Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru.

Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.

Equations for Prediction of Body Adiposity in Patients With Chronic Kidney Disease Undergoing Hemodialysis.

OBJECTIVES: The prevalence of obesity in the population has increased and excess body adiposity is one of the main nutritional disorders in patients with chronic kidney disease (CKD) on hemodialysis (HD). The objective of this study was to develop equations using anthropometric measurements to predict the total and abdominal body adiposity of patients with CKD on HD. METHODS: This is a cross-sectional study evaluating 323 patients with CKD on HD in city in northeastern Brazil. Measurements and anthropometric indicators were correlated with percentage of body fat (%BF) and visceral fat (VF, in kg) measured by dual energy X-ray absorptiometry. Multiple linear regression models based on different combinations of anthropometric measurements were adjusted to develop the equations, with subsequent cross-validation. RESULTS: Of the 323 patients, 62.2% were male and 46.5% were aged between 40 and 59 years. The equation selected to estimate %BF included weight, height, waist and hip circumferences, and triceps and suprailiac skin folds, presenting high predictive capacity (R2 = 0.771). The equation selected to estimate VF included weight, height, waist circumference, hip circumference, and sum of skin folds (R22 = 0.796). CONCLUSIONS: The proposed equations efficiently predicted the %BF and VF (kg) of patients with CKD on HD, thereby serving as viable indicators in clinical practice.

Characterization of larval habitats of Anopheles (Nyssorhynchus) darlingi and associated species in malaria areas in western Brazilian Amazon

BACKGROUND Anopheles darlingi is the most efficient vector of malaria parasites in the Neotropics. Nevertheless, the specificities of its larval habitats are still poorly known. OBJECTIVES Characterize permanent larval habitats, and population dynamics of An. darlingi and other potential vectors in relation to climate, physicochemical variables, insect fauna and malaria cases. METHODS A 14-month longitudinal study was conducted in Porto Velho, Rondônia, western Brazilian Amazon. Monthly, 21 permanent water bodies were sampled. Immature anophelines and associated fauna were collected, physicochemical characteristics, and climate variables were recorded and analyzed. FINDINGS Five types of habitats were identified: lagoon, stream, stream combined with lagoon, stream combined with dam, and fishpond. A total of 60,927 anophelines were collected. The most abundant species in all habitats were Anopheles braziliensis and An. darlingi. The highest density was found in the lagoon, while streams had the highest species richness. Abundance was higher during the transition period wet-dry season. There was a lag of respectively four and five months between the peak of rainfall and the Madeira River level and the highest abundance of An. darlingi larvae, which were positively correlated with habitats partially shaded, pH close to neutrality, increase dissolved oxygen and sulphates. MAIN CONCLUSIONS The present study provides data on key factors defining permanent larval habitats for the surveillance of An. darlingi and other potential vectors as well as a log-linear Negative Binomial model based on immature mosquito abundance and climate variables to predict the increase in the number of malaria cases.

Dissociation Mode of the O-H Bond in Betanidin, pKa-Clusterization Prediction, and Molecular Interactions via Shape Theory and DFT Methods.

Betanidin (Bd) is a nitrogenous metabolite with significant bioactive potential influenced by pH. Its free radical scavenging activity and deprotonation pathway are crucial to studying its physicochemical properties. Motivated by the published discrepancies about the best deprotonation routes in Bd, this work explores all possible pathways for proton extractions on that molecule, by using the direct approach method based on pKa. The complete space of exploration is supported by a linear relation with constant slope, where the pKa is written in terms of the associated deprotonated molecule energy. The deprotonation rounds 1, …, 6 define groups of parallel linear models with constant slope. The intercepts of the models just depend on the protonated energy for each round, and then the pKa can be trivially ordered and explained by the energy. We use the direct approximation method to obtain the value of pKa. We predict all possible outcomes based on a linear model of the energy and some related verified assumptions. We also include a new measure of similarity or dissimilarity between the protonated and deprotonated molecules, via a geometric-chemical descriptor called the Riemann-Mulliken distance (RMD). The RMD considers the cartesian coordinates of the atoms, the atomic mass, and the Mulliken charges. After exploring the complete set of permutations, we show that the successive deprotonation process does not inherit the local energy minimum and that the commutativity of the paths does not hold either. The resulting clusterization of pKa can be explained by the local acid and basic groups of the BD, and the successive deprotonation can be predicted by using the chemical explained linear models, which can avoid unnecessary optimizations. Another part of the research uses our own algorithm based on shape theory to determine the protein's active site automatically, and molecular dynamics confirmed the results of the molecular docking of Bd in protonated and anionic form with the enzyme aldose reductase (AR). Also, we calculate the descriptors associated with the SET and SPLET mechanisms.

Ethnic-Regional Differences in the Allocation of High Complexity Spending in Brazil: Time Analysis 2010-2019.

The following paper presents as a research problem the ethnic-regional differences in the allocation of high complexity spending in Brazil in an analysis from 2010 to 2019. This is a descriptive research in which a generalized linear model (GLM) was developed to analyze these hospital expenditures with high complexity procedures. The total spending on high complexity procedures in Brazil has increased over the past decade. The study shows that the lowest average expenditures are found in the North and Northeast regions. When comparing the spending between different ethnicities, it was observed that the only decrease between the years 2010 and 2019 was in the amount spent on procedures in indigenous people. The spending on male patients was significantly higher compared to female patients. The highest expenditures, on the other hand, are concentrated in the regions of state capitals favoring the strengthening of hub municipalities. Geographic inequalities in access still persist, even with most states already offering almost all procedures. The Brazilian territory is very heterogeneous and needs to organize its health system by regions, therefore integrated public policies and economic and social development are urgently needed.

Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil.

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Epidemiological Study of the Occurrence of Typhus Group Rickettsia Natural Infection in Domiciliated Dogs from a Rural Community in South-Eastern Mexico.

The aim is to describe the Typhus group (TG) Rickettsia infection in dogs and to identify factors associated with this infection. We collected blood samples and gathered exposure and clinical data of 142 dogs from a rural community of Yucatan. The Rickettsia group was determined by semi-nested PCR. Generalized linear models with binomial error distribution were used to model the associated factors from the dog sample for risk ratio (RR) estimation. Thirty-four dogs (23.9%) showed molecular evidence of TG Rickettsia DNA. The multivariate model showed that mixed-breed dogs (RR = 0.06) and dogs that had received antiparasitic treatment (RR = 0.049) had a lower risk of getting infected, taking as reference the purebred group and the non-treated dogs, respectively. Looking at variable interactions, adult dogs without outdoor activities had a lower infection risk than puppies (RR = 0.26). Among dogs with antiparasitic treatment, females had a higher infection risk than male dogs (RR = 26.2). The results showed enzootic TG Rickettsia circulation in dogs of a rural community. The factors outdoor activities, age and previous antiparasitic treatment, as well as the clinical variables signs of hemorrhages and epistaxis, were associated with a less chance of natural infection in the studied dogs. Prevention and control of the enzootic transmission risk of TG Rickettsia should help to reduce the potential zoonotic transmission of this pathogen.

Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in colombia.

Biosecurity protocols (BP) and good management practices are key to reduce the risk of introduction and transmission of infectious diseases into the pig farms. In this observational cross-sectional study, survey data were collected from 176 pig farms with inventories over 100 sows in Colombia. We analyzed a complex survey dataset to explore the structure and identify clustering patterns using Multiple Correspondence Analysis (MCA) of swine farms in Colombia, and estimated its association with Influenza A virus detection. Two principal dimensions contributed to 27.6% of the dataset variation. Farms with highest contribution to dimension 1 were larger farrow-to-finish farms, using self-replacement of gilts and implementing most of the measures evaluated. In contrast, farms with highest contribution to dimension 2 were medium to large farrow-to-finish farms, but implemented biosecurity in a lower degree. Additionally, two farm clusters were identified by Hierarchical Cluster Analysis (HCA), and the odds of influenza A virus detection was statistically different between clusters (OR 7.29, CI: 1.7,66, p = < 0.01). Moreover, after logistic regression analysis, three important variables were associated with higher odds of influenza detection: (1) "location in an area with a high density of pigs", (2) "farm size", and (3) "after cleaning and disinfecting, the facilities are allowed to dry before use". Our results revealed two clustering patterns of swine farms. This systematic analysis of complex survey data identified relationships between biosecurity, husbandry practices and influenza status. This approach helped to identify gaps on biosecurity and key elements for designing successful strategies to prevent and control swine respiratory diseases in the swine industry.

Estimation of actual evapotranspiration in barley crop through a generalized linear model.

