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  1. Free, publicly-accessible full text available October 1, 2023
  2. Fritts et al. (J. Fluid Mech., vol. xx, 2022, xx) describe a direct numerical simulation of interacting Kelvin–Helmholtz instability (KHI) billows arising due to initial billow cores that exhibit variable phases along their axes. Such KHI exhibit strong ‘tube and knot’ dynamics identified in early laboratory studies by Thorpe ( Geophys. Astrophys. Fluid Dyn. , vol. 34, 1985, pp. 175–199). Thorpe ( Q.J.R. Meteorol. Soc. , vol. 128, 2002, pp. 1529–1542) noted that these dynamics may be prevalent in the atmosphere, and they were recently identified in atmospheric observations at high altitudes. Tube and knot dynamics were found by Frittsmore »et al. ( J. Fluid. Mech. , 2022) to drive stronger and faster turbulence transitions than secondary instabilities of individual KH billows. Results presented here reveal that KHI tube and knot dynamics also yield energy dissipation rates $\sim$ 2–4 times larger as turbulence arises and that remain $\sim$ 2–3 times larger to later stages of the flow evolution, compared with those of secondary convective instabilities (CI) and secondary KHI accompanying KH billows without tube and knot influences. Elevated energy dissipation rates occur due to turbulence transitions by tube and knot dynamics arising on much larger scales than secondary CI and KHI where initial KH billows are misaligned. Tube and knot dynamics also excite large-scale Kelvin ‘twist waves’ that cause vortex tube and billow core fragmentation, more energetic cascades of similar interactions to smaller scales and account for the strongest energy dissipation events accompanying such KH billow evolutions.« less
    Free, publicly-accessible full text available June 25, 2023
  3. We perform a direct numerical simulation (DNS) of interacting Kelvin–Helmholtz instabilities (KHI) that arise at a stratified shear layer where KH billow cores are misaligned or exhibit varying phases along their axes. Significant evidence of these dynamics in early laboratory shear-flow studies by Thorpe ( Geophys. Astrophys. Fluid Dyn. , vol. 34, 1985, pp. 175–199) and Thorpe ( J. Geophys. Res. , vol. 92, 1987, pp. 5231–5248), in observations of KH billow misalignments in tropospheric clouds (Thorpe, Q. J. R. Meteorol. Soc. , vol. 128, 2002, pp. 1529–1542) and in recent direct observations of such events in airglow and polarmore »mesospheric cloud imaging in the upper mesosphere reveals that these dynamics are common. More importantly, the laboratory and mesospheric observations suggest that these dynamics lead to more rapid and more intense instabilities and turbulence than secondary convective instabilities in billow cores and secondary KHI in stratified braids between and around adjacent billows. To date, however, no simulations exploring the dynamics and energetics of interacting KH billows (apart from pairing) have been performed. Our DNS performed for Richardson number $Ri=0.10$ and Reynolds number $Re=5000$ demonstrates that KHI tubes and knots (i) comprise strong and complex vortex interactions accompanying misaligned KH billows, (ii) accelerate the transition to turbulence relative to secondary instabilities of individual KH billows, (iii) yield significantly stronger turbulence than secondary KHI in billow braids and secondary convective instabilities in KHI billow cores and (iv) expand the suite of secondary instabilities previously recognized to contribute to KHI dynamics and breakdown to turbulence in realistic geophysical environments.« less
    Free, publicly-accessible full text available June 25, 2023
  4. The lymphatic vascular function is regulated by pulsatile shear stresses through signaling mediated by intracellular calcium [Ca 2+ ] i . Further, the intracellular calcium dynamics mediates signaling between lymphatic endothelial cells (LECs) and muscle cells (LMCs), including the lymphatic tone and contractility. Although calcium signaling has been characterized on LEC monolayers under uniform or step changes in shear stress, these dynamics have not been revealed in LMCs under physiologically-relevant co-culture conditions with LECs or under pulsatile flow. In this study, a cylindrical organ-on-chip platform of the lymphatic vessel (Lymphangion-Chip) consisting of a lumen formed with axially-aligned LECs co-cultured withmore »transversally wrapped layers of LMCs was exposed to step changes or pulsatile shear stress, as often experienced in vivo physiologically or pathologically. Through real-time analysis of intracellular calcium [Ca 2+ ] i release, the device reveals the pulsatile shear-dependent biological coupling between LECs and LMCs. Upon step shear, both cell types undergo a relatively rapid rise in [Ca 2+ ] i followed by a gradual decay. Importantly, under pulsatile flow, analysis of the calcium signal also reveals a secondary sinusoid within the LECs and LMCs that is very close to the flow frequency. Finally, LMCs directly influence the LEC calcium dynamics both under step changes in shear and under pulsatile flow, demonstrating a coupling of LEC–LMC signaling. In conclusion, the Lymphangion-Chip is able to illustrate that intracellular calcium [Ca 2+ ] i in lymphatic vascular cells is dependent on pulsatile shear rate and therefore, serves as an analytical biomarker of mechanotransduction within LECs and LMCs, and functional consequences.« less
    Free, publicly-accessible full text available June 27, 2023
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  10. Chen, Chi-Hua (Ed.)
    Mobile sensing data processed using machine learning models can passively and remotely assess mental health symptoms from the context of patients’ lives. Prior work has trained models using data from single longitudinal studies, collected from demographically homogeneous populations, over short time periods, using a single data collection platform or mobile application. The generalizability of model performance across studies has not been assessed. This study presents a first analysis to understand if models trained using combined longitudinal study data to predict mental health symptoms generalize across current publicly available data. We combined data from the CrossCheck (individuals living with schizophrenia) andmore »StudentLife (university students) studies. In addition to assessing generalizability, we explored if personalizing models to align mobile sensing data, and oversampling less-represented severe symptoms, improved model performance. Leave-one-subject-out cross-validation (LOSO-CV) results were reported. Two symptoms (sleep quality and stress) had similar question-response structures across studies and were used as outcomes to explore cross-dataset prediction. Models trained with combined data were more likely to be predictive (significant improvement over predicting training data mean) than models trained with single-study data. Expected model performance improved if the distance between training and validation feature distributions decreased using combined versus single-study data. Personalization aligned each LOSO-CV participant with training data, but only improved predicting CrossCheck stress. Oversampling significantly improved severe symptom classification sensitivity and positive predictive value, but decreased model specificity. Taken together, these results show that machine learning models trained on combined longitudinal study data may generalize across heterogeneous datasets. We encourage researchers to disseminate collected de-identified mobile sensing and mental health symptom data, and further standardize data types collected across studies to enable better assessment of model generalizability.« less
    Free, publicly-accessible full text available April 27, 2023