The Potential Impact of a Proposed Ban on the Sale of U.S. Horses for Slaughter and Human Consumption

Both federal and state governments in the United States are being asked to enact laws that would make slaughtering of horses for human consumption illegal. In the past, the United States was one of the principal exporters of horsemeat to Europe. This paper examines the impacts of a proposed ban on the U.S. horse industry and the U.S. export market for horsemeat. Findings indicate a loss of approximately $300 per horse in the United States as a result of such a ban. The supply of U.S. exported horsemeat has declined during the past decade. The results suggest that the most significant factors influencing this decline are lower real prices and competing imports.horse slaughter, horsemeat, meat exports, Agricultural and Food Policy, Livestock Production/Industries,

Millennials Versus Boomers: An Asymmetric Pattern of Realistic and Symbolic Threats Drives Intergenerational Tensions in the United States

Intergenerational conflict appears frequently in American public discourse, often framed as clashes between Millennials and Baby Boomers. Building on intergroup threat theory in an exploratory survey, a preregistered correlational study, and a preregistered intervention (N = 1,714), we find that (a) Millennials and Baby Boomers do express more animosity toward each other than toward other generations (Studies 1-3); (b) their animosity reflects asymmetric generational concerns: Baby Boomers primarily fear that Millennials threaten traditional American values (symbolic threat) while Millennials primarily fear that Baby Boomers's delayed transmission of power hampers their life prospects (realistic threat; Studies 2-3); (c) finally, an intervention challenging the entitativity of generational categories alleviates perceived threats and hostility for both generations (Study 3). These findings inform research on intergroup threat, provide a theoretically grounded framework to understand intergenerational relations, and put forward a strategy to increase harmony in aging societies

Automated Prediction of Preferences Using Facial Expressions

We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available

Using mHealth to Increase Treatment Utilization Among Recently Incarcerated Homeless Adults (Link2Care): Protocol for a Randomized Controlled Trial

Background: There is a significant revolving door of incarceration among homeless adults. Homeless adults who receive professional coordination of individualized care (ie, case management) during the period following their release from jail experience fewer mental health and substance use problems, are more likely to obtain stable housing, and are less likely to be reincarcerated. This is because case managers work to meet the various needs of their clients by helping them to overcome barriers to needed services (eg, food, clothing, housing, job training, substance abuse and mental health treatment, medical care, medication, social support, proof of identification, and legal aid). Many barriers (eg, limited transportation, inability to schedule appointments, and limited knowledge of available services) prevent homeless adults who were recently released from incarceration from obtaining available case management, crisis management, substance abuse, and mental health services. Objective: The aim of the Link2Care study is to assess the effectiveness of a smartphone app for increasing case management and treatment service utilization, and in turn reduce homelessness and rearrest. The goals of this research are to (1) assess the impact of an innovative smartphone app that will prompt and directly link recently incarcerated homeless adults to community-based case management services and resources and (2) utilize in-person and smartphone-based assessments to identify key variables (eg, alcohol or drug use, social support, psychological distress, and quality of life) that predict continued homelessness and rearrest. Methods: Homeless adults (N=432) who enroll in a shelter-based Homeless Recovery Program after release from the Dallas County Jail will be randomly assigned to one of the three treatment groups: (1) usual case management, (2) usual case management plus smartphone, and (3) usual case management with a study-provided smartphone that is preloaded with an innovative case management app (smartphone-based case management). Those assigned to smartphone-based case management will receive smartphones that prompt (twice weekly) connections to shelter-based case managers. The app will also offer direct links to case managers (available during normal business hours) and crisis interventionists (available 24 hours a day, 7 days a week) with the touch of a button. Results: Recruitment began in the spring of 2018, and data collection will conclude in 2021. Conclusions: This research represents an important step toward integrated service connection and health care service provision for one of the most underserved, high need, and understudied populations in the United States. Trial Registration: ClinicalTrials.gov NCT03399500; https://clinicaltrials.gov/ct2/show/NCT03399500 (Archived by WebCite at http://www.webcitation.org/6zSJwdgUS) Registered Report Identifier: RR1-10.2196/986

