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Background: Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet. Methodology: Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas. Principal findings: PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases. Author summary: The strategies to control Ae. aegypti require intensive work and considerable financial resources, are time-consuming, and are commonly affected by operational problems requiring urgent improvement. The PCI is a good tool for identifying higher-risk areas; however, its measure requires a high amount of human and material resources, and the aforementioned issues remain. In this paper, we propose a novel approach capable of predicting the PCI of buildings based on street-level images. This first work combines deep learning-based methods with street-level data to predict facade conditions. Considering the good results obtained with PCINet and the good correlations of facade conditions with PCI components, we could use this methodology to classify building conditions without visiting them physically. With this, we intend to overcome the high cost of identifying high-risk areas. Although we have a long road ahead, our results show that PCINet could help to optimize Ae. aegypti and arbovirus surveillance and control, reducing the number of in-person visits necessary to identify buildings or areas at risk. [ABSTRACT FROM AUTHOR]

Copyright of Direito da Cidade is the property of Editora da Universidade do Estado do Rio de Janeiro (EdUERJ) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The particulate matter PM 10 concentrations have been impacting hospital admissions due to respiratory diseases. The air pollution studies seek to understand how this pollutant affects the health system. Since prediction involves several variables, any disparity causes a disturbance in the overall system, increasing the difficulty of the models' development. Due to the complex nonlinear behavior of the problem and their influencing factors, Artificial Neural Networks are attractive approaches for solving estimations problems. This paper explores two neural network architectures denoted unorganized machines: the echo state networks and the extreme learning machines. Beyond the standard forms, models variations are also proposed: the regularization parameter (RP) to increase the generalization capability, and the Volterra filter to explore nonlinear patterns of the hidden layers. To evaluate the proposed models' performance for the hospital admissions estimation by respiratory diseases, three cities of São Paulo state, Brazil: Cubatão, Campinas and São Paulo, are investigated. Numerical results show the standard models' superior performance for most scenarios. Nevertheless, considering divergent intensity in hospital admissions, the RP models present the best results in terms of data dispersion. Finally, an overall analysis highlights the models' efficiency to assist the hospital admissions management during high air pollution episodes. [ABSTRACT FROM AUTHOR]

Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental changes. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance of evaluating the land surface processes of the watershed systematically. We explore the use of local precipitation collection in conjunction with data from a global meteorological reanalysis to simulate the basin hydrology. Our results show that key hydrological fluxes in the basin can be represented by the JULES model simulations. [ABSTRACT FROM AUTHOR]

Copyright of Saúde e Sociedade is the property of Universidade de Sao Paulo, Faculdade de Saude Publica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the relationship between lightning and thunderstorm microphysics, this paper aims to determine the averaged vertical profiles of polarimetric variables for different classes of lightning density according to the GLM grid and then evaluate the potential use of these profiles for data assimilation in models with high spatial and temporal resolutions. Polarimetric variables from an X-band radar located in Campinas-SP and data from the Brazilian Network were used to detect the microphysics properties and atmospheric discharges of clouds (GLM proxy). The main differences between the lightning density class-averaged profiles for the four variables of Z H , Z DR , K DP and ρ HV were observed in the region above the melting layer. For the most intense lightning classes, the signatures associated with high concentrations of ice particles at high altitudes, the presence of supercooled drops above the freezing level and the occurrence of large and more oblate raindrops were observed. To analyze the possible use of reflectivity profiles as a way to indirectly assimilate GLM information into forecast models, two case studies were conducted using the Weather Research and Forecasting model. The analyses and forecasts obtained with the assimilation of radar data (reflectivity factor and Doppler winds) and with the indirect assimilation of GLM lightning density rates through mean reflectivity profiles were evaluated against a control run assimilating no data. Overall, the two assimilation experiments offered substantial improvements over the control run in terms of short-term forecasts of reflectivity patterns and storm motion. These encouraging results supports the ability of the GLM data to positively contribute to nowcasting and forecasting of convective-scale systems, especially over the vast regions of the South American continent currently suffering from limited and, even, an utter lack of observations. • Vertical reflectivity profile can represent systems of different lighting intensity. • The most significant differences among profiles are mainly in high altitudes. • Tests assimilating these profiles suggested they can improve short-term forecast. [ABSTRACT FROM AUTHOR]

