Impact of Bariatric Surgery on metabolic health in a Uruguayan cohort and the emerging predictive role of FSTL1.

Obesity poses significant challenges, necessitating comprehensive strategies for effective intervention. Bariatric Surgery (BS) has emerged as a crucial therapeutic approach, demonstrating success in weight loss and comorbidity improvement. This study aimed to evaluate the outcomes of BS in a cohort of 48 Uruguayan patients and investigate the interplay between BS and clinical and metabolic features, with a specific focus on FSTL1, an emerging biomarker associated with obesity and inflammation. We quantitatively analyzed BS outcomes and constructed linear models to identify variables impacting BS success. The study revealed the effectiveness of BS in improving metabolic and clinical parameters. Importantly, variables correlating with BS success were identified, with higher pre-surgical FSTL1 levels associated with an increased effect of BS on BMI reduction. FSTL1 levels were measured from patient plasma using an ELISA kit pre-surgery and six months after. This research, despite limitations of a small sample size and limited follow-up time, contributes valuable insights into understanding and predicting the success of BS, highlighting the potential role of FSTL1 as a useful biomarker in obesity.

Assessing the Impact of Climate Change on Argyrotaenia sphaleropa (Meyrick, 1909) Voltinism: Implications for Fruit Production in Southern Brazil.

The leafroller Argyrotaenia sphaleropa (Meyrick) is an important pest of temperate fruits. Its biology and population dynamics are strongly influenced by temperature. In this context, this study aims to select a mathematical model that accurately describes the temperature-dependent development rate of A. sphaleropa and applies this model to predict the impact of climate change on the number of annual generations (voltinism) of the pest in southern Brazil. Nine mathematical models were employed to fit the species' developmental rate at different constant temperatures. Voltinism was projected using climate data from the current period (1994-2013) and projections for 2050 and 2070. The Brière-1 model (D(T) = aT(T-TL)(TH-T)1/2) provided the best fit for the temperature-dependent developmental rate of A. sphaleropa. According to this model, the regions with the highest voltinism under current climatic conditions are the northern and central areas of Paraná, the western and northeastern regions of Santa Catarina, and northwestern Rio Grande do Sul. The model also predicts a rise in A. sphaleropa voltinism as a consequence of climate change, especially in the mountainous regions of Santa Catarina and Rio Grande do Sul, with projected increases of up to 25.1%. These regions encompass most areas where temperate fruits used as hosts by the leafroller are cultivated. This study represents a significant advancement in understanding the implications of global warming on A. sphaleropa voltinism and suggests that forthcoming climatic conditions will likely favor the species across much of southern Brazil.

Linear mixed-effect models for correlated response to process electroencephalogram recordings.

A data set of clinical studies of electroencephalogram recordings (EEG) following data acquisition protocols in control individuals (Eyes Closed Wakefulness - Eyes Open Wakefulness, Hyperventilation, and Optostimulation) are quantified with information theory metrics, namely permutation Shanon entropy and permutation Lempel Ziv complexity, to identify functional changes. This work implement Linear mixed-effects models (LMEMs) for confirmatory hypothesis testing. The results show that EEGs have high variability for both metrics and there is a positive correlation between them. The mean of permutation Lempel-Ziv complexity and permutation Shanon entropy used simultaneously for each of the four states are distinguishable from each other. However, used separately, the differences between permutation Lempel-Ziv complexity or permutation Shanon entropy of some states were not statistically significant. This shows that the joint use of both metrics provides more information than the separate use of each of them. Despite their wide use in medicine, LMEMs have not been commonly applied to simultaneously model metrics that quantify EEG signals. Modeling EEGs using a model that characterizes more than one response variable and their possible correlations represents a new way of analyzing EEG data in neuroscience.

Insights into the genetic architecture of Phytophthora capsici root rot resistance in chile pepper (Capsicum spp.) from multi-locus genome-wide association study.

