Towards the general mechanistic prediction of community dynamics

”What controls the distribution and abundance of organisms”? This question, at the heart of the dynamics of ecological communities, would have been familiar to the earliest ecologists. Having lain effectively abandoned for many years, community dynamics today is a vibrant research topic of great conceptual interest with practical import for conservation, ecological management, ecosystem services and the responses of ecological communities to climate change.  We describe how modern coexistence theory can be applied to predict community dynamics through the use of demography. We explore the challenges that limit the deployment of this demographic framework, and the tools from phylogenetic and functional ecology that have been used to surmount them.  Finding existing tools not altogether sufficient, we propose the use of ‘hard’ functional traits and physiological tolerances of environmental conditions and low resource availability to extend the demographic framework so that the dynamics of a broader range of ecological communities can be accurately predicted.  We illustrate these new approaches with two case studies. Given the urgent need to accurately forecast the dynamics of ecological communities, we hope that many ecologists will adopt these tools

The impact of abrupt deglacial climate variability on productivity and upwelling on the southwestern Iberian margin

This study combines high-resolution records of nannofossil abundances, oxygen and carbon stable isotopes, core scanning X-ray fluorescence (XRF), and ice rafted debris (IRD) to assess the paleoceanographic changes that occurred during the last deglaciation on the SW Iberian Margin. Our results reveal parallel centennial-scale oscillations in coccolithophore productivity, nutricline depth and upwelling phenomena not previously observed, explained by means of arrival of iceberg-melting waters, iceberg-induced turbulent conditions, SST changes and riverine discharges. On millennial time-scales, higher primary productivity (PP), shallower nutricline, and upwelling occurrence/invigoration are observed for the Last Glacial Maximum (LGM) and Bølling-Allerød (B/A). The opposite scenario (i.e., lower productivity, deeper nutricline and upwelling weakening/absence) is linked to cold spells such as Heinrich Stadials 2 and 1 (HS2 and HS1) and the Younger Dryas (YD). Such paleoproductivity variations are attributed to latitudinal migrations of the thermal fronts associated with oceanic gyres in the North Atlantic, in parallel to oscillations in the strength of the Atlantic Meridional Overturning Circulation (AMOC). Moderate-to-high PP during the Holocene is ascribed to the development of the modern seasonal surface hydrography, with a more persistent Iberian Poleward Current (IPC) and seasonal wind-induced upwelling

Rape Myths and Hookup Culture: An Exploratory Study of U.S. College Students\u27 Perceptions

The present study provides the first known systematic examination of the association of hookup culture endorsement and rape myth acceptance. Multivariate regression analysis was conducted to test the primary hypothesis that hookup culture endorsement would be the primary predictor of rape myth acceptance levels among a sample of 422 U.S. college students. Findings indicated the existence of a complex relationship in which rape myth acceptance increases or decreases based upon the form of hookup culture endorsement examined. Beliefs that hookups are harmless and elevate social status increased rape myth acceptance, whereas beliefs that hookups express sexual freedom decreased rape myth acceptance. Furthermore, results supported the hypothesis that hookup culture endorsement was the largest predictor of rape myth acceptance. Consistent with previous studies, the predictive power of gender and religiosity in determining levels of rape myth acceptance were shown to be significant. When controlling for levels of hookup culture endorsement, the explanatory power of these variables decreased, and hookup culture endorsement had the largest effect upon rape myth acceptance levels

Functional plasticity in the type IV secretion system of Helicobacter pylori.

Helicobacter pylori causes clinical disease primarily in those individuals infected with a strain that carries the cytotoxin associated gene pathogenicity island (cagPAI). The cagPAI encodes a type IV secretion system (T4SS) that injects the CagA oncoprotein into epithelial cells and is required for induction of the pro-inflammatory cytokine, interleukin-8 (IL-8). CagY is an essential component of the H. pylori T4SS that has an unusual sequence structure, in which an extraordinary number of direct DNA repeats is predicted to cause rearrangements that invariably yield in-frame insertions or deletions. Here we demonstrate in murine and non-human primate models that immune-driven host selection of rearrangements in CagY is sufficient to cause gain or loss of function in the H. pylori T4SS. We propose that CagY functions as a sort of molecular switch or perhaps a rheostat that alters the function of the T4SS and "tunes" the host inflammatory response so as to maximize persistent infection

Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations

Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10−16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10−16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases

A surprisingly simple electrostatic model explains bent vs. linear structures in M+-RG2 species (M = group 1 metal, Li–Fr; RG = rare gas, He–Rn)

It is found that a simple electrostatic model involving competition between the attractive dispersive interaction and induced-dipole repulsion between the two RG atoms performs extremely well in rationalizing the M+-RG2 geometries, where M = Group 1 metal and RG = rare gas. The Li+-RG2 and Na+-RG2 complexes have previously been found to exhibit quasilinear or linear minimum energy geometries, with the Na+-RG2 complexes having an additional bent local minimum [A. Andrejeva, A. M. Gardner, J. B. Graneek, R. J. Plowright, W. H. Breckenridge and T. G. Wright, J. Phys. Chem. A, 2013, 117, 13578]. In the present work, the geometries for M = K–Fr are found to be bent. A simple electrostatic model explains these conclusions and is able to account almost quantitatively for the binding energy of the second RG atom, as well as the form of the angular potential, for all thirty six titular species. Additionally, results of population analyses are presented together with orbital contour plots; combined with the success of the electrostatic model, the expectation that these complexes are all physically bound is confirmed

Effects of a Commercial Insurance Policy Restriction on Lumbar Fusion in North Carolina and the Implications for National Adoption

Analysis of the State Inpatient Database of North Carolina, 2005–2012, and the Nationwide Inpatient Sample, including all inpatient lumbar fusion admissions from non-federal hospitals

Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance

RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments

StarHorse: A Bayesian tool for determining stellar masses, ages, distances, and extinctions for field stars

Understanding the formation and evolution of our Galaxy requires accurate distances, ages and chemistry for large populations of field stars. Here we present several updates to our spectro-photometric distance code, that can now also be used to estimate ages, masses, and extinctions for individual stars. Given a set of measured spectro-photometric parameters, we calculate the posterior probability distribution over a given grid of stellar evolutionary models, using flexible Galactic stellar-population priors. The code (called {\tt StarHorse}) can acommodate different observational datasets, prior options, partially missing data, and the inclusion of parallax information into the estimated probabilities. We validate the code using a variety of simulated stars as well as real stars with parameters determined from asteroseismology, eclipsing binaries, and isochrone fits to star clusters. Our main goal in this validation process is to test the applicability of the code to field stars with known {\it Gaia}-like parallaxes. The typical internal precision (obtained from realistic simulations of an APOGEE+Gaia-like sample) are $\simeq 8\%$ in distance, $\simeq 20\%$ in age,$\simeq 6\$ in mass, and $\simeq 0.04$ mag in $A_V$. The median external precision (derived from comparisons with earlier work for real stars) varies with the sample used, but lies in the range of $\simeq [0,2]\%$ for distances, $\simeq [12,31]\%$ for ages, $\simeq [4,12]\%$ for masses, and $\simeq 0.07$ mag for $A_V$. We provide StarHorse distances and extinctions for the APOGEE DR14, RAVE DR5, GES DR3 and GALAH DR1 catalogues.Comment: 21 pages, 12 figures, accepte

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers

AbstractOur conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability