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Abstract Microbial production of cannabinoids promises to provide a consistent, cheaper, and more sustainable supply of these important therapeutic molecules. However, scaling production to compete with traditional plant-based sources is challenging. Our ability to make strain variants greatly exceeds our capacity to screen and identify high producers, creating a bottleneck in metabolic engineering efforts. Here, we present a yeast-based biosensor for detecting microbially produced Δ⁹-tetrahydrocannabinol (THC) to increase throughput and lower the cost of screening. We port five human cannabinoid G protein-coupled receptors (GPCRs) into yeast, showing the cannabinoid type 2 receptor, CB2R, can couple to the yeast pheromone response pathway and report on the concentration of a variety of cannabinoids over a wide dynamic and operational range. We demonstrate that our cannabinoid biosensor can detect THC from microbial cell culture and use this as a tool for measuring relative production yields from a library of Δ⁹-tetrahydrocannabinol acid synthase (THCAS) mutants. 

Abstract Multi‐view data, which is matched sets of measurements on the same subjects, have become increasingly common with advances in multi‐omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorporate class information, while existing classification methods do not take into account between‐views associations. In this work, we propose a framework for Joint Association and Classification Analysis of multi‐view data (JACA). Our goal is not to merely improve the misclassification rates, but to provide a latent representation of high‐dimensional data that is both relevant for the subtype discrimination and coherent across the views. We motivate the methodology by establishing a connection between canonical correlation analysis and discriminant analysis. We also establish the estimation consistency of JACA in high‐dimensional settings. A distinct advantage of JACA is that it can be applied to the multi‐view data with block‐missing structure, that is to cases where a subset of views or class labels is missing for some subjects. The application of JACA to quantify the associations between RNAseq and miRNA views with respect to consensus molecular subtypes in colorectal cancer data from The Cancer Genome Atlas project leads to improved misclassification rates and stronger found associations compared to existing methods. 

Abstract A vertically aligned carbon nanofiber (VACNF) array with unique conically stacked graphitic structure directly grown on a planar Cu current collector (denoted as VACNF/Cu) is used as a high‐porosity 3D host to overcome the commonly encountered issues of Li metal anodes. The excellent electrical conductivity and highly active lithiophilic graphitic edge sites facilitate homogenous coaxial Li plating/stripping around each VACNF and forming a uniform solid electrolyte interphase. The high specific surface area effectively reduces the local current density and suppresses dendrite growth during the charging/discharging processes. Meanwhile, this open nanoscale vertical 3D structure eliminates the volume changes during Li plating/stripping. As a result, highly reversible Li plating/stripping with high coulombic efficiency is achieved at various current densities. A low voltage hysteresis of 35 mV over 500 h in symmetric cells is achieved at 1 mA cm⁻²with an areal Li plating capacity of 2 mAh cm⁻², which is far superior to the planar Cu current collector. Furthermore, a Li–S battery using a S@PAN cathode and a lithium‐plated VACNF/Cu (VACNF/Cu@Li) anode with slightly higher capacity (2 mAh cm⁻²) exhibits an excellent rate capability and high cycling stability with no capacity fading over 600 cycles.