Influence of microalgal N and P composition on wastewater nutrient remediation

Microalgae have demonstrated the ability to remediate wastewater nutrients efficiently, with methods to further enhance performance through species selection and biomass concentration. This work evaluates a freshwater species remediation characteristics through analysis of internal biomass N:P (nitrogen:phosphorus) and presents a relationship between composition and nutrient uptake ability to assist in species selection. Findings are then translated to an optimal biomass concentration, achieved through immobilisation enabling biomass intensification by modifying bead concentration, for wastewaters of differing nutrient concentrations at hydraulic retention times (HRT) from 3 h to 10 d. A HRT <20 h was found suitable for the remediation of secondary effluent by immobilised Scenedesmus obliquus and Chlorella vulgaris at bead concentrations as low as 3.2 and 4.4 bead·mL−1. Increasing bead concentrations were required for shorter HRTs with 3 h possible at influent concentrations <5 mgP L−1

marchés mondiaux des produits agricoles à l'horizon 2020 : causes et conséquences de la hausse des prix.

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Particle-based methods for parameter estimation and tracking: Numerical experiments

The purpose of this work is to obtain as much intuition as possible, through numerical experiments in a simple case where exact solutions are explicitly available, about the particle approximation of finite signed measures. A prototypical example of a finite signed measure is the derivative, w.r.t. a parameter of the model, of some probability distributions related with a hidden Markov chain. This includes prior, prediction, filtering probability distributions, etc. Two points of view are considered here, to feel the quality of the approximation, at least in a qualitative manner: (i) how accurate is the particle approximation of the finite signed measure, in view of an histogram representation of the weighted particle system? and (ii) considering the log-likelihood function and the score function, how close is the approximate expression provided by the particle approximation to the exact expression? These two questions seem closely related, however the numerical experiments presented in this work show that one of the two particle approximation schemes fails to satisfy the first criteria (quality of the approximation of the finite signed measure), and that both schemes satisfy the second criteria (quality of the approximation of the statistics).L'objectif de ce travail est de mieux comprendre l'approximation particulaire de mesures signées finies, au travers de quelques expériences numériques menées dans un cas simple où les solutions exactes sont connues de manière explicite. Un exemple typique de mesure signée finie est la dérivée, par rapport à un paramètre du modèle, de distributions de probabilité associées à une chaîne de Markov cachée. Cela inclut la distribution a priori, le prédicteur, le filtre, etc. Deux points de vue sont considérés ici pour évaluer la qualité de l'approximation, au moins dans un sens qualitatif: (i) quelle est la précision de l'approximation particulaire de la mesure signée finie, au vu d'une représentation sous forme d'histogramme du système de particules pondérées? et (ii) si on s'intéresse seulement à la fonction de log-vraisemblance ou à la fonction score, quel est l'écart entre l'expression fournie par l'approximation particulaire et l'expression exacte de ces quantités ? Ces deux questions sont évidemment liées, mais les expériences numériques présentées dans ce travail montrent que l'un des deux schémas d'approximation particulaire proposés ne répond pas de manière satisfaisante au premier critère (qualité de l'approximation de la mesure signée finie), et que les deux schémas proposés donnent une bonne approximation pour le second critère (précison de l'approximation des statistiques)

Embedded subspace-based modal analysis and uncertainty quantification on wireless sensor platform PEGASE

International audienceOperational modal analysis is an important step in many methods for vibration-based structural health monitoring. These methods provide the modal parameters (frequencies, damping ratios and mode shapes) of the structure and can be used for monitoring over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to unknown excitation, measurement noise and finite data length. Estimating the standard deviation of the modal parameters on the same dataset offers significant information on the accuracy and reliability of the modal parameter estimates. However, computational and memory usage of such algorithms are heavy even on standard PC systems in Matlab, where reasonable computational power is provided. In this paper, we examine an implementation of the covariance-driven stochastic subspace identification on the wireless sensor platform PEGASE, where computational power and memory are limited. Special care is taken for computational efficiency and low memory usage for an on-board implementation, where all numerical operations are optimized. The approach is validated from an engineering point of view in all its steps, using simulations and field data from a highway road sign structure

Robust uncertainty evaluation for system identification on distributed wireless platforms

