Université de Sherbrooke

About: Université de Sherbrooke is a education organization based out in Sherbrooke, Quebec, Canada. It is known for research contribution in the topics: Population & Receptor. The organization has 14922 authors who have published 28783 publications receiving 792511 citations. The organization is also known as: Universite de Sherbrooke & Sherbrooke University.

TRY - a global database of plant traits

Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET

Abstract: Long-term measurements by the AERONET program of spectral aerosol optical depth, precipitable water, and derived Angstrom exponent were analyzed and compiled into an aerosol optical properties climatology. Quality assured monthly means are presented and described for 9 primary sites and 21 additional multiyear sites with distinct aerosol regimes representing tropical biomass burning, boreal forests, midlatitude humid climates, midlatitude dry climates, oceanic sites, desert sites, and background sites. Seasonal trends for each of these nine sites are discussed and climatic averages presented.

Resonant Formation of DNA Strand Breaks by Low-Energy (3 to 20 eV) Electrons

Abstract: Most of the energy deposited in cells by ionizing radiation is channeled into the production of abundant free secondary electrons with ballistic energies between 1 and 20 electron volts. Here it is shown that reactions of such electrons, even at energies well below ionization thresholds, induce substantial yields of single- and double-strand breaks in DNA, which are caused by rapid decays of transient molecular resonances localized on the DNA's basic components. This finding presents a fundamental challenge to the traditional notion that genotoxic damage by secondary electrons can only occur at energies above the onset of ionization, or upon solvation when they become a slowly reacting chemical species.

The global spectrum of plant form and function

Abstract: The authors found that the key elements of plant form and function, analysed at global scale, are largely concentrated into a two-dimensional plane indexed by the size of whole plants and organs on the one hand, and the construction costs for photosynthetic leaf area, on the other.

Domain-Adversarial Training of Neural Networks.

Abstract: We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.