Biology subjects

Jouanard, N.

Publications and source records attributed to Jouanard, N..

3 recordsLinked to original sources

bioRxiv2025

Rice-Fish Co-Culturing for Sustainability, Food Security, and Disease and Poverty Reduction

Over 140 million households cultivate rice worldwide. However, rice production contributes to exceedances of planetary boundaries, such as freshwater use and biogeochemical flows. Additionally, rice-farming families may face increased risk of acquiring schistosomiasis, a parasitic disease transmitted by freshwater snails that contributes to reinforcing cycles of poverty and disease. Using data from 405 households in rural Senegal, we show that children in rice-farming households had higher Schistosoma mansoni prevalence and S. haematobium intensities than non-farming peers. To address this environmental-health challenge, we integrated native Nile Tilapia (Oreochromis niloticus) and African Bonytongue (Heterotis niloticus) into rice fields. The fish thrived, suppressed insects and snail pests, improved soil nutrients, and boosted rice yields by >25% with a net benefit of 1,805-3,415 USD/ha/year (benefit-to-cost ratio = 7.42). Hence, low-input rice-fish co-culturing offers a scalable planetary health solution that simultaneously advances sustainability, nutrition, health, and rural livelihoods. ResumePlus de 140 millions de foyers a travers le monde cultivent du riz. Cependant, cette production exerce une pression importante sur les ressources naturelles, notamment les reserves deau douce. Par ailleurs, les menages pratiquant la riziculture peuvent etre plus exposes au risq ue de bilharziose, une maladie parasitaire transmise par des escargots deau douce, qui contribue au cycle de pauvrete. A partir de donnees collectees aupres de 405 menages dans des villages senegalais, nous montrons que les enfants issus de familles pratiquant la riziculture presentent une prevalence plus elevee dinfection a Shistosoma mansoni et des intensites dinfection a S. haematobium plus importantes que ceux issues de familles ne cultivant pas le riz. Pour remedier a cela, nous avons integre deux especes locales de poissons, le tilapia du Nil (Oreochromis niloticus) et lHeterotis (Heterotis niloticus), dans les rizieres, afin de reduire les population descargots hotes, ameliorer les rendements agricoles et augmenter les revenus. Ces poissons ont montre une bonne croissance, ont contribue a la regulation des insectes et des escargots nuisibles, ont enrichi le sol en nutriments, et accru les rendements rizicoles de plus de 25%, avec un benefice net estime entre 1 805 et 3 415 USD/ha/an (ratio benefice-cout = 7,42). Ainsi, la co-culture riz-poisson a faibles intrants constitue une solution reproductible en faveur de la sante publique, permettant de concilier durabilite, nutrition et amelioration des moyens de subsistance en milieu rural.

ecology↗

bioRxiv2021

Identifying low risk insecticides to address both food shortages and the biocontrol of human schistosomiasis

Use of agrochemicals, including insecticides, is vital to food production and predicted to increase 2-5 fold by 2050. Previous studies have shown a positive association between agriculture and the human infectious disease schistosomiasis, which is problematic as this parasitic disease infects approximately 250 million people worldwide. Certain insecticides might runoff fields and be highly toxic to invertebrates, such as prawns in the genus Macrobrachium, that are biocontrol agents for snails that transmit the parasites causing schistosomiasis. We used a laboratory dose-response experiment and an observational field study to determine the relative toxicities of three pyrethroid (esfenvalerate, {lambda}-cyhalothrin, and permethrin) and three organophosphate (chlorpyrifos, malathion, and terbufos) insecticides to Macrobrachium prawns. In the lab, pyrethroids were consistently several orders of magnitude more toxic than organophosphate insecticides, and more likely to runoff fields at lethal levels according to modeling data. In the field, we found that Macrobrachium prawn survival at 31 water contact sites in the lower basin of the Senegal River where schistosomiasis is endemic was predicted by pyrethroid application rates to nearby crop fields after controlling for abiotic and prawn-level factors. Our findings suggest that widely used pyrethroid insecticides can have strong non-target effects on Macrobrachium prawns that are biocontrol agents where 400 million people are at risk of human schistosomiasis. Understanding the ecotoxicology of high-risk insecticides may help improve human health in schistosomiasis-endemic regions undergoing agricultural expansion.

ecology↗

bioRxiv2019

Identification of snails and parasites of medical importance via convolutional neural network: an application for human schistosomiasis

Schistosomiasis is a debilitating parasitic disease infecting over 250 million people with nearly 800 million people at risk worldwide, primarily in sub-Saharan Africa. Transmission to humans involves freshwater snails as intermediate hosts, which are particularly prevalent in developing countries where dams and water resource projects have expanded freshwater snail habitat. At our study sites in the lower Senegal River Basin, we have collected more than 5,500 images of the 7 freshwater snail species (grouped into 4 categories) most frequently encountered in this aquatic ecosystem, 5 of which amplify and transmit either urinary or intestinal human schistosomiasis, with the other 2 species responsible for the transmission of less common parasitic diseases of humans and/or livestock. We have also collected over 5,100 images of 11 classes of trematodes, including human and non-human schistosomes. It takes a great deal of training and expertise to accurately classify these organisms morphologically. In recent years, deep convolutional neural networks (CNNs) have proven to be highly efficient for image recognition tasks across many object categories. Here we demonstrate classification capacity for snail and parasite images and test our models performance against 8 highly-trained human parasitologists with experience taxonomically classifying snail and parasite species from the Senegal River Basin in West Africa. We establish and train a single CNN end-to-end directly from images with only pixels and labels as inputs. Applying this state-of-the-art algorithm, we are able to classify images of 4 snail categories with 99.64% accuracy and images of 11 parasite categories with 88.14% accuracy, which rivals highly-trained human parasitologists. The trained algorithm could next be deployed to mobile devices for use in remote field settings by local technicians, and significantly improve monitoring snail and parasite occurrence in the field for disease control purposes.\n\nAuthor SummarySchistosomiasis is a neglected tropical disease (NTD) infecting over 250 million people worldwide. The current approach to mitigate this disease in endemic regions is community or school-based mass drug administration. However, parasites are primarily transmitted through environmental reservoirs where freshwater snails serve as intermediate hosts. People use the contaminated water sources for their daily tasks and get re-infected after drug treatment. Therefore, drug administration alone is not effective for schistosomiasis control in such high transmission settings. Recent studies show that snail population control is essential to reduce disease transmission risks. To discern between parasitic worms of humans in snails and those of other non-human species is a necessary step to precisely quantify transmission risk for human schistosomiasis. However, it takes a great deal of expertise to train lab and field technicians to accurately classify snail and parasite species. Artificial intelligence (AI)-powered computer vision algorithms have recently proven to be highly efficient for image recognition tasks. We have collected thousands of snail and parasite images in Senegal during 2015-2018. Using these images as training data, we developed an AI model that classifies images of 4 snail categories and 11 parasite categories with, in some cases, higher accuracy than well-trained human parasitologists. This model could next be deployed to mobile devices for use in remote field settings to support local technicians to identify transmission hotspots and target control.

bioinformatics↗