Building an effective marketing model for Native American casinos

In 1987 the U.S. Supreme Court recognized that, as sovereign political entities, federally recognized Native American tribal entities could operate gaming facilities free of state regulation. Soon after, Congress introduced the 1988 Indian Gaming Regulatory Act (IGRA), which maps out the conditions under which Native American tribal entities are permitted to operate casinos and bingo parlors (Kilby, Fox, & Lucas, 2005). These terms and conditions for Native American casinos under IGRA regulation are Class II gaming methods. Class II gaming is defined as the game of chance commonly known as bingo (regardless of electronic, computer, or other technological aid) (Kilby et al., 2005). Class II gaming also includes non-banked card games; that is, games that are played exclusively against other players rather than against the house or a player acting as a bank. The most common form of non-banked card games are poker games (Kilby et al., 2005). The IGRA specifically excludes slot machines or electronic facsimiles of any game of chance from the definition of Class II games (Kilby et al, 2005). Tribes retain their authority to conduct, license, and regulate Class II gaming so long as the state in which the tribe is located permits such gaming for any purpose and the tribal government adopts a gaming ordinance approved by the Commission. Tribal governments are responsible for regulating Class II gaming with commission oversight. Native American casinos have the option to create a state compact to allow Class III games such as roulette, black jack, and craps (Kilby et. al., 2005). This allows the state in which the tribe operates to collect a percentage of the revenues generated by the compacted games. With the addition of new gaming options, it is important to implement a marketing model that will satisfy the new gaming market that is being tapped into with the addition of Class III games

Volunteer Participation in the Health eHeart Study: A Comparison with the US Population.

Direct volunteer "eCohort" recruitment can be an efficient way of recruiting large numbers of participants, but there is potential for volunteer bias. We compared self-selected participants in the Health eHeart Study to participants in the National Health And Nutrition Examination Survey (NHANES) 2013-14, a cross-sectional survey of the US population. Compared with the US population (represented by 5,769 NHANES participants), the 12,280 Health eHeart participants with complete survey data were more likely to be female (adjusted odds ratio (ORadj) = 3.1; 95% confidence interval (CI) 2.9-3.5); less likely to be Black, Hispanic, or Asian versus White/non-Hispanic (ORadj's = 0.4-0.6, p < 0.01); more likely to be college-educated (ORadj = 15.8 (13-19) versus ≤high school); more likely to have cardiovascular diseases and risk factors (ORadj's = 1.1-2.8, p < 0.05) except diabetes (ORadj = 0.8 (0.7-0.9); more likely to be in excellent general health (ORadj = 0.6 (0.5-0.8) for "Good" versus "Excellent"); and less likely to be current smokers (ORadj = 0.3 (0.3-0.4)). While most self-selection patterns held for Health eHeart users of Bluetooth blood pressure cuff technology, there were some striking differences; for example, the gender ratio was reversed (ORadj = 0.6 (0.4-0.7) for female gender). Volunteer participation in this cardiovascular health-focused eCohort was not uniform among US adults nor for different components of the study

Solvable senescence model with positive mutations

We build upon our previous analytical results for the Penna model of senescence to include positive mutations. We investigate whether a small but non-zero positive mutation rate gives qualitatively different results to the traditional Penna model in which no positive mutations are considered. We find that the high-lifespan tail of the distribution is radically changed in structure, but that there is not much effect on the bulk of the population. Th e mortality plateau that we found previously for a stochastic generalization of the Penna model is stable to a small positive mutation rate.Comment: 3 figure

MicroRNAs mir‐184 and let‐7 alter Drosophila metabolism and longevity

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140032/1/acel12673.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140032/2/acel12673-sup-0002-FigS1-S8.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140032/3/acel12673_am.pd

Setting and motivation in the decision to participate: An approach to the engagement of diverse samples in mobile research.

Internet and mobile based research are powerful tools in the creation of large, cohort studies (eCohort). However, recent analysis indicates that an underrepresentation of minority and low income groups in these studies might exceed that found in traditional research [1-5]. In this report, we present findings from an experiment in research engagement using the Eureka Research Platform developed to enroll diverse populations in support of biomedical clinical research. This experiment involved the recruitment of African American and Latino participants in a smartphone based survey at a temporary, charitable, dental event sponsored, in part, by the research team, in order to explore the impact of setting and approach on recruitment outcomes. 211 participants enrolled including a significant representation of African Americans (51%) and Latinos (31%) and those with education levels at high school or less (37%). Interviews conducted after the study confirmed that our recruitment efforts within the context of a service event affected the decision to participate. While further research is necessary, this experiment holds promise for the engagement of underrepresented groups in research

Validation of a consumer-grade activity monitor for continuous daily activity monitoring in individuals with multiple sclerosis.

Background:Technological advancements of remote-monitoring used in clinical-care and research require validation of model updates. Objectives:To compare the output of a newer consumer-grade accelerometer to a previous model in people with multiple sclerosis (MS) and to the ActiGraph, a waist-worn device widely used in MS research. Methods:Thirty-one individuals with MS participated in a 7-day validation by the Fitbit Flex (Flex), Fitbit Flex-2 (Flex2) and ActiGraph GT3X. Primary outcome was step count. Valid epochs of 5-min block increments, where there was overlap of ≥1 step/min for both devices were compared and summed to give a daily total for analysis. Results:Bland-Altman plots showed no systematic difference between the Flex and Flex2; mean step-count difference of 25 more steps-per-day more recorded by Flex2 (95% confidence intervals (CI) = 2, 48; p = 0.04),interclass correlation coefficient (ICC) = 1.00. Compared to the ActiGraph, Flex2 (and Flex) tended to record more steps (808 steps-per-day more than the ActiGraph (95% CI= -2380, 765; p < 0.01), although the ICC was high (0.98) indicating that the devices were likely measuring the same kind of activity. Conclusions:Steps from Flex and Flex2 can be used interchangeably. Differences in total step count between ActiGraph and Flex devices can make cross-device comparisons of numerical step-counts challenging particularly for faster walkers

Real-world heart rate norms in the Health eHeart study.

Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18-45, ≤100 in those aged 45-60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data

More than just hormones: H295R cells as predictors of reproductive toxicity

AbstractMany of the commonly observed reproductive toxicities associated with therapeutic compounds can be traced to a disruption of the steroidogenic pathway. We sought to develop an in vitro assay that would predict reproductive toxicity and be high throughput in nature. H295R cells, previously validated as having an intact and functional steroidogenic pathway, were treated with 83 known-positive and 79 known-negative proprietary and public-domain compounds. The assay measured the expression of the key enzymes STAR, 3βHSD2, CYP17A1, CYP11B2, CYP19A1, CYP21A2, and CYP11A1 and the hormones DHEA, progesterone, testosterone, and cortisol. We found that a Random Forest model yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.845, with sensitivity of 0.724 and specificity of 0.758 for predicting in vivo reproductive toxicity with this in vitro assay system