Estimation de la température de l'eau de rivière en utilisant les réseaux de neurones et la régression linéaire multiple

La température de l'eau en rivière est un paramètre ayant une importance majeure pour la vie aquatique. Les séries temporelles décrivant ce paramètre thermique existent, mais elles sont moins nombreuses et souvent courtes, ou comptent parfois des valeurs manquantes. Cette étude présente la modélisation de la température de l'eau en utilisant des réseaux de neurones et la régression linéaire multiple pour relier la température de l'eau à celle de l'air et le débit du ruisseau Catamaran, situé au Nouveau-Brunswick, Canada. Une recherche multidisciplinaire à long terme se déroule présentement sur ce site. Les données utilisées sont de 1991 à 2000 et comprennent la température de l'air de la journée en cours, de la veille et de l'avant-veille, le débit ainsi que le temps transformé en série trigonométrique. Les données de 1991 à 1995 ont été utilisées pour l'entraînement ou la calibration du modèle tandis que les données de 1996 à 2000 ont été utilisées pour la validation du modèle. Les coefficients de détermination obtenus pour l'entraînement sont de 94,2 % pour les réseaux de neurones et de 92,6 % pour la régression linéaire multiple, ce qui donne un écart-type des erreurs de 1,01 C pour les réseaux de neurones et de 1,05 C pour la régression linéaire multiple. Pour la validation, les coefficients de détermination sont de 92,2 % pour les réseaux de neurones et de 91,6 % pour la régression linéaire multiple, ce qui se traduit en un écart-type des erreurs de 1,10 C pour les réseaux de neurones et de 1,25 C pour la régression linéaire multiple. Durant la période d'étude (1991-2000), le biais a été calculé à +0,11 C pour le modèle de réseaux de neurones et à -0,26 °C pour le modèle de régression. Ces résultats permettent de conclure qu'il est possible de prévoir la température de l'eau de petits cours d'eau en utilisant la température de l'air et le débit, aussi bien avec les réseaux de neurones qu'avec la régression linéaire multiple. Les réseaux de neurones semblent donner un ajustement aux données légèrement meilleur que celui offert par la régression linéaire multiple, toutefois ces deux approches de modélisation démontrent une bonne performance pour la prédiction de la température de l'eau en rivière.Water temperature is a parameter of great importance for water resources. For instance, modifications of the thermal regime of a river can have a significant impact on fish habitat. Therefore, understanding and predicting water temperatures is essential in order to help prevent or forecast high temperature problems. In order to predict water temperatures, data series are necessary. Many data series exist for air temperatures, but water temperature series are relatively scarce and those available are often short or have missing values. This study presents the modelling of water temperature using neural networks and multiple linear regression to relate water temperature to air temperature and discharge in Catamaran Brook, New Brunswick, Canada.Catamaran Brook is a small stream (51 km2) where long-term multidisciplinary habitat research is being carried out. Many variables can impact water temperatures in a river, such as air temperature, solar radiation, wind speed, discharge, groundwater flow, etc. For this study, only air temperature and discharge were used. These were judged to be the most often available parameters for modelling temperatures in rivers, and to have the greatest impact on water temperature. More precisely, input variables included current air temperature (°C), air temperature of the previous day (°C), air temperature two days earlier (°C), discharge (m3 /s) and a trigonometric function of time (days). Data used for the analysis were from 1991 to 2000. Data from 1991 to 1995 were used to calibrate the model while data from 1996 to 2000 were used for validation purposes. Observed and predicted water temperatures for each model were presented for the calibration data and the validation data. The coefficient of determination, R2, was used to compare the efficiency of both models as well as the residual standard deviation and the bias. This is equivalent to basing the comparison on the standard deviation (or variance) of the residuals. Coefficients of determination for calibration were 94.2% for the neural networks and 92.6% for the multiple linear regression, which correspond to a residual standard deviation of 1.01°C for the neural networks and of 1.05°C for the multiple linear regression. For validation, coefficients of determination were 92.2% for the neural networks and 91.6% for the multiple linear regression, which correspond to a residual standard deviation of 1.10°C for the neural networks, and of 1.25°C for the multiple regression. The overall bias during the study period (1991-2000) was calculated at +0.11°C for the neural network model and at -0.26°C for the regression model. Results indicated that it was possible to predict water temperature for a small stream using air temperature, flow and time, as input variables, with neural networks and multiple linear regression. The residual series obtained by both models were very similar. Of the two models, neural networks gave slightly better results in terms of fit, but the small difference in results lets us believe that both approaches are equally good in predicting stream water temperatures

Restoration of NK Cell Cytotoxic Function With Elotuzumab and Daratumumab Promotes Elimination of Circulating Plasma Cells in Patients With SLE.

