589 research outputs found

    Population and genetic impacts of a 4-lane highway on black bears in eastern North Carolina

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    A 19.3-km section of U.S. Highway 64 in Washington County, North Carolina was rerouted to a 4-lane, divided highway with 3 wildlife underpasses during 2001–2005. I determined the short-term population and genetic impacts of the new highway on American black bears (Ursus americanus). I used DNA from hair samples collected during 7 weekly sampling periods within the project area of the new highway and a nearby control area during 2000 (pre-construction phase) and 2006 (post-construction phase; n = 70 sites for each study area). DNA from the hair samples was used to obtain genotypes of sampled bears using 10 microsatellite markers. I created capture histories of all identified individuals and used closed mark-recapture models in Program MARK to estimate abundance. Population abundance decreased on the treatment area from 68 (CI = 53–82) before construction to 20 (CI = 14–26) after completion of the highway. On the control area, population abundance decreased from 144 to 101. Using permutation procedures, I determined that the decrease in population abundance on the treatment area was greater compared with the control area (P = 0.0012). Additionally, I used bear visits to the sampling sites with multi-season occupancy models in Program MARK to determine if site occupancy decreased following the construction of the highway and if any decrease was a function of distance from the highway. Following highway construction, site occupancy decreased more on the treatment area than the control area but was not a function of distance from the highway. Finally, I used the microsatellite data to compare gene flow, isolation by distance, heterozygosity, allelic diversity, population assignment, and genetic structure (Fst) before and after completion of the highway. I did not observe any treatment effects for these genetic measures. I speculate that displacement during the construction of the highway and mortality due to bear-vehicle collisions contributed to the population decline and decrease in site occupancy. Although the wildlife underpasses facilitated genetic and demographic connectivity, my study indicates that the potential impact of new highways on black bear population abundance is an important consideration for transportation infrastructure planning

    Responding to sustainability: A model exploring the impacts of boards of directors and organisational strategic flexibility

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    As the strategic apex of decision making, boards of directors have ultimate responsibility in ensuring that firms address economic, environmental and social sustainability. We contend that board information-processing activities act as the mediational pathway by which board composition affects sustainability. Further, because of the complexity of the sustainability paradigm, strategic flexibility is posited to moderate relationships between information-processing activities and sustainable outcomes. The model proposed in this paper offers original insight into the drivers of sustainability in organisations and thus, we conclude the discussion with implications for both research and practice

    K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space

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    <p>Abstract</p> <p>Background</p> <p>Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.</p> <p>Results</p> <p>We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at <url>http://www.sourceforge.net/projects/kopls/</url>. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.</p> <p>Conclusion</p> <p>The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.</p

    Automatic Spectroscopic Data Categorization by Clustering Analysis (ASCLAN): A Data-Driven Approach for Distinguishing Discriminatory Metabolites for Phenotypic Subclasses

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    We propose a novel data-driven approach aiming to reliably distinguish discriminatory metabolites from nondiscriminatory metabolites for a given spectroscopic data set containing two biological phenotypic subclasses. The automatic spectroscopic data categorization by clustering analysis (ASCLAN) algorithm aims to categorize spectral variables within a data set into three clusters corresponding to noise, nondiscriminatory and discriminatory metabolites regions. This is achieved by clustering each spectral variable based on the r(2) value representing the loading weight of each spectral variable as extracted from a orthogonal partial least-squares discriminant (OPLS-DA) model of the data set. The variables are ranked according to r(2) values and a series of principal component analysis (PCA) models are then built for subsets of these spectral data corresponding to ranges of r(2) values. The Q(2)X value for each PCA model is extracted. K-means clustering is then applied to the Q(2)X values to generate two clusters based on minimum Euclidean distance criterion. The cluster consisting of lower Q(2)X values is deemed devoid of metabolic information (noise), while the cluster consists of higher Q(2)X values is then further subclustered into two groups based on the r(2) values. We considered the cluster with high Q(2)X but low r(2) values as nondiscriminatory, while the cluster with high Q(2)X and r(2) values as discriminatory variables. The boundaries between these three clusters of spectral variables, on the basis of the r(2) values were considered as the cut off values for defining the noise, nondiscriminatory and discriminatory variables. We evaluated the ASCLAN algorithm using six simulated (1)H NMR spectroscopic data sets representing small, medium and large data sets (N = 50, 500, and 1000 samples per group, respectively), each with a reduced and full resolution set of variables (0.005 and 0.0005 ppm, respectively). ASCLAN correctly identified all discriminatory metabolites and showed zero false positive (100% specificity and positive predictive value) irrespective of the spectral resolution or the sample size in all six simulated data sets. This error rate was found to be superior to existing methods for ascertaining feature significance: univariate t test by Bonferroni correction (up to 10% false positive rate), Benjamini-Hochberg correction (up to 35% false positive rate) and metabolome wide significance level (MWSL, up to 0.4% false positive rate), as well as by various OPLS-DA parameters: variable importance to projection, (up to 15% false positive rate), loading coefficients (up to 35% false positive rate), and regression coefficients (up to 39% false positive rate). The application of ASCLAN was further exemplified using a widely investigated renal toxin, mercury II chloride (HgCl2) in rat model. ASCLAN successfully identified many of the known metabolites related to renal toxicity such as increased excretion of urinary creatinine, and different amino acids. The ASCLAN algorithm provides a framework for reliably differentiating discriminatory metabolites from nondiscriminatory metabolites in a biological data set without the need to set an arbitrary cut off value as applied to some of the conventional methods. This offers significant advantages over existing methods and the possibility for automation of high-throughput screening in "omics" data
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