Identifying Key Stressors Driving Biological Impairment in Freshwater Streams in the Chesapeake Bay Watershed, USA.

Biological communities in freshwater streams are often impaired by multiple stressors (e.g., flow or water quality) originating from anthropogenic activities such as urbanization, agriculture, or energy extraction. Restoration efforts in the Chesapeake Bay watershed, USA seek to improve biological conditions in 10% of freshwater tributaries and to protect the biological integrity of existing healthy watersheds. To achieve these goals, resource managers need to better understand which stressors are most likely driving biological impairment. Our study addressed this knowledge gap through two approaches: 1) reviewing and synthesizing published multi-stressor studies, and 2) examining 303(d) listed impairments linked to biological impairment as identified by jurisdiction regulatory agencies (the states within the watershed and the District of Columbia). Results identified geomorphology (i.e., physical habitat), salinity, and toxic contaminants as important for explaining variability in benthic community metrics in the literature review. Geomorphology (i.e., physical habitat and sediment), salinity, and nutrients were the most reported stressors in the jurisdictional impairment analysis. Salinity is likely a major stressor in urban and mining settings, whereas geomorphology was commonly reported in agricultural settings. Toxic contaminants, such as pesticides, were rarely measured; more research is needed to quantify the extent of their effects in the region. Flow alteration was also highlighted as an important urban stressor in the literature review but was rarely measured in the literature or reported by jurisdictions as a cause of impairment. These results can be used to prioritize stressor monitoring by managers, and to improve stressor identification methods for identifying causes of biological impairment.

Perception of Carbohydrate Availability Augments High-Intensity Intermittent Exercise Capacity Under Sleep-Low, Train-Low Conditions.

The authors tested the hypothesis that perception of carbohydrate (CHO) availability augments exercise capacity in conditions of reduced CHO availability. Nine males completed a sleep-low train model comprising evening glycogen-depleting cycling followed by an exhaustive cycling protocol the next morning in the fasted state (30 min steady state at 95% lactate threshold followed by 1-min intervals at 80% peak power output until exhaustion). After the evening depletion protocol and prior to sleeping, subjects consumed (a) a known CHO intake of 6 g/kg body mass (TRAIN HIGH) or (b) a perceived comparable CHO intake but 0 g/kg body mass (PERCEPTION) or a known train-low condition of 0 g/kg body mass (TRAIN LOW). The TRAIN HIGH and PERCEPTION trials were conducted double blind. During steady state, average blood glucose and CHO oxidation were significantly higher in TRAIN HIGH (4.01 ± 0.56 mmol/L; 2.17 ± 0.70 g/min) versus both PERCEPTION (3.30 ± 0.57 mmol/L; 1.69 ± 0.64 g/min, p < .05) and TRAIN LOW (3.41 ± 0.74 mmol/L; 1.61 ± 0.59 g/min, p < .05). Exercise capacity was significantly different between all pairwise comparisons (p < .05), where TRAIN LOW (8 ± 8 min) < PERCEPTION (12 ± 6 min) < TRAIN HIGH (22 ± 9 min). Data demonstrate that perception of CHO availability augments high-intensity intermittent exercise capacity under sleep-low, train-low conditions, though this perception does not restore exercise capacity to that of CHO consumption. Such data have methodological implications for future research designs and may also have practical applications for athletes who deliberately practice elements of training in CHO-restricted states.

Approximate Bayesian computation for spatial SEIR(S) epidemic models.

Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas.

Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America.

As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015/2016 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.

An empirically adjusted approach to reproductive number estimation for stochastic compartmental models: A case study of two Ebola outbreaks.

The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach.

The Effect of Receiving Treatment Within a Clinical Trial Setting on Survival and Quality of Care Perception in Advanced Stage Non-Small Cell Lung Cancer.

