In the French West Indies, chlordecone was used on bananas till the beginning of the ‘90s. This resulted in a highly persistent contamination of soils, water and living organisms. In freshwater environments, crustaceans present high concentrations of chlordecone in their organs. Therefore, more severe controls have been made on farms that produce the giant river prawn (Macrobrachium rosenbergii). As the result, the analysis showed that, in several aquaculture farms of Guadeloupe and Martinique, chlordecone levels in the shrimps was over the maximal limit of residues established at 20 µg/kg. Up to now, the studies designed to tackle this problem only addressed the level of contamination of freshwaters and crustaceans, and empirically observed changes in populations of aquatic animals (crustaceans and fish) in contaminated rivers. Many questions remained unsolved regarding the route of entry of chlordecone in the shrimps, on the bioavailability of the compound in freshwater ecosystems, and the capabilities of the shrimps to biotransform and eliminate residues. In addition, intoxication mechanisms which could results in chlordecone-induced alterations of the nervous and endocrine systems remain poorly understood in these invertebrates, so that specific biomarkers of exposure and toxic effects are not available yet. In the MACHLOMA project, which will benefit of interactions with aquaculture farms in Guadeloupe, we propose to use Macrobrachium rosenbergii as a model species to increase the knowledge on (i) the mechanisms of bioconcentration/bioaccumulation of chlordecone, by comparison with the concentrations measured in environmental matrices (water, sediments, particulate materials), (ii) the enzymatic mechanisms of detoxication and depuration of chlordecone, and (iii) the mechanisms of neurotoxicity and endocrine disruption, which are known to occur in mammals. Experiments will be performed (i) in control and contaminated ponds used for shrimp production in local aquaculture farms and (ii) in the laboratory, in order to build concentration-response relations based on environmental realistic exposure concentrations (which can reach 0.5 to 10 µg/L in some locations). Integration of the information acquired on these mechanisms will be facilitate by the use of the same individuals for both residue measurements, detoxication/depuration studies, and effects analysis. Acquisition of a sound knowledge on the fate and effects of chlordecone in M. rosenbergii will be of primary importance (i) to contribute to solve the problem the aquaculture is currently facing in the French West Indies and to propose solutions to maintain this activity which is important in the local economy, and (ii) to identify relevant biomarkers that could be transferred to species naturally present in rivers (e.g., Macrobrachium faustinum) and used for risk assessment and remediation in natural freshwater ecosystems.

More and more human activities are supported by computerized systems. This generates more and more volumes of data to be considered when analysing and monitoring human activities. When the volume of data increases it becomes very expensive (sometimes impossible) to store all available data before processing them: it is necessary to process them 'on the fly' as streams of data. Moreover, many new applications generate directly streams of data produced by a large number of sensors (weather forecast, environmental studies, road traffic, health care, power plants, …). In order to face this increase of available data, much research work has been done in the USA to develop methods and tools to process on the fly streams of structured data (opposed to audio and video streams which are unstructured). A good survey of these approaches can be found in the recent Springer book of C.Aggarwal “Data Streams: Models and Applications”. Two main directions have been explored: (1) Data Stream Management Systems (DSMS) which enable to query streams 'on the fly', (2) data stream mining methods to apply data mining methods directly to the streams without storing them. The main characteristic of these approaches is that all the processing is done 'on the fly' without storing the entire streams. In order to achieve this goal, a common solution is to apply queries and data mining algorithms to a small part of the stream defined as a sliding window containing most recent information. In many applications, there is a need to keep an historical view of the streams, for instance to provide historical aggregate information from the streams or to detect anomalous behaviour of monitored systems. For these applications, applying queries and algorithms to sliding windows prevents from obtaining needed information: it is necessary to keep track of the history of the streams by building and updating summaries on the fly. The MIDAS project is a 'Recherche Fondamentale' project which aims at studying, developing and demonstrating new methods for summarizing data streams. It tackles the following scientific challenges related to the construction of summaries: 􀀹 Summaries are built from infinite streams but must have a fixed or low increasing size; 􀀹 The construction of summaries must be incremental (done 'on the fly'); 􀀹 The amount of CPU used to process each element of the streams must be compatible with the arrival rate of the elements; 􀀹 The summaries must cover the whole stream and enable to build summaries of any past part of the history of a stream. The MIDAS project gathers both academic and industrial partners. The academic partners are already active in the field of data stream management and mining. The industrial partners are very large companies (France Telecom and Electricité de France) who have to face the increase of available data to monitor their activity: they will provide problems and data to direct research and assess new developed approaches. The MIDAS project falls within the scope the 2nd thematic axis of the MDCO call: “Algorithms for processing massive data sets”.