Assessing the impact of small firm dynamics on public mental health amid the pandemic in Latin America.

OBJECTIVE: The aim of our study is to examine the relationship between the economic activity of small firms and the mental well-being of the population in five Latin American countries in the early stages of the pandemic. METHODS: We utilize the search volume of certain keywords on Google Trends (GT), such as "boredom," "frustration," "loneliness," "sleep", "anxiety", and "depression", as an indicator of the well-being of the population. By examining the data from Facebook Business Activity Trends, we investigate how social attention reacts to the activity levels of different economic sectors. RESULTS: Increased business activity is generally associated with reduced levels of boredom, loneliness, sleep problems and anxiety. The effect on depression varies by sector, with positive associations concentrated in onsite jobs. In addition, we observe that strict Non-Pharmaceutical Interventions (NPIs) tend to exacerbate feelings of boredom and loneliness, sleep issues, and anxiety. CONCLUSIONS: Our findings suggest a strong association between different indicators of psychological well-being and the level of activity in different sectors of the economy. Given the essential role of small and medium-sized enterprises (SMEs) in generating employment, especially during crises like the pandemic, it is imperative that they remain resilient and adaptable to support economic recovery and job preservation. To accomplish this, policymakers need to focus on providing financial stability and support for SMEs, fostering social support networks within companies, and incorporating mental health services into workplace environments. This comprehensive strategy can alleviate mental health challenges and enhance public health resilience.

The relationship between online searches and suicide.

AIMS: In this study, we examined the relationship between 131 suicide related Google search terms, grouped into nine categories, and the number of suicide cases per month in Ecuador from January 2011 to December 2021. METHODS: First, we applied time-series analysis to eliminate autocorrelation and seasonal patterns to prevent spurious correlations. Second, we used Pearson's correlation to assess the relationship between Google search terms and suicide rates. Third, cross-correlation analysis was used to explore the potential delayed effects between these variables. Fourth, we extended the correlation and cross-correlation analyses by three demographic characteristics - gender, age, and region. RESULTS: Significant correlations were found in all categories between Google search trends and suicide rates in Ecuador, with predominantly positive and moderate correlations. The terms 'stress' (.548), 'prevention' (.438), and 'disorders' (.435) showed the strongest associations. While global trends indicated moderate correlations, sensitivity analysis revealed higher coefficients in men, young adults, and the Highlands region. Specific patterns emerged in subgroups, such as 'digital violence' showing significant correlations in certain demographics, and 'trauma' presenting a unique temporal pattern in women. In general, cross correlation analysis showed an average negative correlation of -.191 at lag 3. CONCLUSION: Google search data do not provide further information about users, such as demographics or mental health records. Hence, our results are simply correlations and should not be interpreted as causal effects. Our findings highlight a need for tailored suicide prevention strategies that recognize the complex dynamics of suicide risk across demographics and time periods.

Land-cover change in Cuba and implications for the area of distribution of a specialist's host-plant.

Changes in land cover directly affect biodiversity. Here, we assessed land-cover change in Cuba in the past 35 years and analyzed how this change may affect the distribution of Omphalea plants and Urania boisduvalii moths. We analyzed the vegetation cover of the Cuban archipelago for 1985 and 2020. We used Google Earth Engine to classify two satellite image compositions into seven cover types: forest and shrubs, mangrove, soil without vegetation cover, wetlands, pine forest, agriculture, and water bodies. We considered four different areas for quantifications of land-cover change: (1) Cuban archipelago, (2) protected areas, (3) areas of potential distribution of Omphalea, and (4) areas of potential distribution of the plant within the protected areas. We found that "forest and shrubs", which is cover type in which Omphalea populations have been reported, has increased significantly in Cuba in the past 35 years, and that most of the gained forest and shrub areas were agricultural land in the past. This same pattern was observed in the areas of potential distribution of Omphalea; whereas almost all cover types were mostly stable inside the protected areas. The transformation of agricultural areas into forest and shrubs could represent an interesting opportunity for biodiversity conservation in Cuba. Other detailed studies about biodiversity composition in areas of forest and shrubs gain would greatly benefit our understanding of the value of such areas for conservation.

