RESUMO
Self-actualization is a complex psychological construct within Maslow's motivation theory, characterized by numerous gaps in the empirical and measurement spectrums. Therefore, the objectives of this study are to develop, validate, and cross-verify measures for self-actualization attributes and B-values, focusing on job context and theoretical congruence with innovative behavior and human values related to the self-actualization construct (suprapersonal subfunction). This study involved a diverse sample of 621 Brazilian participants from 25 different professions, indicating the broad applicability of the findings. The proposed instruments underwent content and semantic validity assessments, followed by verification of factor validity and internal consistency. Results showed satisfactory content, semantic and factor validity and internal consistency parameters. The study reveals that self-actualization attributes can be understood through achieving one's own potential and work meta-motivation, consistent with the adoption of B-values. Relationships with suprapersonal values (maturity, knowledge, and beauty) and innovative work behavior were also demonstrated, suggesting convergent validity evidence. The validation of SAAS and BVI contributes to understanding self-actualization and B-values in varied Brazilian contexts, offering insights for psychological assessment and intervention.
Assuntos
Motivação , Humanos , Brasil , Feminino , Masculino , Adulto , Inquéritos e Questionários , Pessoa de Meia-Idade , Adulto Jovem , Autoimagem , Reprodutibilidade dos Testes , Psicometria/métodosRESUMO
BACKGROUND: MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. RESULTS: By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. CONCLUSIONS: The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.