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Abstract We introduce a general phenomenological framework for understanding how phenotypic plasticity gives rise to drug persisters. These persisters, often quiescent but sometimes which again return to cycling, survive in the presence of treatment and eventually can lead to mutants with true resistance. Our framework builds on recent experimental observations regarding variations between and among single-cell clones and the possible role of the drug itself in enhancing the survival strategy. Predictions of our approach include the existence of an optimum drug concentration as well as an optimum drug holiday schedule to minimize the persistence-based threat. 

Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series. 

Combination therapies using checkpoint inhibitors with immunostimulatory agonists have attracted great attention due to their synergistic therapeutic effects for cancer treatment. However, such combination immunotherapies require specific timing of doses to show sufficient antitumor efficacy. Sequential treatment usually requires multiple administrations of the individual drugs at specific time points, thus increasing the complexity of the drug regimen and compromising patient compliance. Here, we introduce an injectable porous silicon microparticle (pSiMP) for combination cancer immunotherapy where its multilayered nanopore structure was electrochemically programmed to achieve release of three distinct immunomodulatory drugs in the right sequence at the desired time. We find the optimal sequential treatment timeline of stimulator of interferon genes (STING) agonist, anti-OX40 antibody (aOX40), and anti-PD-1 antibody (aPD-1) for immunosuppressive tumors. We show that a single intratumoral injection of a cocktail of release-programmed pSiMPs coloaded with each antibody and a STING agonist significantly suppresses the tumor growth compared to conventional treatment involving sequential bolus injections, or an injection of pSiMPs configured to release all drugs at the same time, with no delay. With the timely release of immunomodulatory drugs, the programmable pSiMPs offer an effective treatment strategy for combination immunotherapy. 

The ultra-wide bandgap (UWBG) energy (∼5.4 eV) of α-phase Ga2O3 offers the potential to achieve higher power switching performance and efficiency than today's power electronic devices. However, a major challenge to the development of the α-Ga2O3 power electronics is overheating, which can degrade the device performance and cause reliability issues. In this study, thermal characterization of an α-Ga2O3 MOSFET was performed using micro-Raman thermometry to understand the device self-heating behavior. The α-Ga2O3 MOSFET exhibits a channel temperature rise that is more than two times higher than that of a GaN high electron mobility transistor (HEMT). This is mainly because of the low thermal conductivity of α-Ga2O3 (11.9 ± 1.0 W/mK at room temperature), which was determined via laser-based pump-probe experiments. A hypothetical device structure was constructed via simulation that transfer-bonds the α-Ga2O3 epitaxial structure over a high thermal conductivity substrate. Modeling results suggest that the device thermal resistance can be reduced to a level comparable to or even better than those of today's GaN HEMTs using this strategy combined with thinning of the α-Ga2O3 buffer layer. The outcomes of this work suggest that device-level thermal management is essential to the successful deployment of UWBG α-Ga2O3 devices. 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable network. The homogenously integrated cells are implemented in a 2×2 network array to demonstrate the enabled addressability and programmability. The potential for assembling scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.