Diagnosis of the risk of occupational disease among outsourced workers.

Introduction: The outsourcing of work activities has caused new and precarious working conditions, impacting the health and safety of workers, resulting in an increase in disease and accidents given the vulnerability established in the contemporary labor market. Objectives: To identify the occurrence of risk of disease resulting from work, among outsourced workers. Methods: Quantitative, observational, analytical and cross-sectional study, with application of the Inventário de Trabalho e Riscos de Adoecimento in 187 workers of a company that supplies and manages human resources for third parties, under contract with a Federal University, located in the state of Minas Gerais. Results: The Inventário de Trabalho e Riscos de Adoecimento obtained the following averages for the following factors: work organization, 2.9 (standard deviation = 0.6) (critical); working conditions, 2.4 (standard deviation = 0.7) (critical); physical cost, 3.9 (standard deviation = 0.6) (severe); physical damage, 2.1 (standard deviation = 1.3) (critical). Conclusions: The participating workers showed a good perception associated with the Inventory of Inventário de Trabalho e Riscos de Adoecimento factors, resulting in diagnoses with the presence of risks of disease and accidents resulting from work.

Introdução: A terceirização das atividades laborais tem provocado novas e precárias condições de trabalho, impactando na saúde e segurança dos trabalhadores e acarretando o aumento de adoecimentos e de acidentes, dada a vulnerabilidade estabelecida no mercado de trabalho contemporâneo. Objetivos: Identificar risco de adoecimento decorrente do trabalho entre trabalhadores terceirizados. Métodos: Estudo quantitativo, observacional, analítico, transversal com aplicação do Inventário de Trabalho e Riscos de Adoecimento a 187 trabalhadores de uma empresa de fornecimento e gestão de recursos humanos para terceiros sob contrato com uma universidade federal localizada no estado de Minas Gerais. Resultados: O Inventário de Trabalho e Riscos de Adoecimento obteve as seguintes médias para os fatores a seguir: organização do trabalho, 2,9 (desvio-padrão = 0,6) (crítico); condições de trabalho, 2,4 (desvio-padrão = 0,7) (crítico); custo físico, 3,9 (desvio-padrão = 0,6) (grave); danos físicos, 2,1 (desvio-padrão = 1,3) (crítico). Conclusões: Os participantes apresentaram boa percepção associada aos fatores do Inventário de Trabalho e Riscos de Adoecimento, resultando em diagnósticos que apresentaram risco de adoecimentos e de acidentes decorrentes do trabalho.

Enhancing genomic prediction with Stacking Ensemble Learning in Arabica Coffee.

Coffee Breeding programs have traditionally relied on observing plant characteristics over years, a slow and costly process. Genomic selection (GS) offers a DNA-based alternative for faster selection of superior cultivars. Stacking Ensemble Learning (SEL) combines multiple models for potentially even more accurate selection. This study explores SEL potential in coffee breeding, aiming to improve prediction accuracy for important traits [yield (YL), total number of the fruits (NF), leaf miner infestation (LM), and cercosporiosis incidence (Cer)] in Coffea Arabica. We analyzed data from 195 individuals genotyped for 21,211 single-nucleotide polymorphism (SNP) markers. To comprehensively assess model performance, we employed a cross-validation (CV) scheme. Genomic Best Linear Unbiased Prediction (GBLUP), multivariate adaptive regression splines (MARS), Quantile Random Forest (QRF), and Random Forest (RF) served as base learners. For the meta-learner within the SEL framework, various options were explored, including Ridge Regression, RF, GBLUP, and Single Average. The SEL method was able to predict the predictive ability (PA) of important traits in Coffea Arabica. SEL presented higher PA compared with those obtained for all base learner methods. The gains in PA in relation to GBLUP were 87.44% (the ratio between the PA obtained from best Stacking model and the GBLUP), 37.83%, 199.82%, and 14.59% for YL, NF, LM and Cer, respectively. Overall, SEL presents a promising approach for GS. By combining predictions from multiple models, SEL can potentially enhance the PA of GS for complex traits.

Genomic prediction in multi-environment trials in maize using statistical and machine learning methods.

In the context of multi-environment trials (MET), genomic prediction is proposed as a tool that allows the prediction of the phenotype of single cross hybrids that were not tested in field trials. This approach saves time and costs compared to traditional breeding methods. Thus, this study aimed to evaluate the genomic prediction of single cross maize hybrids not tested in MET, grain yield and female flowering time. We also aimed to propose an application of machine learning methodologies in MET in the prediction of hybrids and compare their performance with Genomic best linear unbiased prediction (GBLUP) with non-additive effects. Our results highlight that both methodologies are efficient and can be used in maize breeding programs to accurately predict the performance of hybrids in specific environments. The best methodology is case-dependent, specifically, to explore the potential of GBLUP, it is important to perform accurate modeling of the variance components to optimize the prediction of new hybrids. On the other hand, machine learning methodologies can capture non-additive effects without making any assumptions at the outset of the model. Overall, predicting the performance of new hybrids that were not evaluated in any field trials was more challenging than predicting hybrids in sparse test designs.

Using visual scores for genomic prediction of complex traits in breeding programs.

KEY MESSAGE: An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.

Physical exercise as a friend not a foe in acute kidney diseases through immune system modulation.

Regular and moderate exercise is being used for therapeutic purposes in treating several diseases, including cancer, cardiovascular diseases, arthritis, and even chronic kidney diseases (CKDs). Conversely, extenuating physical exercise has long been pointed out as one of the sources of acute kidney injury (AKI) due to its severe impact on the body's physiology. AKI development is associated with increased tubular necrosis, which initiates a cascade of inflammatory responses. The latter involves cytokine production, immune cell (macrophages, lymphocytes, and neutrophils, among others) activation, and increased oxidative stress. AKI can induce prolonged fibrosis stimulation, leading to CKD development. The need for therapeutic alternative treatments for AKI is still a relevant issue. In this context arises the question as to whether moderate, not extenuating, exercise could, on some level, prevent AKI. Several studies have shown that moderate exercise can help reduce tissue damage and increase the functional recovery of the kidneys after an acute injury. In particular, the immune system can be modulated by exercise, leading to a better recovery from different pathologies. In this review, we aimed to explore the role of exercise not as a trigger of AKI, but as a modulator of the inflammatory/immune system in the prevention or recovery from AKI in different scenarios. In AKI induced by ischemia and reperfusion, sepsis, diabetes, antibiotics, or chemotherapy, regular and/or moderate exercise could modulate the immune system toward a more regulatory immune response, presenting, in general, an anti-inflammatory profile. Exercise was shown to diminish oxidative stress, inflammatory markers (caspase-3, lactate dehydrogenase, and nitric oxide), inflammatory cytokines (interleukin (IL)-1b, IL-6, IL-8, and tumor necrosis factor-α (TNF-α)), modulate lymphocytes to an immune suppressive phenotype, and decrease tumor necrosis factor-ß (TGF-ß), a cytokine associated with fibrosis development. Thus, it creates an AKI recovery environment with less tissue damage, hypoxia, apoptosis, or fibrosis. In conclusion, the practice of regular moderate physical exercise has an impact on the immune system, favoring a regulatory and anti-inflammatory profile that prevents the occurrence of AKI and/or assists in the recovery from AKI. Moderate exercise should be considered for patients with AKI as a complementary therapy.

Population size in QTL detection using quantile regression in genome-wide association studies.

The aim of this study was to evaluate the performance of Quantile Regression (QR) in Genome-Wide Association Studies (GWAS) regarding the ability to detect QTLs (Quantitative Trait Locus) associated with phenotypic traits of interest, considering different population sizes. For this, simulated data was used, with traits of different levels of heritability (0.30 and 0.50), and controlled by 3 and 100 QTLs. Populations of 1,000 to 200 individuals were defined, with a random reduction of 100 individuals for each population. The power of detection of QTLs and the false positive rate were obtained by means of QR considering three different quantiles (0.10, 0.50 and 0.90) and also by means of the General Linear Model (GLM). In general, it was observed that the QR models showed greater power of detection of QTLs in all scenarios evaluated and a relatively low false positive rate in scenarios with a greater number of individuals. The models with the highest detection power of true QTLs at the extreme quantils (0.10 and 0.90) were the ones with the highest detection power of true QTLs. In contrast, the analysis based on the GLM detected few (scenarios with larger population size) or no QTLs in the evaluated scenarios. In the scenarios with low heritability, QR obtained a high detection power. Thus, it was verified that the use of QR in GWAS is effective, allowing the detection of QTLs associated with traits of interest even in scenarios with few genotyped and phenotyped individuals.

Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Fatores associados a qualidade de vida no trabalho de profissionais da saúde da atenção primária à saúde / Factors associated with the quality of life at work of health professionals in primary health care / Factores asociados a la calidad de vida en el trabajo de los profesionales de la salud en la atención primaria de salud

Objetivo: analisar os principais fatores que estão associados a qualidade de vida no trabalho de profissionais da saúde da atenção primária à saúde. Método: Estudo transversal de abordagem quantitativa, realizada com 163 trabalhadores, os quais respon- deram ao questionário Total Quality of Work Life-42 e a um outro sobre características sociodemográficas e laborais. Resultados: Dos respondentes 84% sexo feminino; 47,9% pardos; 55,2% casados; 55,8% com grau de escolaridade médio, 23,9% graduados e 15,5% possuíam pós-graduação. A renda foi observada correlação positiva entre renda e qualidade de vida, além de fatores pessoais como ser do sexo masculino, solteiro, tempo de trabalho no município (anos). Verificou-se que profissionais do sexo feminino apre- sentaram menores escores de qualidade de vida no aspecto econômico e político quando comparados aos do sexo masculino. Conclusão: Medidas devem ser tomadas para pro- mover um ambiente laboral que mantenha a qualidade de vida no trabalho e, por sua vez, favorecer a saúde física e mental dos profissionais de atenção primária à saúde.

Objective: to analyze the main factors that are associated with the quality of life at work of health professionals in primary health care. Method: Cross-sectional study with a quantitative approach, carried out with 163 workers, who answered the Total Quality of Work Life-42 questionnaire and another on sociodemographic and labor characteristics. Results: Of the respondents, 84% were female; 47.9% brown; 55.2% married; 55.8% had a high school education, 23.9% graduated and 15.5% had a postgraduate degree. Income was observed to have a positive correlation between income and quality of life, in addition to personal factors such as being male, single, working time in the municipality (years). It was found that female professionals had lower quality of life scores in the economic and political aspects when compared to males. Conclusion: Measures must be taken to promote a work environment that maintains the quality of life at work and, in turn, favors the physical and mental health of primary health care professionals.

Objetivo: analizar los principales factores que se asocian a la calidad de vida en el trabajo de los profesionales de la salud en la atención primaria de salud. Método: Estudio transversal con enfoque cuantitativo, realizado con 163 trabajadores, quienes respondieron el cuestionario de Total Quality of Work Life-42 y otro sobre características sociodemográficas y laborales. Resultados: De los encuestados, el 84% eran mujeres; 47,9% marrón; 55,2% casados; El 55,8% tenía educación secundaria, el 23,9% se graduó y el 15,5% tenía posgrado. Ingreso, se observó una correlación positiva entre ingreso y calidad de vida, además de factores personales como ser hombre, soltero, tiempo de trabajo en la ciudad (años). Se constató que las mujeres profesionales tenían puntajes de calidad de vida más bajos en los aspectos económico y político en comparación con los hombres. Conclusión: Se deben tomar medidas para promover un ambiente de trabajo que mantenga la calidad de vida en el trabajo y, a su vez, favorezca la salud física y mental de los profesionales de la atención primaria de salud.

Updating knowledge in estimating the genetics parameters: Multi-trait and Multi-Environment Bayesian analysis in rice

Among the multi-trait models selected to study several traits and environments jointly, the Bayesian framework has been a preferred tool when constructing a more complex and biologically realistic model. In most cases, non-informative prior distributions are adopted in studies using the Bayesian approach. However, the Bayesian approach presents more accurate estimates when informative prior distributions are used. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models within a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data on rice. The study involved data pertaining to rice (Oryza sativa L.) genotypes in three environments and five crop seasons (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components, genetic and non-genetic parameters were estimated using the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of individual narrow-sense heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. More informative prior distributions make it possible to detect genetic correlations between traits, which cannot be achieved with non-informative prior distributions. Therefore, this mechanism presented to update knowledge for an elicitation of an informative prior distribution can be efficiently applied in rice breeding programs.

