The visual analysis of microscopic images is often used for soil bacteria recognition in microbiology. Such task can be automated with the aid of machine learning and digital image processing techniques. The best results for soil microorganism identification usually rely on extracting features based on color. However, accommodating in the latter an extra impact of lighting conditions or sample’s preparation on classification accuracy is often omitted. In contrast, this research examines features which are insensitive to the above two factors by focusing rather on bacteria shape and their specific group dispersion. In doing so, the calculation of layout features resorts tok-means and mean shift methods. Additionally, the dependencies between specific distances determined from bacteria cells and the curvature of interpolated bacteria boundary are computed to extract vital geometric shape information. The proposed bacteria recognition tool involves testing four different classification methods for which the parameters are iteratively adjusted. The results obtained here for five selected soil bacteria genera:Enterobacter,Rhizobium,Pantoea,BradyrhizobiumandPseudomonasreach 85.14% classification accuracy upon combining both geometric and dispersion features. The latter forms a promising result as a substitutive tool for color-based feature classification.

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.

As large language models (LLMs) increasingly enter high-stakes decision-making contexts, questions arise about their suitability in domains requiring normative judgment, such as judicial sentencing. This study investigates whether LLMs exhibit bias when tasked with sentencing decisions in Polish criminal law, despite clear legal norms that prohibit considering extralegal factors. The simulated sentencing scenarios for theft offenses use two leading open-source LLMs (LLaMA and Mixtral) and systematically vary three defendant characteristics: gender, number of children, and the value of the stolen item. While none of these variables should legally affect sentence length under Polish law, our results reveal statistically significant disparities, particularly in how female defendants with children are treated. The non-parametric tests (Kruskal–Wallis and Mann–Whitney U) and correlation analysis were applied to quantify these effects. Our findings raise concerns about the normative reliability of LLMs and their alignment with principles of fairness and legality. From a jurisprudential perspective, we contrast the implicit logic of LLM sentencing with theoretical models of adjudication, including Dworkin’s moral interpretivism and Posner’s pragmatism. This work contributes to ongoing debates on the integration of AI in legal systems, highlighting both the empirical risks and the philosophical limitations of computational legal reasoning.