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This article is about the 21st Century Skills that can be enhanced through integrated STEM education. The TRAILS project was featured as a model of integrated STEM curriculum at the high school level to promote integrated STEM and 21st century skills. 

A recent experiment on zebrafish blastoderm morphogenesis showed that the viscosity (η) of a non-confluent embryonic tissue grows sharply until a critical cell packing fraction (ϕ_S). The increase inηup toϕ_Sis similar to the behavior observed in several glass-forming materials, which suggests that the cell dynamics is sluggish or glass-like. Surprisingly,ηis a constant aboveϕ_S. To determine the mechanism of this unusual dependence ofηonϕ, we performed extensive simulations using an agent-based model of a dense non-confluent two-dimensional tissue. We show that polydispersity in the cell size, and the propensity of the cells to deform, results in the saturation of the available free area per cell beyond a critical packing fraction. Saturation in the free space not only explains the viscosity plateau aboveϕ_Sbut also provides a relationship between equilibrium geometrical packing to the dramatic increase in the relaxation dynamics. 

Though historically understudied, due in large part to most species being uncultivable, microbial eukaryotes (i.e. protists) are abundant and widespread across diverse habitats. Recent advances in molecular techniques, including metabarcoding, allow for the characterization of poorly known protist lineages. This study surveys the diversity of SAR (Stramenopila, Alveolata, and Rhizaria), a major eukaryotic clade that is estimated to represent about half of all eukaryotic diversity. SAR lineages use varied metabolic strategies like mixotrophy in dinoflagellates (Alveolata), parasitism in apicomplexans (Alveolata) and labyrinthulids (Stramenopila), and life cycle stages that include encystment and attachment (e.g. in ciliates, Alveolata) to survive in highly dynamic habitats. Using metabarcoding primers designed specifically to target a portion of the 18S small subunit ribosomal RNA (SSU-rRNA) gene of SAR lineages, we compare protist community composition from tide pools in Acadia National Park, Maine, USA. We characterize over 500 lineages, here operational taxonomic units (OTUs), many of which are found abundant in the tide pool environment. We also find that communities vary by month sampled and exhibit patterns by size (i.e. macro-, micro-, and nano-sized). Taken together, these data allow us to further catalog protist diversity in extreme environments (e.g. those subject to extreme fluctuations in temperature and salinity during tidal cycles). Such data are critical in the explorations of biodiversity patterns among microorganisms on our rapidly changing planet. 

Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become “inapplicable” across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.