Improving pest monitoring networks using a simulation-based approach to contribute to pesticide reduction.

Conventional pest management mainly relies on the use of pesticides. However, the negative externalities of pesticides are now well known. More sustainable practices, such as Integrated Pest Management, are necessary to limit crop damage from pathogens, pests and weeds in agroecosystems. Reducing pesticide use requires information to determine whether chemical treatments are really needed. Pest monitoring networks (PMNs) are key contributors to this information. However, the effectiveness of a PMN in delivering relevant information about pests depends on its spatial sampling resolution and its memory length. The trade-off between the monitoring efforts and the usefulness of the information provided is highly dependent on pest ecological traits, the damage they can cause (in terms of crop losses), and economic drivers (production costs, agriculture product prices and incentives). Due to the high complexity of optimising PMNs, we have developed a theoretical model that belongs to the family of Dynamic Bayesian Networks in order to compare several PMNs performances. This model links the characteristics of a PMN to treatment decisions and the resulting pest dynamics. Using simulation and inference tools for graphical models, we derived the proportion of impacted fields, the number of pesticide treatments and the overall gross margins for three types of pest with contrasting levels of endocyclism. The term "endocyclic" refers to an organism whose development is mostly restricted to a field and highly depends on the inoculum present in the considered field. The presence of purely endocyclic pests at a given time increases the probability of reoccurrence. Conversely, slightly endocyclic pests have a low persistence. The simulation analysis considered ten scenarios: an expected margin-based strategy with a spatial resolution of four PMNs and two memory lengths (one year or eight years), as well as two extreme crop protection strategies (systematic treatments on all fields and systematic no treatment). For purely and mainly endocyclic pests (e.g. soil-borne pathogens and most weeds, respectively), we found that increasing the spatial resolution of PMNs made it possible to significantly decrease the number of treatments required for pest control. Taking past observations into account was also effective, but to a lesser extent. PMN information had virtually no influence on the control of non-endocyclic pests (such as flying insects or airborne plant pathogens) which may be due to the spatial coverage addressed in our study. The next step is to extend the analysis of PMNs and to integrate the information generated by PMNs into sustainable pest management strategies, both at the field and the landscape level.

RNAspace.org: An integrated environment for the prediction, annotation, and analysis of ncRNA.

The annotation of noncoding RNA genes remains a major bottleneck in genome sequencing projects. Most genome sequences released today still come with sets of tRNAs and rRNAs as the only annotated RNA elements, ignoring hundreds of other RNA families. We have developed a web environment that is dedicated to noncoding RNA (ncRNA) prediction, annotation, and analysis and allows users to run a variety of tools in an integrated and flexible manner. This environment offers complementary ncRNA gene finders and a set of tools for the comparison, visualization, editing, and export of ncRNA candidates. Predictions can be filtered according to a large set of characteristics. Based on this environment, we created a public website located at http://RNAspace.org. It accepts genomic sequences up to 5 Mb, which permits for an online annotation of a complete bacterial genome or a small eukaryotic chromosome. The project is hosted as a Source Forge project (http://rnaspace.sourceforge.net/).

A search for small noncoding RNAs in Staphylococcus aureus reveals a conserved sequence motif for regulation.

Bioinformatic analysis of the intergenic regions of Staphylococcus aureus predicted multiple regulatory regions. From this analysis, we characterized 11 novel noncoding RNAs (RsaA-K) that are expressed in several S. aureus strains under different experimental conditions. Many of them accumulate in the late-exponential phase of growth. All ncRNAs are stable and their expression is Hfq-independent. The transcription of several of them is regulated by the alternative sigma B factor (RsaA, D and F) while the expression of RsaE is agrA-dependent. Six of these ncRNAs are specific to S. aureus, four are conserved in other Staphylococci, and RsaE is also present in Bacillaceae. Transcriptomic and proteomic analysis indicated that RsaE regulates the synthesis of proteins involved in various metabolic pathways. Phylogenetic analysis combined with RNA structure probing, searches for RsaE-mRNA base pairing, and toeprinting assays indicate that a conserved and unpaired UCCC sequence motif of RsaE binds to target mRNAs and prevents the formation of the ribosomal initiation complex. This study unexpectedly shows that most of the novel ncRNAs carry the conserved C-rich motif, suggesting that they are members of a class of ncRNAs that target mRNAs by a shared mechanism.

GMDPtoolbox: A Matlab library for designing spatial management policies. Application to the long-term collective management of an airborne disease.

Designing management policies in ecology and agroecology is complex. Several components must be managed together while they strongly interact spatially. Decision choices must be made under uncertainty on the results of the actions and on the system dynamics. Furthermore, the objectives pursued when managing ecological systems or agroecosystems are usually long term objectives, such as biodiversity conservation or sustainable crop production. The framework of Graph-Based Markov Decision Processes (GMDP) is well adapted to the qualitative modeling of such problems of sequential decision under uncertainty. Spatial interactions are easily modeled and integrated control policies (combining several action levers) can be designed through optimization. The provided policies are adaptive, meaning that management actions are decided at each time step (for instance yearly) and the chosen actions depend on the current system state. This framework has already been successfully applied to forest management and invasive species management. However, up to now, no "easy-to-use" implementation of this framework was available. We present GMDPtoolbox, a Matlab toolbox which can be used both for the design of new management policies and for comparing policies by simulation. We provide an illustration of the use of the toolbox on a realistic crop disease management problem: the design of long term management policy of blackleg of canola using an optimal combination of three possible cultural levers. This example shows how GMDPtoolbox can be used as a tool to support expert thinking.

EUGENE'HOM: A generic similarity-based gene finder using multiple homologous sequences.

EUGENE'HOM is a gene prediction software for eukaryotic organisms based on comparative analysis. EUGENE'HOM is able to take into account multiple homologous sequences from more or less closely related organisms. It integrates the results of TBLASTX analysis, splice site and start codon prediction and a robust coding/non-coding probabilistic model which allows EUGENE'HOM to handle sequences from a variety of organisms. The current target of EUGENE'HOM is plant sequences. The EUGENE'HOM web site is available at http://genopole.toulouse.inra.fr/bioinfo/eugene/EuGeneHom/cgi-bin/EuGeneHom.pl.