The novel coronavirus SARS-CoV-2, since its initial outbreak in Wuhan, China has led to a worldwide pandemic and has shut down nations. As with any outbreak, there is a general strategy of detection, containment, treatment and/or cure. The authors would argue that rapid and efficient detection is critical and required to successful management of a disease. The current study explores and successfully demonstrates the use of canines to detect COVID-19 disease in exhaled breath. The intended use was to detect the odor of COVID-19 on contaminated surfaces inferring recent deposition of infectious material from a COVID-19 positive individual. Using masks obtained from hospitalized patients that tested positive for COVID-19 disease, four canines were trained and evaluated for their ability to detect the disease. All four canines obtained an accuracy >90% and positive predictive values ranging from ∼73 to 93% after just one month of training.
We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins; natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.
With the advent of the Internet, American Indian/Alaska Native (AI/AN) communities in the Pacific Northwest have new opportunities to access high quality and relevant health information. The Pacific Northwest Regional Medical Library (PNRML), regional headquarters of the National Network of Libraries of Medicine, a program sponsored by the National Library of Medicine, sought to facilitate that access and worked with a selected group of sixteen tribes and native village consortia. The steps were: (1) work with AI/AN communities to arrive at mutually-agreeable health information connectivity objectives and long-term solutions, (2) provide funding to AI/AN communities to ensure Internet connectivity and the presence of Internet workstations for health workers and for the public, and (3) train in effective health information seeking. Community-based approaches helped the PNRML adjust policies and practice for improved information outreach to AI/AN communities in the region. The project participants, collaborating with our staff, successfully carried out many of the community goals and, at the same time, we gained insight about the variables that were barriers or facilitators of success. While we are coming at outreach from a library perspective, the policy and method lessons we learned could apply to a broad variety of outreach endeavors.
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