The 2018 12
Workshop on Recent Issues in Bioanalysis (12th WRIB) took place in Philadelphia, PA, USA on April 9-13, 2018 with an attendance of over 900 representatives from ...pharmaceutical/biopharmaceutical companies, biotechnology companies, contract research organizations and regulatory agencies worldwide. WRIB was once again a 5-day full immersion in bioanalysis, biomarkers and immunogenicity. As usual, it was specifically designed to facilitate sharing, reviewing, discussing and agreeing on approaches to address the most current issues of interest including both small- and large-molecule bioanalysis involving LC-MS, hybrid ligand binding assay (LBA)/LC-MS and LBA/cell-based assays approaches. This 2018 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2018 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 1) covers the recommendations for LC-MS for small molecules, peptides, oligonucleotides and small molecule biomarkers. Part 2 (hybrid LBA/LC-MS for biotherapeutics and regulatory agencies' inputs) and Part 3 (large molecule bioanalysis, biomarkers and immunogenicity using LBA and cell-based assays) are published in volume 10 of Bioanalysis, issues 23 and 24 (2018), respectively.
Airway Management in Prolonged Field Care Dye, Collin; Keenan, Sean; Carius, Brandon M ...
Journal of special operations medicine,
2020-Fall, Letnik:
20, Številka:
3
Journal Article
Recenzirano
This Role 1, prolonged field care (PFC) clinical practice guideline (CPG) is intended to be used after Tactical Combat Casualty Care (TCCC) Guidelines, when evacuation to higher level of care is not ...immediately possible. A provider must first and foremost be an expert in TCCC, the Department of Defense standard of care for first responders. The intent of this PFC CPG is to provide evidence and experience-based solutions to those who manage airways in an austere environment. An emphasis is placed on utilizing the tools and adjuncts most familiar to a Role 1 provider. The PFC capability of airway is addressed to reflect the reality of managing an airway in a Role 1 resource-constrained environment. A separate Joint Trauma System CPG will address mechanical ventilation. This PFC CPG also introduces an acronym to assist providers and their teams in preparing for advanced procedures, to include airway management.
We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning classification of ...photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a `black box' when compared with other classifiers. Another key advantage is that using user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, here we adapt it for multi-band photometric data, i.e. we treat multiple narrow-band filters as very low-resolution spectra. We train our models using specialized mocks from Queiroz et al. (2022). We estimate our redshift precision using the normalized median absolute deviation, \(\sigma_{\rm NMAD}\) applied to our test sample. Our test sample returns an \(f_1\) score (effectively the purity and completeness) of 0.49 for quasars down to magnitude \(r=24.3\) with \(z\geq2.1\) and 0.24 for quasars with \(z<2.1\). For high-z quasars, this goes up to 0.9 for \(r<21.0\). We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a \(\sigma_{NMAD}=0.92\%\) for the entire sample, reduced to 0.81\% for \(r<22.5\) and 0.74\% for \(r<21.3\). Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.
This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over \(\sim\) 1 deg\(^2\) in the AEGIS ...field. We developed two algorithms based on artificial neural networks (ANN) to classify objects into four categories: stars, galaxies, quasars at low redshift (\(z < 2.1)\), and quasars at high redshift (\(z \geq 2.1\)). As inputs, we used miniJPAS fluxes for one of the classifiers (ANN\(_1\)) and colours for the other (ANN\(_2\)). The ANNs were trained and tested using mock data in the first place. We studied the effect of augmenting the training set by creating hybrid objects, which combines fluxes from stars, galaxies, and quasars. Nevertheless, the augmentation processing did not improve the score of the ANN. We also evaluated the performance of the classifiers in a small subset of the SDSS DR12Q superset observed by miniJPAS. In the mock test set, the f1-score for quasars at high redshift with the ANN\(_1\) (ANN\(_2\)) are \(0.99\) (\(0.99\)), \(0.93\) (\(0.92\)), and \(0.63\) (\(0.57\)) for \(17 < r \leq 20\), \(20 < r \leq 22.5\), and \(22.5 < r \leq 23.6\), respectively, where \(r\) is the J-PAS rSDSS band. In the case of low-redshift quasars, galaxies, and stars, we reached \(0.97\) (\(0.97\)), \(0.82\) (\(0.79\)), and \(0.61\) (\(0.58\)); \(0.94\) (\(0.94\)), \(0.90\) (\(0.89\)), and \(0.81\) (\(0.80\)); and \(1.0\) (\(1.0\)), \(0.96\) (\(0.94\)), and \(0.70\) (\(0.52\)) in the same r bins. In the SDSS DR12Q superset miniJPAS sample, the weighted f1-score reaches 0.87 (0.88) for objects that are mostly within \(20 < r \leq 22.5\). Finally, we estimate the number of point-like sources that are quasars, galaxies, and stars in miniJPAS.
Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, ...but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering \(\sim\) 1 deg\(^2\) of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.
In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative ...sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range \(17.5\leq r<24\), we augment our sample of available spectra by shifting the original \(r\)-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.
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