IL-28A (IFN-λ2) modulates lung DC function to promote Th1 immune skewing and suppress allergic airway disease.

IL-28 (IFN-λ) cytokines exhibit potent antiviral and antitumor function but their full spectrum of activities remains largely unknown. Recently, IL-28 cytokine family members were found to be profoundly down-regulated in allergic asthma. We now reveal a novel role of IL-28 cytokines in inducing type 1 immunity and protection from allergic airway disease. Treatment of wild-type mice with recombinant or adenovirally expressed IL-28A ameliorated allergic airway disease, suppressed Th2 and Th17 responses and induced IFN-γ. Moreover, abrogation of endogenous IL-28 cytokine function in IL-28Rα(-/-) mice exacerbated allergic airway inflammation by augmenting Th2 and Th17 responses, and IgE levels. Central to IL-28A immunoregulatory activity was its capacity to modulate lung CD11c(+) dendritic cell (DC) function to down-regulate OX40L, up-regulate IL-12p70 and promote Th1 differentiation. Consistently, IL-28A-mediated protection was absent in IFN-γ(-/-) mice or after IL-12 neutralization and could be adoptively transferred by IL-28A-treated CD11c(+) cells. These data demonstrate a critical role of IL-28 cytokines in controlling T cell responses in vivo through the modulation of lung CD11c(+) DC function in experimental allergic asthma. →See accompanying Closeup by Michael R Edwards and Sebastian L Johnston http://dx.doi.org/10.1002/emmm.201100143.

Fonction ventriculaire gauche après revascularisation pour occlusion d'une coronaire d'une durée de 1-2 heures [Left ventricular function following revascularization for occlusion of a coronary artery lasting 1 to 2 hours].

The time-lag between coronary occlusion and irreversible damage to the myocardium is ill-defined in man. In 10 patients the changes in left ventricular function have been studied after coronary occlusion during diagnostic or therapeutic cardiac catheterization of 1-2 hours' duration. Revascularization was achieved either surgically or through intracoronary streptokinase infusion. The interval between occlusion and onset of extracorporal circulation or reopening was 61 to 119 minutes. Despite enzyme elevation (CPK, CK-MB, SGOT) and appearance of Q-waves in 5 patients, no significant alteration of left ventricular function was noted on repeat cardiac catheterization 10 to 230 days after the accident. These observations, suggest that coronary occlusion of 1-2 hours' duration fails to produce significant irreversible damage to the myocardium despite electrocardiographic and enzymatic signs of myocardial infarction.

A novel Nrf2-miR-29-desmocollin-2 axis regulates desmosome function in keratinocytes.

The Nrf2 transcription factor controls the expression of genes involved in the antioxidant defense system. Here, we identified Nrf2 as a novel regulator of desmosomes in the epidermis through the regulation of microRNAs. On Nrf2 activation, expression of miR-29a and miR-29b increases in cultured human keratinocytes and in mouse epidermis. Chromatin immunoprecipitation identified the Mir29ab1 and Mir29b2c genes as direct Nrf2 targets in keratinocytes. While binding of Nrf2 to the Mir29ab1 gene activates expression of miR-29a and -b, the Mir29b2c gene is silenced by DNA methylation. We identified desmocollin-2 (Dsc2) as a major target of Nrf2-induced miR-29s. This is functionally important, since Nrf2 activation in keratinocytes of transgenic mice causes structural alterations of epidermal desmosomes. Furthermore, the overexpression of miR-29a/b or knockdown of Dsc2 impairs the formation of hyper-adhesive desmosomes in keratinocytes, whereas Dsc2 overexpression has the opposite effect. These results demonstrate that a novel Nrf2-miR-29-Dsc2 axis controls desmosome function and cutaneous homeostasis.

