Multi-scale studies of the relationships between cropping structure and pest and disease regulation services

International audienceThe early detection of DNA mutations such as DNA mismatches is of major interest. Indeed, the accumulation of mismatches into the genome arises from deficiencies of the cellular mismatch repair machinery that is often associated with several types of cancers being resistant to classic chemotherapeutics. In this context, ruthenium(II) compounds bearing a planar extended ligand appear to be excellent candidates as DNA photoprobes since they exhibit high affinity for DNA as well as tuneable luminescence properties. Herein, we report on the synthesis of a novel dissymmetric acridine based Ru(II) complex, [Ru (bpy) 2 napp] 2+ , along with the study of its ability to photodetect DNA mismatches. We also investigated the origin of the ability of the complex to photodetect mismatches via CD-melting assays and bio-layer interferometry. Interestingly, this behaviour may be attributed to a better protection of the excited state of the complex from non-radiative deexcitation sources (e.g., collisions with the solvent, oxygen photosensi-tization, etc.) when intercalated into well-matched compared to mismatched DNA

QUANTIFICATION DE L'EFFICACITE DES PRATIQUES DE CONTROLE DES MALADIES DES GRANDES CULTURES EN FRANCE

A l'heure où l'opinion publique pousse à une réduction de l'utilisation des pesticides, l'efficacité et la nécessité des pratiques agricoles actuelles sur la protection des cultures est sur la sellette (Delbos et al., 2014; Duru et al., 2014; Bonaudo et al. 2014). Les pertes de rendement moyennes induites par les bioagresseurs sont mal connues du fait de la grande variabilité de présence des bioagresseurs et de l'apparition régulière de résistances qui dégradent l'efficacité des traitements (Comins, 1977; Jutsum et al., 1998). De plus les estimations disponibles sont généralement des pertes maximales et non moyennes, estimées en comparant des bandes traitées et non - traitées. Cela ne permet pas l'estimation de l'impact des bioagresseurs non évitée par le traitement. Ici, nous analysons des données de suivis d'épidémiosurveillance de 13 maladies et de 12 insectes dans toute la France sur une période de 9 ans. La pression de chaque bio- agresseur est calculée à l'échelle du département à partir des observations faites sur l'année. Plusieurs modèles sont ensuite testés afin de prédire l'impact d'un groupe de bio-agresseurs sur le rendement départemental de leur culture cible. Nous montrons que la majorité des bio-agresseurs étudiés n'a globalement pas d'impact significatif sur le rendement, ce qui suggère qu'ils sont bien contenus à l'échelle nationale par les différents traitements appliqués sur blé, orge et colza. La septoriose (Septoria tritici) et le puceron des épis (Sitobion avenae) sur blé, ainsi que les charançons du bourgeon et des siliques sur colza apparaissent comme les plus problématiques car ayant un impact négatif significatif dans la plupart des modèles testés

Racial Disparities in Geographic Access to Primary Care in Philadelphia

Although Philadelphia has an adequate supply of primary care providers overall, spatial analysis shows wide variation across neighborhoods, with stark racial disparities. This study identifies six low-access areas within the city that warrant attention

Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images

In this work, we propose to tackle several challenges hindering the development of Automatic Target Detection (ATD) algorithms for ground targets in SAR images. To address the lack of representative training data, we propose a Deep Learning approach to train ATD models with synthetic target signatures produced with the MOCEM simulator. We define an incrustation pipeline to incorporate synthetic targets into real backgrounds. Using this hybrid dataset, we train ATD models specifically tailored to bridge the domain gap between synthetic and real data. Our approach notably relies on massive physics-based data augmentation techniques and Adversarial Training of two deep-learning detection architectures. We then test these models on several datasets, including (1) patchworks of real SAR images, (2) images with the incrustation of real targets in real backgrounds, and (3) images with the incrustation of synthetic background objects in real backgrounds. Results show that the produced hybrid datasets are exempt from image overlay bias. Our approach can reach up to 90% of Average Precision on real data while exclusively using synthetic targets for training

Mapping the Spatial Distribution of a Disease-Transmitting Insect in the Presence of Surveillance Error and Missing Data

