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Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis of variable selection methods for LMEs is more difficult than for linear regression because LME models are nonlinear. In this article we propose a novel optimization strategy that enables a wide range of variable selection methods for LMEs using both convex and nonconvex regularizers, including 𝓁1, Adaptive-𝓁1, SCAD, and 𝓁0. The computational framework only requires the proximal operator for each regularizer to be readily computable, and the implementation is available in an open source python package pysr3, consistent with the sklearn standard. The numerical results on simulated data sets indicate that the proposed strategy improves on the state of the art for both accuracy and compute time. The variable selection techniques are also validated on a real example using a data set on bullying victimization. Supplementary materials for this article are available online. 

Widespread testing and isolation of infected patients is a cornerstone of viral outbreak management, as underscored during the ongoing COVID-19 pandemic. Here, we report a large-area and label-free testing platform that combines surface-enhanced Raman spectroscopy and machine learning for the rapid and accurate detection of SARS-CoV-2. Spectroscopic signatures acquired from virus samples on metal–insulator–metal nanostructures, fabricated using nanoimprint lithography and transfer printing, can provide test results within 25 min. Not only can our technique accurately distinguish between different respiratory and nonrespiratory viruses, but it can also detect virus signatures in physiologically relevant matrices such as human saliva without any additional sample preparation. Furthermore, our large area nanopatterning approach allows sensors to be fabricated on flexible surfaces allowing them to be mounted on any surface or used as wearables. We envision that our versatile and portable label-free spectroscopic platform will offer an important tool for virus detection and future outbreak preparedness. 

Abstract In this third paper of the series reporting on the reverberation mapping campaign of active galactic nuclei with asymmetric H β emission-line profiles, we present results for 15 Palomar–Green quasars using spectra obtained between the end of 2016–2021 May. This campaign combines long time spans with relatively high cadence. For eight objects, both the time lags obtained from the entire light curves and the measurements from individual observing seasons are provided. Reverberation mapping of nine of our targets has been attempted for the first time, while the results for six others can be compared with previous campaigns. We measure the H β time lags over periods of years and estimate their black hole masses. The long duration of the campaign enables us to investigate their broad-line region (BLR) geometry and kinematics for different years by using velocity-resolved lags, which demonstrate signatures of diverse BLR geometry and kinematics. The BLR geometry and kinematics of individual objects are discussed. In this sample, the BLR kinematics of Keplerian/virialized motion and inflow is more common than that of outflow. 

Graphene oxide and graphene quantum dots are attractive fluorophores that are inexpensive, nontoxic, photostable, water‐soluble, biocompatible, and environmentally friendly. They find extensive applications in fluorescent biosensors and chemosensors, in which they serve as either fluorophores or quenchers. As fluorophores, they display tunable photoluminescence emission and the “giant red‐edge effect”. As quenchers, they exhibit a remarkable quenching efficiency through either electron transfer or Förster resonance energy transfer (FRET) process. In this review, the origin of fluorescence and the mechanism of excitation wavelength‐dependent fluorescence of graphene oxide and graphene quantum dots are discussed. Sensor design strategies based on graphene oxide and graphene quantum dots are presented. The applications of these sensors in health care, the environment, agriculture, and food safety are highlighted.