Breaking the mould of executive function research
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Effect of herbal toothpaste on the colour stability, surface roughness, and microhardness of aesthetic restorative materials—an in vitro study
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Coulomb interactions and migrating Dirac cones imaged by local quantum oscillations in twisted graphene
Flat-band moiré graphene systems are a quintessential platform for investigating correlated phases of matter. Various interaction-driven ground states have been proposed, but despite extensive experimental effort, there has been little direct evidence that distinguishes between various phases, in particular near the charge neutrality point. Here we probe the fine details of the density of states and the effects of Coulomb interactions in alternating-twist trilayer graphene by imaging the local thermodynamic quantum oscillations with a nanoscale scanning superconducting quantum interference device. We find that the charging self-energy due to occupied electronic states is most important in explaining the high-carrier-density physics. At half-filling of the conduction flat band, we observe ferromagnetic-driven symmetry breaking, suggesting that it is the most robust mechanism in the hierarchy of phase transitions. Near charge neutrality, where exchange energy dominates over charging self-energy, we find a nematic semimetal ground state, which is theoretically favoured over gapped states in the presence of heterostrain. In this semimetallic phase, the flat-band Dirac cones migrate towards the mini-Brillouin zone centre, spontaneously breaking the threefold rotational symmetry. Our low-field local quantum oscillation technique can be used to explore the ground states of many strongly interacting van der Waals systems.
Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
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