Recent developments in monitoring and modelling airborne pollen, a review

Public awareness of the rising importance of allergies and other respiratory diseases has led to increased scientific effort to accurately and rapidly monitor and predict pollen, fungal spores and other bioaerosols in our atmosphere. An important driving force for the increased social and scientific concern is the realisation that climate change will increasingly have an impact on worldwide bioaerosol distributions and subsequent human health. In this review we examine new developments in monitoring of atmospheric pollen as well as observation and source-orientated modelling techniques. The results of a Scopus® search for scientific publications conducted with the terms ‘Pollen allergy’ and ‘Pollen forecast’ included in the title, abstract or keywords show that the number of such articles published has increased year on year. The 12 most important allergenic pollen taxa in Europe as defined by COST Action ES0603 were ranked in terms of the most ‘popular’ for model-based forecasting and for forecasting method used. Betula, Poaceae and Ambrosia are the most forecast taxa. Traditional regression and phenological models (including temperature sum and chilling models) are the most used modelling methods, but it is notable that there are a large number of new modelling techniques being explored. In particular, it appears that Machine Learning techniques have become more popular and led to better results than more traditional observation-orientated models such as regression and time-series analyses

The SARS-CoV-2 viral load in COVID-19 patients is lower on face mask filters than on nasopharyngeal swabs.

Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3-2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation

Being a Medievalist in the Age of the Pandemic

Matt Clancy writes about how the pandemic shaped his career choices on completing his PhD, and argues that the present challenges facing the profession mean that we should reconsider how we define ourselves as medievalists. He affirms that it is possible to finish a PhD in a pandemic and tentatively begin a career, and that an apparent lack of correspondence between experience as a medievalist and seemingly non-medievalist employment diminishes neither his identity as a medievalist, nor his engagement with the field the enforced change from teaching in person to teaching online is well-suited to the skills medievalists have already developed through teaching Chaucer and other medieval texts, since both present a strange version of the familiar

Wealth and Involvement

2001/11/06. Discuss what it means to support the community and be good stewards of the resources God has provided. Faith and Philanthropy Dinner Seminar

TROPHY: Trust Region Optimization Using a Precision Hierarchy

We present an algorithm to perform trust-region-based optimization for nonlinear unconstrained problems. The method selectively uses function and gradient evaluations at different floating-point precisions to reduce the overall energy consumption, storage, and communication costs; these capabilities are increasingly important in the era of exascale computing. In particular, we are motivated by a desire to improve computational efficiency for massive climate models. We employ our method on two examples: the CUTEst test set and a large-scale data assimilation problem to recover wind fields from radar returns. Although this paper is primarily a proof of concept, we show that if implemented on appropriate hardware, the use of mixed-precision can significantly reduce the computational load compared with fixed-precision solvers.Comment: 14 pages, 2 figures, 2 table

A comprehensive aerobiological study of the airborne pollen in the Irish environment

International audienceAbstract Respiratory allergies triggered by pollen allergens represent a significant health concern to the Irish public. Up to now, Ireland has largely refrained from participating in long-term aerobiological studies. Recently, pollen monitoring has commenced in several sampling locations around Ireland. The first results of the pollen monitoring campaigns for Dublin (urban) and Carlow (rural) concerning the period 2017–2019 and 2018–2019, respectively, are presented herein. Additional unpublished pollen data from 1978–1980 and, 2010–2011 were also incorporated in creating the first pollen calendar for Dublin. During the monitoring period over 60 pollen types were identified with an average Annual Pollen Integral (APIn) of 32,217 Pollen × day/m 3 for Dublin and 78,411 Pollen × day/m 3 for Carlow. The most prevalent pollen types in Dublin were: Poaceae (32%), Urticaceae (29%), Cupressaceae/Taxaceae (11%), Betula (10%), Quercus (4%), Pinus (3%), Fraxinus (2%), Alnus (2%) and Platanus (1%). The predominant pollen types in Carlow were identified as Poaceae (70%), Urticaceae (12%) , Betula (10%), Quercus (2%), Fraxinus (1%) and Pinus (1%). These prevalent pollen types increased in annual pollen concentration in both locations from 2018 to 2019 except for Fraxinus. Although higher pollen concentrations were observed for the Carlow (rural) site a greater variety of pollen types were identified for the Dublin (urban) site. The general annual trend in the pollen season began with the release of tree pollen in early spring, followed by the release of grass and herbaceous pollen which dominated the summer months with the annual pollen season coming to an end in October. This behaviour was illustrated for 21 different pollen types in the Dublin pollen calendar. The correlation between ambient pollen concentration and meteorological parameters was also examined and differed greatly depending on the location and study year. A striking feature was a substantial fraction of the recorded pollen sampled in Dublin did not correlate with the prevailing wind directions. However, using non-parametric wind regression, specific source regions could be determined such as Alnus originating from the Southeast, Betula originating from the East and Poaceae originating from the Southwest