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Path to autonomous soil sampling and analysis by ground-based robots.
J Environ Manage
June 2024
Civil & Environmental Engineering, USA. Electronic address:
Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis.
View Article and Find Full Text PDFDevelopment of a comprehensive cardiovascular disease genetic risk assessment test.
medRxiv
May 2024
Illumina Inc., San Diego, CA 92122.
Article Synopsis
- The study addresses the limitations of current genetic testing for cardiovascular disease (CVD) by introducing a comprehensive clinical genome test with semi-automated interpretation.
- The test assesses various genetic factors, including monogenic conditions, polygenic risk scores, and pharmacogenomics, using data from a broad genomic database.
- Findings suggest that this approach can effectively identify genetic risks for CVD, providing valuable information for patients and potentially enabling broader public health applications.
Portable X-ray fluorescence for autonomous in-situ characterization of chloride in oil and gas waste.
Environ Pollut
January 2023
Department of Civil and Environmental Engineering, Carnegie Mellon University, United States. Electronic address:
Soil salinization resulting from anthropogenic activities affects soil health and productivity. Methods that can provide rapid, inexpensive, and accurate salinity characterization over vast areas of soil and waste materials will help in managing their impacts. The objective of this work was to evaluate the accuracy and precision of portable X-ray Fluorescence (pXRF) Cl measurements of highly saline waste material (WMs) from oil and gas production sites.
View Article and Find Full Text PDFAcute vital signs changes are underrepresented by a conventional electronic health record when compared with automatically acquired data in a single-center tertiary pediatric cardiac intensive care unit.
J Am Med Inform Assoc
June 2022
Divisions of Critical Care Medicine and Cardiology, Department of Pediatrics, Washington University School of Medicine, Saint Louis Children's Hospital, St. Louis, Missouri, USA.
Objective: We sought to evaluate the fidelity with which the patient's clinical state is represented by the electronic health record (EHR) flow sheet vital signs data compared to a commercially available automated data aggregation platform in a pediatric cardiac intensive care unit (CICU).
Methods: This is a retrospective observational study of heart rate (HR), systolic blood pressure (SBP), respiratory rate (RR), and pulse oximetry (SpO2) data archived in a conventional EHR and an automated data platform for 857 pediatric patients admitted postoperatively to a tertiary pediatric CICU. Automated data captured for 72 h after admission were analyzed for significant HR, SBP, RR, and SpO2 deviations from baseline (events).
Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review.
J Am Coll Radiol
February 2022
Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Director, Northwest Screening and Cancer Outcomes Research Enterprise, University of Washington, Seattle, Washington; Deputy Editor, JACR. Electronic address:
Purpose: The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography.
Methods: A systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included.
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