ASU Scholarship Showcase
This growing collection consists of scholarly works authored by ASU-affiliated faculty, staff, and community members, and it contains many open access articles. ASU-affiliated authors are encouraged to Share Your Work in KEEP.
Filtering by
- All Subjects: child disclosure
- All Subjects: Connected and Automated Vehicles
- All Subjects: genome annotation
- All Subjects: scientific organizations
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).
This report documents the results of an empirical study to characterize science diaspora networks and their underlying organizations and to document how network managers characterize operational successes, challenges, future plans, and relations to science diplomacy.
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The current study examined heterogeneity in emerging adult children's routine and self-disclosure to parents using mixture modeling and explored predictors and outcomes associated with the patterns of disclosure. Participants consisted of 449 emerging adults (49% male, 68% European American, 65% college students, 33% single-parent families) who completed questionnaires every year across three waves (Mage at Time 1 = 18.4 years). Latent profile analyses suggested that large groups of emerging adults reported moderate levels of routine disclosure and low levels of self-disclosure to both mothers (79%) and fathers (36%), while other groups (20%) reported high levels of routine and self-disclosure to both parents. Profile membership was associated with predictors (parental autonomy granting, self-disclosure to friend, gender, family structure, college attendance) at Time 1 and outcomes (delinquency, depression, and prosocial behavior) at Time 3. Implications regarding the continued parent-child relationship and disclosure to parents in the third decade of life are discussed.
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The following literature review talks about the driving simulation platforms commercially available for automated vehicle development. It is also a comparison of the simulation packages, their advantages and drawbacks, and an insight into what is missing in the simulators of today. Automated vehicle safety and reliability are the important requirements when developing automated vehicles. These requirements are guaranteed by extensive functional and performance tests. Conducting these tests on real vehicles is extremely expensive and time consuming, and thus it is necessary to develop a simulation platform to perform these tasks. In most cases, it is difficult for system or algorithm developers in the testing process to evaluate the massive design space. To test any algorithm change, developers need to test a functional module alone, and later setting up a whole physical testing environment that consists of several other modules, leading to enormous testing costs. Fortunately, many of the testing tasks can be accomplished by utilizing simulator. The key to the success of a simulation is how accurately the simulator can simulate the physical reality.