Abstract Background: Rheumatoid arthritis (RA) is a common autoimmune systemic inflammatory disease. In addition to joint involvement, RA patients frequently have other comorbidities, such as cardiovascular diseases. Drugs used for RA treatment may increase or decrease the risk of a cardiovascular event. This study aims to analyze cardiovascular risk comorbidities in patients with RA and the correlation with the use of anti-rheumatic drugs. Methods: Cross-sectional study conducted based on the real-life rheumatoid arthritis study database - REAL, a prospective observational cohort study. Associations between the use of anti-rheumatic drugs and the presence of comorbidities were represented by their prevalence ratio and evaluated using the Chi-square or Fisher’s Exact tests. Results: We assessed 1116 patients, 89.4% women, mean age of 55.15 years and predominance of seropositive disease. 63.3% had some cardiovascular comorbidity, predominantly hypertension (49.9%). The use of glucocorticoids was observed in 47.4% of patients and there was a significant tendency of lower use of these drugs in the presence of dyslipidemia (PR: 0.790; p = 0.007). We observed that the presence of cardiovascular comorbidities was associated with higher use of bDMARDs (PR:1.147; p = 0.003). Conclusions: The presence of cardiovascular risk comorbidities was confirmed to be higher in RA patients. Different treatment strategies using less glucocorticoids in the presence of dyslipidemia and more common use of bDMARDs in patients with cardiovascular comorbidities suggest that rheumatologists are aware of the potential influence of the DMARDs in the risk of cardiovascular event. Reinforcing these results, we highlight the need for a better baseline assessment to guide the choice of anti-rheumatic drugs in RA patients who have comorbidities.

Resumo: O objetivo deste trabalho foi avaliar o uso da seleção de covariáveis por conhecimento especializado no desempenho de modelos de predição de classes de solos em uma paisagem complexa, para identificar o melhor modelo preditivo para o mapeamento digital de solos na região Sul do Brasil. Um total de 164 pontos foram amostrados em campo, com uso do hipercubo latino condicionado, tendo-se considerado as covariáveis elevação, declividade e aspecto. A partir do modelo digital de elevação, extraíram-se as covariáveis ambientais que compuseram três conjuntos, formados por: 21 covariáveis, covariáveis após exclusão das multicolineares e covariáveis escolhidas por conhecimento especializado. A predição foi realizada com os seguintes modelos: árvore de decisão, floresta aleatória, regressão logística múltipla e máquina de vetor de suporte. A acurácia dos modelos foi avaliada pelo índice kappa (K), pela acurácia geral (AG) e pela acurácia da classe. Os modelos de previsão foram sensíveis à amostragem desproporcional de classes de solo. O melhor mapa predito obteve AG de 71% e K de 0,59. O uso do conjunto de covariáveis escolhido pelo conhecimento especializado melhora o desempenho do modelo em prever as classes de solo em uma paisagem complexa, e floresta aleatória é o melhor modelo para previsão espacial das classes de solo.

Abstract: The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression, and support vector machine. The accuracy of the models was evaluated by the kappa index (K), general accuracy (GA), and class accuracy. The prediction models were sensitive to the disproportionate sampling of soil classes. The best predicted map achieved a GA of 71% and K of 0.59. The use of the covariate set chosen by expert knowledge improves model performance in predicting soil classes in a complex landscape, and random forest is the best model for the spatial prediction of soil classes.

