Problemas de saúde mental em estudantes de medicina afetam o desempenho acadêmico, levam ao abandono e à avaliação negativa do curso e influenciam a vida profissional futura. Objetivou-se estimar a ...prevalência de problemas de saúde mental e a percepção de qualidade de vida, segundo ciclos do curso. Realizou-se um inquérito epidemiológico, no qual os 1.980 estudantes de graduação em medicina da Universidade Federal de Minas Gerais foram convidados. Em 2018, os estudantes responderam o questionário online (Google Forms), contendo informações sociodemográficas, relacionadas ao curso, comportamentos, saúde mental e qualidade de vida. Realizou-se análise descritiva e comparativa, de acordo com os ciclos (Básico, Teórico-prático e Clínico), por meio do Qui-quadrado de Pearson e ANOVA. A taxa de resposta foi de 74,2% (n=1.470). Depressão (42,8%), ansiedade (19,4%), alterações no sono (70,0%) e pior percepção de qualidade de vida foram mais prevalentes no ciclo Básico; consumo de álcool e de ansiolíticos nos ciclos Teórico-prático (88,3%; 23,5%) e Clínico (86,5%; 27,9%). Maiores prevalências de problemas de saúde mental e pior percepção de qualidade de vida nos dois primeiros anos do curso médico mostram a necessidade de ofertar apoio psicológico aos recém-chegados à universidade, além de melhorar o ambiente educacional.
A mangaba (Hancornia speciosa) é um fruto característico da região Nordeste do Brasil e apresenta excelentes atributos sensoriais e nutritivos, sendo também utilizado na industrialização, sob a forma ...de doces, geleias, compotas, vinho, vinagre, suco e sorvete. Esta pesquisa objetiva otimizar o processo de desidratação osmótica (DO) da mangaba ao analisar a influência de diferentes concentrações de solução osmótica (C) em diferentes períodos de imersão (T) do fruto, sobre as respostas perda de água (PA) e ganho de sólidos (GS), a fim de estabelecer condições ótimas para a melhor conservação do fruto. Para o desenvolvimento da pesquisa, os frutos foram adquiridos no estádio de maturação maduro de um fornecedor local da região de Salvaterra (Pará) e transportados para o Laboratório de Tecnologia de Alimentos (UEPA – Campus Salvaterra), onde se iniciaram os procedimentos de desidratação osmótica. A relação amostra/solução foi de 1 g do fruto para cada 5 mL da solução osmótica, sendo a umidade determinada antes e após o processo. Para avaliar os efeitos das variáveis independentes (C e T), foi utilizado um delineamento composto central rotacional (DCCR) do tipo 22, totalizando 11 ensaios experimentais. As análises estatísticas dos coeficientes de regressão e a análise de variância (ANOVA) foram utilizados para avaliar o grau de ajuste dos modelos propostos aos dados experimentais. As superfícies de respostas apontaram que maiores concentrações de solução osmótica (C) e maiores tempos de imersão (T) favoreceram as maiores PA e GS no fruto. Os resultados obtidos para a otimização simultânea das respostas definiu como condições ótimas para o processo: C = 56 % e T = 55,18 min, visando a maximização da PA e minimização do GS.
Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within ...biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.
Chronic stress could trigger maladaptive changes associated with stress-related mental disorders; however, the underlying mechanisms remain elusive. In this study, we found that exposing juvenile ...male rats to repeated stress significantly impaired the temporal order recognition memory, a cognitive process controlled by the prefrontal cortex (PFC). Concomitantly, significantly reduced AMPAR- and NMDAR-mediated synaptic transmission and glutamate receptor expression were found in PFC pyramidal neurons from repeatedly stressed animals. All these effects relied on activation of glucocorticoid receptors and the subsequent enhancement of ubiquitin/proteasome-mediated degradation of GluR1 and NR1 subunits, which was controlled by the E3 ubiquitin ligase Nedd4-1 and Fbx2, respectively. Inhibition of proteasomes or knockdown of Nedd4-1 and Fbx2 in PFC prevented the loss of glutamatergic responses and recognition memory in stressed animals. Our results suggest that repeated stress dampens PFC glutamatergic transmission by facilitating glutamate receptor turnover, which causes the detrimental effect on PFC-dependent cognitive processes.
