Hierarchical modelling of species sensitivity distribution: development and application to the case of diatoms exposed to several herbicides

The Species Sensitivity Distribution (SSD) is a key tool to assess the ecotoxicological threat of contaminant to biodiversity. It predicts safe concentrations for a contaminant in a community. Widely used, this approach suffers from several drawbacks: i)summarizing the sensitivity of each species by a single value entails a loss of valuable information about the other parameters characterizing the concentration-effect curves; ii)it does not propagate the uncertainty on the critical effect concentration into the SSD; iii)the hazardous concentration estimated with SSD only indicates the threat to biodiversity, without any insight about a global response of the community related to the measured endpoint. We revisited the current SSD approach to account for all the sources of variability and uncertainty into the prediction and to assess a global response for the community. For this purpose, we built a global hierarchical model including the concentration-response model together with the distribution law for the SSD. Working within a Bayesian framework, we were able to compute an SSD taking into account all the uncertainty from the original raw data. From model simulations, it is also possible to extract a quantitative indicator of a global response of the community to the contaminant. We applied this methodology to study the toxicity of 6 herbicides to benthic diatoms from Lake Geneva, measured from biomass reduction

Intégration CMOS analogique de réseaux de neurones à cliques

Artificial neural networks solve problems that classical processors cannot solve without using a huge amount of resources. For instance, multiple-signal analysis and classification are such problems. Moreover, artificial neural networks are more and more integrated on-chip. They aim therefore at increasing processors computational abilities or processing data in embedded systems. In embedded systems, circuit area and energy consumption are critical parameters. However, the amount of connections between neurons is very high. Besides, circuit integration is difficult due to weighted connections and complex activation functions. These limitations exist for most artificial neural networks models and are thus an issue for the integration of a neural network composed of a high number of neurons (hundreds of them or more). Clique-based neural networks are a model of artificial neural networks reducing the network density, in terms of connections between neurons. Its information storage capacity is moreover greater than that of a standard artificial neural networks model such as Hopfield neural networks. This model is therefore suited to implement a high number of neurons on chip, leading to low-complexity and low-energy consumption circuits. In this document, we introduce a mixed-signal circuit implementing clique-based neural networks. We also show several generic network architectures implementing a network of any number of neurons. We can therefore implement clique-based neural networks of up to thousands of neurons consuming little energy. In order to validate the proposed implementation, we have fabricated a 30-neuron clique-based neural network prototype integrated on chip for the Si 65-nm CMOS 1-V supply process. The circuit shows decoding performances similar to the theoretical model and executes the message recovery process in 58 ns. Moreover, the entire network occupies a silicon area of 16,470 µm² and consumes 145 µW, yielding a measured energy consumption per neuron of 423 fJ maximum. These results show that the fabricated circuit is ten times more efficient in terms of occupied silicon area and latency than a digital equivalent circuit.Les réseaux de neurones artificiels permettent de résoudre des problèmes que des processeurs classiques ne peuvent pas résoudre sans utiliser une quantité considérable de ressources matérielles. L'analyse et la classification de multiples signaux en sont des exemples. Ces réseaux sont de plus en plus implantés sur des circuits intégrés. Ils ont ainsi pour but d'augmenter les capacités de calcul de processeurs ou d'effectuer leur traitement dans des systèmes embarqués. Dans un contexte d'application embarquée, la surface et la consommation d'énergie du circuit sont prépondérantes. Cependant, le nombre de connexions entre les neurones est élevé. De plus, les poids synaptiques ainsi que les fonctions d'activation utilisées rendent les implantations sur circuit complexes. Ces aspects, communs dans la plupart des modèles de réseaux de neurones, limitent l'intégration d'un réseau contenant un nombre de neurones de l'ordre de la centaine. Le modèle des réseaux de neurones à cliques permet de réduire la densité de connexions au sein d'un réseau, tout en gardant une capacité de stockage d'information plus grande que les réseaux de Hopfield, qui est un modèle standard de réseaux de neurones. Ce modèle est donc approprié pour implanter un réseau de grande taille, à condition de l'intégrer de façon à garder la faible complexité de ses fonctions, pour consommer un minimum d'énergie. Dans ce document, nous proposons un circuit mixte analogique/numérique implantant le modèle des réseaux de neurones à cliques. Nous proposons également plusieurs architectures de réseau pouvant contenir un nombre indéterminé de neurones. Cela nous permet de construire des réseaux de neurones à cliques contenant jusqu'à plusieurs milliers de neurones et consommant peu d'énergie. Pour valider les concepts décrits dans ce document, nous avons fabriqué et testé un prototype d'un réseau de neurones à cliques contenant trente neurones sur puce. Nous utilisons pour cela la technologie Si CMOS 65 nm, avec une tension d'alimentation de 1 V. Le circuit a des performances de récupération de l'information similaires à celles du modèle théorique, et effectue la récupération d'un message en 58 ns. Le réseau de neurones occupe une surface de silicium de 16 470 µm² et consomme 145 µW. Ces mesures attestent une consommation d'énergie par neurone de 423 fJ au maximum. Ces résultats montrent que le circuit produit est dix fois plus efficace qu'un équivalent numérique en termes de surface de silicium occupée et de latence

IMPLEMENTASI PEMBELAJARAN BERBASIS STEAM (SCIENCE, TECHNOLOGY, ENGINEERING, ARTS, MATHEMATICS) PADA MEDIA LOOSE PART DI TK BOUGENVIL KABUPATEN KUNINGAN

Perkembangan teknologi pada abad 21 memunculkan tantangan dan tuntutan baru dalam dunia pendidikan. Tantangan dan tuntutan baru tersebut meliputi 2 hal yakni mengharuskan pembelajaran lebih mempersiapkan anak dalam menghadapi dunia kerja serta menggabungkan ke 5 bidang (Sain, Teknologi, Teknik, Seni, Matematika) dalam proses pembelajaran. Maka dari itu tujuan dari penelitian ini akan menguraikan secara deskriptif mengenai implementasi pembelajaran STEAM (science, technology, engineering, arts, mathematics) pada media loose part di TK Bougenvil Kabupaten Kuningan. Jenis penelitian yang akan digunakan ialah penelitian kualitatif deskriptif. Sumber data yang digunakan dalam penelitian ini yakni sumber data primer yang didapatkan dari pendidik di TK Bougenvil dan sekunder yang didapatkan dari dokumentasi berupa foto penggunaan media loose part. Teknik pengumpulan data peneliti peroleh melalui wawancara kepada guru kelas, observasi kepada dua puluh delapan anak beserta guru kelas, dan dokumentasi foto pada saat anak dan guru menggunakan media loose part. Sedangkan teknik analisis yang digunakan dalam penelitian ini yakni reduksi data, penyajian data dan verifikasi. Hasil penelitian ditemukan bahwa implementasi pembelajaran bebasis STEAM (science, technology, engineering, arts, mathematics) di TK Bougenvil sudah dilaksanakan sebanyak 2 kali dalam satu bulan. Selama pelaksanaan ini tentunya masih terdapat ketidaksesuaian dengan konsep STEAM pada media loose part seperti terdapatnya instruksi, tidak diterapkan langkah eksplorasi serta tidak ada format RPPH dan penilaian harian. Selain itu, untuk dampak yang diberikan sudah cukup baik terutama pada kemandirian, keaktifan, menghargai teman serta berkomunikasi. Namun pada indikator 2 (imajinatif) masih kurang baik

DRomics: A Turnkey Tool to Support the Use of the Dose–Response Framework for Omics Data in Ecological Risk Assessment

International audienceOmics approaches (e.g. transcriptomics, metabolomics) are promising for ecological risk assessment (ERA) since they provide mechanistic information and early warning signals. A crucial step in the analysis of omics data is the modelling of concentration-dependency which may have different trends including monotonic (e.g. linear, exponential) or biphasic (e.g. U shape, bell shape) forms. The diversity of responses raises challenges concerning detection and modelling of significant responses and effect concentration (EC) derivation. Furthermore, handling high-throughput datasets is time-consuming and requires effective and automated processing routines. Thus, we developed an open source tool (DRomics,available as an R-package and as a web-based service) which, after elimination of molecular responses (e.g. gene expressions from microarrays) with no concentration-dependency and/or high variability, identifies the best model for concentration-response curve description. Subsequently, an EC (e.g. a benchmark dose) is estimated from each curve and curves are classified based on their model parameters. This tool is especially dedicated to manage data obtained from an experimental design favoring a greatnumber of tested doses rather than a great number of replicates and also to handle properly monotonic and biphasic trends. The tool finally restitutes a table of results that can be directly used to perform ERA approaches

