Population genetic structure of Schistosoma haematobium and Schistosoma haematobium x Schistosoma bovis hybrids among school-aged children in Côte d'Ivoire

While population genetics of Schistosoma haematobium have been investigated in West Africa, only scant data are available from Cote d'Ivoire. The purpose of this study was to analyze both genetic variability and genetic structure among S. haematobium populations and to quantify the frequency of S. haematobium x S. bovis hybrids in school-aged children in different parts of Cote d'Ivoire. Urine samples were subjected to a filtration method and examined microscopically for Schistosoma eggs in four sites in the western and southern parts of Cote d'Ivoire. A total of 2692 miracidia were collected individually and stored on Whatman((R)) FTA cards. Of these, 2561 miracidia were successfully genotyped for species and hybrid identification using rapid diagnostic multiplex mitochondrial cox1 PCR and PCR Restriction Fragment Length Polymorphism (PCR-RFLP) analysis of the nuclear ITS2 region. From 2164 miracidia, 1966 (90.9%) were successfully genotyped using at least 10 nuclear microsatellite loci to investigate genetic diversity and population structure. Significant differences were found between sites in all genetic diversity indices and genotypic differentiation was observed between the site in the West and the three sites in the East. Analysis at the infrapopulation level revealed clustering of parasite genotypes within individual children, particularly in Duekoue (West) and Sikensi (East). Of the six possible cox1-ITS2 genetic profiles obtained from miracidia, S. bovis cox1 x S. haematobium ITS2 (42.0%) was the most commonly observed in the populations. We identified only 15 miracidia (0.7%) with an S. bovis cox1 x S. bovis ITS2 genotype. Our study provides new insights into the population genetics of S. haematobium and S. haematobium x S. bovis hybrids in humans in Cote d'Ivoire and we advocate for researching hybrid schistosomes in animals such as rodents and cattle in Cote d'Ivoire

CLASH-VLT: Abell S1063: Cluster assembly history and spectroscopic catalogue

Context. The processes responsible for galaxy evolution in different environments as a function of galaxy mass remain heavily debated. Rich galaxy clusters are ideal laboratories in which to distinguish the role of environmental versus mass quenching because they consist of a full range of galaxies and environments. Aims. Using the CLASH-VLT survey, we assembled an unprecedentedly large sample of 1234 spectroscopically confirmed members in Abell S1063. We found a dynamically complex structure at «zcl»= 0.3457 with a velocity dispersion σv = 1380-32+26 km s-1. We investigated cluster environmental and dynamical effects by analysing the projected phase-space diagram and the orbits as a function of galaxy spectral properties. Methods. We classified cluster galaxies according to the presence and strength of the [OII] emission line, the strength of the Hδ absorption line, and colours. We investigated the relation between the spectral classes of galaxies and their position in the projected phase-space diagram. We separately analysed red and blue galaxy orbits. By correlating the observed positions and velocities with the projected phase-space constructed from simulations, we constrained the accretion redshift of galaxies with different spectral types. Results. Passive galaxies are mainly located in the virialised region, while emission-line galaxies lie beyond r200 and are accreted into the cluster at a later time. Emission-line and post-starburst galaxies show an asymmetric distribution in projected phase-space within r200; emission-line galaxies are prominent at Δv/σ ≲ -1.5 and post-starburst galaxies at Δv/σ ≲ 1.5, suggesting that backsplash galaxies lie at high positive velocities. We find that low-mass passive galaxies are accreted into the cluster before high-mass galaxies. This suggests that we observe as passives only the low-mass galaxies that are accreted early into the cluster as blue galaxies. They had the time to quench their star formation. We also find that red galaxies move on more radial orbits than blue galaxies. This can be explained if infalling galaxies can remain blue by moving on tangential orbits

Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models

Influence of the Gulf of Guinea Coastal and Equatorial Upwellings on the Precipitations along its Northern Coasts during the Boreal Summer Period

International audienceThe Gulf of Guinea (GG) is an area where a seasonal upwelling takes place, along the equator and its northern coasts between Benin and Cote d'Ivoire. The coastal upwelling has a real impact on the local yet documented biological resources. However, climatic impact studies of this seasonal upwelling are paradoxically very rare and disseminated and this impact is still little known, especially on the potential part played by the upwelling onset on the regional precipitation in early boreal summer. This study shows that coastal precipitations of the July-September period are correlated by both the coastal and equatorial sea-surface temperatures (SSTS). This correlation results in a decrease or a rise of rainfall when the SSTs are abnormally cold or warm respectively. The coastal areas that are more subject to coastal and equatorial SSTs influence are located around the Cape Three Points, where the coastal upwelling exhibits the maximum of amplitude

Knowledge and practices of private pharmacy auxiliaries on malaria in Abidjan, Côte d’Ivoire

Abstract Background The emergence of resistance to artemisinin derivatives in Southeast Asia constitutes a serious threat for other malaria endemic areas, particularly in Côte d’Ivoire. To delay this resistance, the application of the control measures recommended by the National Malaria Control Programme (NMCP) for a correct management, in the private pharmacies, is a necessity. The purpose of this study was, therefore, to assess the level of knowledge and practices of private pharmacy auxiliary in Abidjan about the management of malaria. Methods A descriptive cross-sectional study was conducted from April to November 2015. It included auxiliaries of private pharmacies in Abidjan. Data collection material was a structured an open pretested questionnaire. Data analysis was carried out using Package for Social Science (SPSS) software version 21.1. Chi square test was used to compare proportions for a significance threshold of 0.05 for the p value. Results A total, 447 auxiliaries from 163 private pharmacies were interviewed. It was noted that the auxiliaries had a good knowledge of clinical signs of uncomplicated malaria (99.1%), biological examinations (54.6% for the thick film and 40.7% for rapid diagnostic tests (RDTs) and anti-malarial drugs (99.3% for artemether + lumefantrine, AL). The strategies of vector control (long-lasting insecticide-treated mosquito nets (LLITNs, Repellent ointments, cleaning gutters, elimination of larvae breeding site and intermittent preventive treatment with sulfadoxine–pyrimethamine (IPTp-SP) in pregnant women were also known by the auxiliaries, respectively 99.8% and 77.4%. However, the malaria pathogen (25.1%) and the NMCP recommendations (e.g. use of AL or AS + AQ as first-line treatment for uncomplicated malaria and IPTp-SP in pregnant women) were not well known by the auxiliaries (28.2% and 26.9% for uncomplicated and severe malaria). Concerning the practices of the auxiliaries, 91.1% offered anti-malarial drugs to patients without a prescription and 47.3% mentioned incorrect dosages. The combination artemether + lumefantrine was the most recommended (91.3%). The delivery of anti-malarial drugs was rarely accompanied by advice on malaria prevention, neither was it carried out on the result of an RDT. Conclusion The epidemiology and the NMCP recommendations for the diagnostic and therapeutic management of malaria, are not well known to auxiliaries, which may have implications for their practices. These results show the need to sensitize and train private pharmacy auxiliaries, and also to involve them in NMCP activities

Euclid preparation:XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with 90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.</p

Euclid preparation:XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with 90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.</p

Euclid preparation: XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with 90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band

The probability of galaxy- galaxy strong lensing events in hydrodynamical simulations of galaxy clusters

Context. An excess of galaxy galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the cold-dark-matter (CDM) cosmological model has recently been reported. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in CDM cosmology. Aims. We quantify the impact of the numerical resolution and active galactic nucleus (AGN) feedback scheme adopted in cosmological simulations on the predicted GGSL probability, and determine if varying these simulation properties can alleviate the gap with observations. Methods. We analyze cluster-size halos (M200 &gt; 5 × 1014 M ) simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. Our analysis focuses on galaxies with Einstein radii in the range 000 :5 = E = 300. Results. We find that improving the mass resolution by factors of 10 and 25, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor of between 3 and 6. However, we notice that such simulations form overly massive galaxies whose contribution to the lensing cross section would be significant but that their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross sections are galaxies with smaller masses that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent from simulations. Conclusion. Based on these results, we reaffirm the tension between observations of GGSL and theoretical expectations in the framework of the ?CDM cosmological model. The GGSL probability is sensitive to the galaxy formation model implemented in the simulations. Still, all the tested models have difficulty simultaneously reproducing the stellar mass function and the internal structure of galaxies

CLASH-VLT: Abell S1063. Cluster assembly history and spectroscopic catalogue

Context. The processes responsible for galaxy evolution in different environments as a function of galaxy mass remain heavily debated. Rich galaxy clusters are ideal laboratories in which to distinguish the role of environmental versus mass quenching because they consist of a full range of galaxies and environments. Aims: Using the CLASH-VLT survey, we assembled an unprecedentedly large sample of 1234 spectroscopically confirmed members in Abell S1063. We found a dynamically complex structure at ⟨zcl⟩ = 0.3457 with a velocity dispersion σv = 1380−32+26 km s−1. We investigated cluster environmental and dynamical effects by analysing the projected phase-space diagram and the orbits as a function of galaxy spectral properties. Methods: We classified cluster galaxies according to the presence and strength of the [OII] emission line, the strength of the Hδ absorption line, and colours. We investigated the relation between the spectral classes of galaxies and their position in the projected phase-space diagram. We separately analysed red and blue galaxy orbits. By correlating the observed positions and velocities with the projected phase-space constructed from simulations, we constrained the accretion redshift of galaxies with different spectral types. Results: Passive galaxies are mainly located in the virialised region, while emission-line galaxies lie beyond r200 and are accreted into the cluster at a later time. Emission-line and post-starburst galaxies show an asymmetric distribution in projected phase-space within r200; emission-line galaxies are prominent at Δv/σ ≲ −1.5 and post-starburst galaxies at Δv/σ ≳ 1.5, suggesting that backsplash galaxies lie at high positive velocities. We find that low-mass passive galaxies are accreted into the cluster before high-mass galaxies. This suggests that we observe as passives only the low-mass galaxies that are accreted early into the cluster as blue galaxies. They had the time to quench their star formation. We also find that red galaxies move on more radial orbits than blue galaxies. This can be explained if infalling galaxies can remain blue by moving on tangential orbits