Publications by authors named "R Bellotti"
Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such, antimicrobial resistance is considered as one of the most significant and universal challenges to both health and society, as well as the environment. In our research, we employ the explainable artificial intelligence paradigm to identify the factors that most affect the onset of antimicrobial resistance in diversified territorial contexts, which can vary widely from each other in terms of climatic, economic and social conditions.
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- - Advances in DNA sequencing have transformed plant genomics, but predicting plant traits (phenotypes) from genetic data is still difficult, especially in breeding contexts; this study aims to improve prediction accuracy by using explainable AI with machine learning.
- - The research compared various machine learning methods to predict the almond shelling fraction using data from an almond collection, revealing that the Random Forest method provided the best predictions and identified important genetic regions linked to the trait.
- - The study demonstrated that explainable AI not only improves the understanding of genetic factors related to phenotypes but also plays a crucial role in enhancing crop production in sustainable agriculture.
Autism spectrum disorder (ASD) affects social interaction and communication. Emerging evidence links ASD to gut microbiome alterations, suggesting that microbial composition may play a role in the disorder. This study employs explainable artificial intelligence (XAI) to examine the contributions of individual microbial species to ASD.
View Article and Find Full Text PDFImitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept.
J Clin Monit Comput
December 2024
Article Synopsis
- Artificial neural networks (ANNs) can learn and are evaluated in this study for their ability to calculate minute volume changes during spontaneous breathing, specifically in an animal model of metabolic acidosis.
- The study involved ten anesthetized pigs that were subjected to varying pH levels, with data collected on several physiological parameters to train the ANN.
- The trained ANN showed high accuracy in estimating minute volume changes, suggesting they could play a significant role in enhancing closed-loop artificial ventilator systems in the future.
Background: The use of magnetic resonance (MR) imaging for proton therapy treatment planning is gaining attention as a highly effective method for guidance. At the core of this approach is the generation of computed tomography (CT) images from MR scans. However, the critical issue in this process is accurately aligning the MR and CT images, a task that becomes particularly challenging in frequently moving body areas, such as the head-and-neck.
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