Les Simulies d'Afrique du nord : nouvelles données sur la répartition de la faune du Maroc et biogéographie des espèces maghrébines (Diptera, Simuliidae)

Vingt espèces de #Simuliidae$ furent recensées dans plusieurs régions marocaines. Le Haut-Atlas possède la richesse spécifique la plus élevée grâce à la présence de sources et de torrents froids de haute altitude. La faune maghrébine appartient à la zone paléarctique de distribution ouest ou circum-méditerranéenne, atteignant ici sa limite géographique sud. Les éléments d'origine éthiopienne ou orientale sont peu représentés; certaines espèces nord-africaines pénètrent faiblement en Europe par le sud de la péninsule ibérique, d'autres sont endémiques. (Résumé d'auteur

Long-term changes and recurrent patterns in fisheries landings from Large Marine Ecosystems (1950–2004)

The regional dynamics of industrial fisheries within Large Marine Ecosystems (LMEs) boundaries were investigated by means of a historical-descriptive approach. Landings data from the Sea Around Us Project database were used to detect trends in total yields and variations in landings composition by functional groups over time. The temporal and spatial scales covered by this study allowed general issues to be addressed such as the detection of recurrent patterns and synchronies in fisheries landings. An unsuper-vised artificial neural network, Self Organizing Map (SOM), is used as a tool to analyze fisheries landings composition variation over five decades in 51 LMEs all over the world. From the historical analysis of “fishing behaviors” within LMEs a broad distinction between two main types of fisheries emerged: (1) small and medium pelagics fisheries, with stable compositions or cyclic behaviors, occurred in LMEs which share common productive features, despite different geographical locations and (2) demersal fisheries, which are more affected by economic drivers and tend to concentrate in LMEs in the Northern Hemisphere. Our analysis can be regarded as a first step towards the challenging scope of describing the relative influence of environmental and economic drivers on exploited ecosystems

Leukocyte beta2-integrins; genes and disease

Integrins are heterodimeric transmembrane receptors that mediate cell-cell and cell-extracellular matrix interactions. Beta2-integrins are expressed exclusively in leukocytes and mediate many important functions in the immune system. Beta2-integrin genes are important in the pathologies of several diseases and genetic syndromes. These include Leukocyte Adhesion Deficiency (LAD) and Systemic Lupus Erythematosus (SLE), disorders which lie at opposite ends of the spectrum of immunological diseases. In LAD-I and LAD-III syndromes, beta2-integrin expression or function is reduced or absent. In SLE, genetic variants of the ITGAM gene, which encodes for the alpha M/CD11b chain of the beta2-integrin Mac-1,are associated with SLE development. In this mini review we summarise current knowledge regarding the involvement of beta2-integrins in LAD and SLE. Interestingly, dysfunctional beta2-integrins have been linked to both disorders, shedding light on the diverse roles of these receptors in the immune system

Genome sequences of 15 Gardnerella vaginalis strains isolated from the vaginas of women with and without bacterial vaginosis

Gardnerella vaginalis is a predominant species in bacterial vaginosis, a dysbiosis of the vagina that is associated with adverse health outcomes, including preterm birth. Here, we present the draft genome sequences of 15 Gardnerella vaginalis strains (now available through BEI Resources) isolated from women with and without bacterial vaginosis

Contribution à l'étude de la relation entre les différentes formes du phosphore dans les sédiments d'une retenue de barrage eutrophe en climat méditerranéen (El Kansera, Maroc)

