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Hybridisation is a source of genetic diversity, can drive adaptation to new niches and has been found to be a frequent event in lineages harbouring pathogenic fungi. However, little is known about the genomic implications of hybridisation nor its impact on pathogenicity-related traits. A common limitation for addressing these questions is the narrow representativity of sequenced genomes, mostly corresponding to strains isolated from infected patients.

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Food insecurity (FI), the lack of access to adequate food, is linked with negative health and psychological outcomes. FI is typically measured retrospectively over the last year; although this measurement is useful to understand FI prevalence to inform broad policy, it leaves the experience of FI in everyday life poorly understood. Understanding how FI varies across shorter periods of time (days or weeks) can help inform FI prevention and/or intervention.

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Socioeconomic status (SES) is associated with well-being outcomes across studies; however, there is wide variation in its measurement, particularly in adolescence. One key difference in measures of SES concerns whether participants relay objective information-for example, years of education, household income-or subjective perceptions of socioeconomic status, either with or without reference to others or society. Although parents are often considered the best source of SES information-especially objective SES-within families, interviewing parents within the context of adolescent research is costly, time-consuming, and not always feasible.

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Background: Early menarche is associated with both physical and psychosocial problems. Based on psychological and physical health considerations, for girls with early menarche, some parents and physicians may elect to use gonadotropin-releasing hormone agonists (GnRHa) to delay menstruation. This study aimed to explore the effects of GnRHa treatment on the final height of girls with early menarche and build the models to predict the final adult height (FAH).

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Background: To evaluate the performance of different prediction models based on machine learning to predict the presence of early childhood caries.

Material And Methods: Cross-sectional analytical study. The sociodemographic and clinical data used came from a sample of 186 children aged 3 to 6 years and their respective parents or guardians treated at a Hospital in Ica, Peru.

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