Genome-enabled prediction through quantile random forest for complex traits / Predição genômica por meio do random forest quantílitico para características complexas

ABSTRACT: Quantile Random Forest (QRF) is a non-parametric methodology that combines the advantages of Random Forest (RF) and Quantile Regression (QR). Specifically, this approach can explore non-linear functions, determining the probability distribution of a response variable and extracting information from different quantiles instead of just predicting the mean. This evaluated the performance of the QRF in the genomic prediction for complex traits (epistasis and dominance). In addition, compare the accuracies obtained with those derived from the G-BLUP. The simulation created an F2 population with 1,000 individuals and genotyped for 4,010 SNP markers. Besides, twelve traits were simulated from a model considering additive and non-additive effects, QTL (Quantitative trait loci) numbers ranging from eight to 120, and heritability of 0.3, 0.5, or 0.8. For training and validation, the 5-fold cross-validation approach was used. For each fold, the accuracies of all the proposed models were calculated: QRF in five different quantiles and three G-BLUP models (additive effect, additive and epistatic effects, additive and dominant effects). Finally, the predictive performance of these methodologies was compared. In all scenarios, the QRF accuracies were equal to or greater than the methodologies evaluated and proved to be an alternative tool to predict genetic values in complex traits.

RESUMO: Quantile Random Forest (QRF) é uma metodologia não paramétrica, que combina as vantagens do Random Forest (RF) e da Regressão Quantílica (QR). Especificamente, essa abordagem pode explorar funções não lineares, determinando a distribuição de probabilidade de uma variável resposta e extraindo informações de diferentes quantis em vez de apenas prever a média. O objetivo deste trabalho foi avaliar o desempenho do QRF em predizer o valor genético genômico para características com arquitetura genética não aditiva (epistasia e dominância). Adicionalmente, as acurácias obtidas foram comparadas com aquelas advindas do G-BLUP. A simulação criou uma população F2 com 1.000 indivíduos genotipados para 4.010 marcadores SNP. Além disso, doze características foram simuladas a partir de um modelo considerando efeitos aditivos e não aditivos, com número de QTL (Quantitative trait loci) variando de oito a 120 e herdabilidade de 0,3, 0,5 ou 0,8. Para treinamento e validação foi usada a abordagem da validação cruzada 5-fold. Para cada um dos folds foram calculadas as acurácias de todos os modelos propostos: QRF em cinco quantis diferentes e três modelos do G-BLUP (com efeito aditivo, aditivo e epistático, aditivo e dominante). Por fim, o desempenho preditivo dessas metodologias foi comparado. Em todos os cenários, as acurácias do QRF foram iguais ou superiores às metodologias avaliadas e mostrou ser uma ferramenta alternativa para predizer valores genéticos em características complexas.

Efeito da intervenção educativa sobre o nível de conhecimento de acadêmicos de medicina, participantes de uma equipe do Serviço de Atendimento Móvel de Urgência (SAMU), em relação a avulsão de dentes permanentes / Effect of educational intervention on the level of knowledge of participants in a mobile emergency care team (SAMU) in cases of avulsion of permanent teeth

Objetivo: Avaliar o efeito de uma intervenção educativa, sobre avulsão de dentes permanentes, no nível de conhecimento de participantes de uma equipe do SAMU. Métodos: Participaram acadêmicos de Medicina que compunham a equipe do SAMU de Juiz de Fora (MG). A intervenção educativa foi realizada por meio de uma palestra com duração de 15 minutos. Para coleta dos dados foi utilizado um questionário, contendo 13 perguntas sobre avulsão dentária, aplicado em três etapas: antes da palestra (T0), imediatamente após a palestra (T1) e quinze dias após a palestra (T2). Foi realizada análise descritiva e teste McNemar para análise estatística (p < 0,05). Resultados:A amostra foi composta por 36 indivíduos. Nenhum participante relatou ter prestado atendimento a um caso de avulsão dentária. Trinta e quatro acadêmicos informaram não ter recebido orientações anteriores sobre o que fazer diante desse episódio e 35 afirmaram que esse conhecimento é necessário para o médico do SAMU. Houve aumento na porcentagem de acertos após os acadêmicos assistirem a palestra educativa (T1), para sete questões avaliadas. As mesmas questões também apresentaram maior percentual de acertos quinze dias após a palestra educativa (T2). Não houve diferença na capacidade dos acadêmicos reimplantarem um dente avulsionado tanto imediatamente após a palestra educativa (T1), quanto quinze dias após as orientações (T2) (T0-T1: p = 0,999; T0-T2: p = 0,999). Conclusão:A palestra educativa influenciou de forma significativa à melhora do conhecimento sobre avulsão dentária dos acadêmicos de Medicina que fazem parte da equipe do SAMU-JF.

Aim: To evaluate the effect of an educational intervention concerning the avulsion of permanent teeth upon the level of knowledge of members of a SAMU (Ambulance) team. Methods:Medical students who were members of the SAMU team in Juiz de Fora (MG) participated in this study. The educational intervention on the theme was carried out by means of a 15-minute lecture. Data collection was conducted using a structured questionnaire containing 13 objective questions on tooth avulsion, applied to the medical students in three stages: before the lecture (T0), immediately after the lecture (T1), and fifteen days after the lecture (T2). Descriptive analysis and the McNemar test were performed (p < 0.05). Results: The sample consisted of 36 individuals. All participants reported never having attended a dental avulsion case. Thirty-four students reported that they had not received previous guidance on what to do when faced with this type of episode, and 35 stated that information about dental avulsion is necessary for SAMU doctors. For seven of the evaluated questions, an increase was identified in the percentage of correct answers after the students attended the educational lecture (T1). The same questions also showed a higher percentage of correct answers fifteen days after the educational lecture (T2). No difference was found in the students' ability to reimplant an avulsed tooth either immediately after the educational lecture (T1) or fifteen days after receiving guidance on the subject (T2) (T0-T1: p = 0.999; T0-T2: p = 0.999). Conclusion: The educational lecture significantly influenced the improvement of knowledge about dental avulsion among medical students who are members of the SAMU-JF team.

Determination of optimal number of independent components in yield traits in rice

The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

Pacientes trans e cuidado multiprofissional em centros de atendimento psicossocial / Trans patients and multiprofessional care within psychosocial care centers / Pacientes trans y atenciónmultiprofesionalen centros de atenciónpsicosocia

Considerando a discriminação social contra pessoas trans como um determinante para o sofrimento psíquico, este trabalho objetiva analisar a assistência à saúde mental da população trans em dois Centros de Atenção Psicossocial (CAPS) de uma capital brasileira. Entrevistas com nove trabalhadores da equipe multiprofissional possibilitaram identificar práticas, crenças e percepções que permeiam este cuidado. A interpretação dos relatos foi pautada pela análise hermenêutico-dialética. Identifica-se que, mesmo as equipes compreendendo as demandas de saúde mental de pacientes trans como atreladas à discriminação, à violência e à marginalização, são remanescentes compreensões arraigadas em estereótipos, tal como a de que equidade para pacientes trans implicaria privilégio. Consoante à literatura sobre acesso de pessoas trans à saúde pública, o nome social também se afirmou como prática de acolhimento fundamental, porém, insuficiente quando dissociada do maior preparo das equipes nas etapas subsequentes ao atendimento. Sugere-se que a efetividade do cuidado trilha pela garantia do processo terapêutico humanizado, considerando iniquidades sociais enfrentadas (como preconiza o Sistema Único de Saúde) por pacientes trans.

