A new method to quantify atmospheric Poaceae pollen DNA based on the trnT-F cpDNA region

Abstract Background Pollen, mold spores, bacteria and viruses are the main biological substances in the atmosphere causing allergic symptoms and disease. Distinguishing pollen and spores is quite time consuming and requires a trained expert. There is a different approach to identification of these substances such as microscopic analysis. However, DNA based identification of these is becoming popular recently. Objective We evaluated the correlation between the quantity of DNA, which was amplified using trnT-F cpDNA specific primers in samples obtained from a high volume air sampler (HVAS), and concentration of Poaceae pollen collected with a Burkard trap. Materials and methods Here, we present a method for identifying and quantifying airborne Poaceae pollen using a single step polymerase chain reaction (PCR) technique. Forty daily air samples were collected by HVAS. The method was optimised using two different methods (M1 and M2) and the trnT-F cpDNA region was amplified using a Poaceae specific primer pair. The correlation between the quantity of DNA and pollen concentration was tested using R statistical programming language. Results Although a significant correlation was obtained between the M1 and M2 methods (R2=0.655, p<0.01), the M2 method was more correlated with pollen concentration. The correlation between pollen and DNA content changed due to episodes that were observed during the pollen season. DNA concentrations from the PCR data were significantly correlated with pollen concentrations determined by light microscopy (R2=0.767, p<0.01) in episode II using the M2 method and during the entire season (R2=0.469, p<0.01) using M2. Conclusions The M2 method correctly identified Poaceae pollen in mixed air samples from Zonguldak Province. The non-coding trnT-F cpDNA region was used for the first time in aerobiological samples to identify Poaceae pollen. Use of this method that does not require DNA extraction may be a crucial step for real-time pollen monitoring devices to be developed in the future. The correlation strength between pollen and amplified DNA content could be improved using a sampler that has a lower absorption rate, and a more sensitive technique, such as qPCR.

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Neural networks for increased accuracy of allergenic pollen monitoring

Scientific Reports ◽

10.1038/s41598-021-90433-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marcel Polling ◽

Chen Li ◽

Lu Cao ◽

Fons Verbeek ◽

Letty A. de Weger ◽

...

Keyword(s):

Neural Networks ◽

Airborne Pollen ◽

Pollen Grains ◽

Allergenic Pollen ◽

Herbarium Specimens ◽

Pollen Monitoring ◽

Pollen Concentrations ◽

Source Of Information ◽

Urticaceae Pollen

AbstractMonitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

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List mode data acquisition technique for Compton suppression and minimum detectable activity (MDA) reduction in standard high-volume air samples

Radiation Physics and Chemistry ◽

10.1016/j.radphyschem.2022.109964 ◽

2022 ◽

pp. 109964

Author(s):

Mahdi Tourang ◽

Asghar Hadadi ◽

Mitra Athari Allaf ◽

Dariush Sardari

Keyword(s):

Data Acquisition ◽

High Volume ◽

Compton Suppression ◽

Minimum Detectable Activity ◽

List Mode ◽

Air Samples ◽

Detectable Activity ◽

Acquisition Technique

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Main features of Poaceae pollen season in Madeira region (Portugal)

Acta Agrobotanica ◽

10.5586/aa.2015.032 ◽

2015 ◽

Vol 68 (4) ◽

pp. 367-372

Author(s):

Irene Câmara Camacho ◽

Rita Câmara ◽

Roberto Camacho

Keyword(s):

Pollen Season ◽

Airborne Pollen ◽

Growing Season ◽

Onset Date ◽

Critical Levels ◽

Pollen Monitoring ◽

Madeira Island ◽

Poaceae Pollen ◽

The Mean ◽

Low Levels

The pollinic spectrum of the Madeira region is dominated by grass pollen, which also represents an important aeroallergen in Europe. The present work aims to analyze the main features of the Poaceae pollen season in the Madeira region to determine the allergic risk. The study took place in Funchal city, the capital of Madeira Island, over a period of 10 years (2003–2012). The airborne pollen monitoring was carried out with a Hirst type volumetric trap, following well-established guidelines.In the atmosphere of Funchal, the mean annual Poaceae pollen index was 229. The mean Poaceae pollen season lasts 275 days, with an onset date in January/March and an end date in November/December. Poaceae counts showed a seasonal variation with 2 distinct peaks: a higher peak between March and June, and the second one in autumn. The peak values occurred mainly between April and June, and the highest peak was 93 grains/m3, detected on the 27th May of 2010. The Poaceae pollen remaining at low levels during the whole growing season, presenting a nil to low allergenic risk during most of the study period. Higher critical levels of allergens have been revealed after 2006. In general, the pollen risk from Poaceae lasted only a few days per year, despite the very long pollen season and the abundance of grasses in the landscape of Madeira Island.

