Modelling the influence of environmental parameters over marine planktonic microbial communities using artificial neural networks.

Guanabara Bay is a tropical estuarine ecosystem that receives massive anthropogenic impacts from the metropolitan region of Rio de Janeiro. This ecosystem suffers from an ongoing eutrophication process that has been shown to promote the emergence of potentially pathogenic bacteria, giving rise to public health concerns. Although previous studies have investigated how environmental parameters influence the microbial community of Guanabara Bay, they often have been limited to small spatial and temporal gradients and have not been integrated into predictive mathematical models. Our objective was to fill this knowledge gap by building models that could predict how temperature, salinity, phosphorus, nitrogen and transparency work together to regulate the abundance of bacteria, chlorophyll and Vibrio (a potential human pathogen) in Guanabara Bay. To that end, we built artificial neural networks to model the associations between these variables. These networks were carefully validated to ensure that they could provide accurate predictions without biases or overfitting. The estimated models displayed high predictive capacity (Pearson correlation coefficients ≥0.67 and root mean square error ≤ 0.55). Our findings showed that temperature and salinity were often the most important factors regulating the abundance of bacteria, chlorophyll and Vibrio (absolute importance ≥5) and that each of these has a unique level of dependence on nitrogen and phosphorus for their growth. These models allowed us to estimate the Guanabara Bay microbiome's response to changes in environmental conditions, which allowed us to propose strategies for the management and remediation of Guanabara Bay.

Sleep and behavioral/emotional problems in children: a population-based study.

BACKGROUND: The potential relationships between sleep-wake behaviors and emotional/disruptive problems in otherwise healthy school-aged children are unclear. METHODS: A parental questionnaire was developed for the epidemiologic survey of children's sleep and wake behavioral patterns. The questions covered a wide range of features including sleep length (school days, weekends), time to fall asleep, night awakenings, bedtime and nighttime sleep-related behaviors, daytime sleepiness, irritability, and tiredness. To assess psychiatric symptomatology, the Rutter Scale B2 was completed by teachers. In addition to the total score, sub-scores of emotional, hyperactivity, and conduct problems were obtained. The representative population sample comprised 779 children (403 girls), with an age range of 6-11 years. RESULTS: Hyperactivity and conduct problems at school in boys were both associated with parental reports of bedtime resistance. Hyperactivity was also associated with longer sleep duration during weekends. Conduct and emotional problems in girls were associated with earlier bedtime during school days. Emotional problems in girls were also associated with longer sleep durations in school days and weekends. CONCLUSION: Bedtime resistance was the only sleep behavior associated with either hyperactivity or conduct problems in children, and longer sleep durations appear to occur more frequently in children with both hyperactive or emotional problems. Information about good sleep hygiene at bedtime may help parents setting sleep limits.