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Abstract Helmet continuous positive applied pressure is a form of noninvasive ventilation (NIV) that has been used to provide respiratory support to COVID-19 patients. Helmet NIV is low-cost, readily available, provides viral filters between the patient and clinician, and may reduce the need for invasive ventilation. Its widespread adoption has been limited, however, by the lack of a respiratory monitoring system needed to address known safety vulnerabilities and to monitor patients. To address these safety and clinical needs, we developed an inexpensive respiratory monitoring system based on readily available components suitable for local manufacture. Open-source design and manufacturing documents are provided. The monitoring system comprises flow, pressure, and CO2 sensors on the expiratory path of the helmet circuit and a central remote station to monitor up to 20 patients. The system is validated in bench tests, in human-subject tests on healthy volunteers, and in experiments that compare respiratory features obtained at the expiratory path to simultaneous ground-truth measurements from proximal sensors. Measurements of flow and pressure at the expiratory path are shown to deviate at high flow rates, and the tidal volumes reported via the expiratory path are systematically underestimated. Helmet monitoring systems exhibit high-flow rate, nonlinear effects from flow and helmet dynamics. These deviations are found to be within a reasonable margin and should, in principle, allow for calibration, correction, and deployment of clinically accurate derived quantities. 

Cryogenic electron microscopy (cryo-EM) has become one of the most powerful techniques to reveal the atomic structures and working mechanisms of biological macromolecules. New designs of the cryo-EM grids—aimed at preserving thin, uniform vitrified ice and improving protein adsorption—have been considered a promising approach to achieving higher resolution with the minimal amount of materials and data. Here, we describe a method for preparing graphene cryo-EM grids with up to 99% monolayer graphene coverage that allows for more than 70% grid squares for effective data acquisition with improved image quality and protein density. Using our graphene grids, we have achieved 2.6-Å resolution for streptavidin, with a molecular weight of 52 kDa, from 11,000 particles. Our graphene grids increase the density of examined soluble, membrane, and lipoproteins by at least 5-fold, affording the opportunity for structural investigation of challenging proteins which cannot be produced in large quantity. In addition, our method employs only simple tools that most structural biology laboratories can access. Moreover, this approach supports customized grid designs targeting specific proteins, owing to its broad compatibility with a variety of nanomaterials.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.