International audienceHealth monitoring of civil structures by system identification procedures from automatic control is now accepted as a valid approach. These methods provide frequencies and modeshapes from the structure over time. For a continuous monitoring the excitation of a structure is usually ambient, thus unknown and assumed to be noise. Hence, all estimates from the vibration measurements are realizations of random variables with inherent uncertainty due to (unknown) process and measurement noise and finite data length. The underlying algorithms are usually running under Matlab under the assumption of large memory pool and considerable computational power. Even under these premises, computational and memory usage are heavy and not realistic for being embedded in on-site sensor platforms such as the PEGASE platform. Moreover, the current push for distributed wireless systems calls for algorithmic adaptation for lowering data exchanges and maximizing local processing. Finally, the recent breakthrough in system identification allows us to process both frequency information and its related uncertainty together from one and only one data sequence, at the expense of computational and memory explosion that require even more careful attention than before. The current approach will focus on presenting a system identification procedure called multi-setup subspace identification that allows to process both frequencies and their related variances from a set of interconnected wireless systems with all computation running locally within the limited memory pool of each system before being merged on a host supervisor. Careful attention will be given to data exchanges and I/O satisfying OGC standards, as well as minimizing memory footprints and maximizing computational efficiency. Those systems are built in a way of autonomous operations on field and could be later included in a wide distributed architecture such as the Cloud2SM project. The usefulness of these strategies is illustrated on data from a progressive damage action on a prestressed concrete bridge

Phased and synchronous sampling between multiple smart network sensors for modal assessment of large structures

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Human amniotic fluid contaminants alter thyroid hormone signalling and early brain development in Xenopus embryos.

Thyroid hormones are essential for normal brain development in vertebrates. In humans, abnormal maternal thyroid hormone levels during early pregnancy are associated with decreased offspring IQ and modified brain structure. As numerous environmental chemicals disrupt thyroid hormone signalling, we questioned whether exposure to ubiquitous chemicals affects thyroid hormone responses during early neurogenesis. We established a mixture of 15 common chemicals at concentrations reported in human amniotic fluid. An in vivo larval reporter (GFP) assay served to determine integrated thyroid hormone transcriptional responses. Dose-dependent effects of short-term (72 h) exposure to single chemicals and the mixture were found. qPCR on dissected brains showed significant changes in thyroid hormone-related genes including receptors, deiodinases and neural differentiation markers. Further, exposure to mixture also modified neural proliferation as well as neuron and oligodendrocyte size. Finally, exposed tadpoles showed behavioural responses with dose-dependent reductions in mobility. In conclusion, exposure to a mixture of ubiquitous chemicals at concentrations found in human amniotic fluid affect thyroid hormone-dependent transcription, gene expression, brain development and behaviour in early embryogenesis. As thyroid hormone signalling is strongly conserved across vertebrates the results suggest that ubiquitous chemical mixtures could be exerting adverse effects on foetal human brain development

Effects of gender and gonadectomy on growth and plasma cholesterol levels in pigs

We conducted two studies to determine the effect of gender, gonadectomy (GDX) on growth and plasma cholesterol levels in pigs. In experiment 1, five sham-operated and five GDX female Landrace pigs (26 kg) were allowed to have free access to water and feed up to market weight (approximately 100 kg). Body weight and feed consumption were recorded biweekly, and daily body weight gain, daily feed intake and feed efficiency (gain/feed) were calculated during the feeding period. In experiment 2, 10 male (26 kg) and 10 female (26 kg) Landrace pigs were used; five male and five female pigs were assigned to sham-operated or GDX. Pigs were allowed to have free access to water and a diet without added cholesterol (Table 1) until they were 6 months old (male 104 and female 98 kg) and thereafter they were fed a hypercholesterolemic diet (Table 1) containing 0.5% cholesterol and 0.1% cholate for 10 days. GDX of female pigs increased average daily gain (P<0.05), compared with their sham-operated counterparts during the growing-finishing period, but had no effect (P>0.05) on feed efficiency. Plasma cholesterol levels in pigs fed a hypercholesterolemic diet for 10 days were much higher (P<0.05) in females than in males (161 vs 104 mg/100 mL plasma), and were increased by GDX only in male pigs. HDL-cholesterol/LDL+VLDL-cholesterol ratio appeared to be higher in males than in females, and was not influenced by GDX in either sex. Results suggested that the lower growth rate of female pigs than their male counterparts is attributable to the ovarian activity, and the lower plasma cholesterol level in male than in female pigs fed a hypercholesterolemic diet is due to the testicular activity

Stress and estrous cycle affect strategy but not performance of female C57BL/6J mice

Stress induces a switch in learning strategies of male C57BL/6J mice from predominantly spatial to more stimulus-response learning. To study generalization of these findings over sex, we investigated female C57BL/6J mice at three phases of the estrous cycle under non stress and acute (10 min) restraint stress conditions. On a circular hole board (CHB) task, about half of the naive female mice used spatial and stimulus-response strategies to solve the task. Under stress, female mice favored spatial over stimulus-response strategies, with 100% of female mice in the estrus phase. Performance expressed as latency to solve the task is only improved in stressed female mice in the estrus phase. We conclude that the use of learning strategies is influenced by sex and this difference between sexes is aggravated by acute stress