Evapotranspiration is a key variable of the water cycle. Its calculation requires several ground data that frequently are not available. This study contains a detailed method and measurements of meteorological and energy balance variables that can be used to estimate the daily actual evapotranspiration (ETa). A linear generalized model is obtained to calculate the ETa from common variables measured in meteorological stations. The method showed a good performance over a barley crop of easthern Argentine Pampas and can be applied and tested in other great plains. Measurements of soil-plant-atmosphere are included The routines to reproduce the method are included The generalized method allows the calculation of daily ETa over crops and was tested over barley crops.

Trace elements in pelagic Sargassum species in the Mexican Caribbean: Identification of key variables affecting arsenic accumulation in S. fluitans.

Since 2014, the Mexican Caribbean beaches have received massive influxes of the brown seaweed Sargassum (S. fluitans III, S. natans I and S. natans VIII), causing serious ecological and economic effects. Concentrations of arsenic (As), cadmium (Cd), copper (Cu), iron (Fe), lead (Pb), and zinc (Zn) were determined over an annual cycle in pelagic Sargassum species from massive influxes into the Mexican Caribbean. The contribution of trace elements, polysaccharides (alginate and fucoidans), and their main functional groups (uronic acids and sulfate) to arsenic content in Sargassum fluitans - the most abundant species in the Sargassum influx - is discussed. Arsenic was recorded in all samples, yielding mean concentrations of 74.2 ± 2.84 mg kg-1. Significant differences were found between species for As, Cu, Fe, and Pb, but not for Cd and Zn; also, S. fluitans showed significant differences in metal content between seasons for all elements, as well as in alginate and uronic acids from fucoidan. The season of the year, copper, iron, uronic acids, and sulfate content in fucoidan were the main variables associated with arsenic accumulation in S. fluitans as evidenced with a Generalized Linear Model. Arsenic content in Sargassum biomass exceeded the maximum allowable level in the rainy season; therefore, the content of this trace element should be carefully monitored for safe usage of Sargassum biomass.

Fit and genetic parameters of the growth curve of Polled Nellore cattle / Ajuste e parâmetros genéticos da curva de crescimento em bovinos da raça Nelore Mocho

This study was undertaken to compare different non-linear models for fitting growth curves of Polled Nellore animals as well as to estimate genetic parameters for the components of the growth curve. The study involved body weight-age data of 6,717 Polled Nellore cattle from birth to 650 days of age, which belonged to the Brazilian Association of Zebu Breeders (ABCZ), corresponding to the period from 1980 to 2011. Four non-linear models (Brody, Bertalanffy, Logistic, and Gompertz) were fitted and compared by the adjusted coefficient of determination (R2adj), mean absolute deviation of residuals (MAD), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). To estimate the genetic parameters and genetic values of asymptotic weight (A), integration constant (B), and maturation rate (K), the Bayesian inference method was adopted. The Brody model showed the lowest values of MAD, RMSE, AIC, and BIC and the highest R2adj. Heritability estimates for parameters A, B, and K were 0.11, 0.16, and 0.30, respectively, whereas genetic correlations were 0.01 (A-B), -0.91 (A-K), and 0.24 (B-K). The Brody model provided the best fit. The K parameter shows enough genetic variability for selection in the herd. Heavier animals in adulthood tend to exhibit lower growth rates. Despite the low heritability estimate of parameter A, there were genetic gains, indicating that selection is being efficient on asymptotic weight.(AU)

O objetivo deste estudo foi comparar diferentes modelos não lineares para o ajuste das curvas de crescimento de animais da raça Nelore Mocho e estimar os parâmetros genéticos para os componentes da curva de crescimento. Foram utilizados dados de peso corporal-idade do nascimento aos 650 dias de idades de 6.717 bovinos da raça Nelore Mocho, pertencentes à Associação Brasileira de Criadores de Zebu (ABCZ), referentes ao período de 1980 e 2011. Quatro modelos não lineares (Brody, Bertalanffy, Logístico e Gompertz) foram ajustados e comparados pelo coeficiente de determinação ajustado (R2adj), desvio médio absoluto dos resíduos (DMA), raiz quadrada do quadrado médio do resíduo (RMSE), critério de informação de Akaike (AIC) e o critério de informação bayesiano (BIC). Para estimativas dos parâmetros genéticos e valores genéticos do peso assintótico (A), constante de integração (B) e taxa de maturação (K), utilizou-se o método de inferência Bayesiana. O modelo Brody apresentou os menores valores de DMA, RMSE, AIC e BIC e o maior R2adj. As estimativas de herdabilidade foram 0,11; 0,16 e 0,30 para os parâmetros A, B e K, respectivamente, enquanto as correlações genéticas foram de 0,01 (A-B), -0,91 (A-K) e 0,24 (B-K). Constatou-se que o modelo Brody forneceu o melhor ajuste. O parâmetro K apresenta variabilidade genética suficiente para seleção no rebanho. Animais com maior peso na idade adulta tendem a apresentar menores taxas de crescimento. Apesar da baixa estimativa de herdabilidade do parâmetro A, observou-se ganhos genéticos, indicando que a seleção está sendo eficiente sobre o peso assintótico.(AU)

