Complexity of the COVID-19 pandemic in Maringa

While extensive literature exists on the COVID-19 pandemic at regional and national levels, understanding its dynamics and consequences at the city level remains limited. This study investigates the pandemic in Maring\'a, a medium-sized city in Brazil's South Region, using data obtained by actively monitoring the disease from March 2020 to June 2022. Despite prompt and robust interventions, COVID-19 cases increased exponentially during the early spread of COVID-19, with a reproduction number lower than that observed during the initial outbreak in Wuhan. Our research demonstrates the remarkable impact of non-pharmaceutical interventions on both mobility and pandemic indicators, particularly during the onset and the most severe phases of the emergency. However, our results suggest that the city's measures were primarily reactive rather than proactive. Maring\'a faced six waves of cases, with the third and fourth waves being the deadliest, responsible for over two-thirds of all deaths and overwhelming the local healthcare system. Excess mortality during this period exceeded deaths attributed to COVID-19, indicating that the burdened healthcare system may have contributed to increased mortality from other causes. By the end of the fourth wave, nearly three-quarters of the city's population had received two vaccine doses, significantly decreasing deaths despite the surge caused by the Omicron variant. Finally, we compare these findings with the national context and other similarly sized cities, highlighting substantial heterogeneities in the spread and impact of the disease.Comment: 20 pages, 5 figures, supplementary information; accepted for publication in Scientific Report

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure

Optimization of sample unit size for sampling stink bugs (Hemiptera: Pentatomidae) in soybean

Cost-effective and reliable sampling procedures are crucial for integrated pest management. Sweep net sampling is commonly used for stink bugs (Hemiptera: Pentatomidae) in soybean, with sample size being the number of sets of sweeps, and sample unit size the number of sweeps in each set. Sample unit size has received little attention, but can affect sampling parameters. Here, two sample unit sizes (10 vs. 25 sweeps) were compared for the sampling of stink bug taxa. On average, sampling for stink bugs took 3.6 more minutes with the 25-sweep than with the 10-sweep sample unit size. Generally, estimates of the mean number of stink bugs per sweep were similar between the two sample unit sizes for Euschistus spp. and Chinavia hilaris combined (“combined herbivores”) and Euschistus spp. The 25-sweep sample unit size had a higher probability of detecting combined herbivores, Euschistus spp. and Podisus spp., lower standard errors and relative variance for combined herbivores and Euschistus spp., lower standard errors for C. hilaris, and higher relative net precision [which accounts for sampling cost (i.e., time)] for combined herbivores and Euschistus spp. Taken together, the better probability of detection, precision and efficiency of the 25-sweep sample unit size support the continued use of sampling plans developed for that sample unit size. The optimization of sample unit sizes is an important factor that should be accounted for in the development of sampling plans

MazeLogic: Jogo Educacional para Ensino de Lógica de Programação

Resumo. A área de jogos educacionais tem crescido de forma exponencial noBrasil e no mundo, pois estes têm sido usados por muitos alunos como objetode aprendizagem. Os jogos apresentam o conhecimento de modo divertido elúdico e pode ser aplicado a qualquer área e assunto. Este trabalho destina-seao desenvolvimento de um jogo educacional para ensino-aprendizagem para adisciplina de Lógica de Programação.Palavras-chave: Ensino aprendizagem, Jogo Educacional, Lógica deProgramação

The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae)

The study of traditional knowledge of medicinal plants has led to discoveries that have helped combat diseases and improve healthcare. However, the development of quantitative measures that can assist our quest for new medicinal plants has not greatly advanced in recent years. Phylogenetic tools have entered many scientific fields in the last two decades to provide explanatory power, but have been overlooked in ethnomedicinal studies. Several studies show that medicinal properties are not randomly distributed in plant phylogenies, suggesting that phylogeny shapes ethnobotanical use. Nevertheless, empirical studies that explicitly combine ethnobotanical and phylogenetic information are scarce.In this study, we borrowed tools from community ecology phylogenetics to quantify significance of phylogenetic signal in medicinal properties in plants and identify nodes on phylogenies with high bioscreening potential. To do this, we produced an ethnomedicinal review from extensive literature research and a multi-locus phylogenetic hypothesis for the pantropical genus Pterocarpus (Leguminosae: Papilionoideae). We demonstrate that species used to treat a certain conditions, such as malaria, are significantly phylogenetically clumped and we highlight nodes in the phylogeny that are significantly overabundant in species used to treat certain conditions. These cross-cultural patterns in ethnomedicinal usage in Pterocarpus are interpreted in the light of phylogenetic relationships.This study provides techniques that enable the application of phylogenies in bioscreening, but also sheds light on the processes that shape cross-cultural ethnomedicinal patterns. This community phylogenetic approach demonstrates that similar ethnobotanical uses can arise in parallel in different areas where related plants are available. With a vast amount of ethnomedicinal and phylogenetic information available, we predict that this field, after further refinement of the techniques, will expand into similar research areas, such as pest management or the search for bioactive plant-based compounds

Use of nanomaterials in the pretreatment of water samples for environmental analysis

The challenge of providing clean drinking water is of enormous relevance in today’s human civilization, being essential for human consumption, but also for agriculture, livestock and several industrial applications. In addition to remediation strategies, the accurate monitoring of pollutants in water sup-plies, which most of the times are present at low concentrations, is a critical challenge. The usual low concentration of target analytes, the presence of in-terferents and the incompatibility of the sample matrix with instrumental techniques and detectors are the main reasons that renders sample preparation a relevant part of environmental monitoring strategies. The discovery and ap-plication of new nanomaterials allowed improvements on the pretreatment of water samples, with benefits in terms of speed, reliability and sensitivity in analysis. In this chapter, the use of nanomaterials in solid-phase extraction (SPE) protocols for water samples pretreatment for environmental monitoring is addressed. The most used nanomaterials, including metallic nanoparticles, metal organic frameworks, molecularly imprinted polymers, carbon-based nanomaterials, silica-based nanoparticles and nanocomposites are described, and their applications and advantages overviewed. Main gaps are identified and new directions on the field are suggested.publishe