OBJECTIVES: Treatment outcomes of advanced stage (IIIB and IV) non-small cell lung cancer (NSCLC) are poor. In this study, we explore the survival outcomes and the perception of the quality of care delivered in stage IIIB and IV NSCLC patients treated within versus outside a clinical trial. MATERIALS AND METHODS: Data were obtained from the Cancer Care Outcomes Research and Surveillance Consortium (CanCORS). Baseline characteristics according to clinical trial participation were determined. The association between clinical trial enrollment and survival was assessed using a Cox proportional hazard model after adjusting for age, income, primary data collection and research site, comorbidities, self-reported performance status, presence of brain metastasis, stage IIIB versus IV, and cancer histology. RESULTS: Of 815 stage IIIB and IV NSCLC patients, 56 (7%) were enrolled in clinical trials. Median survival for the patients treated within versus outside a clinical trial was 20.5 versus 16.7 months, respectively (P=0.21). Using a multivariate survival model, clinical trial enrollment did not correlate with longer survival (P=0.81). Comparing patients according to clinical trial enrollment, patients treated within a clinical trial setting perceived a better overall quality of care (P<0.01). CONCLUSIONS: Management of stage IIIB and IV NSCLC patients within a clinical trial setting conveyed a perception of superior care that did not translate into survival benefit. These findings suggest that providing cancer care within a clinical trial should not imply a survival benefit when counseling stage IIIB and IV NSCLC patients about entering clinical trials.

A spatial epidemic model for disease spread over a heterogeneous spatial support.

Data from the Iowa mumps epidemic of 2006 were collected on a spatial lattice over a regular temporal interval. Without access to a person-to-person contact graph, it is sensible to analyze these data as homogenous within each areal unit and to use the spatial graph to derive a contact structure. The spatio-temporal partition is fine, and the counts of new infections at each location at each time are sparse. Therefore, we propose a spatial compartmental epidemic model with general latent time distributions (spatial PS SEIR) that is capable of smoothing the contact structure, while accounting for spatial heterogeneity in the mixing process between locations. Because the model is an extension of the PS SEIR model, it simultaneously handles non-exponentially distributed latent and infectious time distributions. The analysis within focuses on the progression of the disease over both space and time while assessing the impact of a large proportion of the infected people dispersing at the same time because of spring break and the impact of public awareness on the spread of the mumps epidemic. We found that the effect of spring break increased the mixing rate in the population and that the spatial transmission of the disease spreads across multiple conduits.

A multivariate CAR model for mismatched lattices.

In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey.

Exposure to the cyanotoxin microcystin arising from interspecific differences in feeding habits among fish and shellfish in the James River Estuary, Virginia.

The cyanotoxin, microcystin (MC), is known to accumulate in the tissues of diverse aquatic biota although factors influencing exposure, such as feeding habits and seasonal patterns in toxin production, are poorly known. We analyzed seasonal variation in the MC content of primary and secondary consumers, and used dietary analysis (gut contents and stable isotopes) to improve understanding of cyanotoxin transport in food webs. Periods of elevated toxin concentration were associated with peaks in the abundance of genes specific to Microcystis and MC toxin production (mcyD). Peak toxin levels in consumer tissues coincided with peak MC concentrations in seston. However, toxins in tissues persisted in overwintering populations suggesting that potential health impacts may not be limited to bloom periods. Interspecific differences in tissue MC concentrations were related to feeding habits and organic matter sources as pelagic fishes ingested a greater proportion of algae in their diet, which resulted in greater MC content in liver and muscle tissues. Sediments contained a greater proportion of allochthonous (terrestrial) organic matter and lower concentrations of MC, resulting in lower toxin concentrations among benthic detritivores. Among shellfish, the benthic suspension feeder Rangia cuneata (wedge clam) showed seasonal avoidance of toxin ingestion due to low feeding rates during periods of elevated MC. Among predators, adult Blue Catfish had low MC concentrations, whereas Blue Crabs exhibited high levels of MC in both muscle and viscera.

A path-specific SEIR model for use with general latent and infectious time distributions.

Most current Bayesian SEIR (Susceptible, Exposed, Infectious, Removed (or Recovered)) models either use exponentially distributed latent and infectious periods, allow for a single distribution on the latent and infectious period, or make strong assumptions regarding the quantity of information available regarding time distributions, particularly the time spent in the exposed compartment. Many infectious diseases require a more realistic assumption on the latent and infectious periods. In this article, we provide an alternative model allowing general distributions to be utilized for both the exposed and infectious compartments, while avoiding the need for full latent time data. The alternative formulation is a path-specific SEIR (PS SEIR) model that follows individual paths through the exposed and infectious compartments, thereby removing the need for an exponential assumption on the latent and infectious time distributions. We show how the PS SEIR model is a stochastic analog to a general class of deterministic SEIR models. We then demonstrate the improvement of this PS SEIR model over more common population averaged models via simulation results and perform a new analysis of the Iowa mumps epidemic from 2006.