Probabilistic electrical resistivity tomography of a CO2 sequestration analog

Electrical resistivity tomography (ERT) is a well-established method for geophysical characterization and has shown potential for monitoring geologic CO2 sequestration, due to its sensitivity to electrical resistivity contrasts generated by liquid/gas saturation variability. In contrast to deterministic inversion approaches, probabilistic inversion provides the full posterior probability density function of the saturation field and accounts for the uncertainties inherent in the petrophysical parameters relating the resistivity to saturation. In this study, the data are from benchtop ERT experiments conducted during gas injection into a quasi-2D brine-saturated sand chamber with a packing that mimics a simple anticlinal geological reservoir. The saturation fields are estimated by Markov chain Monte Carlo inversion of the measured data and compared to independent saturation measurements from light transmission through the chamber. Different model parameterizations are evaluated in terms of the recovered saturation and petrophysical parameter values. The saturation field is parameterized (1) in Cartesian coordinates, (2) by means of its discrete cosine transform coefficients, and (3) by fixed saturation values in structural elements whose shape and location is assumed known or represented by an arbitrary Gaussian Bell structure. Results show that the estimated saturation fields are in overall agreement with saturations measured by light transmission, but differ strongly in terms of parameter estimates, parameter uncertainties and computational intensity. Discretization in the frequency domain (as in the discrete cosine transform parameterization) provides more accurate models at a lower computational cost compared to spatially discretized (Cartesian) models. A priori knowledge about the expected geologic structures allows for non-discretized model descriptions with markedly reduced degrees of freedom. Constraining the solutions to the known injected gas volume improved estimates of saturation and parameter values of the petrophysical relationship. (C) 2014 Elsevier B.V. All rights reserved.

Conditioning of stochastic 3-D fracture networks to hydrological and geophysical data

The geometry and connectivity of fractures exert a strong influence on the flow and transport properties of fracture networks. We present a novel approach to stochastically generate three-dimensional discrete networks of connected fractures that are conditioned to hydrological and geophysical data. A hierarchical rejection sampling algorithm is used to draw realizations from the posterior probability density function at different conditioning levels. The method is applied to a well-studied granitic formation using data acquired within two boreholes located 6 m apart. The prior models include 27 fractures with their geometry (position and orientation) bounded by information derived from single-hole ground-penetrating radar (GPR) data acquired during saline tracer tests and optical televiewer logs. Eleven cross-hole hydraulic connections between fractures in neighboring boreholes and the order in which the tracer arrives at different fractures are used for conditioning. Furthermore, the networks are conditioned to the observed relative hydraulic importance of the different hydraulic connections by numerically simulating the flow response. Among the conditioning data considered, constraints on the relative flow contributions were the most effective in determining the variability among the network realizations. Nevertheless, we find that the posterior model space is strongly determined by the imposed prior bounds. Strong prior bounds were derived from GPR measurements and helped to make the approach computationally feasible. We analyze a set of 230 posterior realizations that reproduce all data given their uncertainties assuming the same uniform transmissivity in all fractures. The posterior models provide valuable statistics on length scales and density of connected fractures, as well as their connectivity. In an additional analysis, effective transmissivity estimates of the posterior realizations indicate a strong influence of the DFN structure, in that it induces large variations of equivalent transmissivities between realizations. The transmissivity estimates agree well with previous estimates at the site based on pumping, flowmeter and temperature data.

Preserved capillary density of dorsal finger skin in treated hypertensive patients with or without type 2 diabetes.

OBJECTIVES: Capillary rarefaction is a hallmark of untreated hypertension. Recent data indicate that rarefaction may be reversed by antihypertensive treatment in nondiabetic hypertensive patients. Despite the frequent association of diabetes with hypertension, nothing is known on the capillary density of treated diabetic patients with hypertension. METHODS: We enrolled 21 normotensive healthy, 25 hypertensive only, and 21 diabetic (type 2) hypertensive subjects. All hypertensive patients were treated with a blocker of the renin-angiotensin system, and a majority had a home blood pressure ≤135/85 mmHg. Capillary density was assessed with videomicroscopy on dorsal finger skin and with laser Doppler imaging on forearm skin (maximal vasodilation elicited by local heating). RESULTS: There was no difference between any of the study groups in either dorsal finger skin capillary density (controls 101 ± 11 capillaries/mm(2) , nondiabetic hypertensive 99 ± 16, diabetic hypertensive 96 ± 18, p > 0.5) or maximal blood flow in forearm skin (controls 666 ± 114 perfusion units, nondiabetic hypertensive 612 ± 126, diabetic hypertensive 620 ± 103, p > 0.5). CONCLUSIONS: Irrespective of the presence or not of type 2 diabetes, capillary density is normal in hypertensive patients with reasonable control of blood pressure achieved with a blocker of the renin-angiotensin system.

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.

Lifelong dynamics of human CD4+CD25+ regulatory T cells: insights from in vivo data and mathematical modeling.

