Leo L. Duan

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.

OBJECTIVE: More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider's response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts. METHODS: We performed a retrospective study of medication alerts over a 24-month period (1/2013-12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions. RESULTS: While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience. CONCLUSION: Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.

BACKGROUND: Lower airway biomarkers of restored cystic fibrosis transmembrane conductance regulator (CFTR) function are limited. We hypothesized that fractional excretion of nitric oxide (FENO), typically low in CF patients, would demonstrate reproducibility during CFTR-independent therapies, and increase during CFTR-specific intervention (ivacaftor) in patients with CFTR gating mutations. METHODS: Repeated FENO and spirometry measurements in children with CF (Cohort 1; n=29) were performed during hospital admission for acute pulmonary exacerbations and routine outpatient care. FENO measurements before and after one month of ivacaftor treatment (150 mg every 12h) were completed in CF patients with CFTR gating mutations (Cohort 2; n=5). RESULTS: Cohort 1: Mean forced expiratory volume in 1s (FEV1 % predicted) at enrollment was 72.3% (range 25%-102%). Mean FENO measurements varied minimally over the two inpatient and two outpatient measurements (9.8-10.9 ppb). There were no clear changes related to treatment of pulmonary exacerbations, gender, genotype or microbiology, and weak correlation with inhaled corticosteroid use (P<0.05). Between the two inpatient measurements, FEV1 % predicted increased by 7.3% (P<0.03) and FENO did not change. In Cohort 2, mean FENO increased from 6.6 ppb (SD=2.19) to 11.8 ppb (SD=4.97) during ivacaftor treatment. Mean sweat chloride dropped by 58 mM and mean FEV1 % predicted increased by 10.2%. CONCLUSIONS: Repeated FENO measurements were stable in CF patients, whereas FENO increased in all patients with CFTR gating mutations treated with ivacaftor. Acute changes in FENO may serve as a biomarker of restored CFTR function in the CF lower airway during CFTR modulator treatment.

<b>Objective</b> To identify phenotypes of type 1 diabetes control and associations with maternal/neonatal characteristics based on blood pressure (BP), glucose, and insulin curves during gestation, using a novel functional data analysis approach that accounts for sparse longitudinal patterns of medical monitoring during pregnancy. <b>Methods</b> We performed a retrospective longitudinal cohort study of women with type 1 diabetes whose BP, glucose, and insulin requirements were monitored throughout gestation as part of a program-project grant. Scores from sparse functional principal component analysis (fPCA) were used to classify gestational profiles according to the degree of control for each monitored measure. Phenotypes created using fPCA were compared with respect to maternal and neonatal characteristics and outcome. <b>Results</b> Most of the gestational profile variation in the monitored measures was explained by the first principal component (82–94%). Profiles clustered into three subgroups of high, moderate, or low heterogeneity, relative to the overall mean response. Phenotypes were associated with baseline characteristics, longitudinal changes in glycohemoglobin A1 and weight, and to pregnancy-related outcomes. <b>Conclusion</b> Three distinct longitudinal patterns of glucose, insulin, and BP control were found. By identifying these phenotypes, interventions can be targeted for subgroups at highest risk for compromised outcome, to optimize diabetes management during pregnancy.