Artificial neural models of concentrations of fungal spores in the air for aerobiological research

This article describes in detail an advanced statistical method, the artificial neural network, and the possibilities for itsapplication in aerobiological analyses. The study and models involve the concentration of fungal spores in the air and their relationshipwith various biological and environmental factors. The author hopes that this work will contribute to a wider use of this method not onlyin the study of spores but also the concentration of pollen grains.

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Agrios GN (2005). Plant Pathology. 5th ed. London, UK: Elsevier Academic Press.
Brown JK, Hovmøller MS (2002). Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297 (5581): 537-541.
Burge HA (1989). Airborne allergenic fungi classification, nomenclature, and distribution. Immunology and Allergy Clinics of North America 9: 307-319.
Daliakopoulus IN, Coulibaly P, Tsanis IK (2005). Groundwater level forecasting using artificial neural networks. Journal of Hydrology 309: 229-240.
D’Amato G, Spieksma FT (1995). Aerobiologic and clinical aspects of mould allergy in Europe. Allergy 50: 870-877.
Feng X, Li Q, Zhu Y, Hou J, Jin L et al. (2015).Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment 107: 118-128.
Fischer G, Schwalbe R, Moller M, Ostrowski R, Dott W (1999). Species-specific production of microbial volatile organic compounds (MVOC) by airborne fungi from a compost facility. Chemosphere 39: 795-810.
Grinn-Gofroń A, Strzelczak A (2008a). Artificial neural network models of relationships between Alternaria spores and meteorological factors in Szczecin (Poland). International Journal of Biometeorology 52: 859-868.
Grinn-Gofroń A, Strzelczak A (2008b). Artificial neural network models of relationships between Cladosporium spores and meteorological factors in Szczecin (Poland). Grana 47: 304- 314.
Grinn-Gofroń A, Strzelczak A (2009). Hourly predictive artificial neural network and multivariate regression tree models of Alternaria and Cladosporium spore concentrations in Szczecin (Poland). International Journal of Biometeorology 53: 555-562.
Grinn-Gofroń A, Strzelczak A (2011). The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air. International Journal of Biometeorology 55: 235-241.
Grinn-Gofroń A, Strzelczak A (2013). Changes in concentration of Alternaria and Cladosporium spores during summer storms. International Journal of Biometeorology 57 (5): 759-776.
Grinn-Gofroń A, Strzelczak A, Wolski T (2011). The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores. Environmental Pollution 159: 602-608
Hardham AR (2001). The cell biology behind Phytophthora pathogenicity. Australasian Plant Pathology 30: 91-98.
Ingold GT (1953). Dispersal in Fungi. New York, NY, USA: Clarendon Press.
James TY, Letcher PM, Longcore JE, Mozley-Standridge SE, Porter D et al. (2006). A molecular phylogeny of the flagellated fungi (Chytridiomycota) and description of a new phylum (Blastocladiomycota). Mycologia 98 (6): 860-871.
Jedryczka M, Strzelczak A, Grinn-Gofron A, Nowak M, Wolski T et al. (2015). Advanced statistical models commonly applied in aerobiology cannot accurately predict the exposure of people to Ganoderma spore-related allergies. Agricultural and Forest Meteorology 201: 209-217.
Kauffman HF, Tomee JF, van de Riet MA, Timmerman AJ, Borger P (2000). Protease-dependent activation of epithelial cells by fungal allergens leads to morphologic changes and cytokine production. Journal of Allergy and Clinical Immunology 105: 1185-1193.
Kurup VP (2003). Fungal allergens. Current Allergy and Asthma Reports 3: 416.
Lacey L (1981). The aerobiology of conidial fungi. In: Cole GT, Kendrick B (editors). Biology of Conidial Fungi. New York, NY, USA: Academic Press, pp. 123-128.
Levetin E, Horner WE (2002). Fungal aerobiology: exposure and measurement. In: Breitenbach M, Crameri R, Lehrer SB (editors). Fungal Allergy and Pathogenicity. Chemical Immunology, Vol. 81. Basel, Switzerland: Karger, pp. 10-27.
Li G, Shi J (2010). On comparing three artificial neural networks for wind speed forecasting. Applied Energy 87 (7): 2313-2320.
Osowski S (1996). Algorithmic Approach to Artificial Neural Networks. Warsaw, Poland: WNT (in Polish).
Palmer A, Monatonó JJ, Sesé A (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management 27 (5): 781-790.
Ščevková J, Hrabovsky M, Kováč J, Rosa S (2019). Intradiurnal variation of predominant airborne fungal spore biopollutants in the Central European urban environment. Environmental Science and Pollution Research (in press). doi: 10.1007/ s11356-019-06616-7
Simon-Nobbe B, Denk U, Pöll V, Rid R, Breitenbach M (2008). The spectrum of fungal allergy. International Archives of Allergy and Immunology 145: 58-86.
Tadeusiewicz R (2001). Introduction to Neural Networks. Krakow, Poland: Statsoft Polska (in Polish).
Trail F (2007). Fungal cannons: explosive spore discharge in the Ascomycota. FEMS Microbiology Letters 276: 12-18.