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Advancing automated identification of airborne fungal spores: guidelines for cultivation and reference dataset creationAbstract
Machine Learning Abstract: Airborne bioparticles, including fungal spores, are of major concern for human and plant health, necessitating precise monitoring systems. While a European norm exists for manual ...
Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
B Sikoparija Source: Aerobiologia, Volume 41, Number 525 (2025) Keywords: Airflow cytometry Automatic identification Culture collection fungal spores Machine Learning Abstract: Airborne bioparticles, including ...
Variability in Alternaria alternata spore characteristics under different culture conditions: implications for automatic detection using air flow cytometry
algorithms by machine learning. Alternaria alternata was cultured on three different media, including exposure to UV light to favor sporulation. Spore morphology was evaluated both macroscopically and ...
Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
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 ...
Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
allowing automated real-time monitoring. Most of them rely on machine learning for the identification of bioaerosols. However, the diverse nature of airborne particles in terms of size, properties and ...
Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.
as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of ...
Novel prediction models for genotoxicity based on biomarker genes in human HepaRG™ cells.
Issue 2 (2023) Keywords: Algorithms Animals Biomarkers DNA Damage Gene Expression Profiling Humans Supervised Machine Learning Abstract: Transcriptomics-based biomarkers are promising new approach ...
Identification of Mycobacterium abscessus Subspecies by MALDI-TOF Mass Spectrometry and Machine Learning.
challenging. In this study, a collection of 325 clinical isolates of M. abscessus was used for MALDI- TOF MS analysis and for the development of machine learning predictive models based on MALDI- TOF MS protein ...
Novel prediction models for genotoxicity based on biomarker genes in human HepaRGTM cells
were developed based on an extended reference dataset of 38 chemicals including existing as well as newly generated gene expression data. Both unsupervised and supervised machine learning algorithms were ...
Advances in the physicochemical characterization of nanoparticles in a regulatory context.
particle detection supported machine learning are proposed. Service: Éléments traces et nanomatériaux Spoorelementen en nanomaterialen Trace elements and nanomaterials Manuscript versions: Version: ...