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease characterized by multiple cellular and molecular dysfunctions of the innate and adaptive immunity. Cytotoxic function of NK cells is compromised in patients with SLE. Herein, we characterized the phenotypic alterations of SLE NK cells in a comprehensive manner to further delineate the mechanisms underlying the cytotoxic dysfunction of SLE NK cells and identify novel potential therapeutic targets. Therefore, we examined PBMC from SLE patients and matched healthy controls by single-cell mass cytometry to assess the phenotype of NK cells. In addition, we evaluated the cell function of NK cells (degranulation and cytokine production) and the killing of B cell subpopulations in a B cell-NK cell in vitro co-culture model. We found that SLE NK cells expressed higher levels of CD38 and were not able to adequately upregulate SLAMF1 and SLAMF7 following activation. In addition, ligation of SLAMF7 with elotuzumab or of CD38 with daratumumab on SLE NK cells enhanced degranulation of both healthy and SLE NK cells and primed them to kill circulating plasma cells in an in vitro co-culture system. Overall, our data indicated that dysregulated expression of CD38, SLAMF1 and SLAMF7 on SLE NK cells is associated with an altered interplay between SLE NK cells and plasma cells, thus suggesting their contribution to the accumulation of (auto)antibody producing cells. Accordingly, targeting SLAMF7 and CD38 may represent novel therapeutic approaches in SLE by enhancing NK cell function and promoting elimination of circulating plasma cell

SLAMF Receptor Expression Identifies an Immune Signature That Characterizes Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown etiology, linked to alterations in both the innate and the adaptive immune system. Due to the heterogeneity of the clinical presentation, the diagnosis of SLE remains complicated and is often made years after the first symptoms manifest, delaying treatment, and worsening the prognosis. Several studies have shown that signaling lymphocytic activation molecule family (SLAMF) receptors are aberrantly expressed and dysfunctional in SLE immune cells, contributing to the altered cellular function observed in these patients. The aim of this study was to determine whether altered co-/expression of SLAMF receptors on peripheral blood mononuclear cells (PBMC) identifies SLE characteristic cell populations. To this end, single cell mass cytometry and bioinformatic analysis were exploited to compare SLE patients to healthy and autoimmune diseases controls. First, the expression of each SLAMF receptor on all PBMC populations was investigated. We observed that SLAMF1+ B cells (referred to as SLEB1) were increased in SLE compared to controls. Furthermore, the frequency of SLAMF4+ monocytes and SLAMF4+ NK were inversely correlated with disease activity, whereas the frequency SLAMF1+ CD4+ TDEM cells were directly correlated with disease activity. Consensus clustering analysis identified two cell clusters that presented significantly increased frequency in SLE compared to controls: switch memory B cells expressing SLAMF1, SLAMF3, SLAMF5, SLAMF6 (referred to as SLESMB) and circulating T follicular helper cells expressing the same SLAMF receptors (referred to as SLEcTFH). Finally, the robustness of the identified cell populations as biomarkers for SLE was evaluated through ROC curve analysis. The combined measurement of SLEcTFH and SLEB1 or SLESMB cells identified SLE patients in 90% of cases. In conclusion, this study identified an immune signature for SLE based on the expression of SLAMF receptors on PBMC, further highlighting the involvement of SLAMF receptors in the pathogenesis of SLE

Does it bite? The role of stimuli characteristics on preschoolers’ interactions with robots, insects and a dog

While there is increasing interest in the impact of animal interactions upon children’s wellbeing and attitudes, there has been less attention paid to the specific characteristics of the animals which attract and engage children. We used a within-subjects design to explore how differences in animal features (such as their animacy, size, and texture) impacted upon pre-school children’s social and emotional responses. This study examined pre-schoolers’ interactions with two animal-like robots (Teksta and Scoozie), two insect types (stick insects and hissing cockroaches) and a dog (Teasel, a West Highland Terrier). Nineteen preschool participants aged 35-57 months were videoed while interacting with the experimenter, a peer and each stimulus (presented individually). We used both verbal and nonverbal behaviours to evaluate interactions and emotional responses to the stimuli and found that these two measures could be incongruent, highlighting the need for systematic approaches to evaluating children’s interactions with animals. We categorised the content of children’s dialogues in relation to psychological and biological attributes of each stimulus and their distinctions between living and non-living stimuli; the majority of comments were biological, with psychological terms largely reserved for the dog and mammal-like robot only. Comments relating to living qualities revealed ambiguity towards attributes that denote differences between living and non-living creatures. We used a range of nonverbal measures, including willingness to approach and touch stimuli, rates of self-touching, facial expressions of emotion, and touch to others. Insects (hissing cockroaches and stick insects) received the most negative verbal and nonverbal responses. The mammal-like robot (rounded, fluffy body shape, large eyes, and sympathetic sounds) was viewed much more positively than its metallic counterpart, as was the real dog. We propose that these interactions provide information on how children perceive animals and a platform for the examination of human socio-emotional and cognitive development more generally. The children engaged in social referencing to the adult experimenter rather than familiar peers when uncertain about the stimuli presented, suggesting that caregivers have a primary role in shaping children’s responses to animals

