematics established from extensive studies of spectroscopic factors and dispersive optical-model analyses of Ca40-49 isotopes. They are, however, inconsistent with the trends obtained in knockout-reaction measurements.

Elastic Scattering of He-6 + p at 82.3 MeV/nucleon

Differential cross sections for the elastic scattering of halo nucleus He-6 on proton target were measured at 82.3 MeV/u. The experimental results are well reproduced by optical model calculations using global potential KD02 with a reduction of the depth of real volume part by a factor of 0.7. A systematic analysis shows that this behavior might be related to the weakly bound property of unstable nuclei.

Surface pigments,algal biomass profiles,and potential production of the euphotic layer:Relationships reinvestigated in view of remote-sensing applications

Maps of surface chlorophyllous pigment (Chl a + Pheo a) are currently produced from ocean color sensors. Transforming such maps into maps of primary production can be reliably done only by using light-production models in conjuction with additional information about the column-integrated pigment content and its vertical distribution. As a preliminary effort in this direction. $\ticksim 4,000$ vertical profiles pigment (Chl a + Pheo a) determined only in oceanic Case 1 waters have been statistically analyzed. They were scaled according to dimensionless depths (actual depth divided by the depth of the euphotic layer, $Z_e$) and expressed as dimensionless concentrations (actual concentration divided by the mean concentration within the euphotic layer). The depth $Z_e$ generally unknown, was computed with a previously develop bio-optical model. Highly sifnificant relationships were found allowing $\langle C \rangle_tot$, the pigment content of the euphotic layer, to be inferred from the surface concentration, $\bar C_pd$, observed within the layer of one penetration depth. According to their $\bar C_pd$ values (ranging from $0.01 to > 10 mg m^-3$), we categorized the profiles into seven trophic situations and computed a mean vertical profile for each. Between a quasi-uniform profile in eutrophic waters and a profile with a strong deep maximum in oligotrophic waters, the shape evolves rather regularly. The wellmixed cold waters, essentially in the Antarctic zone, have been separately examined. On average, their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values of $ρ$, the ratio of Chl a tp (Chl a + Pheo a), have also been obtained for each trophic category. The energy stored by photosynthesizing algae, once normalized with respect to the integrated chlorophyll biomass $\langle C \rangle _tot $ is proportional to the available photosythetic energy at the surface via a parameter $ψ∗$ which is the cross-section for photosynthesis per unit of areal chlorophyll. By tanking advantage of the relative stability of $ψ∗.$ we can compute primary production from ocean color data acquired from space. For such a computation, inputs are the irradiance field at the ocean surface, the "surface" pigment from which $\langle C \rangle _tot$ can be derived, the mean $ρ value pertinent to the trophic situation as depicted by the $\bar C_pd or $\langle C \rangle _tot$ values, and the cross-section $ψ∗$. Instead of a contant $ψ∗.$ value, the mean profiles can be used; they allow the climatological field of the $ψ∗.$ parameter to be adjusted through the parallel use of a spectral light-production model.