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One of the first organizing processes during animal development is the assembly of embryonic cells into epithelia. Common features unite epithelialization across select bilaterians, however, we know less about the molecular and cellular mechanisms that drive epithelial emergence in early branching nonbilaterians. In sea anemones, epithelia emerge both during embryonic development and after cell aggregation of dissociated tissues. Although adhesion is required to keep cells together, it is not clear whether cell polarization plays a role as epithelia emerge from disordered aggregates. Here, we use the embryos of the sea anemoneNematostella vectensisto investigate the evolutionary origins of epithelial development. We demonstrate that lateral cell polarization is essential for epithelial organization in both embryos and aggregates. With disrupted lateral polarization, cell contact in the aggregate is not sufficient to trigger epithelialization and further tissue development. Specifically, knockdown of the conserved lateral polarity and tumor suppressor protein Lethal giant larvae (Lgl) disrupts epithelia in developing embryos and impairs the capacity of dissociated cells to epithelialize from aggregates. In contrast to other systems, cells inNematostella lglknockdown embryos do not undergo excessive proliferation. Cells with reduced Lgl levels lose their columnar shape and proper positioning of their mitotic spindles and basal bodies. Due to misoriented divisions and aberrant shapes, cells arrange nonuniformly without forming a monolayer. Together our data show that, inNematostella,Lgl drives epithelialization in embryos and cell aggregates through its effect on cell shape and organelle localization. 

Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation. We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people. We build RUSS (Rapid Universal Support Service) to tackle this problem. RUSS consists of two models: First, a BERT-LSTM with pointers parses instructions to ThingTalk, a domain-specific language we design for grounding natural language on the web. Then, a grounding model retrieves the unique IDs of any webpage elements requested in ThingTalk. RUSS may interact with the user through a dialogue (e.g. ask for an address) or execute a web operation (e.g. click a button) inside the web runtime. To augment training, we synthesize natural language instructions mapped to ThingTalk. Our dataset consists of 80 different customer service problems from help websites, with a total of 741 step-by-step instructions and their corresponding actions. RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single instructions. It outperforms state-of-the-art models that directly map instructions to actions without ThingTalk. Our user study shows that RUSS is preferred by actual users over web navigation.