Application of magnetic resonance imaging in evaluating ankle motion injury / Aplicação de imagens de ressonância magnética na avaliação de lesões por movimento do tornozelo / Aplicación de la resonancia magnética en la evaluación de la lesión por movimiento del tobillo

ABSTRACT Introduction Discuss the application of magnetic resonance imaging in evaluating ankle motion injury. Objective Verify the influencing factors of magnetic resource imaging (MRI) diagnosis based on the linear regression algorithm model. Methods The experimental group was diagnosed by MRI, while the control group was diagnosed by plain X-ray. After that, the mathematical model of the linear regression algorithm was constructed. Results It could be concluded that the MRI detection rate was 85.71%, and the X-ray plain film detection rate was 77.14%. The linear regression model analysis showed that the P-value of cartilage injury, tendon fracture, bone contusion, and soft tissue swelling was greater than 0.05. Conclusions MRI has more advantages in the application of ankle joint diagnosis. And ligament injury and joint effusion are the influencing factors of MRI diagnosis, which can highly indicate the authenticity of the injury in the ankle joint. Level of evidence II; Therapeutic studies - investigation of treatment results.

RESUMO Introdução Discutir a aplicação da ressonância magnética na avaliação da lesão motora do tornozelo. Objetivo Verificar os fatores que influenciam o diagnóstico de imagens de recursos magnéticos (RM) com base no modelo de algoritmo de regressão linear. Métodos O grupo experimental foi diagnosticado por ressonância magnética, enquanto o grupo controle foi diagnosticado por radiografia simples. Em seguida, foi construído o modelo matemático do algoritmo de regressão linear. Resultados Concluiu-se que a taxa de detecção da ressonância magnética foi de 85,71% e a taxa de detecção da placa de raios X simples foi de 77,14%. A análise do modelo de regressão linear mostrou que o valor P para lesão da cartilagem, fratura do tendão, contusão óssea e edema do tecido mole foi maior que 0,05. Conclusões a ressonância magnética apresenta mais vantagens na aplicação do diagnóstico da articulação do tornozelo. E a lesão ligamentar e derrame articular são os fatores que influenciam o diagnóstico de ressonância magnética, o que pode indicar amplamente a autenticidade da lesão articular do tornozelo. Nível de evidência II; Estudos terapêuticos: investigação dos resultados do tratamento.

RESUMEN Introducción Discutir la aplicación de la resonancia magnética en la evaluación de la lesión por movimiento del tobillo. Objetivo Verificar los factores que influyen en el diagnóstico de imágenes de recursos magnéticos (IRM) basado en el modelo de algoritmo de regresión lineal. Métodos El grupo experimental fue diagnosticado por resonancia magnética, mientras que el grupo control fue diagnosticado por radiografía simple. Después de eso, se construyó el modelo matemático del algoritmo de regresión lineal. Resultados Se pudo concluir que la tasa de detección de resonancia magnética fue del 85,71% y la tasa de detección de la placa simple de rayos X fue del 77,14%. El análisis del modelo de regresión lineal mostró que el valor P de la lesión del cartílago, la fractura del tendón, la contusión ósea y la hinchazón de los tejidos blandos fue superior a 0,05. Conclusiones la RM tiene más ventajas en la aplicación del diagnóstico de la articulación del tobillo. Y la lesión de ligamentos y el derrame articular son los factores que influyen en el diagnóstico de resonancia magnética, que pueden indicar en gran medida la autenticidad de la lesión en la articulación del tobillo. Nivel de evidencia II; Estudios terapéuticos: investigación de los resultados del tratamiento.

Estimating hidden populations by transferring knowledge from geographically misaligned levels.