Using "Adopt a Bacterium" as an e-learning tool for simultaneously teaching microbiology to different health-related university courses.

The COVID-19 pandemic has posed challenges for education, particularly in undergraduate teaching. In this study, we report on the experience of how a private university successfully addressed this challenge through an active methodology applied to a microbiology discipline offered remotely to students from various health-related courses (veterinary, physiotherapy, nursing, biomedicine, and nutrition). Remote teaching was combined with the "Adopt a Bacterium" methodology, implemented for the first time on Google Sites. The distance learning activity notably improved student participation in microbiology discussions, both through word cloud analysis and the richness of discourse measured by the Shannon index. Furthermore, feedback from students about the e-learning approach was highly positive, indicating its effectiveness in motivating and involving students in the learning process. The results also demonstrate that despite being offered simultaneously to students, the methodology allowed for the acquisition of specialized knowledge within each course and sparked student interest in various aspects of microbiology. In conclusion, the remote "Adopt a Bacterium" methodology facilitated knowledge sharing among undergraduate students from different health-related courses and represented a valuable resource in distance microbiology education.

Eficácia da plataforma PIUSE na gestão de pesquisas e sua relação com o uso intensivo de internet por membros de bancas e orientadores de teses / The effectiveness of the PIUSE platform in research management and its relationship with the intensive use of the internet by thesis committee members and advisors / Eficacia de la plataforma PIUSE en la gestión de investigación y su relación con el uso intensivo de internet en jurados y asesores de tesis

O estudo teve como objetivo avaliar a efetividade da plataforma PIUSE na gestão dos processos de pesquisa e o uso excessivo da internet por orientadores e membros de bancas de teses no programa de segunda especialização da Faculdade de Educação da Universidade Nacional do Altiplano de Puno. Esta pesquisa teve um desenho quase-experimental, realizado com 125 professores que participaram como membros de bancas e/ou orientadores, com idade média de 46,86±7,87 anos. No estudo, foram aplicados três instrumentos: questionário para a revisão de projetos e relatórios de pesquisa (α=0,967); questionário para a aprovação de projetos e relatórios de pesquisa (α=0,894); e o questionário para a defesa de projetos e relatórios de pesquisa (α=0,882). Utilizou-se o teste de Wilcoxon para comparar duas amostras relacionadas antes e depois da implementação da plataforma PIUSE e o qui-quadrado de Pearson para a associação entre o uso da internet e o nível de participação. As análises foram realizadas com o software IBM SPSS v.25. A plataforma PIUSE, construída com as ferramentas do Google, influenciou significativamente a eficiência na revisão e aprovação de projetos e relatórios de pesquisa (Z=-9,729; p<0,001), na revisão do intercâmbio de informações (Z=-9,702; p<0,001), e na edição e correção durante a revisão de projetos e relatórios de pesquisa (Z=- 9,766; p<0,001). No entanto, 85,4% dos professores que participaram como membros de bancas em um nível elevado fizeram uso da internet por mais de 4 horas/dia (χ2=43,427; P<0,001).

The study aimed to evaluate the effectiveness of the PIUSE platform in managing research processes and the excessive use of the internet by advisors and thesis committee members in the second specialization program at the Faculty of Education of the National University of Altiplano in Puno. This research had a quasi-experimental design, conducted with 125 professors who participated as committee members and/or advisors, with an average age of 46.86±7.87 years. Three instruments were used in the study: a questionnaire for the review of research projects and reports (α=0.967); a questionnaire for the approval of research projects and reports (α=0.894); and a questionnaire for the defense of research projects and reports (α=0.882). The Wilcoxon test was used to compare two related samples before and after the implementation of the PIUSE platform, and Pearson's chi-square test was used to examine the association between internet use and the level of participation. Analyses were performed using IBM SPSS v.25. The PIUSE platform, built with Google tools, significantly influenced the efficiency in the review and approval of research projects and reports (Z=-9.729; p<0.001), the review of information exchange (Z=-9.702; p<0.001), and the editing and correction during the review of research projects and reports (Z=-9.766; p<0.001). However, 85.4% of the professors who participated as committee members at a high level used the internet for more than 4 hours/day (α2=43.427; P<0.001).