Weak interpolation in Banach spaces

1. Trait-based approaches predict ecosystem functioning under environmental change by relating traits predicting changes in species densities (response traits) to traits driving ecosystem functioning (effect traits). Stressors can however affect ecosystem functioning not only by altering species densities, but also by directly changing species effect traits. 2. We first identified the response traits predicting the cell density of 18 marine benthic diatom strains along gradients of two chemical stressors (a pesticide and a metal, atrazine and copper). We then tested if response traits could predict stressor-induced changes in ecosystem functioning, i.e. changes in the effect traits driving the diatoms’ potential contribution to primary production, sediment stabilization and energy content in intertidal systems. Finally, we examined if changes in density and changes in ecosystem functioning were correlated, to assess whether species capable of growing under stressful conditions could maintain their contribution to ecosystem functioning. 3. The relationship between response traits and stressor-induced changes in density and ecosystem functioning was different depending on stressor type: a set of intercorrelated morphological traits predicted changes in both density and ecosystem functioning under metal stress, with large cells being more stress-resistant. Changes in density and changes in ecosystem functioning were positively related: diatoms whose density was least affected by the metal were also able to sustain functioning under metal exposure. 4. In contrast, the capacity for mixotrophic growth predicted changes in density, but not changes in ecosystem functioning under pesticide stress. Pesticide effects on density and on ecosystem functioning were negatively related for energy content and sediment stabilization, indicating a limited capacity of pesticide-tolerant diatoms to maintain their contribution to ecosystem functioning. Synthesis. Ecosystem functioning under stress can depend on whether response traits driving changes in density also predict direct stress effects on the species’ contribution to ecosystem functioning. Based on our results, we expect a disproportionate loss of functioning when traits driving species densities do not allow to maintain ecosystem functioning under stress

Hydrodynamics Alter the Tolerance of Autotrophic Biofilm Communities Toward Herbicides

Multiple stressors pose potential risk to aquatic ecosystems and are the main reasons for failing ecological quality standards. However, mechanisms how multiple stressors act on aquatic community structure and functioning are poorly understood. This is especially true for two important stressors types, hydrodynamic alterations and toxicants. Here we perform a mesocosm experiment in hydraulic flumes connected as a bypass to a natural stream to test the interactive effects of both factors on natural (inoculated from streams water) biofilms. Biofilms, i.e., the community of autotrophic and heterotrophic microorganisms and their extracellular polymeric substances (EPS) in association with substratum, are key players in stream functioning. We hypothesized (i) that the tolerance of biofilms toward toxicants (the herbicide Prometryn) decreases with increasing hydraulic stress. As EPS is known as an absorber of chemicals, we hypothesize (ii) that the EPS to cell ratio correlates with both hydraulic stress and herbicide tolerance. Tolerance values were derived from concentration-response assays. Both, the herbicide tolerance and the biovolume of the EPS significantly correlated with the turbulent kinetic energy (TKE), while the diversity of diatoms (the dominant group within the stream biofilms) increased with flow velocity. This indicates that the positive effect of TKE on community tolerance was mediated by turbulence-induced changes in the EPS biovolume. This conclusion was supported by a second experiment, showing decreasing effects of the herbicide to a diatom biofilm (Nitzschia palea) with increasing content of artificial EPS. We conclude that increasing hydrodynamic forces in streams result in an increasing tolerance of microbial communities toward chemical pollution by changes in EPS-mediated bioavailability of toxicants

Challenges to water quality assessment in Europe – Is there scope for improvement of the current Water Framework Directive bioassessment scheme in rivers?

The EU Water Framework Directive (WFD) assessment scheme has been putting in force the evaluation of freshwater ecosystems in Europe, including a new paradigm of ecological status. After almost 20 years since the WFD implementation, it is imperative to evaluate the efficiency of its standard assessment scheme and to explore the possibility of learning how to improve its effectiveness. That is the spirit of this review, aiming (i) to explore the existing literature on the WFD bioassessment scheme for assessing freshwater ecosystem health, particularly in lotic ecosystems (where the WFD scheme is most consolidated); (ii) to document which paths are suggested by the scientific community to improve the efficiency of the bioassessment in tackling current challenges. In the specific arena of bioassessment, we first identify the major constraints to the WFD full implementation in rivers. Second, we analyse retrospective Ecological Risk Assessment (ERA) as an evaluation approach supporting management actions that could inspire improvements in the WFD bioassessment scheme. Third, we review the advances and debate on complementary metrics to improve WFD evaluation protocols and/or the feasibility of the evaluation outcome. Fourth, a conceptual scheme for an improved evaluation strategy is presented. Our proposal essentially merges the WFD bioassessment scheme with the ERA philosophy, proposing a tiered approach of increasing complexity and spatial resolution, where expert judgement is included surgically at all decision stages. This scheme requires true integration of chemical, ecological and ecotoxicological LoE for a quantitative estimation of risks, and provides a comprehensive framework that accommodates tools and perspectives already suggested by other authors. Besides providing a literature review on the strengths and weaknesses of the current WFD bioassessment scheme, we wish to open way for the scientific discussion towards an improved conceptual scheme for the evaluation of ecosystem health.CESAM - Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro(UIDP/50017/2020 + UIDB/50017/2020