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Microtubule-kinesin active fluids are distinguished from conventional passive fluids by their unique ability to consume local fuel, ATP, to generate internal active stress. This stress drives internal flow autonomously and promotes micromixing, without the need for external pumps. When confined within a looped boundary, these active fluids can spontaneously self-organize into river-like flows. However, the influence of a moving boundary on these flow behaviors has remained elusive. Here, we investigate the role of a moving boundary on the flow kinematics of active fluids. We confined the active fluid within a thin cuboidal boundary with one side serving as a mobile boundary. Our data reveals that when the boundary's moving speed does not exceed the intrinsic flow speed of the active fluid, the fluid is dominated by chaotic, turbulence-like flows. The velocity correlation length of the flow is close to the intrinsic vortex size induced by the internal active stress. Conversely, as the boundary's moving speed greatly exceeds that of the active fluid, the flow gradually transitions to a conventional cavity flow pattern. In this regime, the velocity correlation length increases and saturates to those of water. Our work elucidates the intricate interplay between a moving boundary and active fluid behavior. *We acknowledge support from the National Science Foundation (NSF-CBET-2045621). 

Humans leverage the dynamics of the environment and their own bodies to accomplish challenging tasks such as grasping an object while walking past it or pushing off a wall to turn a corner. Such tasks often involve switching dynamics as the robot makes and breaks contact. Learning these dynamics is a challenging problem and prone to model inaccuracies, especially near contact regions. In this work, we present a framework for learning composite dynamical behaviors from expert demonstrations. We learn a switching linear dynamical model with contacts encoded in switching conditions as a close approximation of our system dynamics. We then use discrete-time LQR as the differentiable policy class for data-efficient learning of control to develop a control strategy that operates over multiple dynamical modes and takes into account discontinuities due to contact. In addition to predicting interactions with the environment, our policy effectively reacts to inaccurate predictions such as unanticipated contacts. Through simulation and real world experiments, we demonstrate generalization of learned behaviors to different scenarios and robustness to model inaccuracies during execution. 

S100A12 or Calgranulin C is a homodimeric antimicrobial protein of the S100 family of EF-hand calcium-modulated proteins. S100A12 is involved in many diseases like inflammation, tumor invasion, cancer and neurological disorders like Alzheimer’s disease. The binding of transition metal ions to the protein is important as the sequestering of the metal ion induces conformational changes in the protein, inhibiting the growth of various pathogenic microorganisms. In this work, we probe the Cu(II) binding properties of Calgranulin C. We demonstrate that the two Cu(II) binding sites in Calgranulin C show different coordination environments in solution. Electron spin resonance (ESR) spectra of Cu(II)-bound protein clearly show two distinct components at higher Cu(II):protein ratios, which is indicative of the two different binding environments for the Cu(II) ions. The g|| and A|| values are also different for the two components, indicating that the number of directly coordinated nitrogens in each site differs. Furthermore, we perform Continuous Wave (CW)-titrations to obtain the binding affinity of the Ca(II)-loaded protein to Cu2+ ions. We observe a positive cooperativity in binding of the two Cu(II) ions. In order to further probe the Cu2+ coordination, we also perform Electron Spin Echo Envelope Modulation (ESEEM) experiment. We perform ESEEM at two different fields where one Cu(II) binding site dominates over the other. At both sites we see distinct signatures of Cu(II)-histidine coordination. However, we clearly see that the ESEEM spectra corresponding to the two Cu2+ binding sites are significantly different. There is clear change in the intensity of the double quantum (DQ) peak with respect to the nuclear quadrupole interaction (NQI) peak at the two different fields. Furthermore, ESEEM along with Hyperfine Sublevel Correlation (HYSCORE) show that only one of the two Cu(II) binding sites has backbone coordination, confirming our previous observation. Finally, we perform Double Electron Electron Resonance (DEER) spectroscopy to probe if the difference in binding environment is due to the Cu(II) binding to different sites in the protein. We obtain a distance distribution with a sharp peak at ~ 3 nm and a broad peak at ~ 4 nm. The shorter distance agrees with the Cu(II)-Cu(II) distance expected for a dimer from the crystal structure. The longer distance is consistent with the Cu(II)-Cu(II) distance when oligomerization occurs.