Human pulmonary dirofilariasis: a report of seven cases

Human pulmonary dirofilariasis is a rare disease caused by the parasite Dirofilaria immitis. It is usually seen as a solitary pulmonary nodule that mimics lung cancer. Although this disease is considered benign, its diagnosis often requires an excisional lung biopsy. Herein we report the epidemiological, clinical and radiological features observed in seven cases of human pulmonary dirofilariasis from Florianópolis. Six of our seven patients, showed a radiological finding of pulmonary nodule and underwent excisional lung biopsy for diagnosis. In one case, the radiological image was unavailable for review. Therefore, it was not described in this work, and the diagnosis was established through transbronchial biopsy.Dirofilariose pulmonar humana é uma doença rara causada pelo parasita Dirofilaria immitis. Apresenta-se usualmente como um nódulo pulmonar solitário que mimetiza câncer de pulmão. Embora considerada uma doença clinicamente benigna, uma biópsia pulmonar excisional é quase sempre necessária para o diagnóstico. Relatam-se as características epidemiológicas, clínicas e radiológicas de sete casos de dirofilariose pulmonar humana em Florianópolis. De sete pacientes relatados, seis tiveram como achado radiológico um nódulo pulmonar e foram submetidos à biópsia pulmonar excisional para o diagnóstico. Em um paciente, a imagem radiológica não estava disponível para revisão e, portanto, não foi descrita no trabalho; o diagnóstico foi estabelecido pela biópsia transbrônquica.Universidade Federal de São Paulo (UNIFESP)UNIFESPSciEL

An integrated python-based open-source Digital Image Correlation software for in-plane measurements (Part 1)

The authors would like to acknowledge Fundação para a Ciência e a Tecnologia, Portugal (FCT-MCTES) throughout the project PTDC/EMD-EMD/1230/2021 (AneurysmTool) and the support provided by the Brazilian Government funding agencies CAPES , FAPERJ and CNPq . Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joao Filho reports financial support was provided by Coordination of Higher Education Personnel Improvement. Jose Xavier reports financial support was provided by Foundation for Science and Technology. Publisher Copyright: © 2022 The Author(s)The main purpose of this work is to present a new fully-customizable out-of-the-box open-source 2D Digital Image Correlation (2D-DIC) software, so-called iCorrVision-2D. It is implemented in Python, including image acquisition (grabber), numerical correlation and post-processing modules. The proposed software has an intuitive graphical user interface to support selecting all main correlation parameters, calibration and region of interest. The iCorrVision-2D software stands out over other open-source projects due to the great number of functionalities and the control of all important inputs, such as correlation domain, approach (spatial and incremental) and matching criterion, displacement filtering, interpolation techniques, strain window and reconstruction shape functions. Results demonstrate that the iCorrVision-2D software is robust and can be used to measure full-field displacements and strains with satisfactory accuracy and precision. For out-of-plane measurements, iCorrVision-3D will be presented in Part 2 (iCorrVision-3D, SoftwareX, 2022).publishersversionpublishe

Variabilidade de atributos de fertilidade do solo em áreas cultivadas com cana-de-açúcar no estado de Goiás

A amostragem de solo deve representar adequadamente a área avaliada, visando à elaboração de recomendações de medidas de correção da fertilidade e conservação dos solos, a fim de elevar a produtividade e melhorar o aproveitamento de insumos. O objetivo deste trabalho foi avaliar a variabilidade de atributos de fertilidade de Latossolos em áreas de renovação de cana-de-açúcar no Estado de Goiás. Foram selecionadas duas áreas de aproximadamente 8.100 m² na usina Goiasa, município de Goiatuba, consideradas representativas de dois talhões cultivados com cana-de-açúcar, com espaçamento entrelinhas de 1,5 m. Em cada uma das áreas selecionadas, foi realizada amostragem do solo em malha, nas linhas de plantio e nas entrelinhas, com trado holandês. Coletaram-se amostras simples (subamostras) em 49 pontos amostrais nas linhas e 49 nas entrelinhas, nas profundidades de 0,0-0,2 e 0,2-0,4 m, totalizando 196 amostras simples em cada área de estudo, que foram analisadas individualmente. As amostras foram submetidas a análises químicas de fertilidade do solo (pH em CaCl2, acidez potencial, matéria orgânica, P e K, Ca e Mg) e análise granulométrica. Por meio dos dados, foi calculado o número de subamostras requeridas para a estimativa da média de cada atributo, a partir do coeficiente de variação e do erro percentual admitido em torno da média, para probabilidade de 95 %. Os atributos estudados apresentaram variabilidades diferenciadas nas áreas estudadas: alta (P e K); média (acidez potencial, Ca e Mg); e baixa (pH, matéria orgânica e argila). A extrema variabilidade nos teores de P, particularmente na profundidade de 0,2-0,4 m, atribuída à aplicação localizada de doses elevadas de fertilizantes fosfatados no plantio, impõe limitações à avaliação de sua disponibilidade pelo elevado número de subamostras requeridas para composição de uma amostra composta.Soil sampling should provide an accurate representation of a given area so that recommendations for amendments of soil acidity, fertilization and soil conservation may be drafted to increase yield and improve the use of inputs. The aim of this study was to evaluate the variability of soil fertility properties of Oxisols in areas planted to sugarcane in the State of Goias, Brazil. Two areas of approximately 8,100 m² each were selected, representing two fields of the Goiasa sugarcane mill in Goiatuba. The sugarcane crop had a row spacing of 1.5 m and subsamples were taken from 49 points in the row and 49 between the row with a Dutch auger at depths of 0.0-0.2 and 0.2-0.4 m, for a total of 196 subsamples for each area. The samples were individually subjected to chemical analyses of soil fertility (pH in CaCl2, potential acidity, organic matter, P, K, Ca and Mg) and particle size analysis. The number of subsamples required to compose a sample within the acceptable ranges of error of 5, 10, 20 and 40 % of each property were computed from the coefficients of variation and the Student t-value for 95 % confidence. The soil properties under analysis exhibited different variabilities: high (P and K), medium (potential acidity, Ca and Mg) and low (pH, organic matter and clay content). Most of the properties analyzed showed an error of less than 20 % for a group of 20 subsamples, except for P and K, which were capable of showing an error greater than 40 % around the mean. The extreme variability in phosphorus, particularly at the depth of 0.2-0.4 m, attributed to banded application of high rates of P fertilizers at planting, places limitations on assessment of its availability due to the high number of subsamples required for a composite sample

AVALIAÇÃO SENSORIAL E MAPA DE PREFERÊNCIA INTERNO DE MARCAS COMERCIAIS DE REFRIGERANTE SABOR GUARANÁ

O objetivo deste trabalho foi comparar sensorialmente sete marcas de refrigerante sabor guaraná, sendo seis produzidas por pequenas empresas e uma por empresa líder de mercado. A Análise Descritiva Quantitativa (ADQ) foi utilizada para descrever e quantificar os atributos sensoriais das amostras. A aceitabilidade sensorial das marcas de refrigerante foi determinada mediante escala hedônica e os resultados avaliados pela Análise de Componentes Principais, Mapa de Preferência Interno e ANOVA. A maioria dos consumidores preferiu o refrigerante da marca líder de mercado, que apresentou os atributos sabor e aroma característico em maior intensidade e sabor e aroma tutti-fruti em menor intensidade

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost