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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. Dogs (n = 3) showed a decrement in olfactory detection for 1-bromooctane at 10-3 (v/v) dilutions and lower under greater exercise intensity. Dogs' hit rate for the lowest concentration dropped from 0.87 ± 0.04 when walking at low intensity to below 0.45 ± 0.06 when trotting at moderate intensity. This decline had an interaction with the duration of the session in moderate intensity exercise, whereby dogs performed near 100% detection in the first 10 min of the 8 km/h session, but showed 0% detection after 20 min. Hit rates for high odor concentrations (10-2) were relatively stable at both low (1 ± 0.00) and moderate (0.91 ± 0.04) exercise intensities. The paradigm and apparatus developed here may be useful to help further understand causes of operationally relevant olfactory detection threshold decline in dogs.

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While 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. This research assesses the odor release from HME components, ammonium nitrate (AN), urea nitrate (UN), and potassium chlorate (PC), housed in TADDs. Canine odor recognition tests (ORTs) were used with analytical data to determine the detectability of TADDs containing AN, UN, or PC. Headspace analysis by gas chromatography/mass spectrometry (GC/MS) with solid-phase microextraction (SPME) or online cryotrapping were used to measure ammonia or chlorine, as well as other unwanted odorants, emanating from bulk AN, UN, and PC in TADDs over 28 weeks. The analytical data showed variation in the amount of ammonia and chlorine over time, with ammonia from AN and UN decreasing slowly over time and the abundance of chlorine from PC TADDs dependent on the frequency of exposure to ambient air. Even with these variations in odor abundance, canines previously trained to detect bulk explosive HME components were able to detect all three targets in glass and plastic TADDs for at least 18 months after loading. Detection proficiency ranged from 64% to 100% and was not found to be dependent on either age of material.

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Biomedical 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.

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BACKGROUND 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. Patients: Patients admitted to the hospital from March 11, 2020, to August 11, 2020, with a positive COVID-19 status. We followed a time-to-event analysis approach and used Kaplan-Meir curves and log-rank tests for bivariate analyses, and an accelerated failure time model for a multivariable model of hospital LOS. RESULTS: Six hundred and eighty-seven patients discharged alive (mean [SD] age, 60.94 [18.10] years; 339 men [49.34%]; 307 Black/African-American [44.69%]; and 267 White [38.86%]) were included in the investigation. Bivariate analysis using Kaplan-Meir curves showed that patients' age, sex, ethnicity, insurance type, comorbidity of fluid and electrolyte disorder, hypertension, renal failure, diabetes, coagulopathy, congestive heart failure, peripheral vascular disease, neurological disorder, coronary artery disease, and cardiac arrhythmias to be significantly associated with LOS (P<0.05). In the multivariable analysis, patients' age, sex, ethnicity, number of Elixhauser comorbidities, and number of weeks since onset of the pandemic was significantly associated with LOS (P<0.05). Fluid and electrolyte disorder is the only comorbidity independently associated with LOS after adjusting for patients' age, sex, race, ethnicity, number of Elixhauser comorbidities, and weeks since onset of pandemic. CONCLUSION: COVID-19 patients LOS vary based on multiple factors. Understanding these factors are crucial to improving the prediction accuracy of COVID-19 patient census in hospitals for resource planning and care delivery. RELEVANCE FOR PATIENTS: Understanding of the factors associated with LOS of the COVID-19 patients may help the care providers and the patients to better anticipate the LOS, optimize the resources and processes, and prevent protracted stays.

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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. There are also conceptual and theoretical issues, such as the absence in the report of considerations of the influence of group-level processes and their impact on the collective behaviour of evacuating collectivities.

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