Canine visceral leishmaniasis: Seroprevalence and georeferencing in the state of Santa Catarina, Brazil.

Canine visceral leishmaniasis (CVL) has long been considered an endemic disease in the northern and northeastern regions of Brazil, while the southern region remains non-endemic. However, in recent years, several cases of CVL have been reported in southern states. The objective of this work was to determine the seroprevalence of CVL in dogs in the state of Santa Catarina, Brazil, through immunochromatographic tests (DPP®) and ELISA (Enzyme-Linked Immunosorbent Assay) and its correlation with environmental characteristics through georeferencing. Blood samples from dogs (n = 1227) were collected in six mesoregions of the state and evaluated by the rapid test (DPP®). Positive samples were sent to Lacen (Central Public Health Laboratory) in Santa Catarina to be tested using ELISA. Information obtained from the epidemiological questionnaire was subjected to statistical analysis (Chi-square and Student's t-test; P < 0.05) to verify the correlation between serology and the analyzed variables. The locations (GPS) of the samples were used for georeferencing and creating heatmaps (Kernel Method). Four animals that died from CVL were necropsied and organ samples were collected for molecular analysis (PCR), immunohistochemistry, and histopathology (HE). Of the 1227 dogs analyzed, 22 (1.8%) were reactive in the DPP® and of these, 7 (0.6%) were also positive in the ELISA. A correlation (P < 0.01) was observed between positive serology and region, environment, access to the street, and clinical signs. The positive cases were concentrated in the eastern region of the state, in low-altitude areas with average rainfall and higher average temperatures, and in more populated areas close to forest fragments. PCR, HE, and immunohistochemistry, along with serology, have proven to be efficient for characterizing positive cases.

Multi-Drone Cooperation for Improved LiDAR-Based Mapping.

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case.

A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change.

Recent proliferation of GPS technology has transformed animal movement research. Yet, time-series data from this recent technology rarely span beyond a decade, constraining longitudinal research. Long-term field sites hold valuable historic animal location records, including hand-drawn maps and semantic descriptions. Here, we introduce a generalised workflow for converting such records into reliable location data to estimate home ranges, using 30 years of sleep-site data from 11 white-faced capuchin (Cebus imitator) groups in Costa Rica. Our findings illustrate that historic sleep locations can reliably recover home range size and geometry. We showcase the opportunity our approach presents to resolve open questions that can only be addressed with very long-term data, examining how home ranges are affected by climate cycles and demographic change. We urge researchers to translate historical records into usable movement data before this knowledge is lost; it is essential to understanding how animals are responding to our changing world.

Built environment and physical activity of adolescents: an approach with artificial neural networks / Ambiente construído e atividade física de adolescentes: uma abordagem com redes neurais artificiais

The objective of this study was to analyze the association of the level of physical activity (PA) and body composition in relation to the amount and distance of built environments favorable to the practice of PA in relation to the homes of adolescents in the city of Lagoa do Carro/Pernambuco, Brazil. A total of 289 adolescents (153 boys; 10 to 18 years) participated in the study, duly enrolled in schools municipality. The self-administered Physical Activity Questionnaire for Children (PAQ-C) and Physical Activity Questionnaire for Adolescent (PAQ-A) was used to assess the PA level. The Geographic Information System was used to assess the built environments. Buffers of 100 to 500 meters were created from the center of the built environment. The Artificial Neural Network in the Feedforward method was used to assess the association and importance of built environment and body composition variables with PA level. The different distances from the built environment to the place of residence do not present statistical differences. It is noteworthy that the number of buffers up to 500 meters away was the variable that showed the greatest importance for the PA level, along with adolescents who live in places with greater exposure in terms of built environments, being considered more active. It was possible to conclude that the main determinants of the PA level of adolescents were the amount of buffers at 500 meters, sex and the distance to the built environment. However, the variables, housing area, body mass and amounts of buffers at 100 meters were the ones with the lowest power of influence.

O objetivo deste estudo foi analisar a associação do nível de atividade física (AF) e composição corporal em relação à quantidade e distância de ambientes construídos favoráveis à prática da AF em relação ao domicílio de adolescentes da cidade de Lagoa do Carro/Pernambuco, Brasil. Participaram do estudo 289 adolescentes (153 meninos; 10 a 18 anos), devidamente matriculados nas escolas do município. O Physical Activity Questionnaire for Children (PAQ-C) e Physical Activity Questionnaire for Adolescent (PAQ-A) autoaplicável foram utilizados para avaliar o nível de AF. O Sistema de Informação Geográfico foi utilizado para avaliação dos ambientes construídos. Foram criados Buffers de 100 a 500 metros de raio a partir do centro do ambiente construído. A Rede Neural Artificial no método de Feedforward foi utilizada para analisar a associação e a importância das variáveis do ambiente construído e composição corporal com o nível de AF. Não foram observadas diferenças estatisticamente significativas entre o nível de AF e as distâncias do ambiente construído. Ressalta--se que a quantidade de buffers até 500 metros de distância, foi a variável que apresentou maior importância para o nível de AF, juntamente com os adolescentes que residem em locais com maior exposição em quantidade de ambientes construídos, sendo considerados mais ativos. Os principais determinantes do nível da AF dos adolescentes foram à quantidade de buffers a 500 metros, o sexo e a distância para o ambiente construído. Em contrapartida, as variáveis, zona de moradia, massa corporal e quantidades de buffers a 100 metros foram as que apresentaram um menor poder de influência.

The Dolichens database: the lichen biota of the Dolomites.

The Dolichens project provides the first dynamic inventory of the lichens of the Dolomites (Eastern Alps, Italy). Occurrence records were retrieved from published and grey literature, reviewed herbaria, unpublished records collected by the authors, and new sampling campaigns, covering a period from 1820 to 2022. Currently, the dataset contains 56,251 records, referring to 1,719 infrageneric taxa, reported from 1820 to 2022, from hilly to nival belts, and corresponding to about half of the species known for the whole Alpine chain. Amongst them, 98% are georeferenced, although most of them were georeferenced a posteriori. The dataset is available through the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/es/dataset/cea3ee2c-1ff1-4f8e-bb37-a99600cb4134) and through the Dolichens website (https://italic.units.it/dolichens/). We expect that this open floristic inventory will contribute to tracking the lichen diversity of the Dolomites over the past 200 years, and providing the basis for future taxonomic, biogeographical, and ecological studies.

A georeferenced database of the edaphic biota currently available for Argentina.

Background: Soils have been studied and classified in terms of their physical and chemical characteristics, while the knowledge about biodiversity and the ecosystem processes that they support is lagging behind. Furthermore, the advance in scientific knowledge contributed by different researchers is dispersed and it is necessary to collect it to bring the big picture into focus. Today, it is possible to have the findings and data collected by different researchers, compile them and, based on technological advances, have tools that allow the information to be analysed in its entirety. The main objective of this work is to compile and systematise all the bibliographic information available on the main organisms that make up biodiversity in the soil: Acari, Collembola and Crassiclitellata in Argentina. This information will then allow us to link the composition and structure of the soil community with processes and flows in the ecosystem, and to estimate them at different scales and in soils with different anthropic impact. The database presented here gathers presence information on the mentioned taxa, their geographical location for the entire country, while preserving the identity and authorship of each scientific work retrieved. The taxonomic range of the organisms of the edaphic biota collected in this database ranges from class to subspecies and are registered, based on the taxonomic level reported by the original author in their research. The publications were obtained from Google Scholar, Scopus and JSTOR. In addition, records were added from INEDES theses, library searches, information requested from authors cited in other articles and unpublished works. In total, information was collected from 224 scientific publications, as well as personal information requested directly from some authors. The total number of registered individuals so far is 4838 of which 3049 specimens correspond to Acari, 944 to Classiclitellata and 845 belong to Collembola. New information: This work is the first to gather, in a single publication, the entire dataset for all the Acari, Collembola and Clitellata recorded for Argentina.

Screening for canine coronavirus, canine influenza virus, and severe acute respiratory syndrome coronavirus 2 in dogs during the coronavirus disease-2019 pandemic.

Background and Aim: Although most cases of coronavirus disease-2019 (COVID-19) are in humans, there is scientific evidence to suggest that the virus can also infect dogs and cats. This study investigated the circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), canine coronavirus (CCV), and canine influenza virus (CIV) in domiciled and/or stray dogs from different locations in the State of Minas Gerais, Brazil, during the COVID-19 pandemic. Materials and Methods: In total, 86 dogs living in homes, on the streets, or in shelters in the cities of Taiobeiras, Salinas, Araçuaí, and Almenara were randomly selected for this study. The COVID Ag Detect® Self-Test was used to detect SARS-CoV-2. The ACCUVET CCV AG TEST - CANINE CORONAVIROSIS® was used to detect CCV, whereas canine influenza was detected using the ACCUVET CIV AG TEST - INFLUENZA CANINA®. All collected data were mapped using QGIS 3.28.1 for spatial data analysis and the identification of disease distribution patterns. Descriptive analysis of the collected data, prevalence calculations, odds ratios (ORs), and 95% confidence intervals, when possible, was performed. Results: Of the 86 animals tested, only one dog tested positive for SARS-CoV-2 using the rapid test for viral antigen detection. No animals tested positive for CIV. Canine coronavirus was detected in almost half of the animals tested in Almenara. Severe acute respiratory syndrome-CoV-2 had a low prevalence (1.16%), versus 15.62% for CCV. Although the results were not significant, the age and breed of animals appeared to be associated with the occurrence of CCV. The results indicated that younger animals were 2.375-fold more likely to be infected. Likewise, purebred animals were more likely to contract the disease (OR = 1.944). Conclusion: The results indicate the need to maintain preventive measures against CCV, canine influenza, and SARS-CoV-2 in dogs. More studies are needed to better elucidate the panorama of these diseases in dogs, mainly in underdeveloped and developing countries.

Utilising Sentinel-1's Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter.

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite's high orbital stability. By propagating Sentinel-1's orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10-50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.

A New and Automated Method for Improving Georeferencing in Nighttime Thermal ECOSTRESS Imagery.

Georeferencing accuracy plays a crucial role in providing high-quality ready-to-use remote sensing data. The georeferencing of nighttime thermal satellite imagery conducted by matching to a basemap is challenging due to the complexity of thermal radiation patterns in the diurnal cycle and the coarse resolution of thermal sensors in comparison to sensors used for imaging in the visual spectral range (which is typically used for creating basemaps). The presented paper introduces a novel approach for the improvement of the georeferencing of nighttime thermal ECOSTRESS imagery: an up-to-date reference is created for each to-be-georeferenced image, derived from land cover classification products. In the proposed method, edges of water bodies are used as matching objects, since water bodies exhibit a relatively high contrast with adjacent areas in nighttime thermal infrared imagery. The method was tested on imagery of the East African Rift and validated using manually set ground control check points. The results show that the proposed method improves the existing georeferencing of the tested ECOSTRESS images by 12.0 pixels on average. The strongest source of uncertainty for the proposed method is the accuracy of cloud masks because cloud edges can be mistaken for water body edges and included in fitting transformation parameters. The georeferencing improvement method is based on the physical properties of radiation for land masses and water bodies, which makes it potentially globally applicable, and is feasible to use with nighttime thermal infrared data from different sensors.

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models.

BACKGROUND AND OBJECTIVE: SARS-CoV-2 emerged by the end of 2019 and became a global pandemic due to its rapid spread. Various outbreaks of the disease in different parts of the world have been studied, and epidemiological analyses of these outbreaks have been useful for developing models with the aim of tracking and predicting the spread of epidemics. In this paper, an agent-based model that predicts the local daily evolution of the number of people hospitalized in intensive care due to COVID-19 is presented. METHODS: An agent-based model has been developed, taking into consideration the most relevant characteristics of the geography and climate of a mid-size city, its population and pathology statistics, and its social customs and mobility, including the state of public transportation. In addition to these inputs, the different phases of isolation and social distancing are also taken into account. By means of a set of hidden Markov models, the system captures and reproduces virus transmission associated with the stochastic nature of people's mobility and activities in the city. The spread of the virus in the host is also simulated by following the stages of the disease and by considering the existence of comorbidities and the proportion of asymptomatic carriers. RESULTS: As a case study, the model was applied to Paraná city (Entre Ríos, Argentina) in the second half of 2020. The model adequately predicts the daily evolution of people hospitalized in intensive care due to COVID-19. This adequacy is reflected by the fact that the prediction of the model (including its dispersion), as with the data reported in the field, never exceeded 90% of the capacity of beds installed in the city. In addition, other epidemiological variables of interest, with discrimination by age range, were also adequately reproduced, such as the number of deaths, reported cases, and asymptomatic individuals. CONCLUSIONS: The model can be used to predict the most likely evolution of the number of cases and hospital bed occupancy in the short term. By adjusting the model to match the data on hospitalizations in intensive care units and deaths due to COVID-19, it is possible to analyze the impact of isolation and social distancing measures on the disease spread dynamics. In addition, it allows for simulating combinations of characteristics that would lead to a potential collapse in the health system due to lack of infrastructure as well as predicting the impact of social events or increases in people's mobility.

Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments.

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.

Socio-epidemiological factors and comorbidities associated with Chagas disease manifestations in two urban reference health care centres in Rio de Janeiro, Brazil.

BACKGROUND: Chagas disease (CD) is still an important public health issue in Latin America. This study aims to analyse the association between socio-epidemiological factors and comorbidities with clinical manifestations of CD. METHODS: We performed a cross-sectional study of 985 adult patients (65±11 y; 59.5% women) with CD. Data collection was based on questionnaires and medical records review. CD clinical forms (indeterminate, digestive, cardiac and cardiodigestive) and the stages of the cardiac form were classified according to the II Brazilian Consensus on CD. Statistical analyses were based on univariate and multivariate logistic regression. RESULTS: Older age and Brazilian birth state (Minas Gerais and Bahia) were associated with a greater likelihood of the cardiac form of CD. A greater likelihood of the digestive form was seen in men and those of older age. Patients with arterial hypertension and diabetes were less likely to have the digestive form. Men had a greater likelihood of having a more severe cardiac presentation. Those from Minas Gerais and Bahia states had a greater likelihood of having stage B1 or B2. CONCLUSIONS: The results reinforce the aging of the CD population living in urban areas in Brazil, the high prevalence of comorbidities and that epidemiology, sex and the presence of comorbidities may be related to the clinical form of CD.

Consumption of benzylpenicillin as a syphilis control indicator

Abstract Syphilis is a disease with compulsory and mandatory notification to the Notifiable Diseases Information System (SINAN), with benzathine benzylpenicillin being the treatment of choice. The aim of the study was to compare the consumption of benzylpenicillin benzathine, from the dispensation, between the health regions of a capital in the southern region of the country, according to the georeferencing of notified cases of syphilis. This is a descriptive, cross-sectional, retrospective study of the use of benzylpenicillin benzathine and of reported cases of syphilis. Data on syphilis cases were obtained from notifications made in SINAN, and drug consumption data were obtained from the Municipal Health Department computerized system for Drug Dispensing from January 1st, 2019 to December 31st, 2019. Notifications and drug consumption were georeferenced according to 8 health regions. From the compilation of data, the rates of cases and consumption in relation to the population of each region were calculated. A total of 3188 notifications and a total of 35191 vials of benzathine benzylpenicillin were analyzed. The ratio of vials by SINAN notifications showed that each patient took 11 vials of the drug, which is a higher value if we consider that the complete treatment is 2 to 6 vials per case.

Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments.

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.

Vulnerable territories to tuberculosis-diabetes mellitus comorbidity in a northeastern Brazilian scenario.

INTRODUCTION: Epidemiological investigations on tuberculosis-diabetes comorbidity using spatial analysis should be encouraged towards a more comprehensive view of the health of individuals affected by such comorbidity in different contexts. This study analyzes the territories vulnerable to tuberculosis-diabetes comorbidity in a municipality in northeastern Brazil using spatial analysis techniques. METHODS: An ecological study was carried out in Imperatriz, Maranhão, Brazil. Tuberculosis-diabetes cases reported in the Brazilian Notifiable Diseases Information System between 2009 and 2018 were analyzed. Kernel density estimation and spatial scanning techniques were used to identify the areas with the greatest occurrence of spatial clusters. RESULTS: A heterogeneous spatial distribution was found, ranging from 0.00 to 4.12 cases/km2. The spatial scanning analysis revealed three high-risk spatial clusters with statistical significance (p < 0.05), involving eleven strictly urban sectors with a relative risk of 4.00 (95% CI: 2.60-6.80), 5.10 (95% CI: 2.75-7.30), and 6.10 (95% CI: 3.21-8.92), indicating that the population living in these areas had a high risk of tuberculosis-diabetes comorbidity. CONCLUSIONS: The highest concentration of cases/km2, as well as risk clusters, were found in areas with high circulation of people and socio-economic and environmental vulnerabilities. Such findings reinforce the need for public health interventions to reduce social inequalities.

Ship Segmentation and Georeferencing from Static Oblique View Images.

Camera systems support the rapid assessment of ship traffic at ports, allowing for a better perspective of the maritime situation. However, optimal ship monitoring requires a level of automation that allows personnel to keep track of relevant variables in the maritime situation in an understandable and visualisable format. It therefore becomes important to have real-time recognition of ships present at the infrastructure, with their class and geographic position presented to the maritime situational awareness operator. This work presents a novel dataset, ShipSG, for the segmentation and georeferencing of ships in maritime monitoring scenes with a static oblique view. Moreover, an exploration of four instance segmentation methods, with a focus on robust (Mask-RCNN, DetectoRS) and real-time performances (YOLACT, Centermask-Lite) and their generalisation to other existing maritime datasets, is shown. Lastly, a method for georeferencing ship masks is proposed. This includes an automatic calculation of the pixel of the segmented ship to be georeferenced and the use of a homography to transform this pixel to geographic coordinates. DetectoRS provided the highest ship segmentation mAP of 0.747. The fastest segmentation method was Centermask-Lite, with 40.96 FPS. The accuracy of our georeferencing method was (22 ± 10) m for ships detected within a 400 m range, and (53 ± 24) m for ships over 400 m away from the camera.

[Geocoding one million of addresses using API: a semiautomatic multistep procedure]. / Geocodificare un milione di indirizzi mediante l'utilizzo di API: una procedura semiautomatica a più step.

OBJECTIVES: to geocode all residence addresses from Lazio Health Information System in order to obtain a geographical regional database. DESIGN: a semiautomatic and multistep geocoding procedure using several tools and software. SETTING AND PARTICIPANTS: all residence addresses of resident population of Lazio Region (Central Italy) in 2020. MAIN OUTCOME MEASURES: geographic coordinates at residence addresses and accuracy level of geocoding procedure for more than 1 million of addresses. RESULTS: the 99% of residence addresses in the Lazio Region have been geocoded thanks to the purposed procedure; almost 94% of the addresses have been geocoded with a good level of accuracy (more than 56% at civic number level). In the province of Rome, the percentage of addresses geocoded with a good level of accuracy is higher (97.1%), while in the province of Rieti and Frosinone is lower (82.7% and 84.2%, respectively). CONCLUSIONS: this method is useful to obtain accurate geographic coordinates of residences of the entire regional population. This database will be useful for several epidemiological studies in the Region.

Real-Time Georeferencing of Fire Front Aerial Images Using Iterative Ray-Tracing and the Bearings-Range Extended Kalman Filter.

Although Aerial Vehicle images are a viable tool for observing large-scale patterns of fires and their impacts, its application is limited by the complex optical georeferencing procedure due to the lack of distinctive visual features in forest environments. For this reason, an exploratory study on rough and flat terrains was conducted to use and validate the Iterative Ray-Tracing method in combination with a Bearings-Range Extended Kalman Filter as a real-time forest fire georeferencing and filtering algorithm on images captured by an aerial vehicle. The Iterative Ray-Tracing method requires a vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU), a calibrated camera, and a Digital Elevation Map (DEM). The proposed method receives the real-time input of the GPS, IMU, and the image coordinates of the pixels to georeference (computed by a companion algorithm of fire front detection) and outputs the geographical coordinates corresponding to those pixels. The Unscented Transform B is proposed to characterize the Iterative Ray-Tracing uncertainty. A Bearings-Range filter measurement model is introduced in a sequential filtering architecture to reduce the noise in the measurements, assuming static targets. A performance comparison is done between the Bearings-Only and the Bearings-Range observation models, and between the Extended and Cubature Kalman Filters. In simulation studies with ground truth, without filtering we obtained a georeferencing Root Mean Squared Errors (RMSE) of 30.7 and 43.4 m for the rough and flat terrains respectively, while filtering with the proposed Bearings-Range Extended Kalman Filter showed the best results by reducing the previous RMSE to 11.7 and 19.8 m, respectively. In addition, the comparison of both filter algorithms showed a good performance of Bearings-Range filter which was slightly faster. Indeed, these experiments based on the real data conducted to results demonstrated the applicability of the proposed methodology for the real-time georeferencing forest fires.

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications.

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

Association between high-risk pregnancy and environmental contaminants in the Metropolitan Region of Baixada Santista, Brazil.

The Metropolitan Region of Baixada Santista (MRBS) is a highly contaminated area. High-risk pregnancy is one factor that leads to a higher chance of both morbidity and mortality of the mother-fetus binomial. The purpose of this study is to analyze the association between exposure to environmental contaminants and high-risk pregnancy. Case-control study, using a probabilistic and random sample composed of 201 high-risk pregnant women (cases) and 201 no high-risk pregnant women (control) followed up during prenatal care at a Public Hospital. The instrument used was a self-administered questionnaire. Contaminated areas data were obtained from the Environmental Company of the São Paulo State. The participants were georeferenced by their place of residence. Descriptive analysis, Chi-square test, and univariate and multiple logistic regression analysis were performed. The multiple logistic regression model demonstrated that living in a contaminated area (OR = 1.565; 95%CI: 1.033; 2.370), preterm delivery in the current pregnancy (OR = 1.989; 95%CI: 1.239; 3.194), and more than 35 years old (OR = 2.822; 95%CI: 1.692; 4.706) are factors jointly related to high-risk pregnancy. Environmental contaminants play an important role in high-risk pregnancy, and mitigating measures are needed to improve the environment and reduce high-risk pregnancy.