An understanding of ctenophore biology is critical for reconstructing events that occurred early in animal evolution. Towards this goal, we have sequenced, assembled, and annotated the genome of the ctenophore Mnemiopsis leidyi. Our phylogenomic analyses of both amino acid positions and gene content suggests that ctenophores rather than sponges are the sister lineage to all other animals. Mnemiopsis lacks many of the genes found in bilaterian mesodermal cell types, suggesting that these cell types evolved independently. The set of neural genes in Mnemiopsis is similar to that of sponges, indicating that sponges may have lost a nervous system. These results present a new view of early animal evolution that accounts for major losses and/or gains of sophisticated cell types, including nerve and muscle cells.

BackgroundThroughout evolution, the LIM domain has been deployed in many different domain configurations, which has led to the formation of a large and distinct group of proteins. LIM proteins are involved in relaying stimuli received at the cell surface to the nucleus in order to regulate cell structure, motility, and division. Despite their fundamental roles in cellular processes and human disease, little is known about the evolution of the LIM superclass.ResultsWe have identified and characterized all known LIM domain-containing proteins in six metazoans and three non-metazoans. In addition, we performed a phylogenetic analysis on all LIM domains and, in the process, have identified a number of novel non-LIM domains and motifs in each of these proteins. Based on these results, we have formalized a classification system for LIM proteins, provided reasonable timing for class and family origin events; and identified lineage-specific loss events. Our analysis is the first detailed description of the full set of LIM proteins from the non-bilaterian species examined in this study.ConclusionSix of the 14 LIM classes originated in the stem lineage of the Metazoa. The expansion of the LIM superclass at the base of the Metazoa undoubtedly contributed to the increase in subcellular complexity required for the transition from a unicellular to multicellular lifestyle and, as such, was a critically important event in the history of animal multicellularity.

Individuals do not respond uniformly to treatments, such as events or interventions. Sociologists routinely partition samples into subgroups to explore how the effects of treatments vary by selected covariates, such as race and gender, on the basis of theoretical priors. Data-driven discoveries are also routine, yet the analyses by which sociologists typically go about them are often problematic and seldom move us beyond our biases to explore new meaningful subgroups. Emerging machine learning methods based on decision trees allow researchers to explore sources of variation that they may not have previously considered or envisaged. In this article, the authors use tree-based machine learning, that is, causal trees, to recursively partition the sample to uncover sources of effect heterogeneity. Assessing a central topic in social inequality, college effects on wages, the authors compare what is learned from covariate and propensity score–based partitioning approaches with recursive partitioning based on causal trees. Decision trees, although superseded by forests for estimation, can be used to uncover subpopulations responsive to treatments. Using observational data, the authors expand on the existing causal tree literature by applying leaf-specific effect estimation strategies to adjust for observed confounding, including inverse propensity weighting, nearest neighbor matching, and doubly robust causal forests. We also assess localized balance metrics and sensitivity analyses to address the possibility of differential imbalance and unobserved confounding. The authors encourage researchers to follow similar data exploration practices in their work on variation in sociological effects and offer a straightforward framework by which to do so.