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Unmanned Aerial Vehicles (UAVs) have become readily available for both the average consumer and professional due to decreases in price and increases in technological capabilities. This work ventured to explore the feasible use of UAV-technology in the area of roof analysis for facilities management purposes and contrast it to traditional

Unmanned Aerial Vehicles (UAVs) have become readily available for both the average consumer and professional due to decreases in price and increases in technological capabilities. This work ventured to explore the feasible use of UAV-technology in the area of roof analysis for facilities management purposes and contrast it to traditional techniques of inspection. An underlying goal of this work was two-fold. First, it was to calculate the upfront cost of investing in appropriate UAV equipment and training for a typical staff member to become proficient at doing such maintenance work in the practice of actual roof inspections on a sample set of roofs. Secondly, it was to compare the value of using this UAV method of investigation to traditional practices of inspecting roofs manually by personally viewing and walking roofs. The two methods for inspecting roofs were compared using various metrics, including time, cost, value, safety, and other relevant measurables. In addition to the study goals, this research was able to identify specific benefits and hazards for both methods of inspection through empirical trials. These points illustrate the study as Lessons Learned from the experience, which may be of interest to those Facilities Managers who are considering investing resources in UAV training and equipment for industrial purposes. Overall, this study helps to identify the utility of UAV technology in a well-established professional field in a way that has not been previously conducted in academia.
ContributorsBodily, Jordan (Author) / Sullivan, Kenneth (Thesis advisor) / Smithwick, Jake (Committee member) / Stone, Brian (Committee member) / Arizona State University (Publisher)
Created2020
Description

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on dee

Agassiz’s desert tortoise (Gopherus agassizii) is a long-lived species native to the Mojave Desert and is listed as threatened under the US Endangered Species Act. To aid conservation efforts for preserving the genetic diversity of this species, we generated a whole genome reference sequence with an annotation based on deep transcriptome sequences of adult skeletal muscle, lung, brain, and blood. The draft genome assembly for G. agassizii has a scaffold N50 length of 252 kbp and a total length of 2.4 Gbp. Genome annotation reveals 20,172 protein-coding genes in the G. agassizii assembly, and that gene structure is more similar to chicken than other turtles. We provide a series of comparative analyses demonstrating (1) that turtles are among the slowest-evolving genome-enabled reptiles, (2) amino acid changes in genes controlling desert tortoise traits such as shell development, longevity and osmoregulation, and (3) fixed variants across the Gopherus species complex in genes related to desert adaptations, including circadian rhythm and innate immune response. This G. agassizii genome reference and annotation is the first such resource for any tortoise, and will serve as a foundation for future analysis of the genetic basis of adaptations to the desert environment, allow for investigation into genomic factors affecting tortoise health, disease and longevity, and serve as a valuable resource for additional studies in this species complex.

Data Availability: All genomic and transcriptomic sequence files are available from the NIH-NCBI BioProject database (accession numbers PRJNA352725, PRJNA352726, and PRJNA281763). All genome assembly, transcriptome assembly, predicted protein, transcript, genome annotation, repeatmasker, phylogenetic trees, .vcf and GO enrichment files are available on Harvard Dataverse (doi:10.7910/DVN/EH2S9K).

ContributorsTollis, Marc (Author) / DeNardo, Dale F (Author) / Cornelius, John A (Author) / Dolby, Greer A (Author) / Edwards, Taylor (Author) / Henen, Brian T. (Author) / Karl, Alice E. (Author) / Murphy, Robert W. (Author) / Kusumi, Kenro (Author)
Created2017-05-31