Applied Sciences

The stability of spore-forming soil bacteria is crucial for their effective use in agricultural biopreparations. This study evaluated the long-term survivability of selected strains (Paenibacillus amylolyticus, Priestia megaterium, Bacillus velezensis, Bacillus subtilis, and Bacillus licheniformis) with potential applications in biopreparations for crop residue decomposition. The effects of different storage and preservation conditions on vegetative cells and bacterial spores were studied over 12 months. Bacteria were stored at different temperatures (15 °C, 21 °C, 30 °C), pH levels (5, 9, and post-cultivation liquid pH), and osmotic pressures (2%, 5%, and 10% of carbamide, calcium chloride, and multicomponent fertilizer). Additionally, freeze-drying, spray-drying and freezing were performed using cryoprotectants (skimmed milk, trehalose, and glycerol). The results showed that bacterial stability depended on both the strain and storage conditions. Vegetative cells of P. amylolyticus and B. velezensis were most sensitive to temperatures of 30 °C, whereas the spores of most strains demonstrated high temperature resistance. The tested strains exhibited better survivability at pH 5 than pH 9. The addition of calcium chloride, carbamide, or multicomponent fertilizer proved beneficial for maintaining viability, especially increasing spore numbers. Trehalose and skimmed milk were the most effective cryoprotectants overall, though efficacy varied by strain and cell form. These findings provide insight into the optimal conditions for preserving the bacterial viability of spore-producing bacteria in bioformulations, which is crucial for maintaining their effectiveness in agricultural applications.

Access control schemes and models are essential tools for system administrators to protect the integrity of the information. However, they are frequently articulated in natural language, which is a powerful form that guarantees flexibility and expressiveness; however, their inherent ambiguity and unstructured nature pose significant challenges for automated enforcement and rigorous analysis. In this study, we evaluated several transformer-based models for the automated extraction of key components of Natural Language Access Control Policy (NLACP). To this end, we relied on a labeled dataset comprising software requirements specifications from different sectors, such as healthcare and conference management systems. We then conducted a fine-tuning phase, where the BERT model demonstrated optimal performance in extracting entities within a 3-entity paradigm, achieving an F-Measure value of 0.89. ModernBERT proved to be the most promising model in the more complex 5-entity extraction task, with a maximum F-Measure score of 0.84. Furthermore, we introduce an explainability step using layer-wise integrated gradients to gain insight into the decision-making process of these deep models, ensuring that the extracted policy components are both accurate and interpretable.

Advancing health equity requires rigorous analysis of how research initiatives incorporate and address structural disparities across populations. In this study, we apply large language models (LLMs) to systematically analyze research projects registered on the All of Us platform, with a focus on identifying patterns and institutional dynamics associated with health equity research. We examine the relationship between projects that explicitly pursue health equity goals and their use of available demographic data, their institutional composition (e.g., single- vs. multi-institutional teams), and the research tier of participating institutions (R1 vs. R2). Using the capabilities of an established LLM, we automate key tasks including the extraction of relevant attributes from unstructured project descriptions, classification of institutional affiliations, and the summarization of project content into standardized keywords from the Unified Medical Language System vocabulary. This LLM-assisted pipeline enabled scalable, replicable analysis of hundreds of projects with minimal manual overhead. Our findings suggest a strong association between the use of demographic data and health equity aims, and indicate nuanced differences in equity-oriented research participation by institution type and collaborative structure. More broadly, our approach demonstrates how LLMs can support equity-focused computational social science by transforming free-text administrative data into analyzable structures, enabling novel insights in public health, team science, and science-of-science studies.