Search for: All records

ABSTRACT Quorum sensing (QS) enables microorganisms—including bacteria, eukaryotes, and viruses—to coordinate collective behaviors in response to population density. Despite their ecological and evolutionary significance, QS mechanisms in Archaea remain poorly characterized. The halophilic archaeonHaloferax volcaniiprovides a model for archaeal QS, transitioning from motile rods to non-motile disks in a density-dependent response to a secreted disk-forming signal (DFS). To identify components of the DFS regulatory network, we screened for spontaneous mutants that retained motility in DFS-containing soft-agar medium. One candidate,HVO_1357, encodes a predicted response regulator located adjacent to a histidine kinase (HVO_1356) and a second response regulator (HVO_1358), consistent with an extended two-component regulatory system (TCS). Based on our results, these genes encodequorum-sensing-associatedregulators (Qar); therefore, we propose renaming them toqarA(HVO_1357),qarB(HVO_1356), andqarC(HVO_1358). Deletion ofqarAenabled cells to swim on DFS-containing soft-agar plates and conferred hypermotility on standard soft-agar media; however, these phenotypes were not due to changes in motility-related parameters, but a reduced sensitivity to DFS for induction of the non-motile, disk-shaped state. In contrast, ΔqarBand ΔqarCstrains were non-motile and exhibited premature disk formation during normal growth. Suppressor mutations restoring motility to ΔqarBand ΔqarCmapped exclusively toqarA, and epistasis analysis indicated QarA as the central regulator of this system. Phosphoablative variants of QarA, QarB, and QarC failed to complement their respective deletion strains, supporting QarABC as a TCS. Transcriptomic analyses revealed thatqarAdeletion leads to upregulation of genes involved in motility and rod-shape formation. Together, these findings revealqarABCas a DFS-responsive regulatory module and represent the first TCS in archaea shown to control QS-dependent behavior.IMPORTANCEArchaea are ubiquitous and play key roles across diverse ecosystems—including human microbiomes—yet little is known about how they communicate with one another and with other organisms, or how these interactions shape their ecological impact. Such intercellular communication, including quorum sensing (QS), allows microorganisms to coordinate behaviors critical for survival, adaptation, and community organization. In this study, we identify the first archaeal two-component regulatory system that is involved in QS-dependent regulation, providing a foundation for understanding how organisms in this domain sense and respond to population cues. By revealing a previously unknown aspect of archaeal biology, this work represents an important step toward understanding how archaeal communication shapes both their physiology and their interactions within complex microbial communities. 

We introduce a waypoint-based flow capture location model (WbFCLM) for siting facilities on networks with the objective of maximally capturing flows. The advantages of the waypoint-based formulation are that (1) demand is modeled along observed trajectories rather than assumed travel paths, and (2) demand is allowed to vary across geographic space. We demonstrate the WbFCLM using a test dataset derived from vehicle tracking data. Though solving the WbFCLM can require considerable computing power, it can be used to site facilities on networks where both flows and demand vary spatially. 

Sea ice modulates the transfer of shortwave radiative energy fluxes within the Arctic atmosphere-sea-ice-ocean system. Understanding and predicting these fluxes comes with greatest uncertainties during the melt and freeze-up seasons, when the sea ice surface is strongly heterogeneous and changing rapidly. Then, the partitioning of solar radiative fluxes between atmosphere, ice, and ocean has greatest impacts on the surface energy budget, controlling sea ice melt and formation. Here, we investigated changes and impacts of sea ice surface variability by analyzing high-resolution red-green-blue aerial imagery obtained during the Multidisciplinary Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2020. We used pixel brightness from processed aerial images as a proxy of surface albedo, because such data are frequently available and obtainable from commercial digital cameras. The results allowed quantification of fluxes on floe-scales and also revealed the seasonality of sea ice spatial heterogeneity, which was strongest in the middle of melt season driven by melt pond processes. On scales of 10 m × 10 m, a magnitude larger than the traditional single in-situ optical observations (although many are made over larger scales), distinct surface conditions, for example, individual melt ponds, resulted in differences of energy deposition into the ice by more than 600%. The effects of spatial variability were minimized by integrating over areas 200 m × 200 m and larger. We suggest considering these scales for future energy budget studies and airborne observations, because sufficient parts of different surface features are included. The concept of surface brightness and aerial photographs might help to bridge in-situ observations to even larger scales, including fractions of open water. It may also be used to upscale observations of under-ice light regimes by providing spatially continuous surface brightness that governs the light transmittance, thus to improve our understanding of the coupled system, including ecological functions. 

We prove several results showing that every locally finite Borel graph whose large-scale geometry is ‘tree-like’ induces a treeable equivalence relation. In particular, our hypotheses hold if each component of the original graph either has bounded tree-width or is quasi-isometric to a tree, answering a question of Tucker-Drob. In the latter case, we moreover show that there exists a Borel quasi-isometry to a Borel forest, under the additional assumption of (componentwise) bounded degree. We also extend these results on quasi-treeings to Borel proper metric spaces. In fact, our most general result shows treeability of countable Borel equivalence relations equipped with an abstract wallspace structure on each class obeying some local finiteness conditions, which we call aproper walling. The proof is based on the Stone duality between proper wallings and median graphs (i.e., CAT(0) cube complexes). Finally, we strengthen the conclusion of treeability in these results to hyperfiniteness in the case where the original graph has one (selected) end per component, generalizing the same result for trees due to Dougherty–Jackson–Kechris. 

Model predictive control (MPC) has been applied to many platforms in robotics and autonomous systems for its capability to predict a system’s future behavior while incorporating constraints that a system may have. To enhance the performance of a system with an MPC controller, one can manually tunethe MPC’s cost function. However, it can be challenging due to the possibly high dimension of the parameter space as well as the potential difference between the open-loop cost function in MPC and the overall closed-loop performance metric function. This letter presents Difffune-MPC, a novel learning method, to learn the cost function of an MPC in a closed-loop manner. The proposed framework is compatible with the scenario where the time interval for performance evaluation and MPC’s planning horizon have different lengths. We show the auxiliary problem whose solution admits the analytical gradients of MPC and discuss its variations in different MPC settings, including nonlinear MPCs that are solved using sequential quadratic programming. Simulation results demonstrate the learning capability of DiffTune-MPC and the generalization capability of the learned MPC parameters.