Alaska Science Center
This dataset contains results of genetic screening for Poecivirus from samples of black-capped chickadees (BCCH; Poecile atricapillus) with and without clinical signs of avian keratin disorder (AKD). Data include information on detection/non-detection of the virus in tissue collected with buccal swabs, cloacal swabs, blood samples, and fecal samples from up to 124 individuals between 2015 and 2017 from various locations in southcentral Alaska. For an additional 17 symptomatic (and one asymptomatic) individual(s) collected between 2001 and 2015 in the same region, data include measurements of viral load in beak tissue measured by qRT-PCR as well as the amount of actively replicating virus detected by 7 negative-strand and 2 positive-strand oligonucleotide probes, all relative to amounts of host RNA. Ancillary data on beak measurements, clinical signs of AKD (beak overgrowth or hyperkeratosis at the cellular level), locations, and dates of collection are also included for each individual.
This data set includes information on collections of fecal or cloacal samples from wild birds at the landfill in Soldotna, Alaska, USA. Samples were screened for Escherichia coli (E. coli) and tested for resistance to multiple antibiotics.
This dataset includes two spreadsheets associated with a study of three genera of blood parasites (Leucocytozoon, Haemoproteus, Plasmodium) in 185 Spectacled Eiders (Somateria fischeri) sampled in Alaska, 2008-2012. The first spreadsheet provides age, sex, location, and blood parasite (hematozoa) infection data for three different genera of blood parasites. Hematozoa DNA, from eider blood samples, was detected by amplifying a 479bp fragment of the Cytochrome-b gene using a nested PCR technique with primers specific for the three target hematozoa genera. Amplicons were sequenced and assigned to genera by the nucleotide BLAST function of the NCBI GenBank database. Infection data are provided as NCBI GenBank accession numbers for DNA sequences obtained from samples that tested positive for infection. The second spreadsheet provides the geographic coordinates of the five sampling sites.
Bioinformatics software repository containing python scripts intended for search and download of genetic information obtained from GenBank NCBI genetics data resources in support of developing PCR primers, targeted genetic databases, genetic analyses, and data interpretation. Includes multiple functions to streamline this process.
Daily sea ice imagery and charting benefits logistics and navigational planning in the Alaskan Arctic waters, yet access to these data often requires high bandwidth data access and substantial GIS processing. This software script acquires, processes and delivers these data in a format that may be manipulated by openly available virtual globe software, be visualized by software commonly installed on all smart phones and computers, and that may be accessed through moderate band-width data communications available in remote Alaskan communities and offshore research vessels. The script sends daily or weekly e-mails with attached maps images and virtual globe data files of sea ice products, including the National Ice Center Marginal Ice Zone chart, and images of the 6.25 Km resolution passive microwave reflectance optimized to visualize sea ice. In the emailing the script provides links to the NASA MODIS imagery corrected to enhance visualization of sea ice; National Weather Service sea ice and surface forecast products, the NOAA NECP forecasted 24 h sea ice drift map, and the latest NOAA images from the POES AVHRR satellite.
An implementation of Quantitative Fatty Acid Signature Analysis (QFASA) in R. QFASA is a method of estimating the diet composition of predators. The fundamental unit of information in QFASA is a fatty acid signature (signature), which is a vector of proportions describing the fatty acid composition of adipose tissue. Signature data from at least one predator and from samples of all potential prey types are required. Calibration coefficients, which adjust for the differential metabolism of individual fatty acids by predators, are also required. Given those data inputs, a predator signature is modeled as a mixture of potential prey signatures and its diet estimate is obtained as the mixture that minimizes a measure of distance between the observed and modeled signatures. A variety of estimation options, goodness-of-fit diagnostic procedures to assess the suitability of estimates, and simulation capabilities are implemented. Please refer to the package vignette and the documentation files for individual functions for details and references.
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