Early childhood interventions have the potential to promote long-term healthy eating and physical activity habits to prevent obesity. However, research studies including indigenous young children are lacking. This study examined the effectiveness of the Food Friends®: Fun with New Foods™ and Get Movin’ with Mighty Moves™ (FFMM) curricula on willingness to try fruits and vegetables (FV) and gross motor (GM) skills among preschoolers in Guam. A pre-post community-based study included preschoolers from Head Start (HS), gifted and talented education (Pre-GATE), and Pre-Kindergarten programs during school years (SY) 2017–2018 and 2018–2019. In SY2017–2018, the intervention group had a significant increase in imported FV when compared with the other three groups. No significant differences between groups were found on the other FV scales. Regarding gross motor skills, no significant differences between groups were found. In SY2018–2019, the intervention group had a significant increase in all FV scales except imported FV when compared with the enhanced intervention group. With gross motor skills, no significant differences were found between groups on its progress. These results warrant FFMM adaptations for the prevention of obesity among Guam preschoolers.
Since the emergence of the Silk Road market in the early 2010s, dark web 'cryptomarkets' have proliferated and offered people an online platform to buy and sell illicit drugs, relying on cryptocurrencies such as Bitcoin for anonymous transactions. However, recent studies have highlighted the potential for de-anonymization of bitcoin transactions, bringing into question the level of anonymity afforded by cryptomarkets. We examine a set of over 100,000 product reviews from several cryptomarkets collected in 2018 and 2019 and conduct a comprehensive analysis of the markets, including an examination of the distribution of drug sales and revenue among vendors, and a comparison of incidences of opioid sales to overdose deaths in a US city. We explore the potential for de-anonymization of vendors by implementing a Naïve-Bayes classifier to predict the vendor from a given product review, and attempt to link vendors' sales to specific Bitcoin transactions. On the buyer side, we evaluate the efficacy of hierarchical agglomerative clustering for grouping together transactions corresponding to the same buyer. We find that the high degree of specialization among the small subset of high-revenue vendors may render these vendors susceptible to de-anonymization. Further research is necessary to confirm these findings, which are restricted by the scarcity of ground-truth data for validation.
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