BACKGROUND: Phytophthora root rot, a major constraint in chile pepper production worldwide, is caused by the soil-borne oomycete, Phytophthora capsici. This study aimed to detect significant regions in the Capsicum genome linked to Phytophthora root rot resistance using a panel consisting of 157 Capsicum spp. genotypes. Multi-locus genome wide association study (GWAS) was conducted using single nucleotide polymorphism (SNP) markers derived from genotyping-by-sequencing (GBS). Individual plants were separately inoculated with P. capsici isolates, 'PWB-185', 'PWB-186', and '6347', at the 4-8 leaf stage and were scored for disease symptoms up to 14-days post-inoculation. Disease scores were used to calculate disease parameters including disease severity index percentage, percent of resistant plants, area under disease progress curve, and estimated marginal means for each genotype. RESULTS: Most of the genotypes displayed root rot symptoms, whereas five accessions were completely resistant to all the isolates and displayed no symptoms of infection. A total of 55,117 SNP markers derived from GBS were used to perform multi-locus GWAS which identified 330 significant SNP markers associated with disease resistance. Of these, 56 SNP markers distributed across all the 12 chromosomes were common across the isolates, indicating association with more durable resistance. Candidate genes including nucleotide-binding site leucine-rich repeat (NBS-LRR), systemic acquired resistance (SAR8.2), and receptor-like kinase (RLKs), were identified within 0.5 Mb of the associated markers. CONCLUSIONS: Results will be used to improve resistance to Phytophthora root rot in chile pepper by the development of Kompetitive allele-specific markers (KASP®) for marker validation, genomewide selection, and marker-assisted breeding.

Do non-linearity and non-Gaussianity truly matter in streamflow forecasting? A comparative study between PAR(p) and vine copula for Brazilian streamflow time series.

This study evaluates the joint impact of non-linearity and non-Gaussianity on predictive performance in 23 Brazilian monthly streamflow time series from 1931 to 2022. We consider point and interval forecasting, employing a PAR(p) model and comparing it with the periodic vine copula model. Results indicate that the Gaussian hypothesis assumed by PAR(p) is unsuitable; gamma and log-normal distributions prove more appropriate and crucial for constructing accurate confidence intervals. This is primarily due to the assumption of the Gaussian distribution, which can lead to the generation of confidence intervals with negative values. Analyzing the estimated copula models, we observed a prevalence of the bivariate Normal copula, indicating that linear dynamic dependence is frequent, and the Rotated Gumbel 180°, which exhibits lower tail dependence. Overall, the temporal dynamics are predominantly shaped by combining these two types of effects. In point forecasting, both models show similar behavior in the estimation set, with slight advantages for the copula model. The copula model performs better during the out-of-sample analysis, particularly for certain power plants. In interval forecasting, the copula model exhibits pronounced superiority, offering a better estimation of quantiles. Consistently demonstrating proficiency in constructing reliable and accurate intervals, the copula model reveals a notable advantage over the PAR(p) model in interval forecasting.

Prediction of body weight of Curraleiro Pé-Duro cattle based on morphometric measurements.

Cattle weight development is highly correlated with some body measurements. Based on the relationship between morphometric measurements and body mass, our aim was to develop regression equations to estimate the body weight of Curraleiro Pé-Duro (CPD) cattle to be used in farms that lack access to weighting scales. Data from 1023 animals from four farms on withers height (WH), body length (BL), body score (BS), heart girth (HG), permanent teeth (PT), scrotal perimeter (SP), and live weight were used. The animals were classified into five categories depending on age and/or sex: newborns (NB), calves, weaned animals, cows, and bulls. The best models are GLM with Gamma, Gamma, inverse Gaussian, Gaussian, and Gamma distributions for NB, calves, weaned animals, cows, and bulls, respectively. Predictive modeling for bulls was the best performing overall, with a correlation of 0.97 between the estimated by the model and the obtained with a weighting scale. For NB, calves, weaned animals, and cows, the correlation (r) was 0.85, 0.90, 0.95, and 0.87, respectively. The evaluated models are adequate to be used as a technical solution to estimate weight in a cattle production system.

