<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nicolas Bruffaerts</style></author><author><style face="normal" font="default" size="100%">E Graf</style></author><author><style face="normal" font="default" size="100%">Astha Tiwari</style></author><author><style face="normal" font="default" size="100%">I Pyrri</style></author><author><style face="normal" font="default" size="100%">S Erb</style></author><author><style face="normal" font="default" size="100%">M Plaza</style></author><author><style face="normal" font="default" size="100%">Elizabet D'hooge</style></author><author><style face="normal" font="default" size="100%">P Matavulj</style></author><author><style face="normal" font="default" size="100%">B Sikoparija</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation</style></title><secondary-title><style face="normal" font="default" size="100%">European Aerosol Congress 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">fungal spores</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug-2024</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">European Aerosol Society</style></publisher><pub-location><style face="normal" font="default" size="100%">Tampere, Finland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Airborne bioparticles, notably fungal spores, pose health risks, necessitating precise monitoring. While manual methods exist, interest is shifting towards automated systems employing machine learning. However, challenges persist due to diverse particle properties and limited training data. This study, part of SYLVA and COST Action ADOPT, addresses these gaps by establishing best practices for cultivating reference material and creating tailored datasets. Seventeen fungal species were tested on Plair RapidE+ and SwisensPoleno Jupiter. Proof-of-principle models using holography and fluorescence data were developed, achieving variable genus classification accuracy (0.43-0.95). The protocol enhances automatic identification of airborne fungal spores, promising more efficient monitoring systems.&lt;/p&gt;
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