Myriam Maumy

Assessing phytoplankton structure and spatio-temporal dynamics in a freshwater ecosystem using a powerful multiway statistical analysis.

Phytoplankton dynamics and diversity are particularly difficult to analyze, especially when (i) the scale of the analysis is situated at the species level, (ii) such a diversity is high, (iii) the study covers several seasons, and (iv) sampling has been performed at many stations of the ecosystem. Fortunately, some powerful statistical methods have been developed with which each species identified can be considered in detailed spatio-temporal analyses. The Partial Triadic Analysis, a method issued from the STATIS family, was applied on a dataset corresponding to 6 stations of the largest French reservoir (Reservoir Marne) sampled 22 times over two years (2006-2007) between March and September. Three key sampling periods that were consistent with those exhibited with the Plankton Ecology Group model (i.e. early spring, late spring-early summer, late summer-early autumn) were unambiguously recognized, with some specific species associated with each of them. Furthermore, a potential reference sampling station was identified among all stations investigated, an information very relevant to both scientists and water managers. It remains that 3 other stations could also be monitored, regularly or from time to time, because of specific phytoplankton characteristics.

Non standard functional limit laws for the increments of the compound empirical distribution function

Let (Y i , Z i) i≥1 be a sequence of independent, identically distributed (i.i.d.) random vectors taking values in R k × R d , for some integers k and d. Given z ∈ R d , we provide a nonstandard functional limit law for the sequence of functional increments of the compound empirical process, namely ∆n,c(hn, z, ·) := 1 nhn n i=1 1 [0,·) Z i − z hn 1/d Y i. Provided that nhn ∼ c log n as n → ∞, we obtain, under some natural conditions on the conditional exponential moments of Y | Z = z, that ∆n,c(hn, z, ·) Γ almost surely, where denotes the clustering process under the sup norm on [0, 1) d. Here, Γ is a compact set that is related to the large deviations of certain compound Poisson processes.

Development of a micromanipulation method for single cell isolation of prokaryotes and its application in food safety

For nearly a century, conventional microbiological methods have been standard practice for detecting and identifying pathogens in food. Nevertheless, the microbiological safety of food has improved and various rapid methods have been developed to overcome the limitations of conventional methods. Alternative methods are expected to detect low cell numbers, since the presence in food of even a single cell of a pathogenic organism may be infectious. With respect to low population levels, the performance of a detection method is assessed by producing serial dilutions of a pure bacterial suspension to inoculate representative food matrices with highly diluted bacterial cells (fewer than 10 CFU/ml). The accuracy of data obtained by multiple dilution techniques is not certain and does not exclude some colonies arising from clumps of cells. Micromanipulation techniques to capture and isolate single cells from environmental samples were introduced more than 40 years ago. The main limitation of the current micromanipulation technique is still the low recovery rate for the growth of a single cell in culture medium. In this study, we describe a new single cell isolation method and demonstrate that it can be used successfully to grow various types of microorganism from picked individual cells. Tests with Gram-positive and Gram-negative organisms, including cocci, rods, aerobes, anaerobes, yeasts and molds showed growth recovery rates from 60% to 100% after micromanipulation. We also highlight the use of our method to evaluate and challenge the detection limits of standard detection methods in food samples contaminated by a single cell of Salmonella enterica.