Copyright of SMAD Revista Electronica Salud Mental, Alcohol y Drogas is the property of Universidade de Sao Paulo, Escola de Enfermagem de Ribeirao Preto and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article presents a tribute to human rights and anti-slavery advocate Abdias do Nascimento to the African-Brazilian people in Sao Paulo, Brazil. He is remembered for his political activism when he joined with the Afro-Campineiro Congress to protest against racial discrimination in Campinas, Sao Paulo, Brazil. Moreover, his life and career has been regarded in four major parts including his early years up to the end of World War II, to the mid-1960s, and towards his death in May 2011.

Background: Many factors have been associated with circulation of the dengue fever virus and vector, although the dynamics of transmission are not yet fully understood. The aim of this work is to estimate the spatial distribution of the risk of dengue fever in an area of continuous dengue occurrence. Methods: This is a spatial population-based case-control study that analyzed 538 cases and 727 controls in one district of the municipality of Campinas, São Paulo, Brazil, from 2006-2007, considering socio-demographic, ecological, case severity, and household infestation variables. Information was collected by in-home interviews and inspection of living conditions in and around the homes studied. Cases were classified as mild or severe according to clinical data, and they were compared with controls through a multinomial logistic model. A generalized additive model was used in order to include space in a non-parametric fashion with cubic smoothing splines. Results: Variables associated with increased incidence of all dengue cases in the multiple binomial regression model were: higher larval density (odds ratio (OR) = 2.3 (95%CI: 2.0-2.7)), reports of mosquito bites during the day (OR = 1.8 (95%CI: 1.4-2.4)), the practice of water storage at home (OR = 2.5 (95%CI: 1.4, 4.3)), low frequency of garbage collection (OR = 2.6 (95%CI: 1.6-4.5)) and lack of basic sanitation (OR = 2.9 (95%CI: 1.8-4.9)). Staying at home during the day was protective against the disease (OR = 0.5 (95%CI: 0.3-0.6)). When cases were analyzed by categories (mild and severe) in the multinomial model, age and number of breeding sites more than 10 were significant only for the occurrence of severe cases (OR = 0.97, (95%CI: 0.96-0.99) and OR = 2.1 (95%CI: 1.2-3.5), respectively. Spatial distribution of risks of mild and severe dengue fever differed from each other in the 2006/2007 epidemic, in the study area. Conclusions: Age and presence of more than 10 breeding sites were significant only for severe cases. Other predictors of mild and severe cases were similar in the multiple models. The analyses of multinomial models and spatial distribution maps of dengue fever probabilities suggest an area-specific epidemic with varying clinical and demographic characteristics. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the daily activity patterns of adult male and female satyrine butterflies to increase their chances to encounter sexual partner. It states that the study was conducted in two adjacent areas of forest edge in Campinas, São Paulo in southeastern Brazil. The study shows that Hermeuptychia hermes presented two abundance peaks over day, while Paryphthiamoides phronius abundance increase in the morning and remained stable during the afternoon.

This article describes the results of a study on user behavior in relation to environmental comfort conditions. A case study was conducted in school buildings in the region of the city of Campinas, São Paulo, Brazil. The methodology adopted was based on field observations of technical aspects of the school environment and of types of user behavior (interventions) that introduced changes in the classroom space. Questionnaires were applied to users. A follow-up study evaluated user perception of possible interventions. User knowledge on environmental comfort concepts was also assessed. The results of the case study showed few interventions by users in favor of their own comfort. Low participation results may, in part, be attributed to the context, where users, school children, are subject to discipline codes with restricted spontaneous behavior in favor of individual comfort. Environmental awareness should therefore be stimulated by teaching concepts of environmental comfort in primary and secondary schools. [ABSTRACT FROM AUTHOR]