Considering social discrimination against transgender people as a determinant for psychological suffering, this work analyzes the mental health assistance of the trans population in two Psychosocial Care Centers in a Brazilian capital. Interviews with nine workers from the multiprofessional team made possible identify practices, beliefs and perceptions that permeate this care. The interpretation was based on the hermeneutic-dialectic analysis. It is identified that, even though teams understanding the mental health demands of trans patients as linked to discrimination, violence and marginalization, understandings that remain rooted in stereotypes remain, such as that equity for trans patients would imply privilege. Depending on the literature about transgender people and public health, the social name also asserted it self as a welcoming practice, however, insufficient when dissociated from the greater preparation in the subsequent stages of care. It is suggested that the effectiveness of care leads to the guarantee of the humanized therapeutic process, considering social inequities faced (as recommended by SUS) by trans patients.(AU)

Considerando la discriminación social de las personas trans como factor determinante del sufrimiento psicológico, este trabajo analisa la asistencia de salud mental de la población transen dos Centros de Atención Psicosocial en una capital brasileña. Nueve entrevistas com el equipo multiprofesional an identificado prácticas, creencias y percepciones que impregnan esta atención. La interpretación se basó em el análisis hermenéutico-dialéctico. Se identifica que, a pesar de que los equipos entienden las demandas de salud mental de los pacientes trans como vinculados ala discriminación, la violencia y la marginación, los entendimientos enraizados em los estereótipos permanecen, como que la equidad para los pacientes trans implicaría un privilegio. El nombre social también se afirmó como una práctica bienvenida, sin embargo, insuficiente cuando se disocia de la preparación em las etapas posteriores. Se sugiere que la efectividad de la atención depende de lagarantía del processo terapéutico humanizado, considerando las desigualdades sociales que enfrentan (según recomenda el SUS) por los pacientes trans.

Quantile regression in genomic selection for oligogenic traits in autogamous plants: A simulation study.

This study assessed the efficiency of Genomic selection (GS) or genome-wide selection (GWS), based on Regularized Quantile Regression (RQR), in the selection of genotypes to breed autogamous plant populations with oligogenic traits. To this end, simulated data of an F2 population were used, with traits with different heritability levels (0.10, 0.20 and 0.40), controlled by four genes. The generations were advanced (up to F6) at two selection intensities (10% and 20%). The genomic genetic value was computed by RQR for different quantiles (0.10, 0.50 and 0.90), and by the traditional GWS methods, specifically RR-BLUP and BLASSO. A second objective was to find the statistical methodology that allows the fastest fixation of favorable alleles. In general, the results of the RQR model were better than or equal to those of traditional GWS methodologies, achieving the fixation of favorable alleles in most of the evaluated scenarios. At a heritability level of 0.40 and a selection intensity of 10%, RQR (0.50) was the only methodology that fixed the alleles quickly, i.e., in the fourth generation. Thus, it was concluded that the application of RQR in plant breeding, to simulated autogamous plant populations with oligogenic traits, could reduce time and consequently costs, due to the reduction of selfing generations to fix alleles in the evaluated scenarios.

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.

Genomic prediction of leaf rust resistance to Arabica coffee using machine learning algorithms

Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to use DT and its refinements for predicting resistance to orange rust in Arabica coffee. Additionally, DT and its refinements were used to identify the importance of markers related to the characteristic of interest. The results were compared with those from GBLASSO and ANN. Data on coffee rust resistance of 245 Arabica coffee plants genotyped for 137 markers were used. The DT refinements presented equal or inferior values of Apparent Error Rate compared to those obtained by DT, GBLASSO, and ANN. Moreover, DT refinements were able to identify important markers for the characteristic of interest. Out of 14 of the most important markers analyzed in each methodology, 9.3 markers on average were in regions of quantitative trait loci (QTLs) related to resistance to disease listed in the literature.(AU)

α-Synuclein Overexpression Induces Lysosomal Dysfunction and Autophagy Impairment in Human Neuroblastoma SH-SY5Y.

