Earth from Above (KSU)

This Grants Collection for Earth from Above was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/geo-collections/1006/thumbnail.jp

Introduction to Human Geography (KSU)

This Grants Collection for Introduction to Human Geography was created under a Round Eleven ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/geo-collections/1005/thumbnail.jp

Primary Care Appointment Availability and Preventive Care Utilization: Evidence From an Audit Study

Insurance expansions under the Affordable Care Act raise concerns about primary care access in communities with large numbers of newly insured. We linked individual-level, cross-sectional data on adult preventive care utilization from the 2011-2012 Behavioral Risk Factor Surveillance System to novel county-level measures of primary care appointment availability collected from an experimental audit study conducted in 10 states in 2012-2013 and other county-level health service and demographic measures. In multivariate regressions, we found higher county-level appointment availability for privately-insured adults was associated with significantly lower preventive care utilization among adults likely to have private insurance. Estimates were attenuated after controlling for county-level uninsurance, poverty, and unemployment. By contrast, greater availability of Medicaid appointments was associated with higher, but not statistically significant, preventive care utilization for likely Medicaid enrollees. Our study highlights that the relationship between preventive care utilization and primary care access in small areas likely differs by insurance status

Möbius gyrogroups: A Clifford algebra approach

AbstractUsing the Clifford algebra formalism we study the Möbius gyrogroup of the ball of radius t of the paravector space R⊕V, where V is a finite-dimensional real vector space. We characterize all the gyro-subgroups of the Möbius gyrogroup and we construct left and right factorizations with respect to an arbitrary gyro-subgroup for the paravector ball. The geometric and algebraic properties of the equivalence classes are investigated. We show that the equivalence classes locate in a k-dimensional sphere, where k is the dimension of the gyro-subgroup, and the resulting quotient spaces are again Möbius gyrogroups. With the algebraic structure of the factorizations we study the sections of Möbius fiber bundles inherited by the Möbius projectors

Coarse-grained model of entropic allostery

Many signaling functions in molecular biology require proteins to bind to substrates such as DNA in response to environmental signals such as the simultaneous binding to a small molecule. Examples are repressor proteins which may transmit information via a conformational change in response to the ligand binding. An alternative entropic mechanism of "allostery" suggests that the inducer ligand changes the intramolecular vibrational entropy, not just the mean static structure. We present a quantitative, coarse-grained model of entropic allostery, which suggests design rules for internal cohesive potentials in proteins employing this effect. It also addresses the issue of how the signal information to bind or unbind is transmitted through the protein. The model may be applicable to a wide range of repressors and also to signaling in trans-membrane proteins

Primary Care Access for new Patients on the eve of Health Care Reform

Importance: Current measures of access to care have intrinsic limitations and may not accurately reflect the capacity of the primary care system to absorb new patients. Objective: To assess primary care appointment availability by state and insurance status. Design, Setting, and Particpants: We conducted a simulated patient study. Trained field staff, randomly assigned to private insurance, Medicaid, or uninsured, called primary care offices requesting the first available appointment for either routine care or an urgent health concern. The study included a stratified random sample of primary care practices treating nonelderly adults within each of 10 states (Arkansas, Georgia, Illinois, Iowa, Massachusetts, Montana, New Jersey, Oregon, Pennsylvania, and Texas), selected for diversity along numerous dimensions. Collectively, these states comprise almost one-third of the US nonelderly, Medicaid, and currently uninsured populations. Sampling was based on enrollment by insurance type by county. Analyses were weighted to obtain population-based estimates for each state. Main Outcomes and Measures: The ability to schedule an appointment and number of days to the appointment. We also examined cost and payment required at the visit for the uninsured. Results: Between November 13, 2012, and April 4, 2013, we made 12,907 calls to 7788 primary care practices requesting new patient appointments. Across the 10 states, 84.7% (95% CI, 82.6%-86.8%) of privately insured and 57.9% (95% CI, 54.8%-61.0%) of Medicaid callers received an appointment. Appointment rates were 78.8% (95% CI, 75.6%-82.0%) for uninsured patients with full cash payment but only 15.4% (95% CI, 13.2%-17.6%) if payment required at the time of the visit was restricted to $75 or less. Conditional on getting an appointment, median wait times were typically less than 1 week (2 weeks in Massachusetts), with no differences by insurance status or urgency of health concern. Conclusions and Relevance: Although most primary care physicians are accepting new patients, access varies widely across states and insurance status. Navigator programs are needed, not only to help patients enroll but also to identify practices accepting new patients within each plan\u27s network. Tracking new patient appointment availability over time can inform policies designed to strengthen primary care capacity and enhance the effectiveness of the coverage expansions with the Patient Protection and Affordable Care Act

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Investigating the impact of nicotine on executive functions using a novel virtual reality assessment

Aims Nicotine is known to enhance aspects of cognitive functioning in abstinent smokers but the effects on specific areas of executive functions, and in non-smokers are inconclusive. This may be due in part to the poor sensitivity of tests used to assess executive functions. This study used a new virtual reality assessment of executive functions known as JEF (the Jansari assessment of Executive Functions) to address this issue. Design 2x2 design manipulating group (smokers and never-smokers) and drug (nicotine [4mg for smokers; 2mg for never smokers] vs placebo gum). Setting School of Psychology; University of East LondonParticipants 72 participants (aged 18 to 54). 36 minimally-deprived (2 hr) smokers and 36 never-smokers.Measurements Components of executive function were measured using the virtual reality paradigm JEF, which assesses eight cognitive constructs simultaneously as well as providing an overall performance measure. Results Univariate ANOVAs revealed that nicotine improved overall JEF performance, time-based prospective memory and event-based prospective memory in smokers (p < 0.01) but not in never-smokers. Action-based prospective memory was enhanced in both groups (p < 0.01) and never-smokers out-performed smokers on selective thinking and adaptive thinking (p < 0.01). Conclusions. Overall executive functioning and prospective memory can be enhanced by nicotine gum in abstinent smokers. That smokers were only minimally deprived suggests that JEFis a sensitive measure of executive functioning and that prospective memory is particularly susceptible to disruption by abstinence

Fentanyl self-testing outside supervised injection settings to prevent opioid overdose: Do we know enough to promote it?

Since 2013, North America has experienced a sharp increase in unintentional fatal overdoses: fentanyl, and its analogues, are believed to be primarily responsible. Currently, the most practical means for people who use drugs (PWUD) to avoid or mitigate risk of fentanyl-related overdose is to use drugs in the presence of someone who is in possession of, and experienced using, naloxone. Self-test strips which detect fentanyl, and some of its analogues, have been developed for off-label use allowing PWUD to test their drugs prior to consumption. We review the evidence on the off-label sensitivity and specificity of fentanyl test strips, and query whether the accuracy of fentanyl test strips might be mediated according to situated practices of use. We draw attention to the weak research evidence informing the use of fentanyl self-testing strips

Bayesian correlated clustering to integrate multiple datasets

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets. Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods