Flow cytometry as a tool to identify Mycobacterium tuberculosis interaction with the immune system and drug susceptibility

Flow cytometric analysis is a useful and widely employed tool to identify immunological alterations caused by different microorganisms, including Mycobacterium tuberculosis. However, this tool can be used for several others analysis. We will discuss some applications for flow cytometry to the study of M. tuberculosis, mainly on cell surface antigens, mycobacterial secreted proteins, their interaction with the immune system using inflammatory cells recovered from peripheral blood, alveolar and pleura spaces and the influence of M. tuberculosis on apoptosis, and finally the rapid determination of drug susceptibility. All of these examples highlight the usefulness of flow cytometry in the study of M. tuber-culosis infection.

Extensional flow of nematic liquid crystal under electric field gradient

Systematic asymptotic methods are used to formulate a model for the extensional flow of a thin sheet of nematic liquid crystal. With no external body forces applied, the model is found to be equivalent to the so-called Trouton model for Newtonian sheets (and fi bers), albeit with a modi fied "Trouton ratio". However, with a symmetry-breaking electric field gradient applied, behavior deviates from the Newtonian case, and the sheet can undergo fi nite-time breakup if a suitable destabilizing field is applied. Some simple exact solutions are presented to illustrate the results in certain idealized limits, as well as sample numerical results to the full model equations.

Hybrid Multiscale Finite Volume method for two-phase flow in porous media

We present a novel hybrid (or multiphysics) algorithm, which couples pore-scale and Darcy descriptions of two-phase flow in porous media. The flow at the pore-scale is described by the Navier?Stokes equations, and the Volume of Fluid (VOF) method is used to model the evolution of the fluid?fluid interface. An extension of the Multiscale Finite Volume (MsFV) method is employed to construct the Darcy-scale problem. First, a set of local interpolators for pressure and velocity is constructed by solving the Navier?Stokes equations; then, a coarse mass-conservation problem is constructed by averaging the pore-scale velocity over the cells of a coarse grid, which act as control volumes; finally, a conservative pore-scale velocity field is reconstructed and used to advect the fluid?fluid interface. The method relies on the localization assumptions used to compute the interpolators (which are quite straightforward extensions of the standard MsFV) and on the postulate that the coarse-scale fluxes are proportional to the coarse-pressure differences. By numerical simulations of two-phase problems, we demonstrate that these assumptions provide hybrid solutions that are in good agreement with reference pore-scale solutions and are able to model the transition from stable to unstable flow regimes. Our hybrid method can naturally take advantage of several adaptive strategies and allows considering pore-scale fluxes only in some regions, while Darcy fluxes are used in the rest of the domain. Moreover, since the method relies on the assumption that the relationship between coarse-scale fluxes and pressure differences is local, it can be used as a numerical tool to investigate the limits of validity of Darcy's law and to understand the link between pore-scale quantities and their corresponding Darcy-scale variables.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.

Heterogeneity of human monocytes: an optimized four-color flow cytometry protocol for analysis of monocyte subsets.

Monocytes are central mediators in the development of atherosclerotic plaques. They circulate in blood and eventually migrate into tissue including the vessel wall where they give rise to macrophages and dendritic cells. The existence of monocyte subsets with distinct roles in homeostasis and inflammation suggests specialization of function. These subsets are identified based on expression of the CD14 and CD16 markers. Routinely applicable protocols remain elusive, however. Here, we present an optimized four-color flow cytometry protocol for analysis of human blood monocyte subsets using a specific PE-Cy5-conjugated monoclonal antibody (mAb) to HLA-DR, a PE-Cy7-conjugated mAb to CD14, a FITC-conjugated mAb to CD16, and PE-conjugated mAbs to additional markers relevant to monocyte function. Classical CD14(+)CD16(-) monocytes (here termed "Mo1" subset) expressed high CCR2, CD36, CD64, and CD62L, but low CX(3)CR1, whereas "nonclassical" CD14(lo)CD16(+) monocytes (Mo3) essentially showed the inverse expression pattern. CD14(+)CD16(+) monocytes (Mo2) expressed high HLA-DR, CD36, and CD64. In patients with stable coronary artery disease (n = 13), classical monocytes were decreased, whereas "nonclassical" monocytes were increased 90% compared with healthy subjects with angiographically normal coronary arteries (n = 14). Classical monocytes from CAD patients expressed higher CX(3)CR1 and CCR2 than controls. Thus, stable CAD is associated with expansion of the nonclassical monocyte subset and increased expression of inflammatory markers on monocytes. Flow cytometric analysis of monocyte subsets and marker expression may provide valuable information on vascular inflammation. This may translate into the identification of monocyte subsets as selective therapeutic targets, thus avoiding adverse events associated with indiscriminate monocyte inhibition.

Error estimate and control in the MSFV method for multiphase flow in porous media

n this paper the iterative MSFV method is extended to include the sequential implicit simulation of time dependent problems involving the solution of a system of pressure-saturation equations. To control numerical errors in simulation results, an error estimate, based on the residual of the MSFV approximate pressure field, is introduced. In the initial time steps in simulation iterations are employed until a specified accuracy in pressure is achieved. This initial solution is then used to improve the localization assumption at later time steps. Additional iterations in pressure solution are employed only when the pressure residual becomes larger than a specified threshold value. Efficiency of the strategy and the error control criteria are numerically investigated. This paper also shows that it is possible to derive an a-priori estimate and control based on the allowed pressure-equation residual to guarantee the desired accuracy in saturation calculation.

Increased flow of fatty acids toward beta-oxidation in developing seeds of Arabidopsis deficient in diacylglycerol acyltransferase activity or synthesizing medium-chain-length fatty acids.

Synthesis of polyhydroxyalkanoates (PHAs) from intermediates of fatty acid beta-oxidation was used as a tool to study fatty acid degradation in developing seeds of Arabidopsis. Transgenic plants expressing a peroxisomal PHA synthase under the control of a napin promoter accumulated PHA in developing seeds to a final level of 0. 06 mg g(-1) dry weight. In plants co-expressing a plastidial acyl-acyl carrier protein thioesterase from Cuphea lanceolata and a peroxisomal PHA synthase, approximately 18-fold more PHA accumulated in developing seeds. The proportion of 3-hydroxydecanoic acid monomer in the PHA was strongly increased, indicating a large flow of capric acid toward beta-oxidation. Furthermore, expression of the peroxisomal PHA synthase in an Arabidopsis mutant deficient in the enzyme diacylglycerol acyltransferase resulted in a 10-fold increase in PHA accumulation in developing seeds. These data indicate that plants can respond to the inadequate incorporation of fatty acids into triacylglycerides by recycling the fatty acids via beta-oxidation and that a considerable flow toward beta-oxidation can occur even in a plant tissue primarily devoted to the accumulation of storage lipids.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

UOC Algorithms

Aplicació per a iPad a mode de repositori de continguts relacionats amb l'ensenyament d'assignatures d'informàtica.

Stage-specific activity of potential antimalarial compounds measured in vitro by flow cytometry in comparison to optical microscopy and hypoxanthine uptake

The evaluation of new antimalarial agents using older methods of monitoring sensitivity to antimalarial drugs are laborious and poorly suited to discriminate stage-specific activity. We used flow cytometry to study the effect of established antimalarial compounds, cysteine protease inhibitors, and a quinolone against asexual stages of Plasmodium falciparum. Cultured P. falciparum parasites were treated for 48 h with different drug concentrations and the parasitemia was determined by flow cytometry methods after DNA staining with propidium iodide. P. falciparum erythrocytic life cycle stages were readily distinguished by flow cytometry. Activities of established and new antimalarial compounds measured by flow cytometry were equivalent to results obtained with microscopy and metabolite uptake assays. The antimalarial activity of all compounds was higher against P. falciparum trophozoite stages. Advantages of flow cytometry analysis over traditional assays included higher throughput for data collection, insight into the stage-specificity of antimalarial activity avoiding use of radioactive isotopes.