Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

This letter reports designs of adaptive metasurfaces capable of modulating incoming wave fronts of elastic waves through electromechanical-tuning of their cells. The proposed elastic metasurfaces are composed of arrayed piezoelectric units with individually connected negative capacitance elements that are online tunable. By adjusting the negative capacitances properly, accurately formed, discontinuous phase profiles along the elastic metasurfaces can be achieved. Subsequently, anomalous refraction with various angles can be realized on the transmitted lowest asymmetric mode Lamb wave. Moreover, designs to facilitate planar focal lenses and source illusion devices can also be accomplished. The proposed flexible and versatile strategy to manipulate elastic waves has potential applications ranging from structural fault detection to vibration/noise control.

Wave front engineering realized through metasurface synthesis has attracted considerable attention in recent years. Acoustic metasurfaces in deep subwavelength scale have promising potentials in applications such as acoustic focal lenses and acoustic cloaking. Most existing devices, however, lack the tunability in real time. In this paper, an adaptive acoustic metasurface taking advantage of the two-way electro-mechanical coupling of piezoelectric transducers is developed, which enables the manipulation of acoustic waves adaptively. The proposed metasurface consists of units constructed from membranes with back air cavities, wherein the membrane strength is controlled by piezoelectric transducer. With membrane strengths tailored in a tunable manner, an accurate phase profile along the acoustic metasurfaces can be designed, yielding acoustic metasurfaces to steer reflected acoustic waves online without modification of the underlying physical structures. We demonstrate that the adaptive acoustic metasurfaces can successfully achieve abnormal reflections, planar focal lenses and self-accelerating beams. The acoustic cloaking realized by our adaptive acoustic metasurfaces is also illustrated to further manifest the design versatility.

Particle damping has the promising potential for attenuating unwanted vibrations in harsh environments especially under high temperatures where conventional damping materials would not be functional. Nevertheless, a limitation of simple particle damper (PD) configuration is that the damping effect is insignificant if the local displacement/acceleration is low. In this research, we investigate the performance of a tuned mass particle damper (TMPD) in which the particle damping mechanism is integrated into a tuned mass damper (TMD) configuration. The essential idea is to combine the respective advantages of these two damping concepts and in particular to utilize the tuned mass damper configuration as a motion magnifier to amplify the energy dissipation capability of particle damper when the local displacement/acceleration of the host structure is low. We formulate a first-principle-based dynamic model of the integrated system and analyze the particle motion by using the discrete element method (DEM). We perform systematic parametric studies to elucidate the damping effect and energy dissipation mechanism of a TMPD. We demonstrate that a TMPD can provide significant vibration suppression capability, essentially outperforming conventional particle damper.

Background: ABCG2 plays a critical role in multi-drug resistance in a variety of cancers. Its activity is influenced by ATP hydrolysis and substrate transport, which are synergistic processes but in two independent sites. However, there was no QSAR study of the ABCG2 inhibitors for the two binding sites respectively. Methods: in current study, a QSAR model of ATP binding site was built using DCG scoring strategy, it was used to optimize the structure of a tyrosine kinase inhibitor to avoid the potential drug resistance. Results: a series of novel dual target compounds with arylamide skeleton were obtained, which showed notable activity in enzyme activity test. Compound 7c and 7i showed significant inhibitory activity against drug-resistant cells. Conclusions: the QSAR of ATP binding site of ABCG2 was first discussed, the dual-target compound 7c proposed a new strategy to reverse drug resistance.