The estimation of hidden sub-populations is a hard task that appears in many fields. For example, public health planning in Brazil depends crucially of the number of people who holds a private health insurance plan and hence rarely uses the public services. Different sources of information about these sub-populations may be available at different geographical levels. The available information can be transferred between these different geographic levels to improve the estimation of the hidden population size. In this study, we propose a model that use individual level information to learn about the dependence between the response variable and explanatory variables by proposing a family of link functions with asymptotes that are flexible enough to represent the real aspects of the data and robust to departures from the model. We use the fitted model to estimate the size of the sub-population at any desired level. We illustrate our methodology estimating the sub-population that uses the public health system in each neighborhood of large cities in Brazil.

Generalized symmetrical partial linear model.

In this work, we propose a new model called generalized symmetrical partial linear model, based on the theory of generalized linear models and symmetrical distributions. In our model the response variable follows a symmetrical distribution such a normal, Student-t, power exponential, among others. Following the context of generalized linear models we consider replacing the traditional linear predictors by the more general predictors in whose case one covariate is related with the response variable in a non-parametric fashion, that we do not specified the parametric function. As an example, we could imagine a regression model in which the intercept term is believed to vary in time or geographical location. The backfitting algorithm is used for estimating the parameters of the proposed model. We perform a simulation study for assessing the behavior of the penalized maximum likelihood estimators. We use the quantile residuals for checking the assumption of the model. Finally, we analyzed real data set related with pH rivers in Ireland.

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine.

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85-93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

Directly modelling population dynamics in the South American Arid Diagonal using 14C dates.

Large anthropogenic 14C datasets are widely used to generate summed probability distributions (SPDs) as a proxy for past human population levels. However, SPDs are a poor proxy when datasets are small, bearing little relationship to true population dynamics. Instead, more robust inferences can be achieved by directly modelling the population and assessing the model likelihood given the data. We introduce the R package ADMUR which uses a continuous piecewise linear (CPL) model of population change, calculates the model likelihood given a 14C dataset, estimates credible intervals using Markov chain Monte Carlo, applies a goodness-of-fit test, and uses the Schwarz Criterion to compare CPL models. We demonstrate the efficacy of this method using toy data, showing that spurious dynamics are avoided when sample sizes are small, and true population dynamics are recovered as sample sizes increase. Finally, we use an improved 14C dataset for the South American Arid Diagonal to compare CPL modelling to current simulation methods, and identify three Holocene phases when population trajectory estimates changed from rapid initial growth of 4.15% per generation to a decline of 0.05% per generation between 10 821 and 7055 yr BP, then gently grew at 0.58% per generation until 2500 yr BP. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.

Univariate Analysis of Short-Chain Fatty Acids Related to Sudden Infant Death Syndrome.

Sudden infant death syndrome (SIDS) is defined as the death of a child under one year of age, during sleep, without apparent cause, after exhaustive investigation, so it is a diagnosis of exclusion. SIDS is the principal cause of death in industrialized countries. Inborn errors of metabolism (IEM) have been related to SIDS. These errors are a group of conditions characterized by the accumulation of toxic substances usually produced by an enzyme defect and there are thousands of them and included are the disorders of the ß-oxidation cycle, similarly to what can affect the metabolism of different types of fatty acid chain (within these, short chain fatty acids (SCFAs)). In this work, an analysis of postmortem SCFAs profiles of children who died due to SIDS is proposed. Initially, a set of features containing SCFAs information, obtained from the NIH Common Fund's National Metabolomics Data Repository (NMDR) is submitted to an univariate analysis, developing a model based on the relationship between each feature and the binary output (death due to SIDS or not), obtaining 11 univariate models. Then, each model is validated, calculating their receiver operating characteristic curve (ROC curve) and area under the ROC curve (AUC) value. For those features whose models presented an AUC value higher than 0.650, a new multivariate model is constructed, in order to validate its behavior in comparison to the univariate models. In addition, a comparison between this multivariate model and a model developed based on the whole set of features is finally performed. From the results, it can be observed that each SCFA which comprises of the SFCAs profile, has a relationship with SIDS and could help in risk identification.

Sensor Fusion Algorithm Using a Model-Based Kalman Filter for the Position and Attitude Estimation of Precision Aerial Delivery Systems.