Despite their limited proliferation capacity, regulatory T cells (T(regs)) constitute a population maintained over the entire lifetime of a human organism. The means by which T(regs) sustain a stable pool in vivo are controversial. Using a mathematical model, we address this issue by evaluating several biological scenarios of the origins and the proliferation capacity of two subsets of T(regs): precursor CD4(+)CD25(+)CD45RO(-) and mature CD4(+)CD25(+)CD45RO(+) cells. The lifelong dynamics of T(regs) are described by a set of ordinary differential equations, driven by a stochastic process representing the major immune reactions involving these cells. The model dynamics are validated using data from human donors of different ages. Analysis of the data led to the identification of two properties of the dynamics: (1) the equilibrium in the CD4(+)CD25(+)FoxP3(+)T(regs) population is maintained over both precursor and mature T(regs) pools together, and (2) the ratio between precursor and mature T(regs) is inverted in the early years of adulthood. Then, using the model, we identified three biologically relevant scenarios that have the above properties: (1) the unique source of mature T(regs) is the antigen-driven differentiation of precursors that acquire the mature profile in the periphery and the proliferation of T(regs) is essential for the development and the maintenance of the pool; there exist other sources of mature T(regs), such as (2) a homeostatic density-dependent regulation or (3) thymus- or effector-derived T(regs), and in both cases, antigen-induced proliferation is not necessary for the development of a stable pool of T(regs). This is the first time that a mathematical model built to describe the in vivo dynamics of regulatory T cells is validated using human data. The application of this model provides an invaluable tool in estimating the amount of regulatory T cells as a function of time in the blood of patients that received a solid organ transplant or are suffering from an autoimmune disease.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

The cAMP response element binding protein-2 (CREB-2) can interact with the C/EBP-homologous protein (CHOP).

cAMP response element binding protein-2 (CREB-2) is a basic leucine zipper (bZIP) factor that was originally described as a repressor of CRE-dependent transcription but that can also act as a transcriptional activator. Moreover, CREB-2 is able to function in association with the viral Tax protein as an activator of the human T-cell leukemia virus type I (HTLV-I) promoter. Here we show that CREB-2 is able to interact with C/EBP-homologous protein (CHOP), a bZIP transcription factor known to inhibit CAAT/enhancer-dependent transcription. Cotransfection of CHOP with CREB-2 results in decreased activation driven by the cellular CRE motif or the HTLV-I proximal Tax-responsive element, confirming that CREB-2 and CHOP can interact with each other in vivo.

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

On the origin of the ultradeep East Barents Sea basin

Very large subsidence, with up to 20 km thick sediment layers, is observed in the East Barents Sea basin. Subsidence started in early Paleozoic, accelerated in Permo-Triassic times, finished during the middle Cretaceous, and was followed by moderate uplift in Cenozoic times. The observed gravity signal suggests that the East Barents Sea is at present in isostatic balance and indicates that a mass excess is required in the lithosphere to produce the observed large subsidence. Several origins have been proposed for the mass excess. We use 1-D thermokinematic modeling and 2-D isostatic density models of continental lithosphere to evaluate these competing hypotheses. The crustal density in 2-D thermokinematic models resulting from pressure-, temperature-, and composition-dependent phase change models is computed along transects crossing the East Barents Sea. The results indicate the following. (1) Extension can only explain the observed subsidence provided that a 10 km thick serpentinized mantle lens beneath the basin center is present. We conclude that this is unlikely given that this highly serpentinized layer should be formed below a sedimentary basin with more than 10 km of sediments and crust at least 10 km thick. (2) Phase changes in a compositionally homogeneous crust do not provide enough mass excess to explain the present-day basin geometry. (3) Phase change induced densification of a preexisting lower crustal gabbroic body, interpreted as a mafic magmatic underplate, can explain the basin geometry and observed gravity anomalies. The following model is proposed for the formation of the East Barents Sea basin: (1) Devonian rifting and extension related magmatism resulted in moderate thinning of the crust and a mafic underplate below the central basin area explaining initial late Paleozoic subsidence. (2) East-west shortening during the Permian and Triassic resulted in densification of the previously emplaced mafic underplated body and enhanced subsidence dramatically, explaining the present-day deep basin geometry.

Activation of Transient Receptor Potential Canonical 3 (TRPC3)-mediated Ca2+ Entry by A1 Adenosine Receptor in Cardiomyocytes Disturbs Atrioventricular Conduction.