Long-term hearing preservation after microsurgical excision of vestibular schwannoma.

OBJECTIVE: To examine long-term hearing outcomes after microsurgical excision of vestibular schwannoma (VS). STUDY DESIGN: Retrospective case review. SETTING: Tertiary referral center. PATIENTS: Forty-nine subjects at a single institution who had undergone microsurgical excision of a VS via middle cranial fossa (MCF) approach between 1994 and 2007 with immediate postoperative (PO) hearing preservation and for whom long-term audiograms were available. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Word Recognition Score (WRS) is defined by speech discrimination scores (SDS) greater than 70% (grade I), 50% to 70% (grade II), less than 50% (grade III), and 0% (grade IV). RESULTS: For subjects with more than 2 years of follow-up, WRS I hearing was present PO in 42 of 49 patients and was preserved at the latest follow-up in 38 (90%) of 42 patients. No subjects fell beyond WRS II. WRS I hearing was maintained in 23 (88%) of 26 patients with more than 5 years of follow-up. Postoperative WRS I to II hearing was maintained in 28 (96%) of 29 patients with more than 5 years of follow-up. The patient who lost significant hearing in the ear operated on had sensorineural hearing loss that paralleled deterioration in her ear that was not operated on. CONCLUSION: Most subjects maintain their initial PO SDS after microsurgical VS removal, and therefore, the initial PO WRS is predictive of long-term hearing. Postsurgical changes do not alter the natural rate or pattern of progressive bilateral sensorineural hearing loss in individual subjects.

Integrated electromyography and performance outcomes to inertial resistance exercise.

Integrated electromyography (IEMG) and performance outcomes from resistance exercise may be influenced by gender, repetition order, and contractile mode. A novel strength training apparatus employing inertial resistance operates unlike standard exercise equipment and may therefore evoke different IEMG and performance outcomes. Subjects performed 3-set, 10-repetition calf press workouts on an inertial device while IEMG and flywheel velocity were recorded. Dependent variables were examined with 2 (men, women) x 2 (first vs. 10th repetition) x 2 (concentric, eccentric) analyses of variance. Performance outcomes showed gender-by-repetition and gender-by-contractile-mode interactions, with men's 10th-repetition and men's concentric data, respectively, causing these effects. Medial gastrocnemius (MG) IEMG showed a gender-by-repetition interaction resulting from men's first-repetition data. Greater 10th-repetition performance despite higher first-repetition MG IEMG may result from heightened triceps surae elastic energy utilization and/or maintained cross-bridges during transitions from lengthening-to-shortening actions. Inertial strength training may improve performance outcomes without additional motor unit recruitment.

Metabolic responses to the seated calf press exercise performed against inertial resistance.

INTRODUCTION: Future in-flight strength training devices may use inertial resistance to abate mass and strength losses to muscle groups such as the triceps surae, which incurs pronounced deficits from space travel. Yet little data exist regarding physiological outcomes to triceps surae exercise performed against inertial resistance. Two sets of subjects were employed to note either blood lactate (La-) or net caloric cost responses to seated calf presses done on an inertial resistance ergometer. METHODS: Both sets of subjects performed 3 identical 3-set 10-repetition workouts. Blood La- measurements were made pre- and 5 min post-exercise. During workouts, breath-by-breath O2 uptake values were also recorded to help determine the net caloric cost of exercise. RESULTS: Compared to pre-exercise (mean +/- SEM) blood La- (2.01 +/- 0.08 mmol x L(-1)) values, post-exercise (4.73 +/- 0.24 mmol x L(-1)) measurements showed a significant increase. Delta (post/pre differences) La- correlated significantly (r = 0.31-0.34) to several workout performance measures. Net caloric cost averaged 52.82 +/- 3.26 kcals for workouts; multivariate regression showed a subject's height, body mass, and body surface area described the variance associated with energy expenditure. CONCLUSIONS: Workouts evoked minimal energy expenditure, though anaerobic glycolysis likely played a major role in ATP resynthesis. Metabolic and exercise performance measures were likely influenced by series elastic element involvement of the triceps surae-Achilles tendon complex. Ergometer calf presses provided a high-intensity workout stimulus with a minimal metabolic cost.