ABSTRACT Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra. It contains several tools to process spectroscopic data and generate models to predict soil properties. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing techniques, implement several multivariate calibration methods, generate statistics assessment and graphical output, validate the models using independent dataset, and predict unknown variables using soil spectral data. AlradSpectra has four main modules: Import Data, Spectral Preprocessing, Modeling, and Prediction. The implementation of AlradSpectra is demonstrated by applying visible near-infrared reflectance spectroscopy for soil organic carbon (SOC) prediction. The data contains the value of SOC and Vis-NIR reflectance for 595 soil samples. The prediction statistic assessment of SOC was performed applying all spectral preprocessing and methods. The R 2 considering all models ranged from 0.54 to 0.80. In the partial least squares regression (PLSR) models, the performances were similar to multiple linear regression (MLR) and support vector machines (SVM). The lowest error in the SOC prediction was achieved by PLSR method with standard normal variate (SNV) preprocessing reaching an R 2 of 0.80, the smallest root mean square error (RMSE) of 0.47 %, and ratio of performance to inter-quartile distance (RPIQ) of 3.12. The capacity of performing multiple tasks, being free and open-source, easy to operate, and requiring no initial knowledge of R programming language are features that make AlradSpectra a useful tool to perform different modeling approaches and predict the desired soil variable.

ABSTRACT Terrain attributes (TAs) derived from digital elevation models (DEMs) are frequently used in digital soil mapping (DSM) as auxiliary covariates in the construction of prediction models. The DEMs and information extracted from it may be limited with regard to the spatial resolution and error magnitude, and can differ in the behavior of terrain features. The objective of this study was to evaluate the quality and limitations of free DEM data and to evaluate a topographic survey (TS) underlying the choice of a more appropriate model, for use in DSMs at a scale of 1:10,000. The study was conducted in an area of 937 ha in the watershed of Lajeado Giruá, in southern Brazil. The DEMs: DEM-TS, DEM-Topographic Map (TM), DEM-ASTER, DEM-SRTM, and DEM-TOPODATA were evaluated with regard to the precision elevation by statistical tests based on field reference points, the root mean square error (RMSE), identification of the number and size of spurious depressions, and the application of the Brazilian Cartographic Accuracy Standards Law (BCASL) to define the scale of each DEM. In addition, the TA derived from each DEM was compared with the TA from DEM-TS, considered to be terrain reality. The results showed that the elevation data of DEM-TS had the best quality (RMSE = 1.93 m), followed by DEM-SRTM (RMSE = 5.95 m), DEM-Topographic Map (RMSE = 8.28 m), DEM-TOPODATA (RMSE = 9.78 m) and DEM-ASTER (RMSE = 15.57 m). The DEM-TS was well-represented at a 1:10,000 scale, while the DEM-Topographic Map and DEM-SRTM fitted 1:50,000, the DEM-TOPODATA 1:50,000 and the DEM-ASTER a 1:100,000 scale. The results of DEM-SRTM and DEM-TOPODATA were closest to terrain reality (DEM-TS) and had the lowest number of spurious depressions and RMSE values for each evaluated attribute, but were inadequate for not fitting detailed scales compatible with small areas. The techniques for the acquisition of elevation data of each DEM and mainly the flat to gently undulating topography were factors that influenced the results. For a DSM at a scale of 1:10,000 in similar areas, the most appropriate model is DEM-TS.

ABSTRACT Diffuse reflectance spectroscopy (DRS) is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, from different locations in the state of Rio Grande do Sul, Brazil, were measured at wavelengths of 350 to 2,500 nm in the laboratory. The fitting of the linear regression model developed to predict soil clay content from the DRS data was based on a R2 value of 0.74 and 0.75, with a RMSE of 7.82 and 8.51 % for the calibration and validation sets, respectively. Soil texture classification had an overall accuracy of 79.0 % (calibration) and 80.9 % (validation). The heterogeneity of soil samples affected the performance of the prediction models. Future studies should consider a previous classification of soil samples in different groups by soil type, parent material and/or sampling region.

A espectroscopia de reflectância difusa (ERD) pode ser utilizada como alternativa para quantificação de atributos como granulometria e matéria orgânica do solo (MOS). Essa técnica pode ser opção para quantificar esses atributos em grande volume de amostras de solos, visto ser rápida, com menor custo e sem a geração de resíduos químicos. O objetivo deste estudo foi desenvolver modelos usando análise de regressão linear múltipla para predizer o teor de argila, areia, silte e MOS, utilizando dados de ERD em uma área de relevo e geologia complexa localizada na região central do Rio Grande do Sul. No estudo, foram utilizadas 303 amostras coletadas na profundidade de 0,00-0,20 m para determinar os teores de argila, areia, silte e MOS por meio da análise laboratorial e de reflectância espectral. O desempenho dos modelos de predição apresentaram bons resultados, com capacidade de explicação da variância de 77 e 72 % para areia e argila, respectivamente. Mesmo com a complexidade geológica e pedológica, os resultados evidenciaram que a técnica é promissora, sendo possível a aplicação dessa técnica para predição da granulometria e teor de MOS.

Diffuse reflectance spectroscopy (DRS) can be used as an alternative in identifying and quantifying some soil properties such as particle size and soil organic matter (SOM). This technique may be an alternative to quantifying those properties in a large volume of soil samples since it is faster and less costly and does not produce chemical residues. The aim of this study was to develop models using multiple linear regression analysis to predict the content of clay, sand, silt, and SOM using DRS data in an area of complex topography and geology located in the central region of Rio Grande do Sul, Brazil. In the study, 303 samples were collected at a depth of 0.00-0.20 m for determination of clay, sand, silt, and SOM by laboratory and spectral reflectance analysis. The predictive models produced high-quality results, explaining 77 and 72 % of the variance for sand and clay, respectively. The interpolation maps of the observed and predicted properties revealed that the spatial patterns are mainly associated with the topography and geology of the area. Even with the geological and pedological complexity, the results indicated that the technique is promising and it may be applied for prediction of particle size and SOM.

A integração lavoura-pecuária pode ser definida como o sistema que integra duas atividades, visando a maximizar racionalmente o uso da terra e a minimizar os custos de produção. O trabalho teve como objetivo avaliar a semeadura de milho com dois mecanismos sulcadores, em área de pastagem submetida a diferentes manejos, no sistema de integração lavoura-pecuária. O experimento foi conduzido em delineamento de blocos ao acaso, com quatro repetições, e os tratamentos foram compostos pela combinação de quatro intensidades de pastejo (0,25-0,05; 0,30-0,10; 0,35-0,15 m e sem pastejo, para as alturas de entrada e saída dos animais dos piquetes, respectivamente) e dois mecanismos sulcadores (haste e disco duplo). Foram avaliadas a densidade e a macroporosidade do solo no pós-pastejo, bem como a profundidade de semeadura, a área de solo mobilizada, a população de plantas e a produtividade de grãos da cultura do milho. As áreas com maior intensidade de pastejo proporcionaram aumento significativo na densidade do solo. A área de solo mobilizada foi de 0,0071 e 0,0103 m² para o disco duplo e a haste, respectivamente, tendo a haste apresentado maior profundidade de semeadura. Para o mecanismo sulcador tipo disco, a produtividade diminuiu linearmente com o aumento das intensidades de pastejo, sendo que a mesma foi amenizada quando da utilização da haste.

Crop-livestock integration can be defined as a system that integrates both activities in order to maximize rational land use and minimize production costs. The work aimed to evaluate the sowing of maize with two furrowers mechanisms in a grazing area under different managements on integrated crop-livestock system. The experiment was carried out in a randomized block design with four replicates. The treatments consisted by four combination of grazing intensities (0.25-0.05; 0.30-0.10; 0.35-0.15 m and ungrazed, respectively to the pasture height at the entry and exit of animals from the paddocks) and two furrowers mechanisms (shoe and double disc). Bulk density and soil macroporosity were evaluated post graze, as well as sowing depth, area of mobilized soil, plant stand and corn yield. Areas with highest grazing intensity provided a significant increase in bulk density. The area of mobilized soil was 0.0071 and 0.0103 m² for the double disc and rod, respectively, having the rod presented greater sowing depth. For the furrower mechanism of double disc type, the yield decreased linearly with the increase of grazing intensity, and the same was mitigated when the rod was used.

A integração lavoura-pecuária pode ser definida como o sistema que integra duas atividades, visando a maximizar racionalmente o uso da terra e a minimizar os custos de produção. Objetivou-se, com este trabalho, avaliar os efeitos de diferentes alturas remanescentes de pastagem e de mecanismos sulcadores sobre o desenvolvimento inicial e a produtividade da cultura do milho semeado em sequência. Os tratamentos foram compostos pela combinação de quatro intensidades de pastagem (0,05; 0,15; 0,30 m e sem pastejo) e de dois mecanismos sulcadores (haste e disco), utilizados na semeadura pós-pastejo. Após a semeadura, foram avaliados a profundidade de deposição das sementes, o índice de velocidade de emergência de plântulas, o estande final de plantas, a altura inicial e de inserção de espigas e o rendimento de grãos. As diferentes intensidades de pastagem influenciaram significativamente a profundidade de deposição de sementes, a altura inicial de plantas e de inserção da primeira espiga e a produtividade, sendo que a altura de pasto remanescente de 0,05 m apresentou os menores valores de profundidade de deposição de sementes, quando comparada com os demais níveis. Os diferentes mecanismos sulcadores influenciaram apenas a profundidade de deposição de sementes.

Crop-livestock systems may be defined as the integration of two activities aiming to rationally maximize the land use and to decrease production costs. The objective of this study was to evaluate the effects of different heights of remaining pasture and seed drills on the yield of corn grown in sequence. The treatments consisted of combinations of four remaining pasture residue heights (0,05; 0,15; 0,30 m and ungrazed) and two planter furrow openers on seed drills (shoe and disk), used for seeding after grazing. After corn sowing, the depth of seed deposition, the rate of seedling emergence, initial stand of plants, initial and final plant height and crop yield were evaluated. The different grass heights .significantly influenced the depth of seeding, the initial height and insertion of first cob and grain yield. Pasture height of 0,05 m corresponded to the lowest seed depth when compared with the other heights. The different furrow openers affected only the depth of seeding.

A manipulação do arranjo de plantas de milho, através de alterações na densidade de plantas, de espaçamento entre linhas, de distribuição de plantas na linha e na variabilidade entre plantas, é uma das práticas de manejo mais importantes para maximizar a interceptação da radiação solar, otimizar o seu uso e potencializar o rendimento de grãos. Dessa forma, objetivou-se com este trabalho estudar os efeitos da combinação entre diferentes híbridos de milho e espaçamentos entre linhas no desenvolvimento da cultura em sistema de plantio direto na Região Sudoeste do Paraná. O experimento foi realizado na Área Experimental do Curso de Agronomia da UTFPR, no município de Pato Branco - Pr, com delineamento estatístico aleatorizado em blocos em esquema fatorial (3 x 3), com quatro repetições. Os tratamentos foram compostos pela combinação entre três híbridos (DKB 240, PIONEER 30R50 e SG 6010) e três espaçamentos entre linhas (0,45; 0,70 e 0,90 m). Após a semeadura, foram avaliados: a altura de plantas, o diâmetro do colmo, o rendimento de grãos e seus componentes. Os resultados mostraram que em nenhuma das avaliações houve interações entre híbridos e espaçamentos e que a redução no espaçamento entre linhas promoveu aumento no número de espigas por planta e o incremento na produtividade de grãos na cultura do milho.

Corn plant arrangement is one of the most important management tool to improve solar radiation interception and it can be done through changes in plant density, row spacing, distribution of plants in the row and plant variability aiming to optimize its use and maximize the yield. Due to it, the objective of this work was to study the effects of different corn hybrids and row spacing over the corn development cultivated in a no-tillage system in the Southwest of Paraná. The experiment was carried out at the experimental area of the UTFPR Agronomy Course located in Pato Branco - Pr, following a complete randomized block design factorial (3 x 3), with four replications. The treatments consisted of combinations among three hybrids (DKB 240, Pioneer 30R50 and SG 6010) and three row spacing (0.45; 0.70 and 0.90 m). After seeding, were evaluated: plant height, stem diameter, grain yield and its components. According to the results, there were no interaction among hybrids and row spacing, the row spacing reduction promoted greater number of spikes per plant and increased corn grain yield.