University of New Brunswick

About: University of New Brunswick is a education organization based out in Fredericton, New Brunswick, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 10498 authors who have published 20654 publications receiving 474448 citations.

The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project

Abstract: The PREDICTS project—Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (www.predicts.org.uk)—has collated from published studies a large, reasonably representative database of comparable samples of biodiversity from multiple sites that differ in the nature or intensity of human impacts relating to land use. We have used this evidence base to develop global and regional statistical models of how local biodiversity responds to these measures. We describe and make freely available this 2016 release of the database, containing more than 3.2 million records sampled at over 26,000 locations and representing over 47,000 species. We outline how the database can help in answering a range of questions in ecology and conservation biology. To our knowledge, this is the largest and most geographically and taxonomically representative database of spatial comparisons of biodiversity that has been collated to date; it will be useful to researchers and international efforts wishing to model and understand the global status of biodiversity.

Structural and functional diversification of the light-harvesting complexes in photosynthetic eukaryotes

Abstract: Eukaryotes acquired photosynthetic metabolism over a billion years ago, and during that time the light-harvesting antennae have undergone significant structural and functional divergence. The antenna systems are generally used to harvest and transfer excitation energy into the reaction centers to drive photosynthesis, but also have the dual role of energy dissipation. Phycobilisomes formed the first antenna system in oxygenic photoautotrophs, and this soluble protein complex continues to be the dominant antenna in extant cyanobacteria, glaucophytes, and red algae. However, phycobilisomes were lost multiple times during eukaryotic evolution in favor of a thylakoid membrane-integral light-harvesting complex (LHC) antenna system found in the majority of eukaryotic taxa. While photosynthesis spread across different eukaryotic kingdoms via endosymbiosis, the antenna systems underwent extensive modification as photosynthetic groups optimized their light-harvesting capacity and ability to acclimate to changing environmental conditions. This review discusses the different classes of LHCs within photosynthetic eukaryotes and examines LHC diversification in different groups in a structural and functional context.

Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery

Abstract: The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information, which is of great significance for complex land cover mapping. Incorporating spatial features within the classification scheme have been mainly carried out by applying just low-level features, which have shown improvement in the classification result. By contrast, the application of high-level spatial features for classification of satellite imagery has been underrepresented. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland mapping using optical remote sensing data. Designing a fully trained new convolutional network is infeasible due to the limited amount of training data in most remote sensing studies. Thus, we applied fine tuning of a pre-existing CNN. Specifically, AlexNet was used for this purpose. The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely random forest (RF), to evaluate the efficiency of CNN. Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., three features) for CNN compared to RF (i.e., eight features). The proposed classification scheme is the first attempt, investigating the potential of fine-tuning pre-existing CNN, for land cover mapping. It also serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing data.

Organic Composition of C/1999 S4 (LINEAR): A Comet Formed Near Jupiter?

Abstract: In the current paradigm, Oort cloud comets formed in the giant planets' region of the solar nebula, where temperatures and other conditions varied greatly. The measured compositions of four such comets (Halley, Hyakutake, Hale-Bopp, and Lee) are consistent with formation from interstellar ices in the cold nebular region beyond Uranus. The composition of comet C/1999 S4 (LINEAR) differs greatly, which suggests that its ices condensed from processed nebular gas, probably in the Jupiter-Saturn region. Its unusual organic composition may require reevaluation of the prebiotic organic material delivered to the young Earth by comets.