El estudio tuvo como propósito evaluar la efectividad de la plataforma PIUSE en la gestión de procesos de investigación y el uso excesivo de internet en los asesores y jurados de tesis en el programa de segunda especialidad de la Facultad de Educación de la Universidad Nacional del Altiplano de Puno. Esta investigación tuvo un diseño cuasi experimental, realizado con 125 docentes que participaron como jurados y/o asesores, cuya edad promedio fue de 46.86±7.87 años. En este estudio se aplicaron tres instrumentos: cuestionario para la revisión de proyectos e informes de investigación α=0,967; cuestionario para la aprobación de proyectos e informes de investigación α=0,894 y el cuestionario para la sustentación de proyectos e informes de investigación α=0,882. Se empleó la prueba de rangos con signo de Wilcoxon, para comparar dos muestras relacionadas, antes y después de la implementación de la plataforma PIUSE y el chi cuadrado de Pearson para la asociación de uso de internet y nivel de participación. Los análisis se realizaron con el software IBM SPSS v.25. La plataforma PIUSE, construida con las herramientas de Google, influyó significativamente en la eficiencia de revisión y aprobación del proyecto e informe de investigación (Z=- 9,729; p<0.001), en la revisión del intercambio de información (Z=- 9,702; p<0,001), y la edición y corrección en la revisión del proyecto e informe de investigación (Z=- 9,766; p<0,001). Sin embargo, 85,4% de los catedráticos que participaron como jurados en un nivel alto hicieron un uso de internet superior a 4 horas/día (X2 =43,427; P<0,001).

Como indexar una revista científica usando WordPress en Google Scholar

Los avances científicos han facilitado la difusión del conocimiento, encontrándose los más recientes hallazgos rápidamente en Internet, esto ha producido la migración de las revistas desde lo impreso a lo digital, pero este proceso no siempre se realiza adecuadamente, por lo que actualmente existen revistas, y en consecuencia sus publicaciones, que no se logran encontrar en los buscadores académicos, lo que se debe al uso de los softwares inadecuados o en su defecto a una mala configuración de los que se han implementado. En esta línea la recomendación es usar Open Journal System, un software diseñado para la publicación científica, pero varias revistas usan gestores de contenido como WordPress, por su facilidad de implementación y personalización aun cuando estos presenten limitaciones en el aspecto editorial. A continuación, se expone un método para la correcta indexación de revistas confeccionadas en WordPress en el buscador Google Scholar.

Scientific advances have facilitated the dissemination of knowledge, and the latest discoveries can be easily found on the Internet. This has produced the migration of journals from print to digital; however, this process is not always done properly since there are journals, and consequently their publications, which are not currently found in academic search engines due to the inappropriate use of software or otherwise to a misconfiguration of those that have been implemented. In this line, the recommendation is to use Open Journal System, a software designed for scientific publication; on the other hand, several journals use content manager systems such as WordPress because of its easy implementation and customization even when they present editorial constrains. The following is a method for the correct indexing of journals created in WordPress in the Google Scholar search engine.