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The late flowering of invasive species contributes to the increase of Artemisia allergenic pollen in autumn: an analysis of 25 years of aerobiological data (1995–2019) in Trentino-Alto Adige (Northern Italy)

Aerobiologia ◽

10.1007/s10453-020-09663-7 ◽

2020 ◽

Vol 36 (4) ◽

pp. 669-682 ◽

Cited By ~ 1

Author(s):

Antonella Cristofori ◽

Edith Bucher ◽

Michele Rossi ◽

Fabiana Cristofolini ◽

Veronika Kofler ◽

...

Keyword(s):

Invasive Species ◽

Pollen Season ◽

Airborne Pollen ◽

Pollen Grains ◽

Late Summer ◽

Natura 2000 ◽

Allergenic Pollen ◽

Pollen Concentration ◽

Pollen Concentrations ◽

Negative Impacts

AbstractArtemisia pollen is an important aeroallergen in late summer, especially in central and eastern Europe where distinct anemophilous Artemisia spp. produce high amounts of pollen grains. The study aims at: (i) analyzing the temporal pattern of and changes in the Artemisia spp. pollen season; (ii) identifying the Artemisia species responsible for the local airborne pollen load.Daily pollen concentration of Artemisia spp. was analyzed at two sites (BZ and SM) in Trentino-Alto Adige, North Italy, from 1995 to 2019.The analysis of airborne Artemisia pollen concentrations evidences the presence of a bimodal curve, with two peaks, in August and September, respectively. The magnitude of peak concentrations varies across the studied time span for both sites: the maximum concentration at the September peak increases significantly for both the BZ (p < 0.05) and SM (p < 0.001) site. The first peak in the pollen calendar is attributable to native Artemisia species, with A. vulgaris as the most abundant; the second peak is mostly represented by the invasive species A. annua and A. verlotiorum (in constant proportion along the years), which are causing a considerable increase in pollen concentration in the late pollen season in recent years.. The spread of these species can affect human health, increasing the length and severity of allergenic pollen exposure in autumn, as well as plant biodiversity in both natural and cultivated areas, with negative impacts on, e.g., Natura 2000 protected sites and crops.

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Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Atmosphere ◽

10.3390/atmos10110717 ◽

2019 ◽

Vol 10 (11) ◽

pp. 717

Author(s):

Ricardo Navares ◽

José Luis Aznarte

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Airborne Pollen ◽

Missing At Random ◽

Spatial Relations ◽

Computational Time ◽

Pollen Monitoring ◽

Distance Weighting ◽

Poaceae Pollen ◽

Data Points

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.

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Integrating Phenological, Aerobiological and Weather Data to Study the Local and Regional Flowering Dynamics of Four Grapevine Cultivars

Agronomy ◽

10.3390/agronomy10020185 ◽

2020 ◽

Vol 10 (2) ◽

pp. 185

Author(s):

María Fernández-González ◽

Helena Ribeiro ◽

Alba Piña-Rey ◽

Ilda Abreu ◽

F. Javier Rodríguez-Rajo

Keyword(s):

Airborne Pollen ◽

Weather Data ◽

Vitis Vinifera L ◽

Pollen Concentration ◽

Peak Period ◽

Hirst Sampler ◽

Main Pollen Season ◽

Pollen Concentrations ◽

Integral Spin ◽

Bioclimatic Conditions

Phenological, aerobiological, and weather data are useful tools to study local and regional flowering dynamics in crops with economic importance. The present study focuses on four autochthonous grapevine cultivars, namely, ‘Treixadura’, ‘Godello’, ‘Loureira’, and ‘Albariño’ (Vitis vinifera L.), which belong to the Designation of Origin Ribeiro area (located in northwestern Spain) from 2015–2019. The aims of the work were to (1) compare the airborne pollen concentration in the vineyard collected by two different traps, (2) analyze the influence of the main meteorological variables on cultivar phenology and pollen concentration, and (3) test the contribution of the air masses on pollen concentrations in the vineyard. Phenological development has been assessed twice weekly, according to the Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie (BBCH) scale. Airborne pollen concentrations were monitored by using two traps during stage 6 (flowering), namely, a Hirst volumetric sampler and a Cour passive trap. The bioclimatic conditions affected the duration of flowering, ranging from 11 and 24 days. The highest seasonal pollen integral (SPIn) was registered in 2016 for the Hirst sampler, with 302 pollen, and in 2019 for the Cour trap, with 1,797,765 pollen/m2/day. The main variables affecting pollen concentrations were average temperature during the main pollen season, as well as, temperatures and dew points during the pre-peak period. The relationship between pollen data registered by both traps and the obtained harvest indicate that the Hirst trap may be more suitable for predicting a local production and that the Cour sampler is more appropriate for forecasting regional productions.

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Standardising pollen monitoring: quantifying confidence intervals for measurements of airborne pollen concentration

Aerobiologia ◽

10.1007/s10453-020-09656-6 ◽

2020 ◽

Vol 36 (4) ◽

pp. 605-615 ◽

Cited By ~ 1

Author(s):

Beth Addison-Smith ◽

Darren Wraith ◽

Janet M. Davies

Keyword(s):

Confidence Intervals ◽

Airborne Pollen ◽

Pollen Concentration ◽

Pollen Monitoring ◽

Airborne Pollen Concentration

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Antireflection Coating on PMMA Substrates by Atomic Layer Deposition

Coatings ◽

10.3390/coatings10010064 ◽

2020 ◽

Vol 10 (1) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

Pallabi Paul ◽

Kristin Pfeiffer ◽

Adriana Szeghalmi

Keyword(s):

Atomic Layer Deposition ◽

Spectral Range ◽

Microscopic Analysis ◽

Atomic Layer ◽

High Volume ◽

Antireflection Coating ◽

Environmental Stability ◽

Antireflection Coatings ◽

Layer Deposition ◽

Average Reflectance

Antireflection coatings (ARC) are essential for various optical components including such made of plastics for high volume applications. However, precision coatings on plastics are rather challenging due to typically low adhesion of the coating to the substrate. In this work, optimization of the atomic layer deposition (ALD) processes towards conformal optical thin films of Al2O3, TiO2 and SiO2 on poly(methyl methacrylate) (PMMA) has been carried out and a five-layer ARC is demonstrated. While the uncoated PMMA substrates have a reflectance of nearly 8% in the visible (VIS) spectral range, this is reduced below 1.2% for the spectral range of 420–670 nm by applying a double-side ARC. The total average reflectance is 0.7%. The optimized ALD coatings show a good adhesion to the PMMA substrates even after the climate test. Microscopic analysis on the cross-hatch areas on PMMA after the climate test indicates very good environmental stability of the ALD coatings. These results enable a possible route by ALD to deposit uniform, crack free, adhesive and environmentally durable thin film layers on sensitive thermoplastics like PMMA.

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Comments on Brugger and others (2018) ‘A quantitative comparison of microfossil extraction methods from ice cores’

Journal of Glaciology ◽

10.1017/jog.2019.10 ◽

2019 ◽

Vol 65 (250) ◽

pp. 344-346 ◽

Cited By ~ 1

Author(s):

DANIELA FESTI ◽

WERNER KOFLER ◽

KLAUS OEGGL

Keyword(s):

Ice Cores ◽

Extraction Methods ◽

Quantitative Comparison ◽

Qualitative Assessment ◽

Pollen Concentration ◽

Previous Evidence ◽

Pollen Concentrations

ABSTRACTIn our comments, we re-evaluate Brugger and others (2018) Lycopodium/Eucalyptus double marker approach, based on the fact that previous evidence already demonstrated that the batch of Eucalyptus tablets used by Brugger and others (2018) is not suitable for quantitative comparisons as they are characterized by inconsistent pollen concentration. We present clear evidence that the Eucalyptus tablets do feature inaccurate pollen concentrations, and are therefore improper for all quantitative comparisons of microfossil extraction methods. Consequently, the results of the quantitative and qualitative assessment of different pollen extraction methods from ice samples compiled by Brugger and others (2018) are highly questionable due to the use of faulty marker tablets.

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