Among 176 patients who had had severe acute respiratory syndrome (SARS), SARS-specific antibodies were maintained for an average of 2 years, and significant reduction of immunoglobulin G–positive percentage and titers occurred in the third year. Thus, SARS patients might be susceptible to reinfection >3 years after initial exposure.

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Plasmonic gratings constitute a paradigmatic instance of the wide range of applications enabled by plasmonics. While subwavelength metal gratings find applications in optical biosensing and photovoltaics, atomically thin gratings achieved by periodically doping a graphene monolayer perform as metasurfaces for the control of terahertz radiation. In this paper we show how these two instances of plasmonic gratings inherit their spectral properties from an underlying slab with translational symmetry. We develop an analytical formalism to accurately derive the mode spectrum of the gratings that provides a great physical insight.

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Purpose: The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19's transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds. Methods: With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions. Results: The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201). Conclusions: Based on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

Current sediment quality guidelines generally adopt a tiered approach in order to assess sediment quality more cost-effectively. The uncertainties involved in the tiered approach of an integrative assessment, however have not been quantified resulting in a risk of committing type I error or type II error at the final confirmatory stage. This study develops statistical criteria and guidelines for the sediment chemistry component of an integrative assessment of sediment quality. At the tier 1 screening stage, historical data or an initial survey is required to determine the minimum sample numbers that will be required to be representative of the study site. Understanding the guiding principles which have underpinned the setting of sediment quality criteria for contaminant is an important factor in tier 1 evaluations. To reduce cost and uncertainty in data, sampling should include the least number of samples necessary to minimise uncertainties by estimating the probability distribution function (which represents the variability of the natural environment at the location of concern), with subsequent application of kriging and sequential simulation methods on data obtained. Implementation of statistical criteria and guidelines in sediment quality assessments can provide a foundation for a further quantitative cost/benefit analysis and decrease the risk of committing type I and type II statistical errors at later classification stages.