1,419 research outputs found

    Lidar Remote Sensing Variables Predict Breeding Habitat of a Neotropical Migrant Bird

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    A topic of recurring interest in ecological research is the degree to which vegetation structure influences the distribution and abundance of species. Here we test the applicability of remote sensing, particularly novel use of waveform lidar measurements, for quantifying the habitat heterogeneity of a contiguous northern hardwoods forest in the northeastern United States. We apply these results to predict the breeding habitat quality, an indicator of reproductive output of a well-studied Neotropical migrant songbird, the Black-throated Blue Warbler (Dendroica caerulescens). We found that using canopy vertical structure metrics provided unique information for models of habitat quality and spatial patterns of prevalence. An ensemble decision tree modeling approach (random forests) consistently identified lidar metrics describing the vertical distribution and complexity of canopy elements as important predictors of habitat use over multiple years. Although other aspects of habitat were important, including the seasonality of vegetation cover, the canopy structure variables provided unique and complementary information that systematically improved model predictions. We conclude that canopy structure metrics derived from waveform lidar, which will be available on future satellite missions, can advance multiple aspects of biodiversity research, and additional studies should be extended to other organisms and regions

    The Detergent Evaluation Methods and the Washing Machine(PART II)

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    AIC model selection table and associated coefficients for hermit warbler 2013 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt

    第829回千葉医学会例会・第8回千葉精神科集談会

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    AIC model selection table and associated coefficients for Hammond's flycatcher 2012 for all models combined. Column names for the model coefficients use the following notation: coefficient = parameter(covariate) and standard error = SEparameter(covariate). Parameter abbreviations are p = detection probability, psi = initial occupancy, col = colonization/settlement, ext = extinction/vacancy. Parameter(Int) refers to the intercept. ‘nPars’ is the number of parameters estimated in the model. Each model is ranked by its AIC score, which represents how well the model fits the data. A lower ∆AIC (delta) value is indicative of a better model. The probability that the model (of the models tested) would best explain the data is indicated by AICwt

    The Role of the L1 in the L2 Classroom

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    The use of the L1 in L2 classrooms has historically been a controversial issue. Research over the years has greatly influenced the perspectives of the L1 and its purposes in the L2 classroom. In this paper, I will review the traditional views of L1 usage in the L2 classroom as well as discuss the major research studies which have brought new light upon the L1 and its influences on L2 acquisition. The focus of this paper will then shift to the pedagogical implications of such findings and how these findings affect my decisions as a teacher with respect to L1 usage in my classroom

    Taxonomic voucher specimens for study of bee communities in intensively managed Douglas-fir forests in the Oregon Coast Range

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    Understanding how pollinators respond to anthropogenic land use is key to conservation of biodiversity and ecosystem services, but few studies have addressed this topic in coniferous forests, particularly those managed intensively for wood production. This study reports on voucher material generated as part of Zitomer et al. (2023), that assessed changes in wild bee communities with time since harvest in 60 intensively managed Douglas-fir (Pseudotsuga menziesii) stands in the Oregon Coast Range across a gradient in stand age spanning a typical harvest rotation (0-37 years post-harvest). We additionally assessed relationships of bee diversity and community composition to relevant habitat features, including availability of floral resources and nest sites, understory vegetation characteristics, and composition of the surrounding landscape. Specimens were collected using a combination of passive sampling methods-blue vane traps and white, blue, and yellow bowl traps- and hand-netting and were identified to the lowest possible taxonomic level by A.R. Moldenke and L.R. Best. Four hundred and ten taxonomic voucher specimens were deposited into the Oregon State Arthropod Collection (Accession# OSAC_AC_2023_01_09-001-01) to serve as a reference for future research

    Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces [version 3; peer review: 2 approved]

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    Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine. Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures. Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes (i.e. survival time) in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes (e.g. GPCR signalling pathways) containing enriched reactions, providing support for new off-label therapeutic options. Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types (e.g. post-translational modifications) collected for many types of disease
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