This study aimed to investigate effects on growth and development and alterations in the nutrient profiles for Arabidopsis thaliana seedlings following uranium exposure. Seventeen-day-old Arabidopsis thaliana seedlings, grown in hydroponics, were exposed to 0, 0.1, 1, 10 and 100 μM uranium for one, three and seven days. Fresh weight of leaves and roots decreased after exposure to 100 μM uranium for one and three days and after exposure to 1, 10 and 100 μM uranium for seven days. Anthocyanous-colored leaves and stunted yellow roots were observed after exposure to 100 μM uranium. The uranium content of the roots highly increased but the root-to-shoot transfer of uranium was limited. Uranium exposure resulted in disturbed macro- and micronutrient profiles, especially following exposure to 100 μM. This study indicates that elevated uranium concentrations can cause important morphological and physiological effects and disturb the nutrient profiles in Arabidopsis thaliana seedlings.

Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. We estimated the relative risk of dengue disease by census section (a geographical unit composed approximately by 1–20 city blocks) for the period January 2008 to December 2015. We included the covariates normalized difference vegetation index (NDVI) and land surface temperature (LST), obtained by satellite images. We fitted Bayesian areal models at the complete period and annual aggregation time scales for 2008–2015, with fixed and space-varying coefficients for the covariates, using Markov Chain Monte Carlo simulations. In addition, we used Cohen’s Kappa agreement measures to compare the risk from year to year, and from every year to the complete period aggregation. We found the NDVI providing more information than LST for estimating relative risk of dengue, although their effects were small. NDVI was directly associated to high relative risk of dengue. Risk maps of dengue were produced from the estimates obtained by the modeling process. The year to year risk agreement by census section was sligth to fair. The study provides an example of implementation of relative risk estimation using Bayesian models for disease mapping at small spatial scale with covariates. We relate satellite data to dengue disease, using an areal data approach, which is not commonly found in the literature. The main difficulty of the study was to find quality data for generating expected values as input for the models. We remark the importance of creating population registry at small spatial scale, which is not only relevant for the risk estimation of dengue but also important to the surveillance of all notifiable diseases.

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health. Time series analysis of dengue disease case counts are currently employed to establish associations between dengue disease and environmental, socioeconomic and climatic variables and to predict the evolution of dengue epidemics. Nowadays there is acceptance that climatic factors like environmental temperature, rainfall and relative humidity modify the behavior of the dengue vectors, affecting the transmission of the disease. Thus, in the absence of vector data, climatic factors are commonly used to input transmission models of dengue disease on several temporal and spatial scales. We applied hierarchical Bayesian dynamic generalized models to dengue diseases case counts in a medium-sized city in Colombia, with constant and time-varying coefficients for calendar trend, and constant and time-varying coefficients for meteorological variables (temperature, rainfall, solar radiation and relative humidity). We selected a final model useful for exploring of the time-varying association between climatic variables and dengue, and the short-term out-of-sample predictions of dengue counts within the study period. We illustrate the modeling process so a data analyst on a multidisciplinary research team could integrate a time series model accounting for the time-varying nature of the data.

Isometric handgrip or (wall) squat exercise performed three times per week produces reductions in systolic blood pressure (SBP) in adults with hypertension. We aimed to compare these interventions and the potential to retain benefits with one exercise session per week. We compared blood pressure changes following handgrip and squat isometric training interventions with controls in a randomized controlled multicentre trial in 77 unmedicated hypertensive (SBP ≥ 130 mmHg) adults. Exercise sessions were performed in the workplace and consisted of four repetitions—three sessions per week for the first 12 weeks (phase 1), and one session per week for the subsequent 12 weeks (phase 2). Office blood pressure (BP) was measured at baseline, post‐phase 1 and post‐phase 2. Post‐phase 1, mean reductions in SBP were significantly greater in handgrip (–11.2 mmHg, n = 28) and squat (–12.9 mmHg, n = 27) groups than in controls (–.4 mmHg; n = 22) but changes in DBP were not. There were no significant within‐group changes during phase 2 but SBP was 3.8 mmHg lower in the wall squat than the handgrip group—a small magnitude but clinically important difference. While both interventions produced significant SBP reductions, the wall squat appears to be more effective in maintaining benefits with a minimal training dose. The low time investment to achieve and retain clinically significant SBP reductions—42 and 12 min, respectively—and minimal cost, particularly of the wall squat, make it a promising intervention for delivery in public health settings.