Although the activation of the A(1)-subtype of the adenosine receptors (A(1)AR) is arrhythmogenic in the developing heart, little is known about the underlying downstream mechanisms. The aim of this study was to determine to what extent the transient receptor potential canonical (TRPC) channel 3, functioning as receptor-operated channel (ROC), contributes to the A(1)AR-induced conduction disturbances. Using embryonic atrial and ventricular myocytes obtained from 4-day-old chick embryos, we found that the specific activation of A(1)AR by CCPA induced sarcolemmal Ca(2+) entry. However, A(1)AR stimulation did not induce Ca(2+) release from the sarcoplasmic reticulum. Specific blockade of TRPC3 activity by Pyr3, by a dominant negative of TRPC3 construct, or inhibition of phospholipase Cs and PKCs strongly inhibited the A(1)AR-enhanced Ca(2+) entry. Ca(2+) entry through TRPC3 was activated by the 1,2-diacylglycerol (DAG) analog OAG via PKC-independent and -dependent mechanisms in atrial and ventricular myocytes, respectively. In parallel, inhibition of the atypical PKCζ by myristoylated PKCζ pseudosubstrate inhibitor significantly decreased the A(1)AR-enhanced Ca(2+) entry in both types of myocytes. Additionally, electrocardiography showed that inhibition of TRPC3 channel suppressed transient A(1)AR-induced conduction disturbances in the embryonic heart. Our data showing that A(1)AR activation subtly mediates a proarrhythmic Ca(2+) entry through TRPC3-encoded ROC by stimulating the phospholipase C/DAG/PKC cascade provide evidence for a novel pathway whereby Ca(2+) entry and cardiac function are altered. Thus, the A(1)AR-TRPC3 axis may represent a potential therapeutic target.

Use of long-term suppressive acyclovir after hematopoietic stem-cell transplantation: impact on herpes simplex virus (HSV) disease and drug-resistant HSV disease.

The effect that long-term use of suppressive acyclovir (ACV) has on both overall herpes simplex virus (HSV) disease and ACV-resistant HSV disease was examined in 3 consecutive cohorts of hematopoietic stem-cell transplant (HCT) recipients (n=2049); cohort 1 received ACV for 30 days after HCT, cohort 2 received it for 1 year after HCT, and cohort 3 received it for an extended period (i.e., >1 year) if the patient's immunosuppression continued after 1 year. The 2-year probability of HSV disease was 31.6% (95% confidence interval [CI], 28.0%-35%) in cohort 1, 3.9% (95% CI, 2.7%-5.2%) in cohort 2, and 0% in cohort 3 (P<.001). ACV-resistant HSV disease developed in 10 patients in cohort 1 (2-year probability, 1.3% [95% CI, 0.8%-2.7%]), in 2 patients in cohort 2 (2-year probability, 0.2% [95% CI, 0%-0.8%]; P=.006), and in 0 patients in cohort 3 (cohort 2 vs. cohort 3, P=.3). Long-term use of suppressive prophylactic ACV appears to prevent the emergence of drug-resistant HSV disease in HCT.

Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis.

Metabolites from intestinal microbiota are key determinants of host-microbe mutualism and, consequently, the health or disease of the intestinal tract. However, whether such host-microbe crosstalk influences inflammation in peripheral tissues, such as the lung, is poorly understood. We found that dietary fermentable fiber content changed the composition of the gut and lung microbiota, in particular by altering the ratio of Firmicutes to Bacteroidetes. The gut microbiota metabolized the fiber, consequently increasing the concentration of circulating short-chain fatty acids (SCFAs). Mice fed a high-fiber diet had increased circulating levels of SCFAs and were protected against allergic inflammation in the lung, whereas a low-fiber diet decreased levels of SCFAs and increased allergic airway disease. Treatment of mice with the SCFA propionate led to alterations in bone marrow hematopoiesis that were characterized by enhanced generation of macrophage and dendritic cell (DC) precursors and subsequent seeding of the lungs by DCs with high phagocytic capacity but an impaired ability to promote T helper type 2 (TH2) cell effector function. The effects of propionate on allergic inflammation were dependent on G protein-coupled receptor 41 (GPR41, also called free fatty acid receptor 3 or FFAR3), but not GPR43 (also called free fatty acid receptor 2 or FFAR2). Our results show that dietary fermentable fiber and SCFAs can shape the immunological environment in the lung and influence the severity of allergic inflammation.