16,800 research outputs found
Unraveling spatiotemporal variability of arbuscular mycorrhizal fungi in a temperate grassland plot
© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldmann, K., Boeddinghaus, R. S., Klemmer, S., Regan, K. M., Heintz-Buschart, A., Fischer, M., Prati, D., Piepho, H., Berner, D., Marhan, S., Kandeler, E., Buscot, F., & Wubet, T. Unraveling spatiotemporal variability of arbuscular mycorrhizal fungi in a temperate grassland plot. Environmental Microbiology, 22(3),(2020): 873-888, doi:10.1111/1462-2920.14653.Soils provide a heterogeneous environment varying in space and time; consequently, the biodiversity of soil microorganisms also differs spatially and temporally. For soil microbes tightly associated with plant roots, such as arbuscular mycorrhizal fungi (AMF), the diversity of plant partners and seasonal variability in trophic exchanges between the symbionts introduce additional heterogeneity. To clarify the impact of such heterogeneity, we investigated spatiotemporal variation in AMF diversity on a plot scale (10 Ă 10 m) in a grassland managed at low intensity in southwest Germany. AMF diversity was determined using 18S rDNA pyrosequencing analysis of 360 soil samples taken at six time points within a year. We observed high AMF alphaâ and betaâdiversity across the plot and at all investigated time points. Relationships were detected between spatiotemporal variation in AMF OTU richness and plant species richness, root biomass, minimal changes in soil texture and pH. The plot was characterized by high AMF turnover rates with a positive spatiotemporal relationship for AMF betaâdiversity. However, environmental variables explained only â20% of the variation in AMF communities. This indicates that the observed spatiotemporal richness and community variability of AMF was largely independent of the abiotic environment, but related to plant properties and the cooccurring microbiome.We thank the managers of the three Exploratories, Kirsten ReichelâJung, Swen Renner, Katrin Hartwich, Sonja Gockel, Kerstin Wiesner, and Martin Gorke for their work in maintaining the plot and project infrastructure; Christiane Fischer and Simone Pfeiffer for giving support through the central office, Michael Owonibi and Andreas Ostrowski for managing the central data base, and Eduard Linsenmair, Dominik Hessenmöller, Jens Nieschulze, ErnstâDetlef Schulze, Wolfgang W. Weisser and the late Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. The work has been funded by the DFG Priority Program 1374 âInfrastructureâBiodiversityâExploratoriesâ (BU 941/22â1, BU 941/22â3, KA 1590/8â2, KA 1590/8â3). Field work permits were issued by the responsible state environmental office of BadenâWĂŒrttemberg (according to § 72 BbgNatSchG). Likewise, we kindly thank Beatrix Schnabel, Melanie GĂŒnther and Sigrid HĂ€rtling for 454 sequencing in Halle. AHB gratefully acknowledges the support of the German Centre for Integrative Biodiversity Research (iDiv) HalleâJenaâLeipzig funded by the German Research Foundation (FZT 118). Authors declare no conflict of interests
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment
Many localization algorithms use a spatiotemporal window of sensory
information in order to recognize spatial locations, and the length of this
window is often a sensitive parameter that must be tuned to the specifics of
the application. This letter presents a general method for environment-driven
variation of the length of the spatiotemporal window based on searching for the
most significant localization hypothesis, to use as much context as is
appropriate but not more. We evaluate this approach on benchmark datasets using
visual and Wi-Fi sensor modalities and a variety of sensory comparison
front-ends under in-order and out-of-order traversals of the environment. Our
results show that the system greatly reduces the maximum distance traveled
without localization compared to a fixed-length approach while achieving
competitive localization accuracy, and our proposed method achieves this
performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation
Letters. v2: incorporated reviewer feedbac
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