Sonification, Musification, and Synthesis of Absolute Program Music

Presented at the 22nd International Conference on Auditory Display (ICAD-2016)When understood as a communication system, a musical work can be interpreted as data existing within three domains. In this interpretation an absolute domain is interposed as a communication channel between two programatic domains that act respectively as source and receiver. As a source, a programatic domain creates, evolves, organizes, and represents a musical work. When acting as a receiver it re-constitutes acoustic signals into unique auditory experience. The absolute domain transmits physical vibrations ranging from the stochastic structures of noise to the periodic waveforms of organized sound. Analysis of acoustic signals suggest recognition as a musical work requires signal periodicity to exceed some minimum. A methodological framework that satisfies recent definitions of sonification is outlined. This framework is proposed to extend to musification through incorporation of data features that represent more traditional elements of a musical work such as melody, harmony, and rhythm

The Population Genetic Signature of Polygenic Local Adaptation

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We first describe a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of $Q_{ST}/F_{ST}$ comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.Comment: 42 pages including 8 figures and 3 tables; supplementary figures and tables not included on this upload, but are mostly unchanged from v

Disentangling the effects of geographic and ecological isolation on genetic differentiation

Populations can be genetically isolated both by geographic distance and by differences in their ecology or environment that decrease the rate of successful migration. Empirical studies often seek to investigate the relationship between genetic differentiation and some ecological variable(s) while accounting for geographic distance, but common approaches to this problem (such as the partial Mantel test) have a number of drawbacks. In this article, we present a Bayesian method that enables users to quantify the relative contributions of geographic distance and ecological distance to genetic differentiation between sampled populations or individuals. We model the allele frequencies in a set of populations at a set of unlinked loci as spatially correlated Gaussian processes, in which the covariance structure is a decreasing function of both geographic and ecological distance. Parameters of the model are estimated using a Markov chain Monte Carlo algorithm. We call this method Bayesian Estimation of Differentiation in Alleles by Spatial Structure and Local Ecology (BEDASSLE), and have implemented it in a user-friendly format in the statistical platform R. We demonstrate its utility with a simulation study and empirical applications to human and teosinte datasets

Influencers: Not So Fluent in Disclosure Compliance

The Fyre Festival is one of the most infamous disasters in music festival history. Lesser known to the public is that the influencers involved in Fyre Festival’s influencer marketing campaign were required to disclose their payments for endorsing the event. These types of disclosures are regulated by the Federal Trade Commission (“FTC”) pursuant to its authority granted under the Federal Trade Commission Act (“FTC Act”). The disclosure requirement is set forth in the FTC’s Guides Concerning the Use of Endorsements and Testimonials in Advertising (“Endorsement Guides”), which are nonbinding instructions that educate influencers on how to comply with Section 5 of the FTC Act. While the FTC pursues companies and influencers that violate the disclosure requirements, its attempts are futile due to the Endorsement Guides’ nonbinding nature. With influencer marketing growing rapidly and fraudulent practices becoming rampant, the FTC must make two changes to become more effective. First, the agency must use its rulemaking authority under the FTC Act to codify elements of the Endorsement Guides, and other FTC works into formal rules that will allow it to seek monetary penalties and consumer redress against violators. Second, the FTC must mandate disclosure provisions to be present in every influencer-company endorsement contract to prevent the prevalent deceptive business practice. By implementing these two changes, the FTC will have adequate tools at its disposal to prevent and punish violators who previously remained outside its grasp

Capturing Architecture in Words

Open architecture. How or who or what is that? Or rather, how should we think, plan, build in a world which is daily becoming more tattered? Should we fear these tatters, suppress them and flee into the safe world of architecture

Nursing Students\u27 Knowledge of Alcohol - Interactive Medications

In 2018, nearly 57% of American adults reported drinking alcohol in the past month, and 41.5% of current drinkers reported taking alcohol-interactive (AI) medications. Consuming alcohol and medications concurrently may result in adverse effects. The purpose of this study was to determine the effectiveness of a one-hour lecture about AI medications in a class of undergraduate nursing students (N = 48) at a small Midwestern university. The Jarvis Nursing Knowledge of Alcohol-Interactive Medications survey was distributed on August 27, 2018, and again on November 19, 2018. A significant increase was found between pretest and posttest on correct identification of mechanism (27.47 ± 14.18 vs. 37.33 ± 16.60; t =3.15; p \u3c .02). A significant increase was also found between pretest and posttest scores on the ability to discriminate a medication as AI or Non-AI (68.5% ± 6.3 vs 71.5% ± 6.5; t= 2.5; p \u3c. 02). While scores increased significantly, students failed to consistently recognize the correct medication-alcohol interaction. A one-hour lecture emphasizing AI medications in the pre-licensure program enhanced students’ knowledge; however, future research is needed to determine retention of AI medication knowledge