► Repeated stress impairs PFC-dependent recognition memory ► Repeated stress depresses PFC glutamatergic transmission in juvenile male rats ► Repeated stress increases GluR1 and NR1 ubiquitination and degradation in PFC ► Nedd4 and Fbx2 are the E3 ubiquitin ligases involved in the stress effects in PFC
Yuen et al. reveal that exposing juvenile male rats to repeated stress facilitates ubiquitin/proteasome-mediated degradation of glutamate receptor subunits. This leads to significant dampening of glutamatergic transmission in prefrontal cortex (PFC) and detrimental effects on PFC-dependent cognitive processes.
•Both methods are equally effective for developing and analyzing the structural relationship.•CB-SEM demands a lot from the data, whereas PLS-SEM is quite lenient.•For a factor-based model, CB-SEM ...should be used.•For a composite-based model, PLS-SEM should be considered.•CB-SEM and PLSc-SEM methods provide almost similar results.
This study compares the two widely used methods of Structural Equation Modeling (SEM): Covariance based Structural Equation Modeling (CB-SEM) and Partial Least Squares based Structural Equation Modeling (PLS-SEM). The first approach is based on covariance, and the second one is based on variance (partial least squares). It further assesses the difference between PLS and Consistent PLS algorithms. To assess the same, empirical data is used. Four hundred sixty-six respondents from India, Saudi Arabia, South Africa, the USA, and few other countries are considered. The structural model is tested with the help of both approaches. Findings indicate that the item loadings are usually higher in PLS-SEM than CB-SEM. The structural relationship is closer to CB-SEM if a consistent PLS algorithm is undertaken in PLS-SEM. It is also found that average variance extracted (AVE) and composite reliability (CR) values are higher in the PLS-SEM method, indicating better construct reliability and validity. CB-SEM is better in providing model fit indices, whereas PLS-SEM fit indices are still evolving. CB-SEM models are better for factor-based models like ours, whereas composite-based models provide excellent outcomes in PLS-SEM. This study contributes to the existing literature significantly by providing an empirical comparison of all the three methods for predictive research domains. The multi-national context makes the study relevant and replicable universally. We call for researchers to revisit the widely used SEM approaches, especially using appropriate SEM methods for factor-based and composite-based models.
•When the numbers of samples and folds are both large enough, we proved that CV variance and its accuracy have the quadratic relationship.•The relationships between CV variance and its factors have ...been derived, allowing to predict which variance is less before applying k-fold CV.•Theoretical explanations have been given for some empirical evidences of Rodriguez and Kohavi from the respect of variance analysis.•The proposed normalized variance has significant correlation with the error and is unrelated to k so that it can serve as a stable error measurement.
Cross-validation (CV) is often used to estimate the generalization capability of a learning model. The variance of CV error has a considerable impact on the accuracy of CV estimator and the adequacy of the learning model, so it is very important to analyze CV variance. The aim of this paper is to investigate how to improve the accuracy of the error estimation based on variance analysis. We first describe the quantitative relationship between CV variance and its accuracy, which can provide guidance for improving the accuracy by reducing the variance. We then study the relationships between variance and relevant variables including the sample size, the number of folds, and the number of repetitions. These form the basis of theoretical strategies of regulating CV variance. Our classification results can theoretically explain the empirical results of Rodríguez and Kohavi. Finally, we propose a uniform normalized variance which not only measures model accuracy but also is irrelative to fold number. Therefore, it simplifies the selection of fold number in k-fold CV and normalized variance can serve as a stable error measurement for model comparison and selection. We report the results of experiments using 5 supervised learning models and 20 datasets. The results indicate that it is reliable to determine which variance is less before k-fold CV by the proposed theorems, and thus the accuracy of error estimation can be promoted by reducing variance. In so doing, we are more likely to select the best parameter or model.
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