Surface-attached bacterial biofilms are self-replicating active liquid crystals and the dominant form of bacterial life on earth ( 1 – 4 ). In conventional liquid crystals and solid-state materials, the interaction potentials between the molecules that comprise the system determine the material properties. However, for growth-active biofilms it is unclear whether potential-based descriptions can account for the experimentally observed morphologies, and which potentials would be relevant. Here, we overcame previous limitations of single-cell imaging techniques ( 5 , 6 ) to reconstruct and track all individual cells inside growing three-dimensional (3D) biofilms with up to 10,000 individuals. Based on these data, we identify, constrain, and provide a microscopic basis for an effective cell-cell interaction potential, which captures and predicts the growth dynamics, emergent architecture, and local liquid crystalline order of Vibrio cholerae biofilms. Furthermore, we show how external fluid flows control the microscopic structure and 3D morphology of biofilms. Our analysis implies that local cellular order and global biofilm architecture in these active bacterial communities can arise from mechanical cell-cell interactions, which cells can modulate by regulating the production of particular matrix components. These results establish an experimentally validated foundation for improved continuum theories of active matter and thereby contribute to solving the important problem of controlling biofilm growth.

Bacterial cells in nature are frequently exposed to changes in their chemical environment 1,2. For such stimuli, the response mechanisms of isolated cells have been investigated in great detail. By contrast, little is known about the emergent multicellular responses to environmental changes, such as antibiotic exposure 3-7 , which may hold the key to understanding the structure and functions of the most common bacterial communities: biofilms. Here, by monitoring all individual cells in Vibrio cholerae biofilms during exposure to commonly administered antibiotics for cholera infections, we discovered that translational inhibitors cause strong effects on cell size and shape, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

SINGLE CELL AGENT-BASED SIMULATIONS Model descriptionOur single cell model is based on the agent-based framework described in [1], with modifications to include the effect of the flow on the cells and cell-cell polar adhesion. Cells are modeled as ellipsoids of half-length l and half-width r; each cell is described by its position x, orientationn and effective local viscosity µ . The dynamics of the cells are approximated as over-damped, as cells live at low Reynolds number Re ≈ 10 −4 [1]. Denoting the identity matrix by I and the dynamic viscosity of water by µ w , the over-damped translational and orientational dynamics for a single cell are

We present a robust neural abstractive summarization system for cross-lingual summarization. We construct summarization corpora for documents automatically translated from three low-resource languages, Somali, Swahili, and Tagalog, using machine translation and the New York Times summarization corpus. We train three language-specific abstractive summarizers and evaluate on documents originally written in the source languages, as well as on a fourth, unseen language: Arabic. Our systems achieve significantly higher fluency than a standard copy-attention summarizer on automatically translated input documents, as well as comparable content selection.

Recent advances in microscopy techniques make it possible to study the growth, dynamics, and response of complex biophysical systems at single-cell resolution, from bacterial communities to tissues and organoids. In contrast to ordered crystals, it is less obvious how one can reliably distinguish two amorphous yet structurally different cellular materials. Here, we introduce a topological earth mover's (TEM) distance between disordered structures that compares local graph neighborhoods of the microscopic cell-centroid networks. Leveraging structural information contained in the neighborhood motif distributions, the TEM metric allows an interpretable reconstruction of equilibrium and nonequilibrium phase spaces and embedded pathways from static system snapshots alone. Applied to cell-resolution imaging data, the framework recovers time ordering without prior knowledge about the underlying dynamics, revealing that fly wing development solves a topological optimal transport problem. Extending our topological analysis to bacterial swarms, we find a universal neighborhood size distribution consistent with a Tracy-Widom law.