Le présent travail porte sur l'évaluation des différentes formes de phosphore, dans les sédiments d'un réservoir eutrophe en climat méditerranéen (El Kansera, Maroc) et la détermination des relations entre ces formes. L'application d'un schéma de fractionnement chimique du phosphore a permis de déterminer les proportions de quatre formes différentes : le phosphore lié au fer Fe(OOH)-P, le phosphore lié au calcium CaCO3-P, le phosphore organique soluble dans l'acide POSA et le phosphore organique résiduel POR. La forme biodisponible a été déterminée par ailleurs, par des bioessais en utilisant une Chlorophycée (Scenedesmus crassus) isolée à partir des eaux de la retenue du barrage étudié.Une étude par analyse factorielle des correspondances a mis en évidence l'influence de la nature des sédiments étudiés et de leurs propriétés physico-chimiques sur la répartition des formes du phosphore dans les sédiments. En période de basses eaux, les sédiments sont caractérisés par la prédominance des formes inorganiques ; alors qu'en période de hautes eaux, la forme POSA est la plus abondante. La confrontation des résultats obtenus par la méthode des bioessais avec ceux relatifs aux extractions chimiques séquentielles de phosphore a montré par ailleurs, que la forme biodisponible du phosphore est fortement corrélée aux formes inorganiques {Fe(OOH)-P et CaCO3-P} et indépendante vis à vis des formes organiques {POSA et POR}.We have studied in this work the different forms of phosphorus in the sediments of a eutrophic reservoir in a Mediterranean climate (El Kansera – Morocco). The studied sediments have been collected at eight sampling stations located at different points in the middle of the lake during two sampling sessions in contrasting weather conditions: the first sampling occurred in November 1992, a period of autumn mixing and low water levels, whereas the second took place in January 1994 during a high water level period, just after the first winter floods of the Beht river whose waters are retained in the studied reservoir.The evaluation of the different forms of phosphorus was carried out using both a chemical extraction method and a bioassay method. The application of the chosen chemical extraction scheme allowed the determination of the proportions of four different phosphorus forms: iron-bound {Fe(OOH)-P}, calcium-bound {CaCO3-P}, acid-soluble organic {POSA} and residual organic {POR}. The bioavailable form {Pbio}, representing less than 32 % of the total sediment phosphorus content, has been evaluated using a Scenedesmus crassus algal strain isolated from El Kansera reservoir waters.According to the results of our analyses, the concentrations of iron-bound phosphorus are higher overall than those of calcium-bound phosphorus. The sediments collected during the low water period contain spatially stable values of POSA (0.03 – 0.09 mg.g-1). These values are lower than those determined in the sediments collected during the high water period (0.24 – 0.42 mg.g-1). This form represents less than 11% of the total phosphorus in the low water period whereas it represents more than 25% and up to 56% of the total phosphorus in the filling period. This result can be explained by the freshness of the organic matter supplied by the continuous water flow in the high water period and the degradation of this organic matter during the low water period. Moreover, the anaerobic conditions of the hypolimnetic layers and the low external supply during the low water period favour organic phosphorus mineralisation.A Factorial Correspondence analysis study has shown the influence of the nature and the physico-chemical properties of the studied sediments on the distribution of the different phosphorus forms. In the low level period, the predominance of the inorganic forms is obvious while POSA is the most abundant form in the period of rising water levels. This differentiation can be explained by the different transport dynamics of suspended organic or inorganic particles according to the water level and also to the external or internal supply of the organic matter.We have also determined the statistical relations among the estimated different forms of phosphorus in El Kansera reservoir sediments. This statistical analysis was carried out to establish the correlation between the bioavailable phosphorus determined by the bioassay method and the phosphorus forms determined by chemical speciation. The goal of the study was to determine, according to statistically significant correlations, the chemical forms of the bioavailable phosphorus contained in the studied sediments.The comparison between the results we have obtained by the bioassay method and those obtained by sequential chemical extraction has shown that the bioavailable form of phosphorus is strongly correlated with the inorganic forms {Fe(OOH)-P} and {CaCO3-P}. The high correlation found between the iron-bound form of phosphorus and the bioavailable one is in good agreement with previous work described in the literature that has shown that an important part of the iron-bound and aluminium- bound phosphorus is bioavailable, whatever the extraction method may be.It has also been demonstrated that the forms {Pbio}, {Fe(OOH)-P}, {CaCO3-P} and {Pinorg}are notably independent from the {POSA} and {Porg} forms, the correlation coefficients being relatively low. The residual organic form of phosphorus {POR} does not seem to be linked to any variable. Therefore, we can say that this form is not bioavailable for the Scenedesmus crassus species

Modélisation de la relation pluie-débit à l'aide des réseaux de neurones artificiels