Although the etiology of Parkinson's disease (PD) is multifactorial, it has been linked to abnormal accumulation of α-synuclein (α-syn) in dopaminergic neurons, which could lead to dysfunctions on intracellular organelles, with potential neurodegeneration. Patients with familial early-onset PD frequently present mutation in the α-syn gene (SNCA), which encodes mutant α-syn forms, such as A30P and A53T, which potentially regulate Ca2+ unbalance. Here we investigated the effects of overexpression of wild-type α-syn (WT) and the mutant forms A30P and A53T, on modulation of lysosomal Ca2+ stores and further autophagy activation. We found that in α-syn-overexpressing cells, there was a decrease in Ca2+ released from endoplasmic reticulum (ER) which is related to the increase in lysosomal Ca2+ release, coupled to lysosomal pH alkalization. Interestingly, α-syn-overexpressing cells showed lower LAMP1 levels, and a disruption of lysosomal morphology and distribution, affecting autophagy. Interestingly, all these effects were more evident with A53T mutant isoform when compared to A30P and WT α-syn types, indicating that the pathogenic phenotype for PD is potentially related to impairment of α-syn degradation. Taken together, these events directly impact PD-related dysfunctions, being considered possible molecular targets for neuroprotection.

When tongue strength exercises reflect in the cervical region. / Quando exercícios de fortalecimento da língua refletem na região cervical.

PURPOSE: To compare the impact of isokinetic exercise (tongue suction on the palate) in the cervical region of Class I and Class II / 2nd Division participants, considering the average and the symmetry of Root Means Square (RMS) of suprahyoid and suboccipital muscles and cervical sensory reports. METHOD: 11 participants Class I and 19 Class II / 2nd Division, both genders, mean age 33.4 ± 14.1 years. For the analysis of RMS average and symmetry, electromyography was performed in the suboccipital and suprahyoid muscles, bilaterally, at rest and suction of water in the initial, intermediate and final phases. The cervical sensation was evaluated qualitatively during the exercises. RESULTS: the mean RMS did not differ between Classes (p=0.7), but showed an increase in the intermediate phase in the suboccipital musculature (p=0.0001) and decrease in the suprahyoid musculature. In symmetry, the suprahyoid musculature showed a significant difference between classes (p=0.0001) during the intermediate phase. In the Class I participant the symmetry was reestablished in the final phase, a fact that did not occur in Class II / 2nd Division. Regarding the cervical sensation, only the Class II / 2nd Division had expressive complaints. CONCLUSION: The Isokinetic suctioning exercise of the tongue against the palate, had an expressive repercussion with reports of discomfort and neck pain in the Class II / 2nd Division participants. On average RMS, there was no difference between the classes, but in the intermediate phase, the suboccipital muscles showed a significant increase in the activity. Symmetry in the suprahyoid musculature had a significant difference between the classes and asymmetry in the intermediate phase.

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.(AU)

Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.(AU)

Nonlinear quantile regression to describe the dry matter accumulation of garlic plants / Regressão quantílica não linear para descrever o acúmulo de matéria seca de alho

ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of fit of the models. Accessions were grouped using the UPGMA algorithm, with the estimates of the parameters with biological interpretation as variables. The nonlinear quantile regression is efficient for the adjustment of models for dry matter accumulation in garlic plants over time. The estimated parameters are more uniform and robust in the presence of asymmetry in the distribution of the data, heterogeneous variances, and outliers.

RESUMO: Este trabalho teve como objetivo ajustar modelos de regressão quantílica não linear para o estudo do acúmulo de matéria seca total em plantas de alho ao longo do tempo, e compará-los com modelos ajustados pelo método dos mínimos quadrados. A matéria seca total de nove acessos de alho pertencentes ao Banco de Germoplasma de Hortaliças da Universidade Federal de Viçosa (BGH/UFV) foi avaliada em quatro períodos (60, 90, 120 e 150 dias após plantio), e estes valores foram utilizados para o ajuste de modelos de regressão - não linear - logística. Para cada acesso, foram ajustados um modelo de regressão quantílica (τ=0,5) e um modelo pela metodologia dos mínimos quadrados. Para avaliar a qualidade de ajuste dos modelos foi utilizado o Critério de Informação de Akaike. Os acessos foram agrupados pelo algoritmo UPGMA, utilizando as estimativas dos parâmetros com interpretação biológica como variáveis. A regressão quantílica não linear foi eficiente no ajuste de modelos para descrição do acúmulo de matéria seca ao longo do tempo. As estimativas de parâmetros foram mais uniformes e robustas na presença de dados assimétricos, variâncias heterogêneas e de valores discrepantes.