AIMC Topic: Antigens, Plant

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Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Environmental monitoring and assessment
Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant trigger for several of these allergic diseases. Ambrosia (ragweed) is known for its abundant production of pollen and its potent allergic effect in Nort...

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Environmental monitoring and assessment
Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the rise due to increased pollen levels caused by global warming and the spread of highly invasive weeds. The production, release, and dispersal of pollen de...

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Pollen.

International journal of environmental research and public health
Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, accurately predicting the daily concentration of airborne pollen is of significant public benefit in providing timely alerts. This study presents a me...

Structure-function properties of hypolipidemic peptides.

Journal of food biochemistry
This review addresses the structure-function properties of hypolipidemic peptides. The cholesterol-lowering peptide (lactostatin: IIAEK) operates via a new regulatory pathway in the calcium-channel-related mitogen-activated protein kinase (MAPK) sign...

Why Protein Modifications Matter for Digestibility: The Case of Ara h 1 Peanut Allergen and Trypsin Cleavage.

Journal of agricultural and food chemistry
Trypsin is the principal intestinal endopeptidase and proteomics digestion tool, yet the impact of protein modifications (PMs) on digestibility and allergenicity remains underexplored. We employed a proteomic approach to assess trypsin cleavage effic...