University of New Mexico

About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.

Deep learning for neuroimaging: A validation study

Abstract: Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

Eutrophication weakens stabilizing effects of diversity in natural grasslands

Abstract: Studies of experimental grassland communities have demonstrated that plant diversity can stabilize productivity through species asynchrony, in which decreases in the biomass of some species are compensated for by increases in others. However, it remains unknown whether these findings are relevant to natural ecosystems, especially those for which species diversity is threatened by anthropogenic global change. Here we analyse diversity-stability relationships from 41 grasslands on five continents and examine how these relationships are affected by chronic fertilization, one of the strongest drivers of species loss globally. Unmanipulated communities with more species had greater species asynchrony, resulting in more stable biomass production, generalizing a result from biodiversity experiments to real-world grasslands. However, fertilization weakened the positive effect of diversity on stability. Contrary to expectations, this was not due to species loss after eutrophication but rather to an increase in the temporal variation of productivity in combination with a decrease in species asynchrony in diverse communities. Our results demonstrate separate and synergistic effects of diversity and eutrophication on stability, emphasizing the need to understand how drivers of global change interactively affect the reliable provisioning of ecosystem services in real-world systems.

Observation of a large-scale anisotropy in the arrival directions of cosmic rays above 8 × 1018 eV

Abstract: Cosmic rays are atomic nuclei arriving from outer space that reach the highest energies observed in nature Clues to their origin come from studying the distribution of their arrival directions Using 3 × 10 4 cosmic rays with energies above 8 × 10 18 electron volts, recorded with the Pierre Auger Observatory from a total exposure of 76,800 km 2 sr year, we determined the existence of anisotropy in arrival directions The anisotropy, detected at more than a 52σ level of significance, can be described by a dipole with an amplitude of 65 − 09 + 13 percent toward right ascension α d = 100 ± 10 degrees and declination δ d = − 24 − 13 + 12 degrees That direction indicates an extragalactic origin for these ultrahigh-energy particles