Mechanisms, functions and ecology of colour vision in the honeybee.

notes: PMCID: PMC4035557types: Journal Article© The Author(s) 2014.This is an open access article that is freely available in ORE or from Springerlink.com. Please cite the published version available at: http://link.springer.com/article/10.1007%2Fs00359-014-0915-1Research in the honeybee has laid the foundations for our understanding of insect colour vision. The trichromatic colour vision of honeybees shares fundamental properties with primate and human colour perception, such as colour constancy, colour opponency, segregation of colour and brightness coding. Laborious efforts to reconstruct the colour vision pathway in the honeybee have provided detailed descriptions of neural connectivity and the properties of photoreceptors and interneurons in the optic lobes of the bee brain. The modelling of colour perception advanced with the establishment of colour discrimination models that were based on experimental data, the Colour-Opponent Coding and Receptor Noise-Limited models, which are important tools for the quantitative assessment of bee colour vision and colour-guided behaviours. Major insights into the visual ecology of bees have been gained combining behavioural experiments and quantitative modelling, and asking how bee vision has influenced the evolution of flower colours and patterns. Recently research has focussed on the discrimination and categorisation of coloured patterns, colourful scenes and various other groupings of coloured stimuli, highlighting the bees' behavioural flexibility. The identification of perceptual mechanisms remains of fundamental importance for the interpretation of their learning strategies and performance in diverse experimental tasks.Biotechnology and Biological Sciences Research Council (BBSRC

Predictors of disease worsening defined by progression of organ damage in diffuse systemic sclerosis: a European Scleroderma Trials and Research (EUSTAR) analysis.

Objectives Mortality and worsening of organ function are desirable endpoints for clinical trials in systemic sclerosis (SSc). The aim of this study was to identify factors that allow enrichment of patients with these endpoints, in a population of patients from the European Scleroderma Trials and Research group database. Methods Inclusion criteria were diagnosis of diffuse SSc and follow-up over 12\ub13 months. Disease worsening/organ progression was fulfilled if any of the following events occurred: new renal crisis; decrease of lung or heart function; new echocardiography-suspected pulmonary hypertension or death. In total, 42 clinical parameters were chosen as predictors for the analysis by using (1) imputation of missing data on the basis of multivariate imputation and (2) least absolute shrinkage and selection operator regression. Results Of 1451 patients meeting the inclusion criteria, 706 had complete data on outcome parameters and were included in the analysis. Of the 42 outcome predictors, eight remained in the final regression model. There was substantial evidence for a strong association between disease progression and age, active digital ulcer (DU), lung fibrosis, muscle weakness and elevated C-reactive protein (CRP) level. Active DU, CRP elevation, lung fibrosis and muscle weakness were also associated with a significantly shorter time to disease progression. A bootstrap validation step with 10 000 repetitions successfully validated the model. Conclusions The use of the predictive factors presented here could enable cohort enrichment with patients at risk for overall disease worsening in SSc clinical trial

Phenotypes Determined by Cluster Analysis and Their Survival in the Prospective European Scleroderma Trials and Research Cohort of Patients With Systemic Sclerosis

Objective: Systemic sclerosis (SSc) is a heterogeneous connective tissue disease that is typically subdivided into limited cutaneous SSc (lcSSc) and diffuse cutaneous SSc (dcSSc) depending on the extent of skin involvement. This subclassification may not capture the entire variability of clinical phenotypes. The European Scleroderma Trials and Research (EUSTAR) database includes data on a prospective cohort of SSc patients from 122 European referral centers. This study was undertaken to perform a cluster analysis of EUSTAR data to distinguish and characterize homogeneous phenotypes without any a priori assumptions, and to examine survival among the clusters obtained. / Methods: A total of 11,318 patients were registered in the EUSTAR database, and 6,927 were included in the study. Twenty‐four clinical and serologic variables were used for clustering. / Results: Clustering analyses provided a first delineation of 2 clusters showing moderate stability. In an exploratory attempt, we further characterized 6 homogeneous groups that differed with regard to their clinical features, autoantibody profile, and mortality. Some groups resembled usual dcSSc or lcSSc prototypes, but others exhibited unique features, such as a majority of lcSSc patients with a high rate of visceral damage and antitopoisomerase antibodies. Prognosis varied among groups and the presence of organ damage markedly impacted survival regardless of cutaneous involvement. / Conclusion: Our findings suggest that restricting subsets of SSc patients to only those based on cutaneous involvement may not capture the complete heterogeneity of the disease. Organ damage and antibody profile should be taken into consideration when individuating homogeneous groups of patients with a distinct prognosis