A Perspective on Efflux Transport Proteins in the Liver

Detailed knowledge regarding the influence of hepatic transport proteins on drug disposition has advanced at a rapid pace over the past decade. Efflux transport proteins located in the basolateral and apical (canalicular) membranes of hepatocytes play an important role in the hepatic elimination of many endogenous and exogenous compounds, including drugs and metabolites. This review focuses on the role of these efflux transporters in hepatic drug excretion. The impact of these proteins as underlying factors for disease is highlighted, and the importance of hepatic efflux proteins in the efficacy and toxicity of drugs is discussed. In addition, a brief overview of methodology to evaluate the function of hepatic efflux transport proteins is provided. Current challenges in predicting the impact of altered efflux protein function on systemic, intestinal and hepatocyte exposure to drugs and metabolites are highlighted

Search for new Higgs bosons via same-sign top quark pair production in association with a jet in proton-proton collisions at s=13TeV

A search is presented for new Higgs bosons in proton-proton (pp) collision events in which a same-sign top quark pair is produced in association with a jet, via the pp→tH/A→ttc‾ and pp→tH/A→ttu‾ processes. Here, H and A represent the extra scalar and pseudoscalar boson, respectively, of the second Higgs doublet in the generalized two-Higgs-doublet model (g2HDM). The search is based on pp collision data collected at a center-of-mass energy of 13 TeV with the CMS detector at the LHC, corresponding to an integrated luminosity of 138fb−1. Final states with a same-sign lepton pair in association with jets and missing transverse momentum are considered. New Higgs bosons in the 200–1000 GeV mass range and new Yukawa couplings between 0.1 and 1.0 are targeted in the search, for scenarios in which either H or A appear alone, or in which they coexist and interfere. No significant excess above the standard model prediction is observed. Exclusion limits are derived in the context of the g2HDM

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors