Tests d'évaluation du degré de pollution des sédiments marins : effets sur la production de larves et la consommation d'algues chez le copépode Tigriopus brevicornis

L'eau interstitielle ou l'eau de lessivage d'un sédiment est mise en contact avec des lots de 50 femelles ovigères du copépode Tigriopus brevicornis. Notons que ce microcrustacé marin est sensible à la pollution mais résistant aux caractéristiques physico-chimiques des types d'eaux testés (salinité et température notamment). On évalue ensuite les effets de ces eaux sur la production larvaire en 10 jours et sur l'ingestion en 4 jours par les copépodes d'une suspension de Phaeodactylum tricornutum. Nous observons ici, pour des lots de sédiments provenant de la région de Marseille, des réductions de la production de larves variant de 61 % (Les Embiez) à 90 % (Vaine). Le test "consommation d'algues", bien moins sensible aboutit pour les mêmes stations à des réductions de 19 % à 35 %. Le test "production larvaire" du fait de sa plus grande sensibilité doit être préféré au test "consommation d'algues".Most pollutants discharged into the sea are found in sediments, generally after temporary fixation in planktonic organisms. The slightest discharge leaves a trace in the soft bottoms. Thus, it may be said that these behave as good "data storage indicators" testifying to the degree of pollution present. Sediments therefore represent a privileged field in research on the state of pollution in the aquatic ecosystem. How can the degree of pollution in this field be evaluated?A chemical analysis of pollutants in sediments is a good means of investigation to detect of degradation in the quality of waters. Most pollutants however are difficult to detect and dose. Moreover, in many cases dosage is tedious and costly. It is from this point of view that biological assays were considered essential.Interstitial water or water used to wash a sediment was placed in contact with batches of 50 ovigerous females of the copepod Tigriopus brevicornis. This marine microcrustacean is known to be sensitive to pollution, though resisting the physical and chemical effects of the waters tested (salinity and temperature, in particular). An evaluation was the made on the effects of these waters; first on larval production during a period of ten days, and then on ingestion by copepeds of a suspension of Pheodactylum tricornutum for four days.The results obtained here with batches of sediment from the Marseilles region show that the larval production test is the most sensitive one. Indeed, the inhibition percentages found by the larval production test range from 35 to 100 % (figure 1), whereas they vary from 5 to 55 % with the algae consumption test (figure 2). With the larval production test, it is possible to classify sediments according to their ecological quality.From this test, moreover, the presence of harmful substances in the sediments can be rapidly detected. Being reproductible and not expensive, it supplements the far too restrictive traditional chemical analyses. When applied to estuarine sedimentary zones, combined with other tests, it should help establish a quality coefficient for sediments based on experimentation

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma

The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorized to have filled the Universe shortly after the Big Bang. A critical challenge in studying the QGP is that, to reconcile experimental observables with theoretical parameters, one requires many simulation runs of a complex physics model over a high-dimensional parameter space. Each run is computationally very expensive, requiring thousands of CPU hours, thus limiting physicists to only several hundred runs. Given limited training data for high-dimensional prediction, existing surrogate models often yield poor predictions with high predictive uncertainties, leading to imprecise scientific findings. To address this, we propose a new Additive Multi-Index Gaussian process (AdMIn-GP) model, which leverages a flexible additive structure on low-dimensional embeddings of the parameter space. This is guided by prior scientific knowledge that the QGP is dominated by multiple distinct physical phenomena (i.e., multiphysics), each involving a small number of latent parameters. The AdMIn-GP models for such embedded structures within a flexible Bayesian nonparametric framework, which facilitates efficient model fitting via a carefully constructed variational inference approach with inducing points. We show the effectiveness of the AdMIn-GP via a suite of numerical experiments and our QGP application, where we demonstrate considerably improved surrogate modeling performance over existing models

Introducing a rainfall compound distribution model based on weather patterns sub-sampling

This paper presents a probabilistic model for daily rainfall, using sub-sampling based on meteorological circulation. We classified eight typical but contrasted synoptic situations (weather patterns) for France and surrounding areas, using a "bottom-up" approach, i.e. from the shape of the rain field to the synoptic situations described by geopotential fields. These weather patterns (WP) provide a discriminating variable that is consistent with French climatology, and allows seasonal rainfall records to be split into more homogeneous sub-samples, in term of meteorological genesis. <br><br> First results show how the combination of seasonal and WP sub-sampling strongly influences the identification of the asymptotic behaviour of rainfall probabilistic models. Furthermore, with this level of stratification, an asymptotic exponential behaviour of each sub-sample appears as a reasonable hypothesis. This first part is illustrated with two daily rainfall records from SE of France. <br><br> The distribution of the multi-exponential weather patterns (MEWP) is then defined as the composition, for a given season, of all WP sub-sample marginal distributions, weighted by the relative frequency of occurrence of each WP. This model is finally compared to Exponential and Generalized Pareto distributions, showing good features in terms of robustness and accuracy. These final statistical results are computed from a wide dataset of 478 rainfall chronicles spread on the southern half of France. All these data cover the 1953–2005 period

Retinitis pigmentosa: rapid neurodegeneration is governed by slow cell death mechanisms

For most neurodegenerative diseases the precise duration of an individual cell's death is unknown, which is an obstacle when counteractive measures are being considered. To address this, we used the rd1 mouse model for retinal neurodegeneration, characterized by phosphodiesterase-6 (PDE6) dysfunction and photoreceptor death triggered by high cyclic guanosinemono-phosphate (cGMP) levels. Using cellular data on cGMP accumulation, cell death, and survival, we created mathematical models to simulate the temporal development of the degeneration. We validated model predictions using organotypic retinal explant cultures derived from wild-type animals and exposed to the selective PDE6 inhibitor zaprinast. Together, photoreceptor data and modeling for the first time delineated three major cell death phases in a complex neuronal tissue: (1) initiation, taking up to 36 h, (2) execution, lasting another 40 h, and finally (3) clearance, lasting about 7 h. Surprisingly, photoreceptor neurodegeneration was noticeably slower than necrosis or apoptosis, suggesting a different mechanism of death for these neurons. Cell Death and Disease (2013) 4, e488; doi: 10.1038/cddis.2013.12; published online 7 February 201

A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss