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The analysis of membranous extracellular vesicles, such as exosomes vesicles (EV) opens a new direction for the rapid disease diagnosis because EVs can carry molecular constituents of their originating cells. Secreted by mammalian cells, the size of most membrane-bound phospholipid EVs ranges from 50 to 150 nm in diameter. Recent studies have demonstrated the potential of using EVs for cancer diagnosis and treatment monitoring. To diagnose infectious diseases using EVs, the ability to discriminate EVs from host cells and parasites is key. Here, we report a rapid EV analysis assay that can discriminate EVs based on a host-specific transmembrane protein (CD63 antigen) using a label-free optical biosensor.

The optical resonances of the silicon nanopost array patterned on a silicon-on-insulator (SOI) substrate have been investigated. The fabricated device supports optical resonances in the range of 1.55 μm with a variable Q factor depending on the angle of incidence. By sealing the device on top of the nanoposts, we demonstrated a lateral flow-through label-free biosensor built on SOI. The biosensor exhibits the refractive index sensitivity of 800 nm/RIU and the femtomolar sensitivity for detection of a breast cancer biomarker (ErbB2).

The Askaryan Radio Array (ARA) is an ultrahigh energy (UHE, >10^17  eV) neutrino detector designed to observe neutrinos by searching for the radio waves emitted by the relativistic products of neutrino-nucleon interactions in Antarctic ice. In this paper, we present constraints on the diffuse flux of ultrahigh energy neutrinos between 1016 and 1021  eV resulting from a search for neutrinos in two complementary analyses, both analyzing four years of data (2013–2016) from the two deep stations (A2, A3) operating at that time. We place a 90% CL upper limit on the diffuse all flavor neutrino flux at 1018  eV of EF(E)=5.6×10^−16  cm^−2 s^−1 sr^−1. This analysis includes four times the exposure of the previous ARA result and represents approximately 1/5^th the exposure expected from operating ARA until the end of 2022.

Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

Abstract The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.