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Background: Unintentional exposure to medications is a noted problem in pediatric populations despite the prevalent use of child-resistant (CR) packaging and educational campaigns informing consumers about appropriate storage. Objective: Conduct a proof-of concept study that evaluates how package designs that engage the attention of children in meaningless ways affect opening time and number of openings. Study design: Non-CR vials with or without distracters were provided to 108 children (24–51 months) in pairs. Each participant was handed a vial and instructed to “do whatever you want to with it.” Successful opening and time to opening were recorded. Data were analyzed using generalized linear mixed models. Results: Older children were approximately four times more likely than younger children to successfully open a vial with a visual distracter (P = 0.049); when distracters were not present, no evidence for differences was apparent between age groups (P = 0.64). For successful openings of either age group, distracter presence significantly prolonged time to opening (P = 0.0375); vials containing distracters took nearly three times longer to open than those without. Conclusions: Existing CR designs almost exclusively rely on late stages of information processing (e.g. difficult to understand or open). Our results suggest that packaging designs that target early stage processing (i.e. perception) represent a potential paradigm for creating effective CR designs. It should be acknowledged that visual distracters, by their very nature, have the potential to act as "attractive nuisances" (i.e. if it were to be so effective that it drew children to the hazard). Further studies designed to specifically investigate this possibility are advised. [ABSTRACT FROM AUTHOR]

Assessing geographic patterns of species richness is essential to develop biological conservation as well as to understand the processes that shape these patterns. We aim to improve geographic prediction of tree species richness (TSR) across eastern USA by using: 1) gridded point-sample data rather than spatially generalized range maps for the TSR outcome variable, 2) new predictor variables (forest area FA; mean frost day frequency MFDF) and 3) regression models that account for spatial autocorrelation. TSR was estimated in 50 km by 50 km grids using Forest Inventory and Analysis (FIA) point-sample data. Eighteen environmental predictor variables were employed, with the most effective set selected by a LASSO that reduced multicollinearity. Those predictors were then employed in Generalized linear models (GLMs), and in Eigenvector spatial filtering (ESF) models that accounted for spatial autocorrelation. Models were evaluated by model fit statistics, spatial patterns of TSR predictions, and spatial autocorrelation. Our results showed gridded TSR was best-predicted by the ESF model that used, in descending order of influence: precipitation seasonality, mean precipitation in the driest quarter, FA, and MFDF. ESF models, by accounting for spatial autocorrelation, outperformed GLMs regardless of the predictors employed, as indicated by percent deviance explained and spatial autocorrelation of residuals. Small regions with low TSR, such as the Midwest prairie peninsula, were successfully predicted by ESF models, but not by GLMs or other studies. Gridded TSR in Florida was only correctly predicted by the ESF model with FA and MFDF, and was over-predicted by all other models. [ABSTRACT FROM AUTHOR]

The rate of lexical replacement estimates the diachronic stability of word forms on the basis of how frequently a proto-language word is replaced or retained in its daughter languages. Lexical replacement rate has been shown to be highly related to word class and word frequency. In this paper, we argue that content words and function words behave differently with respect to lexical replacement rate, and we show that semantic factors predict the lexical replacement rate of content words. For the 167 content items in the Swadesh list, data was gathered on the features of lexical replacement rate, word class, frequency, age of acquisition, synonyms, arousal, imageability and average mutual information, either from published databases or gathered from corpora and lexica. A linear regression model shows that, in addition to frequency, synonyms, senses and imageability are significantly related to the lexical replacement rate of content words–in particular the number of synonyms that a word has. The model shows no differences in lexical replacement rate between word classes, and outperforms a model with word class and word frequency predictors only. [ABSTRACT FROM AUTHOR]