Maps of the distribution of epidemiological data often ignore surveillance error or possible correlations between missing information and outcomes. We analyse presence–absence data at the household level (12050 points) of a disease‐carrying insect in Mariano Melgar, Peru, collected as part of the Arequipan Ministry of Health\u27s efforts to control Chagas disease. We construct a Bayesian hierarchical model to locate regions that are vulnerable to under‐reporting due to surveillance error, accounting for variability in participation due to infestation status. The spatial correlation in the data allows us to identify relative inspector sensitivity and to elucidate the relationship between participation and infestation. We show that naive estimates of prevalence would be biased by surveillance error and missingness at random assumptions. We validate our results through simulations and observe how randomized inspector assignments may improve prevalence estimates. Our results suggests that bias due to imperfect observations and missingness at random can be assessed and corrected in prevalence estimates of spatially auto-correlated binary variables

Landscape drivers of pests and pathogens abundance in arable crops

Farmers' use of fungicides and insecticides constitutes a major threat to biodiversity that is also endangering agriculture itself. Landscapes could be designed to take advantage of the dependencies of pests, pathogens and their natural enemies on elements of the landscape. Yet the complexity of the interactions makes it difficult to establish general rules. In our study, we sought to characterize the impact of the landscape on pest and pathogen prevalence, taking into account both crop and semi-natural areas. We drew on a nine-year national survey of 30 major pests and pathogens of arable crops, distributed throughout the latitudes of metropolitan France. We performed binomial LASSO generalized linear regressions on the pest and pathogen prevalence as a function of the landscape composition in a total of 39 880 field × year × pest observation series. We observed a strong disequilibrium between the number of pests or pathogens favored (15) and disadvantaged (2) by the area of their host crop in the landscape during the previous growing season. The impact of the host crop area during the ongoing growing season was different on pests than on pathogens: the density of most pathogens increased (11 of 17, and no decreases) while the density of a small majority of pests decreased (7 of 13, and four increases). We also found that woodlands, scrublands, hedgerows and grasslands did not have a consistent effect on the studied spectrum of pests and pathogens. Although overall the estimated effect of the landscape is small compared to the effect of the climate, a territorial coordination that generally favors crop diversity but excludes a crop at risk in a given year might prove useful in reducing pesticide use

Is Participation Contagious? Evidence From a Household Vector Control Campaign in Urban Peru

Objective: High rates of household participation are critical to the success of door-to-door vector control campaigns. We used the Health Belief Model to assess determinants of participation, including neighbour participation as a cue to action, in a Chagas disease vector control campaign in Peru. Methods: We evaluated clustering of participation among neighbours; estimated participation as a function of household infestation status, neighbourhood type and number of participating neighbours; and described the reported reasons for refusal to participate in a district of 2911 households. Results: We observed significant clustering of participation along city blocks (p\u3c0.0001). Participation was significantly higher for households in new versus established neighbourhoods, for infested households, and for households with more participating neighbours. The effect of neighbour participation was greater in new neighbourhoods. Conclusions: Results support a ‘contagion’ model of participation, highlighting the possibility that one or two participating households can tip a block towards full participation. Future campaigns can leverage these findings by making participation more visible, by addressing stigma associated with spraying, and by employing group incentives to spray

The Effects of City Streets on an Urban Disease Vector.

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran’s spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran’s decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p,0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies

Evaluation of Spatially Targeted Strategies to Control Non-Domiciliated Triatoma dimidiata Vector of Chagas Disease

Chagas disease is one of the most important parasitic diseases in Latin America. Since the 1980's, many national and international initiatives have contributed to eliminate vectors developing inside human domiciles. Today's challenge is to control vectors that are non-adapted to the human domicile, but still able to transmit the parasite through regular short stay in the houses. Here, we assess the potential of different control strategies applied in specific spatial patterns using a mathematical model that reproduces the dynamic of dispersion of such ‘non-domiciliated’ vectors within a village of the Yucatan Peninsula, Mexico. We show that no single strategy applied in the periphery of the village, where the insects are more abundant, provides satisfying protection to the whole village. However, combining the use of insect screens in houses at the periphery of the village (to simultaneously fight insects dispersing from the garden and the forest), and the cleaning of the peri-domicile areas of the centre of the village (where sylvatic insects are absent), would provide a cost-effective control. This type of spatially mixed strategy offers a promising way to reduce the cost associated with the repeated interventions required to control non-domiciliated vectors that permanently attempt to infest houses