Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

The trend of e-cigarette use among teens is ever increasing. Here we show the dysbiotic oral microbial ecology in e-cigarette users influencing the local host immune environment compared with non-smoker controls and cigarette smokers. Using 16S rRNA high-throughput sequencing, we evaluated 119 human participants, 40 in each of the three cohorts, and found significantly altered beta-diversity in e-cigarette users (p = 0.006) when compared with never smokers or tobacco cigarette smokers. The abundance of Porphyromonas and Veillonella (p = 0.008) was higher among vapers. Interleukin (IL)-6 and IL-1b were highly elevated in e-cigarette users when compared with non-users. Epithelial cell-exposed e-cigarette aerosols were more susceptible for infection. In vitro infection model of premalignant Leuk-1 and malignant cell lines exposed to e-cigarette aerosol and challenged by Porphyromonas gingivalis and Fusobacterium nucleatum resulted in elevated inflammatory response. Our findings for the first time demonstrate that e-cigarette users are more prone to infection.

Oral mucositis (OM) is among the most common, painful, and debilitating toxicities of cancer regimen-related treatment, resulting in the formation of ulcers, which are susceptible to increased colonization of microorganisms. Novel discoveries in OM have focused on understanding the host-microbial interactions, because current pathways have shown that major virulence factors from microorganisms have the potential to contribute to the development of OM and may even prolong the existence of already established ulcerations, affecting tissue healing. Additional comprehensive and disciplined clinical investigation is needed to carefully characterize the relationship between the clinical trajectory of OM, the local levels of inflammatory changes (both clinical and molecular), and the ebb and flow of the oral microbiota. Answering such questions will increase our knowledge of the mechanisms engaged by the oral immune system in response to mucositis, facilitating their translation into novel therapeutic approaches. In doing so, directed clinical strategies can be developed that specifically target those times and tissues that are most susceptible to intervention.

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Butterflies are one of the best‐known insect groups, and they have been the subject of numerous studies in ecology and evolution, especially in the tropics. Much attention has been given to the fruit‐feeding butterfly guild in biodiversity conservation studies, due to the relative ease with which taxa may be identified and specimens sampled using bait traps. However, there remain many uncertainties about the macroecological and biogeographical patterns of butterflies in tropical ecosystems. In the present study, we gathered information about fruit‐feeding butterfly species in local communities from the Atlantic Forests of South America. The ATLANTIC BUTTERFLIES data set, which is part of ATLANTIC SERIES data papers, results from a compilation of 145 unpublished inventories and 64 other references, including articles, theses, and book chapters published from 1949 to 2018. In total, the data set contains 7,062 records (presence) of 279 species of fruit‐feeding butterflies identified with taxonomic certainty, from 122 study locations. The Satyrini is the tribe with highest number of species (45%) and records (30%), followed by Brassolini, with 13% of species and 12.5% of records. The 10 most common species correspond to 14.2% of all records. This data set represents a major effort to compile inventories of fruit‐feeding butterfly communities, filling a knowledge gap about the diversity and distribution of these butterflies in the Atlantic Forest. We hope that the present data set can provide guidelines for future studies and planning of new inventories of fruit‐feeding butterflies in this biome. The information presented here also has potential use in studies across a great variety of spatial scales, from local and landscape levels to macroecological research and biogeographical research. We expect that such studies be very important for the better implementation of conservation initiatives, and for understanding the multiple ecological processes that involve fruit‐feeding butterflies as biological indicators. No copyright restrictions apply to the use of this data set. Please cite this Data paper when using the current data in publications or teaching events.

Oil spill detection and mapping (OSPM) is an extremely relevant issue from a scientific point of view due to the environmental impact on coastal and marine ecosystems. In this study, we present a new approach to assess scientific literature for the past 50 years. In this sense, our study aims to perform a bibliometric and network analysis using a literature review on the application of OSPM to assess researchers and trends in this field of science. In methodological terms we used the Scopus base to search for articles in the literature, then we used bibliometric tools to access information and reveal quantifying patterns in this field of literature. Our results suggest that the detection of oil in the sea has undergone a great evolution in the last decades and there is a strong relationship between the technological evolution aimed at detection with the improvement of remote sensing data acquisition methods. The most relevant contributions in this field of science involved countries such as China, the United States, and Canada. We revealed aspects of great importance and interest in OSPM literature using a bibliometric and network approach to give a clear overview of this field’s research trends.

Introduction: Tobacco use is one of the main causes of periodontitis. E-cigarette are gaining in popularity, and studies are needed to better understand the impact of e-cigarettes on oral health.Objective: To perform a longitudinal study to evaluate the adverse effects of e-cigarettes on periodontal health.Methods: Naïve E-cigarette users, cigarette smokers, and non-smokers were recruited using newspaper and social media. Age, gender, and ethnicity, were recorded. Participants were scheduled for two visits 6 months apart. At each visit, we collected data on the frequency and magnitude of e-cigarette and cigarette use, and alcohol consumption. Carbon monoxide (CO) levels, cotinine levels, salivary flow rate, periodontal probing depth (PD), bleeding on probing (BoP), and clinical attachment loss (CAL) were also determined at both baseline and follow-up visits and compared between groups with two-way repeated measures ANOVA. Periodontal diagnosis and other categorical variables were compared between groups with the chi-square statistic and logistic regression.Results: We screened 159 subjects and recruited 119 subjects. One-hundred-one subjects (31 cigarette smokers, 32 e-cigarette smokers, and 38 non-smokers) completed every assessment in both visits. The retention and compliance rate of subjects was 84.9%. The use of social media and craigslist was significant in recruiting e-cigarette subjects. Ethnicity and race differed between groups, as did average age in the male subjects. Carbon monoxide and salivary cotinine levels were highest among cigarette smokers. Bleeding on probing and average PDs similarly increased over time in all three groups, but CAL uniquely increased in e-cigarette smokers. Rates of severe periodontal disease were higher in cigarette smokers and e-cigarette users than non-smokers, but interpretation is confounded by the older age of the cigarette smokers.Conclusion: Among the recruited participants, CAL after 6 months was significantly worse only in the e-cigarette smokers. This study design and protocol will assist in future larger studies on e-cigarette and oral health.

The new Covid-19 pandemic has left traces of suffering and devastation to individuals of almost all countries worldwide and severe impact on the global economy. Understanding the clinical characteristics, interactions with the environment, and the variables that favor or hinder its dissemination help the public authorities in the fight and prevention, leading for a rapid response in society. Using models to estimate contamination scenarios in real time plays an important role. Population compartments models based on ordinary differential equations (ODE) for a given region assume two homogeneous premises, the contact mechanisms and diffusion rates, disregarding heterogeneous factors as different contact rates for each municipality and the flow of contaminated people among them. This work considers a hybrid model for covid-19, based on local SIR models and the population flow network among municipalities, responsible for a complex lag dynamic in their contagion curves. Based on actual infection data, local contact rates ( ) are evaluated. The epidemic evolution at each municipality depends on the local SIR parameters and on the inter-municipality transport flow. When heterogeneity of values and flow network are included, forecasts differ from those of the homogeneous ODE model. This effect is more relevant when more municipalities are considered, hinting that the latter overestimates new cases. In addition, mitigation scenarios are assessed to evaluate the effect of earlier interventions reducing the inter-municipality flux. Restricting the flow between municipalities in the initial stage of the epidemic is fundamental for flattening the contamination curve, highlighting advantages of a contamination lag between the capital curve and those of other municipalities in the territories.