arXiv subjects

Yingqi Zhao

Publications and source records attributed to Yingqi Zhao.

21 records · Page 2Linked to original sources

arXiv2018

Controlled Intracellular Delivery of Single Particles in Single Cells by 3D Hollow Nanoelectrodes

We present an electrophoretic platform based on 3D hollow nanoelectrodes capable of controlling and quantifying the intracellular delivery of single nanoparticles in single selected cells by surface-enhanced Raman spectroscopy (SERS). The gold-coated hollow nanoelectrode has a sub-femtoliter inner volume that allows the confinement and enhancement of electromagnetic fields upon laser illumination to distinguish the SERS signals of a single nanoparticle flowing through the nanoelectrode. The tight wrapping of cell membranes around the nanoelectrodes enables effective membrane electroporation such that single gold nanorods are delivered into a living cell with a delivery rate subject to the applied bias voltage. The capability of the 3D hollow nanoelectrodes to porate cells and reveal single emitters from the background under live flow is promising for the analysis of both intracellular delivery and sampling.

physics.app-ph↗

arXiv2018

Nanostructured Hyperbolic Meta-Antennas Enable Arbitrary Control of Scattering vs Absorption

We show that meta-antennas made of a composite material displaying type II hyperbolic dispersion enable precise and controlled spectral separation of absorption and scattering processes in the visible/near-infrared frequency range. The experimental evidence is supported by a comprehensive theoretical study. We demonstrate that the physical mechanism responsible for the aforementioned effect lies in the different natures of the plasmonic modes excited within the hyperbolic meta-antennas. We prove that it is possible to have a pure scattering channel if an electric dipolar mode is induced, while a pure absorption process can be obtained if a magnetic dipole is excited. Also, by varying the geometry of the system, the relative weight of scattering and absorption can be tuned, thus enabling an arbitrary control of the decay channels. Importantly, both modes can be efficiently excited by direct coupling with the far-field radiation, even when the radiative channel (scattering) is almost totally suppressed, hence making the proposed architecture suitable for practical applications.

physics.app-ph↗

arXiv2016

Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens

Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. The complexity, patient heterogeneity and chronicity of many diseases and disorders call for learning optimal DTRs which best dynamically tailor treatment to each individual's response over time. Proliferation of personalized data (e.g., genetic and imaging data) provides opportunities for deep tailoring as well as new challenges for statistical methodology. In this work, we propose a robust hybrid approach referred as Augmented Multistage Outcome-Weighted Learning (AMOL) to integrate outcome-weighted learning and Q-learning to identify optimal DTRs from the Sequential Multiple Assignment Randomization Trials (SMARTs). We generalize outcome weighted learning (O-learning; Zhao et al.~2012) to allow for negative outcomes; we propose methods to reduce variability of weights in O-learning to achieve numeric stability and higher efficiency; finally, for multiple-stage SMART studies, we introduce doubly robust augmentation to machine learning based O-learning to improve efficiency by drawing information from regression model-based Q-learning at each stage. The proposed AMOL remains valid even if the Q-learning model is misspecified. We establish the theoretical properties of AMOL, including the consistency of the estimated rules and the rates of convergence to the optimal value function. The comparative advantage of AMOL over existing methods is demonstrated in extensive simulation studies and applications to two SMART data sets: a two-stage trial for attention deficit and hyperactive disorder (ADHD) and the STAR*D trial for major depressive disorder (MDD).

stat.ME↗