Efecto de factores sociodemográficos en la mortalidad del COVID-19 en Costa Rica: un enfoque geográfico

Introducción: La enfermedad por coronavirus (COVID-19) se ha extendido entre la población de todo el país y ha tenido un gran impacto a nivel mundial. Sin embargo, existen diferencias geográficas importantes en la mortalidad de COVID-19 entre las diferentes regiones del mundo y en Costa Rica. Objetivo: Explorar el efecto de algunos de los factores sociodemográficos en la mortalidad de COVID-19 en pequeñas divisiones geográficas o cantones de Costa Rica. Métodos: Usamos registros oficiales y aplicamos un modelo de regresión clásica de Poisson y un modelo de regresión ponderada geográficamente. Resultados: Obtuvimos un criterio de información de Akaike (AIC) más bajo con la regresión ponderada (927.1 en la regresión de Poison versus 358.4 en la regresión ponderada). Los cantones con un mayor riesgo de mortalidad por COVID-19 tuvo una población más densa; bienestar material más alto; menor proporción de cobertura de salud y están ubicadas en el área del Pacífico de Costa Rica. Conclusiones: Una estrategia de intervención de COVID-19 específica debería concentrarse en áreas de la costa pacífica con poblaciones más densas, mayor bienestar material y menor población por unidad de salud.

Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great global impact. However, there are important geographic differences in mortality from COVID-19 among world regions and within Costa Rica. Objective: To explore the effect of some sociodemographic factors on COVID-19 mortality in the small geographic divisions or cantons of Costa Rica. Methods: We used official records and applied a classical epidemiological Poisson regression model and a geographically weighted regression model. Results: We obtained a lower Akaike Information Criterion with the weighted regression (927.1 in Poisson regression versus 358.4 in weighted regression). The cantons with higher risk of mortality from COVID-19 had a denser population; higher material well-being; less population by health service units and are located near the Pacific coast. Conclusions: A specific COVID-19 intervention strategy should concentrate on Pacific coast areas with denser population, higher material well-being and less population by health service units.

Long-term exposure to ambient fine particulate matter and carotid intima media thickness at bilateral, left and right in adults from Mexico City: Results from GEA study.

BACKGROUND: PM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AIM: To evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. METHODS: This study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 µg/m3 increase) at different lag years (1-4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). RESULTS: The median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) µm, respectively. Annual average PM2.5 exposure was 26.64 µg/m3, with median and IQR, of 24.46 (23.5-25.46) µg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; -0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. CONCLUSIONS: Our results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

Heteroscedastic partially linear model under skew-normal distribution with application in ragweed pollen concentration.

We introduce a new class of heteroscedastic partially linear model (PLM) with skew-normal distribution. Maximum likelihood estimation of the model parameters by the ECM algorithm (Expectation/Conditional Maximization) as well as influence diagnostics for the new model are investigated. In addition, a Likelihood Ratio test for assessing the homogeneity of the scale parameter is presented. Simulation studies for assessing the performance of the ECM algorithm and the Likelihood Ratio test statistics for homogeneity of variance are developed. Also, a study for misspecification of the structure function is considered. Finally, an application of the new heteroscedastic PLM to a real data set on ragweed pollen concentration is presented to show that it provides a better fit than the classic homocedastic PLM. We hope that the proposed model may attract applications in different areas of knowledge.

Growth Curve of Brazilian Creole Chickens (Canela-Preta Breed) Raised in Two Different Rearing Systems under Tropical Climate

The objective of this study was to describe the growth curve of Brazilian Creole chickens of the Canela-Preta breed raised in two different rearing systems using non-linear growth models. A total of 400 birds were divided into two groups of 200 animals (of both genders), which were kept in confined or semi-confined systems. The confined birds were housed in an experimental masonry shed and the semi-confined animals were housed in another shed with access to pasture from 29 days of age. Birds were individually weighed every seven days during six months for determination of the growth curves of body weight using 10 non-linear models. The parameters of the models were estimated using the Gauss Newton method. The performance of the models was assessed using mean squared error (MSE), coefficient of determination (R2), percentage of convergence, and residual mean absolute deviation (MAD). With the exception of the Inverse Polynomial, all the other models had R2 values close to one. Therefore, the best models were chosen based on the lowest MSE and MAD values, with the Richards model ranking first followed by the Von Bertalanffy model. Gender and rearing system effects significantly influenced (p<0.05) some parameters of the Richards model. In conclusion, the Richards model was the most adequate to describe the growth of Canela-Preta chickens. Gender and rearing system significantly influenced the growth of the birds. The growth rates observed indicated that management strategies can be performed to increase the production efficiency of Canela-Preta chickens.(AU)

Proposal of a non-linear model to adjust in vitro gas production at different incubation times