Atmospheric deposition in southeastern North Carolina and its impact on the Cape Fear River estuary

Concentrations of NHx, NO3 -, free amino acids, total and organic nitrogen, and inorganic anions were determined for 78 rain events between September 1, 2002 and August 31, 2003, on the campus of the University of North Carolina at Wilmington in southeastern North Carolina. The majority of N in Wilmington rain (78%) is inorganic, occurring as NO3 - and NHx in approximately equal proportions. Free amino acids make up a small portion of ON (11%). Correlation analysis and back trajectory analysis indicate that regional sources, rather than local emissions, determine the concentrations of inorganic N in UNCW rainwater and particle dry deposition. NO3 - concentration in rainwater at UNCW has decreased from 1990 to the present while wet deposition increased due to increased rainfall and possibly increased emissions from growing population and expanding industry in North Carolina. Wet and dry deposition of NHx are respectively 53% and 26% higher than a decade ago reflecting an increase in regional and possibly local emissions. Similar temporal patterns in are seen at UNCW and the NADP monitored sites with the greatest increase in Clinton, NC, where NHx concentrations and depositions have increased 58% and 107% respectively. Concentrations of H+ and major N analytes, with the exception of ON, vary significantly as a function of air mass origin with major increases in analytes correlating with locations of known anthropogenic sources. Seasonal and diurnal influences have a significant impact on the concentrations of H+ and N analytes in UNCW rainwater with significant concentration maximums in the spring and significantly lower concentrations from 6:00AM to 10:00PM. Direct atmospheric deposition is not a major source of bioavailable nitrogen to the Cape Fear River Estuary. The average total daily NHx and NO3 - dry depositions were small in comparison to their total amounts in the CFRE. Wet depositions of NHx reached 20% for a single event with an average per event deposition equivalent to 1.3% of NHx in the CFRE. Dry deposition accounts for 17% of inorganic N deposition to the CFRE; though it is possibly underestimated in this study

Cooperative interactions among subunits of a voltage-dependent potassium channel. Evidence from expression of concatenated cDNAs

Four copies of the coding sequence for a voltage-dependent potassium channel (RBK1, rat Kv1.1) were ligated contiguously and transcribed in vitro. The resulting RNA encodes four covalently linked subunit domains ([4]RBK1). Injection of this RNA into Xenopus oocytes resulted in the expression of voltage-dependent potassium currents. A single amino acid substitution, Tyr--\u3eVal, located within the outer mouth of the pore, introduced into the equivalent position of any of the four domains, reduced affinity for external tetraethylammonium by approximately the same amount. In constructs containing 0, 1, 2, 3, or 4 Tyr residues the free energy of binding tetraethylammonium was linearly related to the number of Tyr residues. A different amino acid substitution, Leu--\u3eIle, located in the S4 region, was made in the equivalent position of one, two, three, or four domains. The depolarization required for channel activation increased approximately linearly with the number of Ile residues, whereas models of independent gating of each domain predict marked nonlinearity. Expression of this concatenated channel provides direct evidence that voltage-dependent potassium channels have four subunits positioned symmetrically around a central permeation pathway and that these subunits interact cooperatively during channel activation

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On

Accuracy of Inferring Self-and Other-Preferences from Spontaneous Facial Expressions

Abstract Participants' faces were covertly recorded while they rated the attractiveness of people, the decorative appeal of paintings, and the cuteness of animals. Ratings employed a continuous scale. The same participants then returned and tried to guess ratings from 3-s videotapes of themselves and other targets. Performance was above chance in all three stimulus categories, thereby replicating the results of an earlier study (North et al. in J Exp Soc Psychol 46(6):1109-1113, 2010) but this time using a more sensitive rating procedure. Across conditions, accuracy in reading one's own face was not reliably better than otheraccuracy. We discuss our findings in the context of ''simulation'' theories of face-based emotion recognition (Goldman in The philosophy, psychology, and neuroscience of mindreading. Oxford University Press, Oxford, 2006) and the larger body of accuracy research

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions