Exploration of moisture activated dry granulation for the development of gastroretentive tablets aided by SeDeM diagram.

Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2 min), long floating duration (>4 h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

Overweight, Obesity, and Age Are the Main Determinants of Cardiovascular Risk Aggregation in the Current Mexican Population: The FRIMEX III Study.

Background: The Mexican population exhibits several cardiovascular risk factors (CVRF) including high blood pressure (HBP), dysglycemia, dyslipidemia, overweight, and obesity. This study is an extensive observation of the most important CVFRs in six of the most populated cities in Mexico. Methods: In a cohort of 297,370 participants (54% female, mean age 43 ± 12.6 years), anthropometric (body mass index (BMI)), metabolic (glycemia and total cholesterol (TC)), and blood pressure (BP) data were obtained. Results: From age 40, 40% and 30% of the cohort's participants were overweight or obese, respectively. HBP was found in 27% of participants. However, only 8% of all hypertensive patients were controlled. Fifty percent of the subjects 50 years and older were hypercholesterolemic. Glycemia had a constant linear relation with age. BMI had a linear correlation with SBP, glycemia, and TC, with elevated coefficients in all cases and genders. The ß1 coefficient for BMI was more significant in all equations than the other ß, indicating that it greatly influences the other CVRFs. Conclusions: TC, glycemia, and SBP, the most critical atherogenic factors, are directly related to BMI.

Biopsychosocial factors associated with the state of disability after hemiparesis in the chronic phase of stroke: exploratory analysis based on the International Classification of Functioning, Disability and Health.

OBJECTIVE: To identify the main biopsychosocial factors associated with disability level after stroke using the International Classification of Functioning, Disability and Health (ICF) model. METHODS: A cross-sectional study was conducted with chronic stroke survivors. Disability was assessed using the World Health Disability Assessment Schedule 2.0. The independent variables were: Body functions: emotional functioning and whether the dominant upper limb was affected. For the Activities & Participation component, satisfaction regarding the execution of activities and participation were assessed using the SATIS-Stroke, as well as the locomotion ability for adults (ABILOCO), manual ability (ABILHAND) and the return to work. For environmental factors, income and facilitators and obstacles were assessed using the Measure of the Quality of the Environment (MQE). Personal factors: age and sex. Multiple Linear Regression was employed. RESULTS: Limited locomotor ability (ß = -0.281; t = -3.231 p = 0.002), dissatisfaction regarding activities and participation (ß = -0.273; t = -3.070 p = 0.003), and the non-return to work (ß = 0.162; t = 2.085 p = 0.04) were associated with disability. CONCLUSION: The reduction in locomotor ability, dissatisfaction regarding activities and participation and the non-return to work were associated with disability in the chronic phase following a stroke.

The reduction in locomotion ability, dissatisfaction regarding activities and participation, and the non-return to work were associated with disability in the chronic phase following a stroke.Clinicians will be able to develop rehabilitation strategies focused on diminishing locomotor limitations, increasing satisfaction with activities and participation, and improving vocational planning for the return to work after a strokeThese findings underscore the importance of assessments and intervention strategies based on the individual rather than the disease as well as focusing on social and personal issues to guide clinical decision making.

Morphological Description and Live Weight Prediction from Body Measurements of Socorro Island Merino Lambs.

The aim of this study was to describe the morphology and estimate live weight from body measurements of Socorro Island Merino lambs. A group of Socorro Island Merino lambs was recorded from birth to year for live weight, rump width, rump length, withers height, body length, cannon bone perimeter, and chest girth, width, and depth. The effect of the lamb type on body measurements and live weight was analyzed using ANOVA, Pearson's correlation analysis was performed to estimate the relationship between body measurements and live weight, multiple linear regressions were fitted to obtain prediction equations of live weight from the body measurements and finally, chest girth was used to generate prediction equations using linear and exponential models. At birth and at year, differences were observed in body measurements, especially those related to the thoracic region, with crossbred males showing the highest values. Live weight was correlated with almost all the body measurements, with the highest coefficients observed with chest girth, chest width, and chest depth. Live weight can be accurately predicted from multiple regression equations using several body measurements, but using only chest girth (CG) as a predictor, the exponential equation W0-365 = 0.9142 exp(0.0462 CG) showed the best accuracy.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches.

Globally, pesticides are toxic substances with wide applications. However, the widespread use of pesticides has received increasing attention from regulatory agencies due to their various acute and chronic effects on multiple organisms. In this study, Quantitative Structure-Toxicity Relationship (QSTR) models were established using Multiple Linear Regression (MLR) and five Machine Learning (ML) algorithms to predict pesticide toxicity in Americamysis bahia. The most influential descriptors included in the MLR model are RBF, JGI2, nCbH, nRCOOR, nRSR, nPO4 and 'Cl-090', with positive contributions to the dependent variable (negative decimal logarithm of median lethal concentration at 96-h). The Random Forest (RF) regression model was superior amongst the five ML models. We observed higher values of R2 (0.812) and lower values of RMSE (0.595) and MAE (0.462) in the cross-validation training set and external validation set. Similarly, this study had a high level of fitness and was internally robust and externally predictive compared to models presented in similar studies. The results suggest that the developed QSTR models are suitable for reliably predicting the aquatic toxicity of structurally diverse pesticides and can be used for screening, prioritising new pesticides, filling data gaps and overcoming the limitations of in vivo and in vitro tests.

Impacto das mudanças climáticas e da qualidade do ar em hospitalizações por doenças respiratórias em municípios da Região Metropolitana de São Paulo (RMSP), Brasil / Impact of climate change and air quality on hospitalizations for respiratory diseases in municipalities in the Metropolitan Region of São Paulo (MRSP), Brazil

Resumo Este estudo teve por objetivo analisar os possíveis impactos das mudanças climáticas na saúde respiratória nos municípios de Santo André e São Caetano do Sul. Foram analisados dados meteorológicos históricos (temperatura, precipitação, umidade relativa e pressão atmosférica), de qualidade do ar (concentrações de MP10 e O3) e de saúde respiratória (taxas de incidência de internações por doenças respiratórias - TIIDR), relacionados através de modelos estatísticos de Regressão Linear Múltipla (RLM). Dados meteorológicos de projeções climáticas futuras (2019-2099) de três modelos climáticos (um global e dois regionalizados) em dois cenários de emissão foram aplicados aos modelos de RLM. Os resultados das projeções mostraram um aumento de até 10% nas TIIDR em relação aos níveis atuais para São Caetano do Sul no período de 2070-2099. Em Santo André as projeções indicaram redução de até 26% nas TIIDR. A variável de maior peso nos modelos de RLM de Santo André foi a temperatura (-2,15x) indicando que o aquecimento é inversamente proporcional ao aumento nas TIIDR, enquanto em São Caetano do Sul a pressão atmosférica teve o maior peso (2,44x). Para próximos trabalhos recomenda-se a inclusão de projeções futuras de concentrações de poluentes atmosféricos.

Abstract The scope of this study was to analyze the possible impacts of climate change on respiratory health in the municipalities of Santo André and São Caetano do Sul. Historical meteorological data (temperature, precipitation, relative humidity and atmospheric pressure), air quality data (concentrations of PM10 and O3) and respiratory health data (incidence rates of hospitalizations for respiratory diseases - IRHRD) were related through statistical models of Multiple Linear Regression (MLR). Meteorological data from future climate projections (2019-2099) from three different climate models (one global and two regionalized) in two emission scenarios were applied to the MLR models. The results showed that the IRHRD will suffer an increase of up to 10% in relation to the current levels for São Caetano do Sul in the 2070-2099 period. In Santo André, projections indicated a reduction of up to 26% in IRHRD. The most important variable in the MLR models for Santo André was temperature (-2,15x), indicating an inverse relationship between global warming and an increase in IRHRD, while in São Caetano the atmospheric pressure had the greatest weight (2.44x). For future studies, the inclusion of future projections of PM10 concentrations is recommended.

Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches.

Characterizing the spatiotemporal variability of the Urban Heat Island (UHI) and its drivers is a key step in leveraging thermal comfort to create not only healthier cities, but also to enhance urban resilience to climate change. In this study, we developed specific daytime and nighttime multiple linear regression (MLR) and random forest (RF) models to analyze and predict the spatiotemporal evolution of the Urban Heat Island intensity (UHII), using the air temperature (Tair) as the response variable. We profited from the wealth of in situ Tair data and a comprehensive pool of predictors variables - including land cover, population, traffic, urban geometry, weather data and atmospheric vertical indices. Cluster analysis divided the study period into three main groups, each dominated by a combination of weather systems that, in turn, influenced the onset and strength of the UHII. Anticyclonic circulations favored the emergence of the largest UHII (hourly mean of 5.06 °C), while cyclonic circulations dampened its development. The MLR models were only able to explain a modest percentage of variance (64 and 34% for daytime and nighttime, respectively), which we interpret as part of their inability to capture key factors controlling Tair. The RF models, on the other hand, performed considerably better, with explanatory power over 96% of the variance for daytime and nighttime conditions, capturing and mapping the fine-scale Tair spatiotemporal variability in both periods and under each cluster condition. The feature importance analysis showed that the meteorological variables and the land cover were the main predictors of the Tair. Urban planners could benefit from these results, using the high-performing RF models as a robust framework for forecasting and mitigating the effects of the UHI.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration.

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

The impact of air pollution on COVID-19 pandemic varied within different cities in South America using different models.

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman's correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO2 and O3 are significant independent variables, however, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America.

Assessment of performance indicators for different sizes of water supply systems in Minas Gerais, Brazil.

Indicators are important tools to improve the efficiency of water supply systems. Considering that the performance could vary according to the systems' sizes, this research proposed financial, operational, and water quality indicators for water supply systems of municipalities with different populations in Minas Gerais, Brazil. The organisation and selection of the sample were based on available information in the National Sanitation Data System of 2014. We selected 363 municipalities of Minas Gerais and 56 predictors. Through multiple linear regression (MLR), we found that the commitment of revenues with expenditures and the ratio among revenues and expenses are the most relevant variables to describe the financial performance. Furthermore, water loss per connection and water-billing index were the most important to describe the operational performance. Finally, models related to water quality performance could not be established due to the low value of the coefficient of determination. We observed that supply systems have distinct variables to describe their financial and operational performance, according to their sizes. Small municipalities have a strong relationship with financial performance and expenses. Large counterparts have their performance related to the collection, which can be explained by the economy of scale. Considering the operational performance, we observed that larger municipalities have a strong relationship between their operational performance and water loss. These models are potential tools in the decision-making processes, which can be used to promote improvements in water supply systems.

Multiple linear regression models for predicting the n­octanol/water partition coefficients in the SAMPL7 blind challenge.

A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log PN) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as "TFE-MLR", presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.

Statistical modeling of sea ice concentration in the northwest region of the Antarctic Peninsula.

Sea ice is one of the main components of the cryosphere that modifies the exchange of heat and moisture between the ocean and atmosphere, regulating the global climate. In this sense, it is important to identify the concentration of sea ice in different regions of Antarctica in order to measure the impact of environmental changes on the region's ecosystem. The objective of this study was to evaluate the performance of the multiple linear regression and Box-Jenkins methods for predicting the concentration of sea ice along the northwest coast of the Antarctic Peninsula. Sea ice concentration data from May to November for the period 1979-2018 were extracted from passive remote sensors including a scanning multichannel microwave radiometer, special sensor microwave imager, and special sensor microwave imager/sounder. Meteorological variables from the atmospheric reanalysis model ERA5 of the European Center for Medium-Range Weather Forecasts were used as predictor variables, and the leave-one-out cross-validation technique was used to calibrate and validate the models. It was found that both statistical models have similar performance when analyzing residual analysis results, root mean square error of cross-validation, and final accuracy and residual standard deviation, these responses being related to the regionalization of the study area and to the Box-Jenkins presents strong, homogeneous, and stable correlations in the time series modeled for each pixel.

Influence of changes in land use and land cover and rainfall on the streamflow regime of a watershed located in the transitioning region of the Brazilian Biomes Atlantic Forest and Cerrado.

Climate change and the intensification of anthropogenic activities in watersheds have been substantially changing the streamflow regime, which is a problem for water resource managers. This study assesses the influence of the changes in land use and land cover and rainfall on the streamflow regime. This study also models the pattern of these streamflows according to the rainfall and land use and land cover in the Santo Antônio River watershed, located in the transitioning region of the Brazilian Biomes Atlantic Forest and Cerrado. To assess the dynamic relationship between land use and land cover and the streamflow regime, five classes of land use and land cover were used. To characterize the hydrological pattern, data from six streamflow gauges and 24 rainfall gauges that influence the study area were used. Multiple regression models were adjusted to estimate streamflow using the explanatory variables rainfall and land use and land cover. As result, a direct relationship was found, as the decrease in streamflow in some drainage areas was influenced by the decrease in rainfall over the base period. The relationship between land use and land cover and streamflow was not significant. The reductions in the streamflow regimes over the years in the watershed were influenced by reductions in annual rainfall, which reduced about 19% while the water withdrawals from 2003 to 2014 increased 2350%. The results found in this study are useful to the water managers since they can estimate streamflow in any part of the studied river through rainfall and land use and land cover data. This helps to reduce the risks associated with the water allocation process.

Applications of an electronic nose in the prediction of oxidative stability of stored biodiesel derived from soybean and waste cooking oil.

Waste cooking oil (WCO) is a valuable feedstock for the synthesis of biodiesel but the product exhibits poor oxidative stability. Techniques available for assessing this parameter are generally expensive and time-consuming, hence the purpose of this study was to develop and validate a rapid and reliable predictive system based on signals from the sensors of a commercial hand-held e-nose instrument. Biodiesels were synthesized from soybean oil and six samples of WCO, and their physicochemical characteristics and oxidative stabilities determined before and after storage in different types of containers for 30 or 60 days at room temperature or 43 °C. Linear regression models were constructed based on principal component analysis of the signals generated by all 32 e-nose sensors and stochastic modeling of signal profiles from individual sensors. The regression model with principal components as predictors was unable to explain the oxidative stability of biodiesels, while the regression model with stochastic parameters (combining signals from 11 sensors) as predictors showed an excellent goodness of fit (R2 = 0.91) with a 45-sample training set and a good quality of prediction (R2 = 0.84) with a 18-sample validation set. The proposed e-nose system was shown to be accurate and efficient and could be used to advantage by producers/distributors of biodiesel in the assessment fuel quality.

Indicadores demográficos en la incidencia de la covid-19 en Santiago de Cuba / Indicadores demográficos na incidência da Covid-19 em Santiago de Cuba / Demographic indicators in the incidence of Covid-19 in Santiago de Cuba

Los indicadores demográficos han sido empleados por algunos investigadores para estimar el número de personas infectadas por la covid-19. El presente trabajo tiene como primer objetivo determinar en qué medida la incidencia de casos con covid-19 en los municipios de la provincia de Santiago de Cuba puede ser explicada a partir de determinados indicadores demográficos. El segundo objetivo es construir una jerarquía de grupos de municipios de acuerdo al comportamiento diferenciado de los indicadores demográficos seleccionados. Se desarrolló un estudio ecológico, exploratorio, de grupos múltiples, comparando los nueve municipios de la provincia Santiago de Cuba según variables del nivel global, supuestamente relacionadas con la cantidad de casos con covid-19 confirmados desde el 15 de octubre de 2020 hasta el 16 de enero de 2021. Se aplicó el análisis de regresión lineal múltiple para seleccionar el modelo que describiera mejor el comportamiento de los datos y el análisis de clúster para visualizar la agrupación de los municipios. Se evidenció una correlación significativa entre la cantidad de casos con covid-19, la densidad de población y el grado de urbanización. En cambio, en el modelo de regresión solo resultó significativa la densidad poblacional cuando se consideraron los nueve municipios y el índice de masculinidad, cuando se excluyó el municipio atípico, Santiago de Cuba. El índice de masculinidad resultó ser una variable espuria condicionada por la densidad poblacional como variable confusora. El análisis de clúster reveló la formación de tres grupos de municipios, quedando Santiago de Cuba aislado del resto de los municipios.

Some researchers have used demographic indicators to estimate the number of people infected by COVID-19. The first goal of this study is to determine to what extent the incidence of cases of COVID-19 in the municipalities of the province of Santiago de Cuba can be explained by certain demographic indicators. The second goal is to construct a hierarchy of groups of municipalities according to the differentiated behavior of the selected demographic indicators. An ecological, exploratory, multi-group study was developed, comparing the nine municipalities of Santiago de Cuba province according to global level variables, supposedly related to the number of cases with COVID-19 confirmed from October 15, 2020 to January 16, 2021. Multiple linear regression analysis was applied to select the model that best described the behavior of the data and cluster analysis to visualize the grouping of the municipalities. A significant correlation was found between the number of cases with COVID-19, population density and urbanization level. On the other hand, in the regression model, only population density was significant when the nine municipalities were considered and the masculinity index, when the atypical municipality, Santiago de Cuba, was excluded. The masculinity index turned out to be a spurious variable conditioned by population density as a confounding variable. The cluster analysis revealed the formation of three groups of municipalities, with Santiago de Cuba being isolated from the rest of the municipalities.

Indicadores demográficos têm sido usados por alguns pesquisadores para estimar o número de pessoas infectadas pela Covid-19. O primeiro objetivo deste estudo é determinar até que ponto a incidência de casos de Covid-19 nos municípios da província de Santiago de Cuba pode ser explicada por certos indicadores demográficos. O segundo objetivo é construir uma hierarquia de grupos de municípios de acordo com o comportamento diferenciado dos indicadores demográficos selecionados. Foi desenvolvido um estudo ecológico, exploratório e multigrupo, comparando os nove municípios da província de Santiago de Cuba de acordo com variáveis de nível global, supostamente relacionadas ao número de casos de Covid-19 confirmados entre 15 de outubro de 2020 e 16 de janeiro de 2021. A análise de regressão linear múltipla foi aplicada para selecionar o modelo que melhor descrevia o comportamento dos dados e a análise de agrupamento para visualizar o agrupamento dos municípios. Foi encontrada uma correlação significativa entre o número de casos de Covid-19, a densidade populacional e o nível de urbanização. Por outro lado, no modelo de regressão, apenas a densidade populacional era significativa quando os nove municípios foram considerados e o índice de masculinidade, quando o município atípico, Santiago de Cuba, foi excluído. O índice de masculinidade revelou-se uma variável espúria condicionada pela densidade populacional como uma variável confusa. A análise de agrupamento revelou a formação de três grupos de municípios, com Santiago de Cuba sendo isolado do resto dos municípios.

SARS-CoV-2 Infections in the World: An Estimation of the Infected Population and a Measure of How Higher Detection Rates Save Lives.

This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide, on July 20, it has been estimated above 160 million individuals infected by SARS-CoV-2. Moreover, it is found that only about 1 out of 11 infected individuals are detected. In an information context in which population-based seroepidemiological studies are not frequently available, this study shows a parsimonious alternative to provide estimates of the number of SARS-CoV-2 infected individuals. By comparing our estimates with those provided by the population-based seroepidemiological ENE-COVID study in Spain, we confirm the utility of our approach. Then, using a cross-country regression, we investigated if differences in detection rates are associated with differences in the cumulative number of deaths. The hypothesis investigated in this study is that higher levels of detection of SARS-CoV-2 infections can reduce the risk exposure of the susceptible population with a relatively higher risk of death. Our results show that, on average, detecting 5 instead of 35 percent of the infections is associated with multiplying the number of deaths by a factor of about 6. Using this result, we estimated that 120 days after the pandemic outbreak, if the US would have tested with the same intensity as South Korea, about 85,000 out of their 126,000 reported deaths could have been avoided.

Entrada de viajeros y densidad poblacional en la propagación de la COVID-19 en Cuba / Arrival of travelers and population density in the spread of COVID-19 in Cuba

Introducción: El comportamiento no homogéneo de la cantidad de casos confirmados con COVID-19 en diferentes regiones de Cuba aún no se ha esclarecido, lo cual resultaría de utilidad para la toma de decisiones en futuras epidemias en el país. Objetivo: Determinar la influencia de la entrada de viajeros y la densidad poblacional sobre la distribución no homogénea de la cantidad de casos con COVID-19 por provincias en Cuba. Métodos: Se desarrolló un estudio ecológico, exploratorio, de grupos múltiples, comparando las provincias cubanas según variables del nivel global y agregado, relacionadas con la cantidad de casos con COVID-19, confirmados durante la epidemia en Cuba. Se aplicó el análisis de regresión lineal múltiple para seleccionar el modelo que mejor describe el comportamiento de los datos y el análisis de clúster para visualizar la agrupación de las provincias. Resultados: Se evidenció una correlación significativa entre la cantidad de casos con COVID-19 y la cantidad de viajeros con COVID-19, la cantidad total de viajeros que arribaron al país en marzo y los eventos de trasmisión. En el modelo de regresión resultaron significativas la densidad poblacional y las cantidades de viajeros total y con COVID-19. El análisis de clúster reveló la formación de cuatro grupos de provincias. Conclusiones: La cantidad de casos con COVID-19 por provincia se relaciona con la cantidad de viajeros que entraron al país, con y sin COVID-19, y la densidad poblacional. Se forman cuatro grupos de provincias por su similitud en los aspectos identificados en la regresión(AU)

Introduction: The non-homogeneous behavior of the number of COVID-19 confirmed cases in different regions of Cuba has not yet been clarified, which would be useful for decision-making in future epidemics in the country. Objective: To determine the influence of the arrival of travelers and the population density on the non-homogeneous distribution of the number of COVID-19 cases by provinces in Cuba. Methods: An ecological, exploratory, multiple group study was carried out, comparing Cuban provinces according to variables of the global and aggregate levels, related to the number of COVID-19 cases, confirmed during the epidemic in Cuba. Multiple linear regression analysis was applied to select the model that best describes the behavior of the data and cluster analysis to visualize the grouping of the provinces. Results: A significant correlation was proved between the number of COVID-19 cases and the number of travelers with COVID-19, the total number of travelers who arrived in Cuba in March, and transmission events. In the regression model, the population density and the total number of travelers and those with COVID-19 were significant. The cluster analysis revealed the formation of four groups of provinces. Conclusions: The number of cases with COVID-19 by province is related to the number of travelers who arrived in the country, with and with no COVID-19, and the population density. Four groups of provinces are formed by their similarity in the aspects identified at regression(AU)

Do hip strength, flexibility and running biomechanics predict dynamic valgus in female recreational runners?

BACKGOUND: Dynamic valgus has been the focus of many studies to identify its association to an increased risk of running-related injuries. However, it is not known which physical and biomechanical variables are associated with this movement dysfunction. RESEARCH QUESTION: This study aimed to test the correlation between strength, flexibility and biomechanical variables and dynamic valgus in female runners. METHODS: Twenty-nine healthy females ran on a treadmill at 2.92 m/s and performed strength, range of motion and endurance tests. Pelvic, hip and ankle kinematics were measured with a 3D motion analysis system. Six multiple linear regression models were used to identify the ability of physical and biomechanical variables to predict excursion and peak of contralateral pelvic drop, hip adduction and internal rotation. RESULTS: Contralateral pelvic drop and hip adduction were positively correlated to ankle eversion and step cadence. Hip internal rotation had a negative correlation with ankle eversion. Despite significance, predictor variables explained less than 30% of dynamic valgus variance during running. No interest variable had significant correlation with the hip strength and hip and ankle passive range of motion. SIGNIFICANCE: The results showed that distal joint kinematics and spatiotemporal variables should be considered during biomechanical running analysis to identify their possible relationship with joint overload caused by dynamic valgus. Caution should be taken when linking hip disorders during running to posterolateral hip strength and stiffness, core endurance, and ankle dorsiflexion range of motion since no correlation occurred amongstthese variables in this sample of female runners.

Laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) data fusion to predict the concentration of K, Mg and P in bean seed samples.

The present study aims to develop a fast and simple method for the determination of potassium (K), magnesium (Mg) and phosphor (P) in bean seed samples employing a data fusion strategy in the low-level with laser-induced breakdown spectroscopy (LIBS) and wavelength dispersive X-ray fluorescence (WDXRF) techniques combined with direct solid sample analysis. Univariate and multivariate (multiple linear regression, MLR) calibration and leave-one-out cross validation strategies were evaluated to build the calibration models correlated with reference values obtained by inductively coupled plasma optical emission spectrometry (ICP OES) after microwave-assisted acid digestion. The proposed calibration models for WDXRF and LIBS were tested using 14 samples, where the best results were obtained using the data fusion of both techniques. The standard error of cross validation (SECV) obtained were: 0.12% for K, 0.019% for Mg and 0.10% for P, and the trueness ranged between 89 and 124% for K, 82 to 125% for Mg and 73 to 128% for P. These values showed a good accuracy, precise and robustness of the method and a greater reliability of the results. In addition, the predicted concentrations ranged from 0.97 to 1.55% for K, 0.14 to 0.28% for Mg, and 0.27 to 0.82% for P.

Agrometeorological models to forecast açaí (Euterpe oleracea Mart.) yield in the Eastern Amazon.

BACKGROUND: The increasing demand in Brazil and the world for products derived from the açaí palm (Euterpe oleracea Mart) has generated changes in its production process, principally due to the necessity of maintaining yield in situations of seasonality and climate fluctuation. The objective of this study was to estimate açaí fruit yield in irrigated system (IRRS) and rainfed system or unirrigated (RAINF) using agrometeorological models in response to climate conditions in the eastern Amazon. Modeling was done using multiple linear regression using the 'stepwise forward' method of variable selection. Monthly air temperature (T) values, solar radiation (SR), vapor pressure deficit (VPD), precipitation + irrigation (P + I), and potential evapotranspiration (PET) in six phenological phases were correlated with yield. The thermal necessity value was calculated through the sum of accumulated degree days (ADD) up to the formation of fruit bunch, as well as the time necessary for initial leaf development, using a base temperature of 10 °C. RESULTS: The most important meteorological variables were T, SR, and VPD for IRRS, and for RAINF water stress had the greatest effect. The accuracy of the agrometeorological models, using maximum values for mean absolute percent error (MAPE), was 0.01 in the IRRS and 1.12 in the RAINF. CONCLUSION: Using these models yield was predicted approximately 6 to 9 months before the harvest, in April, May, November, and December in the IRRS, and January, May, June, August, September, and November for the RAINF. © 2019 Society of Chemical Industry.