This work aims to propose a new model named Gompertz-Von Bertalanffy bicompartmental (GVB), a combination of the models Gompertz and Von Bertalanffy. The GVB models is applied to fit the kinetic curve of cumulative gas production (CGP) of four foods (SS ­ sunflower silage; CS ­ corn silage; and the mixtures 340SS ­ 660 gkg-1 of corn silage and 340 gkg-1 of sunflower silage; and 660SS ­ 340 gkg-1 of corn silage and 660 gkg-1 of sunflower silage). The GVB fit is compared to models Logistic-Von Bertalanffy bicompartmental (LVB) and bicompartmental logistic (BL). All the process studied employed the semi-automatic "in vitro" technique of producing gases used in ruminant nutrition. The gas production readout was performed at times 2, 4, 6, 8, 10, 12, 15, 19, 24, 30, 48, 72, and 96 h. The data generated were used to estimate the models' parameters by the least squared method with the iterative Gauss-Newton process. The data fit quality of the models was verified using the adjusted coefficient of determination criterion (), mean residual square (MRS), Akaike information criterion (AIC), and mean absolute deviation (MAD). Among the analyzed models, the LVB model presented the best quality of fit evaluators for CS. In contrast, the GVB model showed better quality of fit to describe CGP over time for 340SS, 660SS, and SS, presenting the highest values of () and the lowest values of MSR, AIC, and MAD.

Mortalidade de professores no estado de Goiás: análise de série temporal / Mortality of teachers in the state of Goiás: a time serie analysis

Análise de perfil epidemiológico e a tendência da mortalidade de professores da educação básica e do ensino superior no Estado de Goiás, no período de 2008 a 2017. Método: série temporal, com dados do Sistema de Informações sobre Mortalidade. Para a análise da tendência da mortalidade utilizou-se modelos de regressão linear e considerou-se p<0,05. Resultados: foram levantados 2.439 óbitos, maior frequência de óbitos no sexo feminino, em indivíduos de cor branca e com idade entre 50 e 69 anos. Entre as mulheres, as neoplasias malignas foram as principais causas de óbito, enquanto entre os homens destacaram-se as doenças do aparelho circulatório. Identificou-se tendência temporal de aumento dos óbitos (0,134 para as neoplasias malignas, 0,132 para as doenças do aparelho circulatório, 0,252 para as causas externas e 0,212 para as doenças do aparelho respiratório). Considerando todas as causas de óbito o incremento foi de 0,040 (p<0,000). Conclusão: há aumento de mortalidade de professores por causas evitáveis

Objective: to analyze the epidemiologic profile and the trends in mortality of teachers from basic education and higher education professors in the state of Goiás, in the period from 2008 to 2017. Method: time series, with data from the Mortality Information System. In order to analyze the trend in mortality, a linear regression model was used, considering p<0.05. Results: 2,439 deaths were recorded, with higher frequency of deaths within female sex, white and aged from 50 to 69 years. Among the women, malignant neoplasms were the main causes of death, whilst among men, circulatory system diseases stood out. It was identified a temporal trend of increase in deaths (0.134 for malignant neoplasms, 0.132 for circulatory system diseases, 0.252 for external causes and 0.212 for respiratory system diseases). Considering all the causes of death, the increase was 0.040 (p<0.000). Conclusion: there is an increase in mortality of teachers and professors due to avoidable causes

PredictION: a predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2.

The optimization of resources for research in developing countries forces us to consider strategies in the wet lab that allow the reuse of molecular biology reagents to reduce costs. In this study, we used linear regression as a method for predictive modeling of coverage depth given the number of MinION reads sequenced to define the optimum number of reads necessary to obtain >200X coverage depth with a good lineage-clade assignment of SARS-CoV-2 genomes. The research aimed to create and implement a model based on machine learning algorithms to predict different variables (e.g., coverage depth) given the number of MinION reads produced by Nanopore sequencing to maximize the yield of high-quality SARS-CoV-2 genomes, determine the best sequencing runtime, and to be able to reuse the flow cell with the remaining nanopores available for sequencing in a new run. The best accuracy was -0.98 according to the R squared performance metric of the models. A demo version is available at https://genomicdashboard.herokuapp.com/.

Can the stable isotope variability in a zooplankton time series be explained by its key species?

