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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates

Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
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Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...

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European and U.S. perspectives on the sharing and integration of weather information into ATM decisions

Published in:
ATM2011, 9th USA/Europe Air Traffic Management Research and Development Seminar, 14 June 2011.

Summary

Weather is a major source of operational air traffic delays, accounting for 25 to 70 percent of all delays dependent of the geographical region. In today's Air Traffic Management (ATM) systems, a variety of weather information is available to help tactical and strategic planners better anticipate weather events that impact airspace capacity. Regretfully, the information is not always shared amongst all the stakeholders involved or well integrated into the existing ATM environment. This paper describes the high-level concepts for an improved sharing and integration or weather information into Air Traffic Management Decisions, as well as the current state and anticipated capabilities or the underlying information Management infrastructure.
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Summary

Weather is a major source of operational air traffic delays, accounting for 25 to 70 percent of all delays dependent of the geographical region. In today's Air Traffic Management (ATM) systems, a variety of weather information is available to help tactical and strategic planners better anticipate weather events that impact...

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CompositeMatch: detecting n-ary matches in ontology alignment

Published in:
OM 2009: Proc. 4th Int. Workshop on Ontology Matching, 25 October 2009, pp. 250-251.

Summary

The field of ontology alignment still contains numerous unresolved problems, one of which is the accurate identification of composite matches. In this work, we present a context-sensitive ontology alignment algorithm, CompositeMatch, that identifies these matches, along with the typical one-to-one matches, by looking more broadly at the information that a concept's relationships confer. We show that our algorithm can identify composite matches with greater confidence than current tools.
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Summary

The field of ontology alignment still contains numerous unresolved problems, one of which is the accurate identification of composite matches. In this work, we present a context-sensitive ontology alignment algorithm, CompositeMatch, that identifies these matches, along with the typical one-to-one matches, by looking more broadly at the information that a...

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