Detection canines serve critical roles to support the military, homeland security and border protection. Some explosive detection tasks are physically demanding for dogs, and prior research suggests this can lead to a reduction in olfactory detection sensitivity. To further evaluate the effect of exercise intensity on olfactory sensitivity, we developed a novel olfactory paradigm that allowed us to measure olfactory detection thresholds while dogs exercised on a treadmill at two different exercise intensities.
View Article and Find Full Text PDFWhile canines are most commonly trained to detect traditional explosives, such as nitroaromatics and smokeless powders, homemade explosives (HMEs), such as fuel-oxidizer mixtures, are arguably a greater threat. As such, it is imperative that canines are sufficiently trained in the detection of such HMEs. The training aid delivery device (TADD) is a primary containment device that has been used to house HMEs and HME components for canine detection training purposes.
View Article and Find Full Text PDFBiomedical detection dogs offer incredible advantages during disease outbreaks that are presently unmatched by current technologies, however, dogs still face hurdles of implementation due to lack of inter-governmental cooperation and acceptance by the public health community. Here, we refine the definition of a biomedical detection dog, discuss the potential applications, capabilities, and limitations of biomedical detection dogs in disease outbreak scenarios, and the safety measures that must be considered before and during deployment. Finally, we provide recommendations on how to address and overcome the barriers to acceptance of biomedical detection dogs through a dedicated research and development investment in olfactory sciences.
View Article and Find Full Text PDFBackground And Aim: This study aims to determine COVID-19 patient demographics and comorbidities associated with their hospital length of stay (LOS).
Methods: Design: Single-site, retrospective study. Setting: A suburban 700-bed community hospital in Newark, Delaware, USA.
This paper criticises the conclusions and the unanswered questions in the National Institute of Standards and Technology (NIST)'s official report on the evacuation of the World Trade Center in New York City, United States, on 11 September 2001. It reviews the extent to which the report disregards several conventional statistical methods and comments on the NIST's refusal to share the machine-readable data file with the scientific community for replication and further analysis. Problems lie in the sampling methods employed, the treatment of missing data, the use of ordinary least squares (OLS) with binary dependent variables, the failure to document the scalability of the scales used, the lack of tests to check for constant error variance, and the absence of overall fit tests of the model.
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