Comparison of lower airway sampling strategies in children with protracted bacterial bronchitis

2021 ◽  
Author(s):  
Francis Gilchrist
CHEST Journal ◽  
2019 ◽  
Vol 155 (4) ◽  
pp. 778-786 ◽  
Author(s):  
Robyn L. Marsh ◽  
Heidi C. Smith-Vaughan ◽  
Alice C.H. Chen ◽  
Julie M. Marchant ◽  
Stephanie T. Yerkovich ◽  
...  

2003 ◽  
Vol 62 (2) ◽  
pp. 121-129 ◽  
Author(s):  
Astrid Schütz ◽  
Franz Machilek

Research on personal home pages is still rare. Many studies to date are exploratory, and the problem of drawing a sample that reflects the variety of existing home pages has not yet been solved. The present paper discusses sampling strategies and suggests a strategy based on the results retrieved by a search engine. This approach is used to draw a sample of 229 personal home pages that portray private identities. Findings on age and sex of the owners and elements characterizing the sites are reported.


Critical Care ◽  
2014 ◽  
Vol 18 (Suppl 1) ◽  
pp. P341 ◽  
Author(s):  
F Van Someren Gréve ◽  
KF Van der Sluijs ◽  
NP Juffermans ◽  
T Winters ◽  
SP Rebers ◽  
...  

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.


2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Man Zhang ◽  
Bogdan Marculescu ◽  
Andrea Arcuri

AbstractNowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.


Respiration ◽  
2021 ◽  
pp. 1-5
Author(s):  
Catherine L. Oberg ◽  
Reza Ronaghi ◽  
Erik E. Folch ◽  
Colleen L. Channick ◽  
Tao He ◽  
...  

<b><i>Background:</i></b> The coronavirus disease 2019 (COVID-19) pandemic has drastically affected hospital and operating room (OR) workflow around the world as well as trainee education. Many institutions have instituted mandatory preoperative SARS-CoV-2 PCR nasopharyngeal swab (NS) testing in patients who are low risk for COVID-19 prior to elective cases. This method, however, is challenging as the sensitivity, specificity, and overall reliability of testing remains unclear. <b><i>Objectives:</i></b> The objective of this study was to assess the concordance of a negative NS in low risk preoperative patients with lower airway bronchoalveolar lavage (BAL) specimens obtained from the same patients. <b><i>Methods:</i></b> We prospectively sent intraoperative lower airway BAL samples collected within 48 h of a negative mandatory preoperative NS for SARS-CoV-2 PCR testing. All adult patients undergoing a scheduled bronchoscopic procedure for any reason were enrolled, including elective and nonelective cases. <b><i>Results:</i></b> One-hundred eighty-nine patients were included. All BAL specimens were negative for SARS-CoV-2 indicative of 100% concordance between testing modalities. <b><i>Conclusions:</i></b> These results are promising and suggest that preoperative nasopharyngeal SARS-CoV-2 testing provides adequate screening to rule out active COVID-19 infection prior to OR cases in a population characterized as low risk by negative symptom screening. This information can be used for both pre-procedural screening and when reintroducing trainees into the workforce.


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