Purpose: The purpose of this study was to design and synthesize the ug46 peptide, incorporate its fibrils into composite materials, and evaluate its structural and antimicrobial properties. Another objective was to utilize spectroscopy and molecular simulation, enhanced by Machine Vision methods, to monitor the aggregation process of the ug46 peptide and assess its potential as a scaffold for an antimicrobial peptide. Methods: The structural analysis of the ug46 peptide reveals its dynamic conformational changes. Initially, the peptide exhibits a disordered structure with minimal α-helix content, but as incubation progresses, it aggregates into fibrils rich in β-sheets. This transformation was validated by CD and ThT assays, which showed decreased molar ellipticity and an increase in ThT fluorescence. Results: Laser-induced fluorescence and molecular dynamics simulations further revealed the transition from a compact native state to extended “worm-like” filament structures, influenced by peptide concentration and temperature. TEM and AFM confirmed these changes, showing the evolution of protofibrils into mature fibrils with characteristic twists. When incorporated into chitosan- bioglass composites, these fibrils significantly enhanced antimicrobial activity against pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. Conclusion: Overall, ug46 peptide fibrils show promise as a multifunctional scaffold with structural and antimicrobial benefits in composite biomaterials.

The aim of this research is to create an automated system for identifying soil microorganisms at the genera level based on raw microscopic images of monocultural colonies grown in laboratory environment. The examined genera are: Fusarium, Trichoderma, Verticillium, Purpureolicillium and Phytophthora. The proposed pipeline deals with unprocessed microscopic images, avoiding additional sample marking or coloration. The methodology includes several stages: image preprocessing, segmenting images to isolate microorganisms from the background, calculating features related to image color and texture for classification. Using an extensive dataset of 2866 images from the National Institute of Horticultural Research in Skierniewice the Extreme Learning Machine model was trained and validated. The model showcases high accuracy and computational efficiency compared to other Machine Learning state-of-the art methods e.g. CatBoost, Random Forest or Convolutional Neural Networks. Statistical techniques, including Multivariate Analysis of Variance were employed to confirm significant differences among the datasets, enhancing the model’s robustness. Nevertheless, Shapley Additive Explanations values provided transparency into the model’s decision-making process. This approach has the potential to improve early detection and management of soil pathogens, promoting sustainable agriculture and demonstrating machine learning’s potential in environmental monitoring, microbial ecology or industrial microbiology.