Stable isotope (SI) analysis is a standard tool to study marine food webs, usually based on the measurement of a few individuals from a small list of subjectively pre-defined species. The main objective of this study was to find out which species are significantly associated with the temporal variability of the SI composition of zooplankton in a tropical marine ecosystem. We investigated this by means of a novel species-biomass-isotopes-mixture (SBIM) approach that uses a relative biomass matrix to explain the SI signature of the zooplankton community. Furthermore, SBIM was applied to detect key taxa that can be considered bioindicators for important descriptors of ecosystem state (e.g., oligotrophy, carbon sources, mean trophic level). Plankton samples (64 µm mesh size) were obtained in Tamandaré Bay (northeastern Brazil) from June 2013 to August 2019. One aliquot of each sample was taken for stable isotope measurements and one for taxonomic identification and estimation of size and relative biomass. Total zooplankton biomass differed significantly between years, seasons and stations. Total zooplankton δ13C values ranged from -21.0 to -18.2‰ (mean ± standard deviation: -19.7 ± 0.7‰ in the dry season, and -19.4 ± 0.8‰ in the rainy season). Total zooplankton δ15N values ranged from 3.8 to 9.0‰ (7.0 ± 1.0‰ in the dry season, and 6.5 ± 1.2‰ rainy season). Total zooplankton C/N ratios ranged from 3.5 to 5.0 (4.2 ± 0.4 in the dry season and 4.2 ± 0.3 in the rainy season). The sparsely abundant and relatively large-sized copepod Pseudodiaptomus acutus was the most important species for explaining the variability in δ15N (22% of the total variability). Relative biomass (%) of P. acutus showed a strong positive correlation with δ15N, indicating a high trophic level (TL). Our results highlight the importance of less abundant taxa for marine food webs. Small-sized invertebrate larvae were negatively correlated with δ15N, indicating a TL below average. The copepod Dioithona oculata was the most important organism in explaining the δ13C of zooplankton (17.7% of the total variability, positive correlation with δ13C), indicating possible selective use of a13C-enriched food source (e.g., diatoms) by this cyclopoid copepod. Oithona spp. juveniles showed a negative relationship with zooplankton C/N ratio, which can be indicators of an oligotrophic ecosystem state and lipid-poor zooplankton. The tintinnid F. ehrenbergii showed a positive correlation with C/N, being an indicator for turbid "green waters'', during the rainy season, when the ecosystem was in a eutrophic state, with high lipid contents in the zooplankton community. The proposed SBIM approach opens up a novel pathway to understanding the factors and species that shape the temporal variability of food webs.

O efeito das medidas de distanciamento social sobre a incidência de COVID-19 no Brasil: Uma análise multissetorial / The effect of social distancing measures on the incidence of COVID-19 in Brazil: A multisectoral analysis

Objetivo: Este artigo estima o impacto das medidas de distanciamento social sobre a incidência de COVID-19 a partir de uma perspectiva multissetorial. Métodos: O desenho de pesquisa utiliza um modelo de regressão em painel para analisar a relação entre restrições de mobilidade em diferentes setores econômicos e a dinâmica longitudinal da doença nos estados do Brasil. Resultados: Os principais resultados indicam que apenas os coeficientes das variáveis que representam os setores de restaurantes (p-valor < 0,05), compras (p-valor < 0,05) e transporte (p-valor < 0,001) obtiveram significância estatística. Em especial, o transporte (ß= -0,674) é a variável que mais influencia a variação do número de casos de COVID-19. Conclusões: As evidências reportadas nesta pesquisa podem auxiliar o processo de tomada de decisão dos gestores governamentais a respeito da eficácia de intervenções não farmacológicas como instrumento para reduzir a disseminação da COVID-19 (AU)

Objective: This article estimates the impact of social distancing measures on the incidence of COVID-19 from a multisectoral perspective. Methods: The research design uses a panel regression model to analyze the relationship between mobility restrictions in different economic sectors and the longitudinal dynamics of the infection across Brazilian states. Results: The main results indicate that only the coefficients for the restaurant (p-value < 0.05), shopping (p-value < 0.05), and transport sectors (p-value < 0.001) reached statistical significance. In particular, transport (ß = -0.674) is the variable with the strongest impact on the variation in the number of COVID-19 cases. Conclusions: The evidence reported in this research can assist the decision-making process of government managers regarding the effectiveness of non-pharmacological interventions as a tool to reduce the spread of COVID-19

Association between coffee consumption with serum lipid profile in ELSA-Brasil study: a metabolomic approach.

PURPOSE: This study evaluated the association between coffee consumption and serum lipid profile in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). METHODS: This is a cross-sectional study on baseline data from participants of the cohort ELSA-Brasil. Only participants of São Paulo Research Center who underwent a nuclear magnetic resonance (NMR) spectroscopy examination of lipid profile were included (N = 4736). Coffee intake was categorized into four categories (cups/day, in reference cup size of 50 mL, which is the household measure adopted in Brazil): never/almost never, ≤ 1, 1-3, and > 3. Serum lipid profile [i.e., Total Cholesterol (TC), Total Triglycerides (TG), Very Low-Density Lipoprotein-cholesterol (VLDL-c), Low-Density Lipoprotein-cholesterol (LDL-c), High-Density Lipoprotein-cholesterol (HDL-c), Triglyceride-rich Lipoprotein Particles (TRLP) and subfractions particles] was analyzed. To estimate the effect of coffee consumption on serum lipid profile, multivariate Generalized Linear Models were performed. RESULTS: Compared to participants who never or almost never drink coffee, individuals who consumed more than 3 cups/day showed an increase in concentrations of TC (ß: 4.13; 95% CI 0.81, 7.45), TG (ß: 9.53; 95% CI 1.65, 17.42), VLDL-c (ß: 1.90; 95% CI 0.38, 3.42), TRLP (ß: 8.42; 95% CI 1.24, 15.60), and Very Small-TRLP and Medium-TRLP subfractions (ß: 7.36; 95% CI 0.21, 14.51; ß: 2.53; 95% CI 0.89, 4.16, respectively), but not with HDL-c and LDL-c. Among individuals with low (≤ 1 cup/day) and moderate (1-3 cups/day) coffee consumption, no significant associations with lipids was observed. CONCLUSION: High coffee consumption (more than 3 cups per day) was associated with an increase in serum lipids, namely TC, TG, VLDL-c, and TRL particles, highlighting the importance of a moderate consumption of this beverage.

The influence of abiotic and biotic variables on the patent parasitemias of Trypanosoma spp. in Thrichomys fosteri (Rodentia: Echimyidae) in the southern Pantanal.

Parasitism is a dynamic ecological phenomenon that is constantly influenced by the environment and intrinsic factors of the host. We aimed to evaluate the influence of vegetation, environmental temperature, reproductive conditions, sex, and body condition (BC) on the detection of Trypanosoma spp. in the blood of Thrichomys fosteri in the Pantanal region, an enzootic area for trypanosomiasis. Whole blood was collected from the tip of the tail, and nPCR was performed for Trypanosoma spp. detection from the DNA extracted from the resultant blood clot. Statistical analyses were performed using generalized linear models. Our results showed that there is a greater probability of detection of Trypanosoma spp. in the bloodstream of animals with the highest BC values in periods with mild temperatures. Since T. fosteri is an abundant and common prey for carnivores, even in periods with low temperatures and consequent decrease in the reproduction and activities of the blood-sucking arthropod vectors, the maintenance of Trypanosoma spp. in the studied area would be guaranteed via predation (trophic network) of T. fosteri individuals with good BC and patent parasitemia. Furthermore, T. fosteri, which displays Trypanosoma spp. in the bloodstream, would be reproducing adequately because we found no influence between the reproductive condition and the detection of Trypanosoma spp. in T. fosteri. The caviomorph rodent T. fostei is an important species for the maintenance of Trypanosoma spp. in the Pantanal biome.

Overview of Major Computer Packages for Genomic Prediction of Complex Traits.

Genomic prediction models are showing their power to increase the rate of genetic gain by boosting all the elements of the breeder's equation. Insight into the factors associated with the successful implementation of this prediction model is increasing with time but the technology has reached a stage of acceptance. Most genomic prediction models require specialized computer packages based mainly on linear models and related methods. The number of computer packages has exploded in recent years given the interest in this technology. In this chapter, we explore the main computer packages available to fit these models; we also review the special features, strengths, and weaknesses of the methods behind the most popular computer packages.

Tendência temporal da leptospirose e sua associação com variáveis climáticas e ambientais em Santa Catarina, Brasil / Temporal trends in leptospirosis incidence and association with climatic and environmental factors in the state of Santa Catarina, Brazil

Resumo A leptospirose é uma zoonose que apresenta potencial epidêmico, principalmente após fortes chuvas que acarretam inundações, alagamentos e enxurradas. Algumas características da região costeira de Santa Catarina, localizada no Sul do Brasil, influenciam nesses processos. Portanto, a partir do estudo da leptospirose nos seis municípios do estado com as maiores incidências e picos epidêmicos de 2000 a 2015, buscou-se conhecer a tendência dessa doença e as variáveis climáticas e ambientais associadas à sua ocorrência, ajustando dois modelos com resposta binomial negativa. As maiores incidências foram encontradas em 2008 e 2011, com picos no mesmo mês ou no posterior aos eventos de desastres. A incidência apresentou forte comportamento sazonal, sendo maior nos meses do verão. Observou-se tendência de queda na incidência dos municípios estudados, estimada em 3,21% ao ano. Os fatores climáticos e ambientais mais fortemente associados foram o número de dias de chuva, a temperatura máxima e a presença de enxurrada e de inundação, com diferentes impactos entre os municípios. Houve interações significativas, indicando que o efeito de inundações na incidência não é o mesmo em todos os municípios e que as diferenças nas incidências entre os municípios dependem da ocorrência ou não de inundações.

Abstract Leptospirosis is a zoonosis with epidemic potential, especially after heavy rainfall causing river, urban and flash floods. Certain features of Santa Catarina's coastal region influence these processes. Using negative binomial regression, we investigated trends in the incidence of leptospirosis in the six municipalities with the highest epidemic peaks between 2000 and 2015 and the climatic and environmental variables associated with the occurrence of the disease. Incidence was highest in 2008 and 2011, and peaks occurred in the same month or month after disasters. Incidence showed a strong seasonal trend, being higher in summer months. There was a decrease trend in incidence across the six municipalities (3.21% per year). The climatic and environmental factors that showed the strongest associations were number of rainy days, maximum temperature, presence of flash floods, and river flooding. The impact of these variables varied across the municipalities. Significant interactions were found, indicating that the effect of river flooding on incidence is not the same across all municipalities and differences in incidence between municipalities depend on the occurrence of river flooding.

Estimation of lactation curves of Gyr cattle and some associated production parameters in the Colombian low tropic

Background: The Gyr breed is widely used in Colombian low tropic dairy production systems. During the last 10 years, the Asociación Colombiana de Criadores de Ganado Cebú† ASOCEBU, has been leading a dairy milk control program which led to the creation of a dataset that permits to carry out the first analysis of milk yield in Gyr cattle in the country using records from several herds. Objectives: To study milk production dynamics of Gyr cattle in the Colombian low tropic through the estimation of lactation curves and four derived production parameters: total milk yield between 5 and 305 days (TMY305), peak milk yield (PMY), days at peak (DP) and persistency (P). Methods: 13,798 daily milk yield records from 1,510 cows performing in 103 herds were used; the total number of lactations was 2,480. Four models were considered: Wood, Wiltmink, Papajcsik & Bordero, and a second-degree polynomial. Mean square error, mean absolute error, mean square error of prediction, Akaike and Bayesian information criteria were used to select the model better describing each lactation using the majority rule, that is, the model selected by most criteria was the chosen one. The shape of each fitted lactation curve was checked using basic results from calculus which permitted the classification of the estimated curves into two groups: typical and atypical; only typical functions were used to compute the four aforementioned production parameters. Results: The second-order polynomial was the model most frequently selected, while the Papajcsik & Bordero model had the lowest frequency. Average TMY305, PMY, DP and P were 3,489.86 kg, 17.28 kg, 57.17 days, and 0.83, respectively, with coefficients of variation: 0.27, 0.21, 0.41, and 0.16. Conclusions: This study permitted to identify individuals with outstanding phenotypic performance. To the best of our knowledge, this is the first study of this kind involving thousands of lactations from Gyr cows performing in several regions of Colombian low tropic.

Antecedentes: La raza Gyr es ampliamente utilizada en las lecherías de trópico bajo en Colombia. Durante los últimos 10 años, la Asociación Colombiana de Criadores de Ganado Cebú ASOCEBU, ha liderado un programa de control lechero que generó una base de datos que permite llevar a cabo el primer análisis de producción de leche de la raza Gyr en el país, considerando individuos de varias fincas. Objetivos: Caracterizar la dinámica de producción de leche de ganado Gyr en el trópico bajo colombiano mediante la estimación de curvas de lactancia y cuatro parámetros de producción derivados: producción total entre 5 y 305 días (TMY305), pico de lactancia (PMY), días al pico (DP) y persistencia (P) Métodos: Se utilizaron 13.798 registros de producción diaria de leche de 1.510 vacas provenientes de 103 fincas, el total de lactancias fue 2.480. Se consideraron cuatro modelos: Wood, Wiltmink, Papajcsik & Bordero, y un polinomio de segundo grado. Los criterios usados para elegir el modelo que mejor describió cada lactancia fueron: error cuadrático medio, error absoluto medio, error cuadrático medio de predicción, criterio de información de Akaike y criterio de información Bayesiano. Se utilizó el criterio de mayoría, esto es, el modelo seleccionado fue aquel elegido por más criterios. La forma de cada una de las curvas de lactancia estimadas fue chequeada utilizando resultados básicos del cálculo, esto permitió clasificar las curvas estimadas en dos grupos: típicas y atípicas; solamente las curvas típicas fueron empleadas para calcular los cuatro parámetros antes mencionados. Resultados: El polinomio de segundo grado fue el modelo que se seleccionó con mayor frecuencia, mientras que el modelo Papajcsik & Bordero tuvo la menor frecuencia. Los promedios para TMY305, PMY, DP y P fueron 3.489,86 kg, 17,28 kg, 57,17 días, y 0,83, respectivamente, con coeficientes de variación 0,27, 0,21, 0,41 y 0,16 Conclusiones: Este estudio permitió identificar individuos con desempeño fenotípico sobresaliente. De acuerdo al estado del arte, este es el primer estudio de este tipo que considera miles de lactancias de vacas Gyr provenientes de varias regiones del trópico bajo colombiano.

Antecedentes: Gir é uma raça Bos indicus amplamente utilizada em sistemas de produção leiteira no trópico baixo Colombiano. A Asociación Colombiana de Criadores de Ganado CebúASOCEBU, lidera um programa de controle de leite nos últimos 10 anos, o que permitiu à coleta de um conjunto de dados para realizar a primeira análise de produção de leite em bovinos Gir no país com informações de vários rebanhos. Objetivo: Estudar a dinâmica da produção de leite por meio da estimativa das curvas de lactação e quatro parâmetros de produção derivados: produção total de leite entre 5 e 305 dias (TMY305), produção de leite no pico de lactação (PMY), dias em produção de leite no pico de lactação (DP) e persistência (P). Métodos: Foram utilizados 13.798 registros de produção diária de leite de 1.510 vacas de 103 fazendas , totalizando 2.480 lactações. Foram considerados quatro modelos: Wood, Wiltmink, Papajcsik & Bordero e um polinômio de segundo grau. Para selecionar o modelo que melhor descreve cada lactação foram utilizados os seguintes critérios: erro quadrado médio, erro absoluto médio, e erro quadrado médio de predição. O modelo selecionado pela maioria dos parâmetros, de acordo com os critérios de informação de Akaike e Bayesiano, foi o escolhido. A forma de cada curva de lactação ajustada foi verificada utilizando os resultados básicos do cálculo, isso permitiu classificar as curvas estimadas em dois grupos: típico e atípico, e apenas funções típicas foram utilizadas para calcular os quatro parâmetros de produção acima mencionados Resultados: O polinômio de segunda ordem foi o modelo mais frequentemente selecionado, enquanto o modelo Papajcsik & Bordero apresentou a menor frequência. A média de TMY305, PMY, DP e P foram 3.489,86 kg, 17,28 kg, 57,17 d e 0,83, respectivamente, com coeficientes de variação de 0,27, 0,21, 0,41 e 0,16. Conclusões: Este estudo permitiu identificar indivíduos com excelente desempenho fenotípico. De acordo com a literatura atual, este é o primeiro estudo envolvendo milhares de lactações de vacas Gir em várias regiões do trópico baixo colombiano.