In this research, we focus on the use of Unmanned Aerial Vehicles (UAVs) for the delivery of payloads and navigation towards safe-landing zones, specifically on the modeling of flight dynamics of lightweight vehicles denoted Precision Aerial Delivery Systems (PADSs). While a wide range of nonlinear models has been developed and tested on high-end applications considering various degrees of freedom (DOF), linear models suitable for low-cost applications have not been explored thoroughly. In this study, we propose and compare two linear models, a linearized version of a 6-DOF model specifically developed for micro-lightweight systems, and an alternative model based on a double integrator. Both linear models are implemented with a sensor fusion algorithm using a Kalman filter to estimate the position and attitude of PADSs, and their performance is compared to a nonlinear 6-DOF model. Simulation results demonstrate that both models, when incorporated into a Kalman filter estimation scheme, can determine the flight dynamics of PADSs during smooth flights. While it is validated that the double integrator model can adequately operate under the proposed estimation scheme for up to small acceleration changes, the linearized model proves to be capable of reproducing the nonlinear model characteristics even during moderately steep turns.

Risk assessment of temperature and air pollutants on hospitalizations for mental and behavioral disorders in Curitiba, Brazil.

BACKGROUND: Extreme ambient temperatures and air quality have been directly associated with various human diseases from several studies around the world. However, few analyses involving the association of these environmental circumstances with mental and behavioral disorders (MBD) have been carried out, especially in developing countries such as Brazil. METHODS: A time series study was carried out to explore the associations between daily air pollutants (SO2, NO2, O3, and PM10) concentrations and meteorological variables (temperature and relative humidity) on hospital admissions for mental and behavioral disorders for Curitiba, Brazil. Daily hospital admissions from 2010 to 2016 were analyzed by a semi-parametric generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM). RESULTS: Significant associations between environmental conditions (10 µg/m3 increase in air pollutants and temperature °C) and hospitalizations by MBD were found. Air temperature was the environmental variable with the highest relative risk (RR) at 0-day lag for all ages and sexes analyzed, with RR values of 1.0182 (95% CI: 1.0009-1.0357) for men, and 1.0407 (95% CI: 1.0230-1.0587) for women. Ozone exposure was a risk for all women groups, being higher for the young group, with a RR of 1.0319 (95% CI: 1.0165-1.0483). Elderly from both sexes were more susceptible to temperature variability, with a RR of 1.0651 (95% CI: 1.0213-1.1117) for women, and 1.0215 (95% CI: 1.0195-1.0716) for men. CONCLUSIONS: This study suggests that temperatures above and below the thermal comfort threshold, in addition to high concentrations of air pollutants, present significant risks on hospitalizations by MBD; besides, there are physiological and age differences resulting from the effect of this exposure.

Distribution of the acoustic occurrence of dolphins during the summers 2011 to 2015 in the Upper Gulf of California, Mexico.

Baseline knowledge of spatial and temporal distribution patterns is essential for cetacean management and conservation. Such knowledge is particularly important in areas where gillnet fishing occurs, as the Upper Gulf of California, which increases the probability of bycatch of cetaceans. In this area, the vaquita porpoise (Phocoena sinus) has been widely studied, but the knowledge of other cetaceans is scarce and based on traditional visual survey methods. We used data collected by an array of acoustic click detectors (C-PODs) during the summers 2011 to 2015 to analyze the distribution of dolphins in the Vaquita Refuge in the Upper Gulf of California. We recorded 120,038 echolocation click trains of dolphins during 12,371 days of recording effort at 46 sampling sites. Based on simultaneous visual and acoustic data, we estimated a false positive acoustic detection rate of 19.4%. Dolphin acoustic activity varied among sites, with higher activity in the east of the Vaquita Refuge. Acoustic activity was higher at night than during the day. We used negative binomial generalized linear models to study the count of clicks of dolphins in relation to spatial, temporal, physical, biological and anthropogenic explanatory variables. The best model selected for the response variable included sampling site, day-night condition, and vertical component of tide speed. Patterns in the spatial distribution of predicted acoustic activity of dolphins were similar to the acoustic activity observed per sampling season. Higher acoustic activity was predicted at night, but the tide speed variable was not relevant under this condition. Acoustic activity patterns could be related to the availability of prey resources since echolocation click trains are associated with foraging activities of dolphins. This is the first study of the distribution of dolphins in Mexico using medium-term systematic passive acoustic monitoring, and the results can contribute to better management to the natural protected area located in the Upper Gulf of California.