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

Molecular profiling of signet ring cell colorectal cancer provides a strong rationale for genomic targeted and immune checkpoint inhibitor therapies

We would like to thank all patients whose samples were used in this study. We are also thankful to the Northern Ireland Biobank and Grampian Biorepository for providing us with tissue blocks and patient data; and Dr HG Coleman (Queen’s University Belfast) for her advice on statistical analyses. This work has been carried out with financial support from Cancer Research UK (grant: C11512/A18067), Experimental Cancer Medicine Centre Network (grant: C36697/A15590 from Cancer Research UK and the NI Health and Social Care Research and Development Division), the Sean Crummey Memorial Fund and the Tom Simms Memorial Fund. The Northern Ireland Biobank is funded by HSC Research and Development Division of the Public Health Agency in Northern Ireland and Cancer Research UK through the Belfast CRUK Centre and the Northern Ireland Experimental Cancer Medicine Centre; additional support was received from Friends of the Cancer Centre. The Northern Ireland Molecular Pathology Laboratory which is responsible for creating resources for the Northern Ireland Biobank has received funding from Cancer Research UK, Friends of the Cancer Centre and Sean Crummey Foundation.Peer reviewedPublisher PD

Assessing the conservation value of waterbodies: the example of the Loire floodplain (France)

In recent decades, two of the main management tools used to stem biodiversity erosion have been biodiversity monitoring and the conservation of natural areas. However, socio-economic pressure means that it is not usually possible to preserve the entire landscape, and so the rational prioritisation of sites has become a crucial issue. In this context, and because floodplains are one of the most threatened ecosystems, we propose a statistical strategy for evaluating conservation value, and used it to prioritise 46 waterbodies in the Loire floodplain (France). We began by determining a synthetic conservation index of fish communities (Q) for each waterbody. This synthetic index includes a conservation status index, an origin index, a rarity index and a richness index. We divided the waterbodies into 6 clusters with distinct structures of the basic indices. One of these clusters, with high Q median value, indicated that 4 waterbodies are important for fish biodiversity conservation. Conversely, two clusters with low Q median values included 11 waterbodies where restoration is called for. The results picked out high connectivity levels and low abundance of aquatic vegetation as the two main environmental characteristics of waterbodies with high conservation value. In addition, assessing the biodiversity and conservation value of territories using our multi-index approach plus an a posteriori hierarchical classification methodology reveals two major interests: (i) a possible geographical extension and (ii) a multi-taxa adaptation

Rare variants of the 3'-5' DNA exonuclease TREX1 in early onset small vessel stroke

Background: Monoallelic and biallelic mutations in the exonuclease TREX1 cause monogenic small vessel diseases (SVD). Given recent evidence for genetic and pathophysiological overlap between monogenic and polygenic forms of SVD, evaluation of TREX1 in small vessel stroke is warranted. Methods: We sequenced the TREX1 gene in an exploratory cohort of patients with lacunar stroke (Edinburgh Stroke Study, n=290 lacunar stroke cases). We subsequently performed a fully blinded case-control study of early onset MRI-confirmed small vessel stroke within the UK Young Lacunar Stroke Resource (990 cases, 939 controls). Results: No patients with canonical disease-causing mutations of TREX1 were identified in cases or controls. Analysis of an exploratory cohort identified a potential association between rare variants of TREX1 and patients with lacunar stroke. However, subsequent controlled and blinded evaluation of TREX1 in a larger and MRI-confirmed patient cohort, the UK Young Lacunar Stroke Resource, identified heterozygous rare variants in 2.1% of cases and 2.3% of controls. No association was observed with stroke risk (odds ratio = 0.90; 95% confidence interval, 0.49-1.65 p=0.74). Similarly no association was seen with rare TREX1 variants with predicted deleterious effects on enzyme function (odds ratio = 1.05; 95% confidence interval, 0.43-2.61 p=0.91). Conclusions: No patients with early-onset lacunar stroke had genetic evidence of a TREX1-associated monogenic microangiopathy. These results show no evidence of association between rare variants of TREX1 and early onset lacunar stroke. This includes rare variants that significantly affect protein and enzyme function. Routine sequencing of the TREX1 gene in patients with early onset lacunar stroke is therefore unlikely to be of diagnostic utility, in the absence of syndromic features or family history

Multi-level evidence of an allelic hierarchy of USH2A variants in hearing, auditory processing and speech/language outcomes.

Language development builds upon a complex network of interacting subservient systems. It therefore follows that variations in, and subclinical disruptions of, these systems may have secondary effects on emergent language. In this paper, we consider the relationship between genetic variants, hearing, auditory processing and language development. We employ whole genome sequencing in a discovery family to target association and gene x environment interaction analyses in two large population cohorts; the Avon Longitudinal Study of Parents and Children (ALSPAC) and UK10K. These investigations indicate that USH2A variants are associated with altered low-frequency sound perception which, in turn, increases the risk of developmental language disorder. We further show that Ush2a heterozygote mice have low-level hearing impairments, persistent higher-order acoustic processing deficits and altered vocalizations. These findings provide new insights into the complexity of genetic mechanisms serving language development and disorders and the relationships between developmental auditory and neural systems

Rare germline variants in DNA repair genes and the angiogenesis pathway predispose prostate cancer patients to develop metastatic disease

Background Prostate cancer (PrCa) demonstrates a heterogeneous clinical presentation ranging from largely indolent to lethal. We sought to identify a signature of rare inherited variants that distinguishes between these two extreme phenotypes. Methods We sequenced germline whole exomes from 139 aggressive (metastatic, age of diagnosis < 60) and 141 non-aggressive (low clinical grade, age of diagnosis ≥60) PrCa cases. We conducted rare variant association analyses at gene and gene set levels using SKAT and Bayesian risk index techniques. GO term enrichment analysis was performed for genes with the highest differential burden of rare disruptive variants. Results Protein truncating variants (PTVs) in specific DNA repair genes were significantly overrepresented among patients with the aggressive phenotype, with BRCA2, ATM and NBN the most frequently mutated genes. Differential burden of rare variants was identified between metastatic and non-aggressive cases for several genes implicated in angiogenesis, conferring both deleterious and protective effects. Conclusions Inherited PTVs in several DNA repair genes distinguish aggressive from non-aggressive PrCa cases. Furthermore, inherited variants in genes with roles in angiogenesis may be potential predictors for risk of metastases. If validated in a larger dataset, these findings have potential for future clinical application

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases

Erythrocytes lacking the Langereis blood group protein ABCB6 are resistant to the malaria parasite Plasmodium falciparum.

The ATP-binding cassette transporter ABCB6 was recently discovered to encode the Langereis (Lan) blood group antigen. Lan null individuals are asymptomatic, and the function of ABCB6 in mature erythrocytes is not understood. Here, we assessed ABCB6 as a host factor for Plasmodium falciparum malaria parasites during erythrocyte invasion. We show that Lan null erythrocytes are highly resistant to invasion by P. falciparum, in a strain-transcendent manner. Although both Lan null and Jr(a-) erythrocytes harbor excess porphyrin, only Lan null erythrocytes exhibit a P. falciparum invasion defect. Further, the zoonotic parasite P. knowlesi invades Lan null and control cells with similar efficiency, suggesting that ABCB6 may mediate P. falciparum invasion through species-specific molecular interactions. Using tandem mass tag-based proteomics, we find that the only consistent difference in membrane proteins between Lan null and control cells is absence of ABCB6. Our results demonstrate that a newly identified naturally occurring blood group variant is associated with resistance to Plasmodium falciparum

Analysis of exome data for 4293 trios suggests GPI-anchor biogenesis defects are a rare cause of developmental disorders.

Over 150 different proteins attach to the plasma membrane using glycosylphosphatidylinositol (GPI) anchors. Mutations in 18 genes that encode components of GPI-anchor biogenesis result in a phenotypic spectrum that includes learning disability, epilepsy, microcephaly, congenital malformations and mild dysmorphic features. To determine the incidence of GPI-anchor defects, we analysed the exome data from 4293 parent-child trios recruited to the Deciphering Developmental Disorders (DDD) study. All probands recruited had a neurodevelopmental disorder. We searched for variants in 31 genes linked to GPI-anchor biogenesis and detected rare biallelic variants in PGAP3, PIGN, PIGT (n=2), PIGO and PIGL, providing a likely diagnosis for six families. In five families, the variants were in a compound heterozygous configuration while in a consanguineous Afghani kindred, a homozygous c.709G>C; p.(E237Q) variant in PIGT was identified within 10-12 Mb of autozygosity. Validation and segregation analysis was performed using Sanger sequencing. Across the six families, five siblings were available for testing and in all cases variants co-segregated consistent with them being causative. In four families, abnormal alkaline phosphatase results were observed in the direction expected. FACS analysis of knockout HEK293 cells that had been transfected with wild-type or mutant cDNA constructs demonstrated that the variants in PIGN, PIGT and PIGO all led to reduced activity. Splicing assays, performed using leucocyte RNA, showed that a c.336-2A>G variant in PIGL resulted in exon skipping and p.D113fs*2. Our results strengthen recently reported disease associations, suggest that defective GPI-anchor biogenesis may explain ~0.15% of individuals with developmental disorders and highlight the benefits of data sharing