Identifier tous les processus physiques élémentaires du cycle hydrologique qui peuvent avoir lieu dans un bassin versant et attribuer à chacun d'eux une description analytique permettant la prévision conduisent à des structures complexes employant un nombre élevé de paramètres difficilement accessibles. En outre, ces processus, même simplifiés, sont généralement non linéaires. Le recours à des modèles à faible nombre de variables, capables de traiter la non-linéarité, s'avère nécessaire.C'est dans cette optique que nous proposons une méthode de modélisation de la relation pluie et débit basée sur l'utilisation de réseaux neuronaux. Les performances de ces derniers dans la modélisation non linéaire ont été déjà prouvées dans plusieurs domaines scientifiques (biologie, géologie, chimie, physique). Dans ce travail, nous utilisons l'algorithme de la rétropropagation des erreurs avec un réseau à 3 couches de neurones. La fonction de transfert appliquée est de type sigmoïde. Pour prédire le débit à un moment donné, on présente à l'entrée du réseau des valeurs de pluies et de débits observés à des instants précédents. La structure du réseau est optimisée pour obtenir une bonne capacité prévisionnelle sur des données n'ayant pas participé au calage.L'application du réseau à des données pluviométriques et débimétriques du bassin de l'oued Beth permet d'obtenir de bonnes prévisions d'un ou plusieurs pas de temps, aussi bien journalières qu'hebdomadaires. Pour les données n'ayant pas participé au calage, les coefficients de corrélation entre les valeurs observées et les valeurs estimées par les différents modèles sont élevés. Ils varient de 0.72 à 0.91 pour les coefficients de corrélation de Pearson et de 0.73 à 0.95 pour les coefficients de Spearman.Identification of the elementary processes of the hydrological cycle in a drainage basin, and the comprehensive description of each of them, lead to hydrological models with a complex structure including a high number of relatively inaccessible parameters. Moreover these processes, even when simplified, are generally non-linear. Using models with a smaller number of parameters, in order to cope with non-linearity, is therefore necessary.In this perspective, we propose an artificial neural network for rainfall-runoff modeling. Performances of this method in non-linear modeling have been already demonstrated in several scientific fields (biology, geology, chemistry, physics). In the present work, we use the error back-propagation algorithm with a three-layer neural network. The transfer functions belong to the sigmoidal type at each layer. To predict the runoff at a given moment, the input variables are the rainfall and the runoff values observed for the previous time period. The structure of the network (number of hidden nodes, learning coefficient and momentum values) is optimized to guarantee a good prediction of the runoff, using a set of test data (validation set) not used in the training phase.Data compiled in our model are a ten year set of rainfall-runoff values collected by the Rabat hydraulic administration (September 1983 to April 1993) in the Beth Wadi catchment. In this study, we develop two types of models according to two different time steps (daily and weekly). The data are subdivided into two sets: a first set to train the model (training set) and a second set to test the model (validation set). For the daily timestep model, we used data of the last two years: April 1991 to April 1993. The initial 365 data (April 1991- April 1992) constitute the training set and the 365 remaining data constitute the validation set. For the weekly data (Monday to Sunday averages), we have 502 pairs of values. We worked by preserving the last 120 values as the validation set and trained the neural network with the remaining data, i.e. 382 pairs of values of weekly rainfall-runoff.Three types of estimation have been carried out:1. at instant prediction: prediction of runoff at time t taking into account rainfall values at time t, as well as runoff and rainfall values at preceding times (until t-1); 2. one step ahead prediction: prediction of runoff at time t from rainfall and runoff values at the preceding times (until t-1); 3. multistep prediction: prediction of runoff values for a period from t-jh until t, given that values of the runoff for the period 1 to t-jh-1 and values of the rainfall at times 1 to t are available (h is the timestep). The step time is daily for the at instant prediction and weekly for one step ahead and multistep predictions. The choice of input variables is determined by autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses on runoff values, and cross-correlation function (CCF) analysis between rainfall and runoff values. For the at instant prediction, the input vector is composed by runoff values of the four days preceding day t, and rainfall values for the three last preceding days as well as its value on day t. For the one step ahead prediction, the input vector is composed of runoff values of the five weeks preceding week t, and rainfall values for the three preceding weeks (without considering the rainfall at time t). Finally, for the multistep prediction, the input vector is the same as for the one step ahead prediction but rainfall values include time t. The runoff values for the week t-jh+1, as well as for the following weeks, are computed by feed backing to the input vector the runoff value predicted for the preceding week.The rainfall-runoff models allow a good estimation for one or several timesteps, daily as well as weekly. In the validation set, correlation coefficients between observed and estimated values are high. In the at instant prediction, we obtain the Pearson correlation coefficient R=0.772 and the Spearman correlation coefficient CR=0.958. The weak value of R as compared to CR is explained by a few extremely high values of error of prediction. In the one step ahead prediction (R=0.887 and CR=0.782) and multistep prediction (R=0.908 and CR=0.727), the R coefficients are higher that CR. This confirms that predicted values are in good agreement with the peaks of observed values (absence of large exceptional errors). In all cases, the results obtained are better than those obtained with linear methods. The neural network models can thus be recommended for time series studies in environmental sciences

A gut-to-brain signal of fluid osmolarity controls thirst satiation.

Satiation is the process by which eating and drinking reduce appetite. For thirst, oropharyngeal cues have a critical role in driving satiation by reporting to the brain the volume of fluid that has been ingested1-12. By contrast, the mechanisms that relay the osmolarity of ingested fluids remain poorly understood. Here we show that the water and salt content of the gastrointestinal tract are precisely measured and then rapidly communicated to the brain to control drinking behaviour in mice. We demonstrate that this osmosensory signal is necessary and sufficient for satiation during normal drinking, involves the vagus nerve and is transmitted to key forebrain neurons that control thirst and vasopressin secretion. Using microendoscopic imaging, we show that individual neurons compute homeostatic need by integrating this gastrointestinal osmosensory information with oropharyngeal and blood-borne signals. These findings reveal how the fluid homeostasis system monitors the osmolarity of ingested fluids to dynamically control drinking behaviour