Ribosomal S6 Kinase Regulates the Timing and Entrainment of the Mammalian Circadian Clock Located in the Suprachiasmatic Nucleus.

Previous work in the suprachiasmatic nucleus (SCN), the locus of the principal circadian clock, has shown that the activation state of the ERK/MAPK effector p90 ribosomal S6 kinase (RSK) is responsive to photic stimulation and is modulated across the circadian cycle. These data raise the prospect that RSK signaling contributes to both SCN clock timing and entrainment. Here, we found marked expression of the three main RSK isoforms (RSK1/2/3) within the SCN of C57/Bl6 mice. Further, using a combination of immunolabeling and proximity ligation assays, we show that photic stimulation led to the dissociation of RSK from ERK and the translocation of RSK from the cytoplasm to the nucleus. To test for RSK functionality following light treatment, animals received an intraventricular infusion of the selective RSK inhibitor, SL0101, 30 min prior to light (100 lux) exposure during the early circadian night (circadian time 15). Notably, the disruption of RSK signaling led to a significant reduction (∼45 min) in the phase delaying effects of light, relative to vehicle-infused mice. To test the potential contribution of RSK signaling to SCN pacemaker activity, slice cultures from a per1-Venus circadian reporter mouse line were chronically treated with SL0101. Suppression of RSK signaling led to a significant lengthening of the circadian period (∼40 min), relative to vehicle-treated slices. Together, these data reveal that RSK functions as a signaling intermediate that regulates light-evoked clock entrainment and the inherent time keeping properties of the SCN.

Community-Informed Mobile COVID-19 Testing Model to Addressing Health Inequities.

CONTEXT: The New York City (NYC) Test & Trace Corps (Test & Trace), under New York City Health + Hospitals (NYC H+H), set out to provide universal access to COVID-19 testing. Test & Trace partnered with numerous organizations to direct mobile COVID-19 testing from concept through implementation to reduce COVID-19-related health inequities. PROGRAM: Test & Trace employs a community-informed mobile COVID-19 testing model to deliver testing to the hardest-hit, underserved communities. Community partners, uniquely knowledgeable of the residents they serve, are engaged as decision makers and operational partners in mobile COVID-19 testing delivery. IMPLEMENTATION: Through several mobile testing methods, community partners choose testing locations and tailor outreach to their community. Test & Trace assumes logistical responsibility for mobile testing but defers critical programmatic decisions and community engagement to partners. Integral to the success of this program is responsive, bidirectional communication. EVALUATION: During the reporting period of December 1, 2020, to April 30, 2021, Test & Trace's community-informed mobile COVID-19 testing model provided testing to 150351 unique patients and processed 274083 tests in total. The available outcomes data and qualitative feedback provided by community partners illustrate that this intervention, combined with robust governmental investment, successfully ensured that NYC-identified, low-resource neighborhoods had greater access to COVID-19 testing. DISCUSSION: Making community partners decision makers reduced inequities in access to testing for communities of color. In addition, the model has served as the framework for Test & Trace's community-informed mobile COVID-19 vaccination program, operated in concert with NYC's Vaccine Command Center, and is a foundation for addressing health inequities at scale, including during public health crises.

Rodeo Algorithm for Quantum Computing.

We present a stochastic quantum computing algorithm that can prepare any eigenvector of a quantum Hamiltonian within a selected energy interval [E-ε,E+ε]. In order to reduce the spectral weight of all other eigenvectors by a suppression factor δ, the required computational effort scales as O[|logδ|/(pε)], where p is the squared overlap of the initial state with the target eigenvector. The method, which we call the rodeo algorithm, uses auxiliary qubits to control the time evolution of the Hamiltonian minus some tunable parameter E. With each auxiliary qubit measurement, the amplitudes of the eigenvectors are multiplied by a stochastic factor that depends on the proximity of their energy to E. In this manner, we converge to the target eigenvector with exponential accuracy in the number of measurements. In addition to preparing eigenvectors, the method can also compute the full spectrum of the Hamiltonian. We illustrate the performance with several examples. For energy eigenvalue determination with error ε, the computational scaling is O[(logε)^{2}/(pε)]. For eigenstate preparation, the computational scaling is O(logΔ/p), where Δ is the magnitude of the orthogonal component of the residual vector. The speed for eigenstate preparation is exponentially faster than that for phase estimation or adiabatic evolution.