Asociación entre las tendencias de búsqueda en internet con el número de casos confirmados por la viruela símica: estudio multinacional en diez países / Association between internet search trends and the number of confirmed monkeypox cases: a multinational study in ten countries

INTRODUCCIÓN: La viruela símica es una infección zoonótica que se ha distribuido por todo el mundo. La búsqueda de información en internet refleja el interés y concientización de la población acerca de salud. OBJETIVO: Determinar la asociación entre el volumen relativo de búsquedas en internet con el número de casos confirmados por la viruela símica en diez países. MÉTODOS: Se realizó un estudio obser- vacional, analítico, retrospectivo, utilizando la herramienta Google Trends (GT™) para encontrar el volumen relativo búsqueda (VRB) sobre viruela símica desde 01 de enero al 31 de agosto del 2022 usando términos de búsqueda en el idioma oficial de los 10 países con mayor número de casos en dichas fechas, registrado por Our World in Data. Para establecer la relación lineal entre el VRB con los nuevos de casos por día se usó el coeficiente de correlación de Pearson con un nivel de significancia (p ≤ 0,05). RESULTADOS: Se encontró un coeficiente de correlación de Pearson fuerte en Brasil (Rp = 0,562,p = 0,001), y débil en países como Alemania (Rp = 0,281, p = 0,004), Estados Unidos de Norteamérica (Rp = 0,255, p = 0,008), España (Rp = 0,122, p = 0,213), Perú (Rp = 0,120, p = 0,333), Canadá (Rp = 0,116, p = 0,238), Francia (Rp = 0,095, p = 0,335), Reino Unido (Rp = 0,085, p = 0,362), Portugal (Rp = 0,024, p = 0,805) y Países Bajos (Rp = 0,067, p = 0,497). CONCLUSIÓN: Nuestro estudio evidencio que el VRB presento una relación positiva con el número de nuevos casos de viruela símica. Asimismo, se observo un coeficiente de correlación fuerte en Brasil, y en el resto de países fue débil.

BACKGROUND: Smallpox is a zoonotic infection that has been distributed worldwide. The search for information on the Internet reflects the interest and awareness of the population about health. AIM: To determine the correlation between the relative volume of internet searches and the number of confirmed cases of smallpox in ten countries. METHODS: An observational, analytical, retrospective study was conducted using the Google Trends (GT™) tool to find the relative search volume (RSV) on monkeypox from January 1 to August 31, 2022 using search terms in the official language of the 10 countries with the highest number of cases on those dates, as recorded by Our World in Data. To establish the relationship between RSV and new cases per day, Spearman's correlation was used with a significance level (p ≤ 0.05). RESULTS: A. strong Pearson correlation coefficient was found in Brazil (Rp = 0.562, p = 0.001), and weak in countries like Germany (Rp = 0.281, p = 0.004), United States (Rp = 0.255, p = 0.008), Spain (Rp = 0. 122, p = 0.213), Peru (Rp = 0.120, p = 0.333), Canada (Rp = 0.116, p = 0.238), France (Rp = 0.095, p = 0.335), United Kingdom (Rp = 0.085, p = 0.362), Portugal (Rp = 0.024, p = 0.805) and Netherlands (Rp = 0.067, p = 0.497). CONCLUSION: Our study showed that RSV had a positive relationship with the number of new cases of smallpox. Also, a strong correlation coefficient was observed in Brazil, while the rest of the countries showed a weak correlation coefficient.

Population well-being and the COVID-19 vaccination program in Chile: evidence from Google Trends.

OBJECTIVE: We analyze the dynamics of the mental well-being of the Chilean population in response to the progress of the vaccination strategy implemented by the government. STUDY DESIGN: This study aims at investigating the possibility of using Google Trends as an instrument for tracking mental well-being of the Chilean population. METHODS: We use the volume of searches for keywords in Google Trends (GT) related to Anguish, Anxiety, Depression, and Stress as a proxy for population well-being. Using event study methods, we analyze social attention reactions to news about the vaccination program. We implement a Difference-in-Difference-in-Differences estimation to estimate changes in population welfare by socio-economic status induced by the progress of inoculation. RESULTS: We show that social attention to mental health problems is sensitive to news about the vaccination program. Moreover, and most importantly, we find that mental well-being responds positively to the percentage of inoculated people. This phenomenon appear to be permanent and affected by socio-economic status, with the wealthier population experiencing greater improvements than the less wealthy. CONCLUSIONS: During the COVID-19 vaccination program in Chile, social attention to mental health problems appears to be sensitive to news about the vaccination program. There is also strong evidence of socio-economic status-induced heterogeneity in population responses to program implementation. The above phenomena appears to be permanent and cannot be attributed to either socio-economic segregation in access to vaccines or to the highly stratified schedule of the vaccination program.

Spatial-temporal dynamics of decaying stages of pelagic Sargassum spp. along shorelines in Puerto Rico using Google Earth Engine.

Coastal social-ecological systems in the Caribbean are affected by pelagic Sargassum spp. influxes and decomposition, but most satellite monitoring efforts focus on offshore waters. We developed a method to detect and spatial-temporally assess sargassum accumulations and their decaying stages along the shoreline and nearshore waters. A multi-predictor Random Forest model combining Sentinel-2 MultiSpectral Instrument reflectance bands and several vegetation, seaweed, water, and water quality indices was developed within the online Google Earth Engine platform. The model achieved 97 % overall accuracy and identified both fresh and decomposing sargassum, as well as the Sargassum-brown-tide generated from decomposing sargassum. We identified three hotspots of sargassum accumulation in La Parguera, Puerto Rico and found that sargassum was present every month in at least one of its forms during the entire time series (September 2015-January 2022). This research provides information to understand sargassum impacts and areas where mitigation efforts need to focus.

Economic analysis through alternative data and big data techniques: what do they tell about Brazil?

Alternative data are now widely used in economic analyses worldwide but still infrequent in studies on the Brazilian economy. This research demonstrates how alternative data extracted from Google Trends and Google Mobility contribute to innovative economic analysis. First, it demonstrates that the search for the future on the internet is correlated (R = 0.62) with the average household income in Brazilian states. The three Brazilian states with the most people looking for the future on the internet have an average household income 1.6 times higher than people from states that do not have this behavior. The search for the future represents 10.9% of the economic development potential of the states, while the proportion of people with university degrees, scientific publications, and researchers represents another 60.4%. The reduction in mobility in retail/recreation locations averaged 34.28% in Brazil, Ecuador, Paraguay, and Uruguay. This group of countries had COVID-19 infection and death rates 1.25 and 1.74 times higher than in countries that reduced their mobility in retail/recreation locations by 45.03%. The impact of reduced mobility in retail/recreation locations on the unemployment rate, gross domestic product degrowth, and inflation in countries such as Brazil was 1.1, 2.2, and 2.6 times lower than in countries that reduced mobility more of people. The research contributions are associated with identifying new indicators extracted from alternative data and their application to carry out innovative economic analyses.

Deforestation and fires in the Brazilian Amazon from 2001 to 2020: Impacts on rainfall variability and land surface temperature.

Deforestation and fires in the Amazon are serious problems affecting climate, and land use and land cover (LULC) changes. In recent decades, the Amazon biome area has suffered constant fires and deforestation, causing severe environmental problems that considerably impact the land surface temperature (LST) and hydrological cycle. The Amazon biome lost a large forest area during this period. Thus, this study aims to analyze the deforestation and burned areas in the Amazon from 2001 to 2020, considering their impacts on rainfall variability and LST. This study used methods and procedures based on Google Earth Engine for analysis: (a) LULC evolution mapping, (b) vegetation cover change analysis using vegetation indices, (c) mapping of fires, (d) rainfall and LST analyses, and (e) analysis of climate influence and land cover on hydrological processes using the geographically weighted regression method. The results showed significant LULC changes and the main locations where fires occurred from 2001 to 2020. The years 2007 and 2010 had the most significant areas of fires in the Brazilian Amazon (233,401 km2 and 247,562 km2, respectively). The Pará and Mato Grosso states had the region's largest deforested areas (172,314 km2 and 144,128 km2, respectively). Deforestation accumulated in the 2016-2020 period is the greatest in the period analyzed (254,465 km2), 92% higher than in the 2005-2010 period and 82% higher than in the 2001-2005 period. The study also showed that deforested areas have been increasing in recent decades, and the precipitation decreased, while an increase is observed in the LST. It was also concluded that indigenous protection areas have suffered from anthropic actions.

Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework.

Terrestrial mammals face a severe crisis of habitat loss worldwide. Therefore, assessing information on habitat loss throughout different time periods is crucial for assessing species' conservation statuses based on the IUCN Red List system. To support the national extinction risk assessment in Brazil (2016-2022), we developed a script that uses the MapBiomas Project 6.0 data source of land cover and land use (annual maps at 30 m scale) within the Google Earth Engine (GEE) platform to calculate habitat loss. We defined suitable habitats from the MapBiomas Project land cover classification for 190 mammalian taxa, according to each species range map and ecological characteristics. We considered a period of three generation lengths to assess habitat loss in accordance with the Red List assessment criteria. We used the script to estimate changes in available habitat throughout the analyzed period within the species' known ranges. The results indicated that habitat loss occurred within 94.3% of the analyzed taxa range, with the Carnivora order suffering the greatest habitat loss, followed by the Cingulata order. These analyses may be decisive for applying criteria, defining categories during the assessment of at least 17 species (9%), enriching discussions, and raising new questions for several other species. We considered the outcome of estimating habitat loss for various taxa when applying criterion A, which refers to population reduction, thus supporting more accurate inferences about past population declines.

An anonymised longitudinal GPS location dataset to understand changes in activity-travel behaviour between pre- and post-COVID periods.

Collecting GPS data using mobile devices is essential to understanding human mobility. However, getting this type of data is tricky because of some specific features of mobile operating systems, the high-power consumption of mobile devices, and users' privacy concerns. Therefore, data of this kind are rarely publicly available for scientific purposes, while private companies that own the data are often reluctant to share it. Here we present a large anonymous longitudinal dataset of Activity Point Location (APL) generated from mobile devices' GPS tracking. The GPS data were collected by using the Google Location History (GLH), accessible in the Google Maps application. Our dataset, named AnLoCOV hereafter, includes anonymised data from 338 persons with corresponding socio-demographics over approximately ten years (2012-2022), thus covering pre- and post-COVID periods, and calculates over 2 million weekly-classified APL extracted from approximately 16 million GPS tracking points in Ecuador. Furthermore, we made our models publicly available to enable advanced analysis of human mobility and activity spaces based on the collected datasets.

Vegetational succession assessment in a fragment of the Brazilian Atlantic Forest.

Vegetational succession assessment is an important step for better management practices, providing relevant quantitative and qualitative information. With the advancements of remote sensing algorithms and access to data, land use and land cover (LULC) monitoring has become increasingly feasible and important for the evaluation of changes in the landscape at different spatial and temporal scales. This study aims to analyze the vegetation succession achieved by a project funded by the Brazilian Environmental Ministry (Ministério do Meio Ambiente, in Portuguese) intended to recover degraded areas. A 2014 and a 2019 LULC map was generated using high-resolution (10 cm) images. Given the great challenge of classifying high-resolution images, three classification algorithms were compared. The techniques to regenerate degraded areas were efficient to increase arboreal vegetation area by more than 30% between 2014 and 2019. Land cover and land use change monitoring is of paramount importance to strengthen sustainable practices, especially in the highly threatened Atlantic Forest biome. This study also shows that funding opportunities are essential for projects that make such actions possible, including the present research and the analysis of environmental regeneration.

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.

Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.

The COVID-19 infodemic in Brazil: trends in Google search data.

Background: Since the beginning of the new coronavirus pandemic, there has been much information about the disease and the virus has been in the spotlight, shared and commented upon on the Internet. However, much of this information is infodemics and can interfere with the advancement of the disease and that way that populations act. Thus, Brazil is a country that requires attention, as despite the fact that in almost two years of pandemic it has shown a devastating numbers of deaths and number of cases, and generates false, distorted and malicious news about the pandemic. This work intends to understand the attitudes of the Brazilian population using infodemic queries from the Google Trends search tool and social and income variables. Methods: Data from infodemic research carried out on Google Trends, between January 1, 2020 and June 30, 2021, with socioeconomic data, such as income and education, were unified in a single database: standardization and exploratory and multivalued techniques based on grouping were used in the study. Results: In the analysis of the search trend of infodemic terms, it is clear that the categories of Prevention and Beliefs should stand out in Brazil, where there is a diverse culture. It is followed by the COVID-19 Treatment category, with treatments that were not those recommended by the authorities. Income transfer programs and information on socioeconomic variables did not have much impact on infodemic surveys, but it was observed that states where President Bolsonaro has more supporters had researched more infodemic information. Conclusions: In a country as geographically large as Brazil, it is important that political authorities go to great lengths to disseminate reliable information and monitor the infodemic in the media and on the internet. It was concluded that the denial of the pandemic and the influence of political leaders influenced the search for infodemic information, contributing to a disorganization in the control of the disease and prevention measures.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil.

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

Augmented reality in interventional radiology education: a systematic review of randomized controlled trials

ABSTRACT BACKGROUND: Augmented reality (AR) involves digitally overlapping virtual objects onto physical objects in real space so that individuals can interact with both at the same time. AR in medical education seeks to reduce surgical complications through high-quality education. There is uncertainty in the use of AR as a learning tool for interventional radiology procedures. OBJECTIVE: To compare AR with other learning methods in interventional radiology. DESIGN AND SETTING: Systematic review of comparative studies on teaching techniques. METHODS: We searched the Cochrane Library, MEDLINE, Embase, Tripdatabase, ERIC, CINAHL, SciELO and LILACS electronic databases for studies comparing AR simulation with other teaching methods in interventional radiology. This systematic review was performed in accordance with PRISMA and the BEME Collaboration. Eligible studies were evaluated using the quality indicators provided in the BEME Collaboration Guide no. 11, and the Kirkpatrick model. RESULTS: Four randomized clinical trials were included in this review. The level of educational evidence found among all the papers was 2B, according to the Kirkpatrick model. The Cochrane Collaboration tool was applied to assess the risk of bias for individual studies and across studies. Three studies showed an improvement in teaching of the proposed procedure through AR; one study showed that the participants took longer to perform the procedure through AR. CONCLUSION: AR, as a complementary teaching tool, can provide learners with additional skills, but there is still a lack of studies with a higher evidence level according to the Kirkpatrick model. SYSTEMATIC REVIEW REGISTRATION NUMBER: DOI 10.17605/OSF.IO/ACZBM in the Open Science Framework database.

Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine.

Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops.

Socioeconomic inequalities and the surface heat island distribution in Santiago, Chile.

Surface urban heat islands (SUHIs) are an important socio-environmental problem associated with large cities, such as the Santiago Metropolitan Area (SMA), in Chile. Here, we analyze daytime and nighttime variations of SUHIs for each season of the year during the period 2000-2020. To evaluate socioeconomic inequities in the distribution of SUHIs, we establish statistical relationships with socioeconomic status, land price, and urban vegetation. We use the MODIS satellite images to obtain the land surface temperatures and the normalized difference vegetation index (NDVI) through the Google Earth Engine platform. The results indicate more intense SUHIs during the nighttime in the eastern sector, coinciding with higher socioeconomic status and larger green areas. This area during the day is cooler than the rest of the city. The areas with lower and middle socioeconomic status suffer more intense SUHIs (daytime and nighttime) and match poor environmental and urban qualities. These results show the high segregation of SMA. Urban planning is subordinated to land prices with a structure maintained over the study period. The lack of social-climate justice is unsustainable, and such inequalities may be exacerbated in the context of climate change. Thus, these results can contribute to the planning of the SMA.