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- Comparative Analysis of Secondary Metabolite Production by Cophinforma mamane and Aspergillus niger Preserved for Long-Term MethodsPublication . Padrón-Antonio, Yuliana; Falcão, Lucas; Souza, Anne; Andrade, Cleudiane; Silva, Paulo; Barbosa, Larissa; Jr, Sérgio; Correia, Ana Cristina; Jordão, António; Albuquerque, PatriciaEndophytic fungi are recognized for their capacity to produce bioactive metabolites, yet their metabolic output is strongly modulated by preservation strategies. This study evaluated the influence of three long-term preservation methods—PDA slants with mineral oil (PB), Castellani’s method (PC), and filter paper with mineral oil (PF)—on the production of phenolic compounds and antioxidant activity by the Amazonian endophytes Cophinforma mamane and Aspergillus niger. Post-reactivation morphological analyses revealed method-dependent changes in colony pigmentation, texture, and sporulation intensity. PB preservation induced the most vigorous growth and enhanced sporulation in A. niger. For extracellular metabolites (AcOEt extracts), PB yielded the highest total phenolic content in A. niger (258.24 mg GAE/g), whereas PF and PC favored C. mamane (154.48 and 153.33 mg GAE/g). For intracellular ethanolic extracts, PB was most effective for C. mamane, producing 206.19 mg GAE/g for total phenols and the highest antioxidant activities (EC50 = 1.40 mg/mL and FRAP = 95.61 μmol TE/g). HPLC–DAD identified protocatechuic acid as the predominant compound, which was especially abundant in PC and PF-preserved A. niger (60.76 and 85.38 μg/mg, respectively). Multivariate correlations indicated syringic (r = 0.53) and p-coumaric (r = 0.63) acids as the main contributors to antioxidant potential. These findings demonstrate that preservation methods may positively or negatively modulate fungal biosynthetic capacity, underscoring the importance of species-specific preservation protocols for maintaining metabolic stability in culture collections.
- Navigating the Dual Challenge: A Strategic Framework for Ethical and Pedagogical Integration of Generative AI in Higher EducationPublication . Nina de Morais, Nídia Salomé; de Almeida Gouveia, Teresa Sofia; Raposo, Rui; Rito, Pedro Neves; Carneiro de Sousa, Catarina; Ferreira, Sónia; Antunes Sobral, Filomena; Gomes, HelenaThe exponentially rapid ascent of Generative Artificial Intelligence (GenAI) and merging into multiple levels and niches of the higher education ecosystem has created a demand for approaches capable of tending to an ever-growing number of teaching staff who feel as if they are falling behind and incapable of keeping up and catching up with all that is being done regarding GenAI. This paper proposes a Strategic Framework for the ethical integration of Generative AI, developed through an exploratory initiative at the Viseu School of Education (ESEV), which has tackled these challenges and has worked towards the iterative and participative design of a strategic and scalable framework for the critical and its responsible integration of GenAI into faculty teaching practice. The work details the methodological architecture and operational indicators of this framework to ensure its scalability across higher education contexts. The multi-phase methodology adopted employs a robust mixed-methods approach, which includes a comprehensive assessment to diagnose faculty AI literacy, adoption patterns, and ethical practice and risk perceptions. Data collected informs the subsequent systematic inventory of high-utility tools and the establishment of a dedicated, interdisciplinary Study Group for continuous knowledge of co-creation. The project also includes the design and deployment of subject-specific, practical workshops focused on developing both the technical proficiency and critical pedagogical competencies required for effective integration of GenAI in teaching activi- 2 ties. It culminates in the delivery of evidence-based pedagogical recommendations and a comprehensive Faculty Manual for Ethical AI Integration. This project is not merely a context-specific case study but rather provides a transferable and potentially replicable framework for higher education institutions, aiming to enhance the quality of teaching and proactively cultivate an ethically grounded culture of innovation in the age of AI.