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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]

This paper presents partial results of a more comprehensive research on housing quality and the performance of social housing projects (EHIS) in the Metropolitan Region of Campinas (RMC). The overall goal is improving social housing production quality by valuing Brazilian advances in the regulatory context. More specifically, the goal was contributing to the consolidation of an EHIS knowledge base in Santa Barbara d'Oeste, SP in view of the Brazilian ABNT NBR 15575 Standard on Housing Performance. Documentary and bibliographic studies were conducted. After the completion of a comprehensive inventory and detailed characterization, case study projects were selected. The design projects were verified regarding compliance to a selection of NBR15575 requirements. Research allowed reflections on the importance of compliance to normative references in housing design phase. EHIS knowledge base can establish the foundation for future performance evaluations and design improvements for these and new projects. [ABSTRACT FROM AUTHOR]

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]

Th e objective of this paper is to analyze a set of technology-based enterprises connected to incubators and the University of Campinas, both located in the Metropolitan Region of Campinas, Brazil. We call these, respectively, "induced" and "spontaneous" companies. A survey was conducted directly with the companies and reveled an answer rate of 31%for our population connected to incubators and 24%for those connected with the university. We propose a discussion of fi rm's endogenous and exogenous characteristics, as well as their relations with the geographic space. Our main conclusions are as follows: 1) the space emerges as fundamental in the creation and development of technology-based endeavors, 2) both groups of companies do not diff er substantially in successful growth potential, 3) diff erences emerge with the level of integration with the university and network access, and 4) their main limitation is connected with fi nancing access. [ABSTRACT FROM AUTHOR]

• Polyether ionophores residues were monitored in pasteurized milk samples. • Monensin was the only ionophore found in 18 of 40 samples. • Concentrations of 0.1 to 0.27 μg kg−1 were quantified in 13 samples. • Monensin dietary intake through milk do not indicate a consumer risk. This paper aimed to survey the occurrence of six polyether ionophores (lasalocid, maduramycin, monensin, narasin, salinomycin and semduramycin) in pasteurized milk using a validated method by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). For that, 40 samples of pasteurized milk, from seven brands, collected in six locations (supermarkets, grocery stores and greengrocers) in the metropolitan region of São Paulo and Campinas, São Paulo State, Brazil, were evaluated. Sampling included whole (18), skimmed (8) and semi-skimmed (10) milk as well as lactose-free (4) products. Only monensin antibiotic residues were found in the analyzed samples. This ionophore was confirmed in 45% of the samples and quantified in 32.5% at concentrations ranging from 0.1 to 0.27 μg kg−1. The positive samples were whole or semi-skimmed milk. Considering the levels of monensin reported in the present study and data on milk consumption in Brazil, an exposure assessment indicated that the intake of this antibiotic does not represent a risk to health. [ABSTRACT FROM AUTHOR]

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]