The objective was to analyze the impact of the Brazilian Traffic Code and the Law Against Drinking and Driving on mortality from traffic accidents in the State of Paraná, Brazil, from 1980 to 2014. This was an ecological time series study on mortality from traffic accidents in residents 15 to 49 years of age, stratified by the sex, age, and categories of victims, with data from the Mortality Information System. The time trend study used a segmented linear regression model and the Cochrane-Orcutt iterative procedure. The assumption of independence of residuals was verified by correlograms and the Box-Pierce test. The highest mortality rates during the period were in males 20 to 29 years of age. After enactment of the Brazilian Traffic Code, there was a decrease of 9.69 deaths/100,000 inhabitants per year for all categories of traffic accidents (p < 0.001), 6.90 for pedestrians (p = 0.001), and 1.96 for vehicle occupants (p < 0.001). As for age bracket, the greatest impact on mortality was in pedestrians 15 to 19 years of age (p < 0.001) and all victims 20 to 29 years of age (p < 0.001). Following enactment of the Drinking and Driving Law, the data displayed variability and the trends were not significant. However, there was a decrease in overall and pedestrian mortality. The rates for motorcyclists and vehicle occupants stabilized. The results showed an impact on traffic accident mortality after enactment of the new Brazilian Traffic Code and Drinking and Driving Law, followed by an increase in the rates. The study evidenced the need for more effective enforcement and progress with public policies in order to avoid a reversal of the gains achieved.

† This paper is an extended version of our paper published in Kupssinskü, L.; Guimarães, T.; Freitas, R.; de Souza, E.; Rossa, P.; Ademir Marques, J.; Veronez, M.; Junior, L.G.; Mauad, F.; Cazarin, C. Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks.Abstract: Total Suspended Solids (TSS) and chlorophyll-a concentration are two critical parameters to monitor water quality. Since directly collecting samples for laboratory analysis can be expensive, this paper presents a methodology to estimate this information through remote sensing and Machine Learning (ML) techniques. TSS and chlorophyll-a are optically active components, therefore enabling measurement by remote sensing. Two study cases in distinct water bodies are performed, and those cases use different spatial resolution data from Sentinel-2 spectral images and unmanned aerial vehicles together with laboratory analysis data. In consonance with the methodology, supervised ML algorithms are trained to predict the concentration of TSS and chlorophyll-a. The predictions are evaluated separately in both study areas, where both TSS and chlorophyll-a models achieved R-squared values above 0.8.Sensors 2020, 20, 2125 2 of 18 bodies [3]. Moreover, the suspended solids may indicate erosive processes within the watershed and additional water pollution since it can carry or store pollutants [4].Generally, once the samples get to a laboratory, their concentrations are analyzed, and manual measurements are taken to extract these parameters. The involved processes are costly, time-consuming, and require skilled personnel [5]. Although there are accurate evaluations, they do not indicate the individual spatio-temporal variation of water quality [6].In this sense, applications involving remote sensing can overcome the drawbacks of conventional monitoring since they allow real-time, spatial, continuous, and long-term monitoring in large areas [7,8]. The presence of components alters the spectral characteristics of the pure water, called Optically Active Components (OAC). The main OACs in water bodies are chlorophyll, suspended solids, and organic material [4]. The possibility of monitoring non-accessible areas and composing time series of water quality from historical data of remote sensing [9] are also factors that make this tool essential and its use promising.Among the most common satellites in OAC remote sensing studies are Landsat 8 and Sentinel-2. Although Landsat is more widely used, the European Sentinel-2 satellite has promising characteristics: it has higher spatial resolution than Landsat 8 and also spectral bands in the region called the red edge, which is of great interest for water and vegetation studies.Remote sensing serves as a powerful technique for monitoring environmental and seasonal changes, and its ability to remotely monitor water resources has increased in recent decades because of the quality and availability of satellite imagery data [10]. Even so, the analysis of small water bodies may not be adequ...

The composition of a Brazilian green propolis ethanolic extract (Et-Bra) and its effect on Trypanosoma cruzi trypomastigotes and other pathogenic microorganisms have already been reported. Here, we further investigated Et-Bra targets in T. cruzi and its effect on experimental infection of mice. The IC50/4 days for inhibition of amastigote proliferation was 8.5 ± 1.8 μg mL−1, with no damage to the host cells. In epimastigotes Et-Bra induced alterations in reservosomes, Golgi complex and mitochondrion. These effects were confirmed by flow cytometry analysis. In trypomastigotes, Et-Bra led to the loss of plasma membrane integrity. The in vitro studies indicate that Et-Bra interferes in the functionality of the plasma membrane in trypomastigotes and of reservosomes and mitochondrion in epimastigotes. Acutely infected mice were treated orally with Et-Bra and the parasitemia, mortality and GPT, GOT, CK and urea levels were monitored. The extract (25–300 mg kg−1 body weight/day for 10 days) reduced the parasitemia, although not at significant levels; increased the survival of the animals and did not induce any hepatic, muscular lesion or renal toxicity. Since Et-Bra was not toxic to the animals, it could be assayed in combination with other drugs. Et-Bra could be a potential metacyclogenesis blocker, considering its effect on reservosomes, which are an important energy source during parasite differentiation.

The wavelet transform is used to reduce the high frequency multipath of pseudorange and carrier phase GPS double differences (DDs). This transform decomposes the DD signal, thus separating the high frequencies due to multipath effects. After the decomposition, the wavelet shrinkage is performed by thresholding to eliminate the high frequency component. Then the signal can be reconstructed without the high frequency component. We show how to choose the best threshold. Although the high frequency multipath is not the main multipath error component, its correction provides improvements of about 30% in pseudorange average residuals and 24% in carrier phases. The results also show that the ambiguity solutions become more reliable after correcting the high frequency multipath.

BackgroundRoad traffic injuries (RTI) are a major public health epidemic killing thousands of people daily. Low and middle-income countries, such as Brazil, have the highest annual rates of road traffic fatalities. In order to improve road safety, this study mapped road traffic fatalities on a Brazilian highway to determine the main environmental factors affecting road traffic fatalities.Methods and FindingsFour techniques were utilized to identify and analyze RTI hotspots. We used spatial analysis by points by applying kernel density estimator, and wavelet analysis to identify the main hot regions. Additionally, built environment analysis, and principal component analysis were conducted to verify patterns contributing to crash occurrence in the hotspots. Between 2007 and 2009, 379 crashes were notified, with 466 fatalities on BR277. Higher incidence of crashes occurred on sections of highway with double lanes (ratio 2∶1). The hotspot analysis demonstrated that both the eastern and western regions had higher incidences of crashes when compared to the central region. Through the built environment analysis, we have identified five different patterns, demonstrating that specific environmental characteristics are associated with different types of fatal crashes. Patterns 2 and 4 are constituted mainly by predominantly urban characteristics and have frequent fatal pedestrian crashes. Patterns 1, 3 and 5 display mainly rural characteristics and have higher prevalence of vehicular collisions. In the built environment analysis, the variables length of road in urban area, limited lighting, double lanes roadways, and less auxiliary lanes were associated with a higher incidence of fatal crashes.ConclusionsBy combining different techniques of analyses, we have identified numerous hotspots and environmental characteristics, which governmental or regulatory agencies could make use to plan strategies to reduce RTI and support life-saving policies.