Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models.

A reliable and practical determination of a chemical species' solubility in water continues to be examined using empirical observations and exhaustive experimental studies alone. Predictions of chemical solubility in water using data-driven algorithms can allow us to create a rationally designed, efficient, and cost-effective tool for next-generation materials and chemical formulations. We present results from two machine learning (ML) modeling studies to adequately predict various species' solubility using data for over 8400 compounds.

New geographic records for in coyotes and moose from Nova Scotia, Canada.

spp. tapeworms can cause serious diseases in mammals, including humans. Within the species complex, metacestodes produce unilocular cysts that are responsible for cystic echinococcosis in animal intermediate hosts.

Examining the relations between spatial skills and mathematical performance: A meta-analysis.

Much recent research has focused on the relation between spatial skills and mathematical skills, which has resulted in widely reported links between these two skill sets. However, the magnitude of this relation is unclear. Furthermore, it is of interest whether this relation differs in size based on key demographic variables, such as gender and grade-level, and the extent to which this relation can be accounted for by shared domain-general reasoning skills across the two domains.