Karst simulation with Lindenmayer-systems and ODSIM

International audienceKarstic systems are geological structures that strongly impact underground flows. Despite intensive explorations by speleologists, they remain partially described as many conduits are not accessible to humans. Paleokarsts are buried karstic systems with a significant reservoir potential. But they are not easily identifiable on seismic images. In those contexts, a huge uncertainty subsists on the network location and the conduit geometry. Stochastic simulations help to better assess that uncertainty. The difficulty is to reproduce the system connectivity at different scales while integrating as much geological knowledge as possible without involving poorly constrained parameters (e.g. paleo-climate, boundary conditions...). In this paper we propose to work on two aspects and scales of karstic systems. At large scale, we stochastically simulate karst network skeletons with a new method based on a formal grammar, the Lindenmayer-system. Based on an alphabet, an axiom and user-defined rules, the method puts together segments to build the network skeleton. The definition of proper rules and the introduction of karst-dedicated parameters generate curves reproducing the complex architectures encountered in those systems, mixing branchwork and anastomotic patterns. At the conduit scale, we propose to build a 3D envelope around these skeletons with an enhanced Object Distance based Simulation Method. It uses a custom distance field from the skeleton which takes into account geological features influencing karstogenesis (horizons, faults or fractures). This controls the first-order shape of the conduits. It is then combined to a custom random threshold controlling finer-scale features of the conduits. This threshold is generated with several parameter values depending on the involved geological structures. This workflow is demonstrated on a synthetic case, showing the potentialities of the approach at both scales. Results are encouraging and various improvements are in focus. Data conditioning, both to karst observations and local shape information has to be enhanced. The network simulation has the advantage to be grid-free, meaning that no background grid is needed to perform the simulation. Thus, it avoids the stair-step effect that can be observed in other techniques. On the opposite, the method used to simulate the conduit shapes relies on a grid, necessary to compute the distance fields and to perform the threshold geostatistical simulation. For detailed conduit geometry, the grid requires a high resolution, which impacts directly the computational efficiency. Finally, it would be interesting to test the approach on a real dataset and to develop a coupling with a flow simulator to evaluate the impact of the shape and of the network connections on the flow response

Parametric Excitation and Squeezing in a Many-Body Spin System

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We present in vivo single-cell FRET measurements in the Escherichia coli chemotaxis system that reveal pervasive signaling variability, both across cells in isogenic populations and within individual cells over time. We quantify cell-to-cell variability of adaptation, ligand response, as well as steady-state output level, and analyze the role of network design in shaping this diversity from gene expression noise. In the absence of changes in gene expression, we find that single cells demonstrate strong temporal fluctuations. We provide evidence that such signaling noise can arise from at least two sources: (i) stochastic activities of adaptation enzymes, and (ii) receptor-kinase dynamics in the absence of adaptation. We demonstrate that under certain conditions, (ii) can generate giant fluctuations that drive signaling activity of the entire cell into a stochastic two-state switching regime. Our findings underscore the importance of molecular noise, arising not only in gene expression but also in protein networks

Anti-inflammatory and cell proliferative effect of the 1270 nm laser irradiation on the BALB/c Nude mouse model involves activation of the cell antioxidant system

Recently, many interdisciplinary community researchers have focused their efforts on study of the low-level light irradiation effects (photobiomodulation, PBM) as a promising therapeutic technology. Among the priorities, a search of new wavelength ranges of laser radiation to enhance the laser prospects in treatment of autoimmune and cancer diseases commonly accompanied by disorders in the antioxidant system of the body. The laser wavelengths within 1265-1270 nm corresponds to the maximum oxygen absorption band. Therefore, PBM effects on a model organism within this spectrum range are of particular interest for preclinical research. Here, we report comprehensive biomolecular studies of the changes in the BALB/c nude mice skin after an exposure to the continuous laser radiation at the 1270 nm wavelength and energy densities of 0.12 and 1.2 J/cm2. Such regime induces both local and systemic PBM effects, presumably due to the short-term increase in ROS levels, which in turn activate the cell antioxidative system

Unveiling the optimal regression model for source apportionment of the oxidative potential of PM₁₀

The capacity of particulate matter (PM) to generate reactive oxygen species (ROS) in vivo leading to oxidative stress is thought to be a main pathway in the health effects of PM inhalation. Exogenous ROS from PM can be assessed by acellular oxidative potential (OP) measurements as a proxy of the induction of oxidative stress in the lungs. Here, we investigate the importance of OP apportionment methods for OP distribution by PM10 sources in different types of environments. PM10 sources derived from receptor models (e.g., EPA positive matrix factorization (EPA PMF)) are coupled with regression models expressing the associations between PM10 sources and PM10 OP measured by ascorbic acid (OPAA) and dithiothreitol assay (OPDTT). These relationships are compared for eight regression techniques: ordinary least squares, weighted least squares, positive least squares, Ridge, Lasso, generalized linear model, random forest, and multilayer perceptron. The models are evaluated on 1 year of PM10 samples and chemical analyses at each of six sites of different typologies in France to assess the possible impact of PM source variability on PM10 OP apportionment. PM10 source-specific OPDTT and OPAA and out-of-sample apportionment accuracy vary substantially by model, highlighting the importance of model selection according to the datasets. Recommendations for the selection of the most accurate model are provided, encompassing considerations such as multicollinearity and homoscedasticity.</p