Polysaccharides in plant cell walls serve as a rich carbon and energy source, yet their structural complexity presents a barrier to efficient degradation. To address this, anaerobic microorganisms like R. flavefaciens have developed sophisticated multi-enzyme complexes known as cellulosomes, which enable the efficient breakdown of these recalcitrant polysaccharides. These complexes are assembled through high-affinity interactions between cohesin (Coh) modules in scaffoldin proteins and dockerin (Doc) modules in cellulosomal enzymes. R. flavefaciens FD-1 harbors one of the most intricate cellulosomes described to date, comprising over 200 Doc-containing proteins encoded in its genome. Despite substantial research on this cellulosome, the role of a group of truncated but functional dockerins, known as group-2 Docs, remains unclear. In this study, we present a detailed structural and binding analysis of a Coh-Doc complex involving the cohesin from the cell-anchoring scaffoldin ScaE and a group-2 Doc that bears only one of the two Ca+2-coordinating loops that characterise the canonical Docs. Our findings reveal a novel tripartite binding mechanism, in which the cohesin can simultaneously bind two distinct dockerin units in three alternative conformations. This discovery provides new insights into the modular versatility of the R. flavefaciens cellulosome and sheds light on the mechanisms that enhance its efficiency in polysaccharide degradation.

The European Metal sector is at a critical juncture, navigating through the complex landscape of the twin transition towards digitalization and sustainability. This transformative journey, though essential, is fraught with challenges that if not addressed, could undermine the sector's resilience, competitiveness and social equity. The Metallica project emerges as a pivotal initiative in this context, aiming to mitigate these challenges by fostering enhanced worker engagement, participation and conflict resolution.

The European Metal sector is at a critical juncture, navigating through the complex landscape of the twin transition towards digitalization and sustainability. This transformative journey, though essential, is fraught with challenges that if not addressed, could undermine the sector's resilience, competitiveness and social equity. The Metallica project emerges as a pivotal initiative in this context, aiming to mitigate these challenges by fostering enhanced worker engagement, participation and conflict resolution.

The digitalisation of logistics and assembly lines in the Portuguese automotive sector is reshaping manufacturing processes, work organisation and competence requirements. The research aims to assess the challenges and opportunities associated with these technological changes and their impact on workforce skills and organisational structures. Findings suggest that digitalisation is progressing unevenly across logistics and production, with logistics leading in automation while production struggles with legacy systems and high costs. The shift to alternative engines supports digitalisation, but significant workforce skill gaps in AI and digital systems remain a challenge. Companies are implementing internal training, yet outdated vocational programs fail to address Industry 4.0 needs. Rather than full automation, digitalisation is fostering new human-machine collaboration, requiring cooperation between AI specialists, managers, and workers. To ensure success, policymakers and industry leaders must prioritise workforce reskilling and align digital strategies with labour market realities.

The Upper Jurassic beds of the Lusitanian Basin in central Portugal yield diverse dinosaurian fauna, dated to the Kimmeridgian–Tithonian interval. Saurischian dinosaurs are, overall, more abundant than their ornithischian counterparts, in terms of both specimens collected and species recognized. Iguanodontians are so far represented by the styracosternan Draconyx loureiroi, the dryosaurid Eousdryosaurus nanohallucis and the enigmatic dryomorphan Hesperonyx martinhotomasorum. Here we aim to highlight the diversity of this clade in the Late Jurassic of Portugal, presenting evidence for yet another species of ankylopollexian iguanodontian dinosaur, represented by the specimen SHN.JJS.015, which is housed at the Sociedade de História Natural, Torres Vedras. Detailed comparisons rule out attribution to previously known taxa, and phylogenetic analyses that include SHN.JJS.015 indicate early-diverging ankylopollexian affinities for this specimen. As there is no robust diagnosis, we do not erect a new formal species for it at this stage. Nevertheless, this specimen represents a previously unreported taxon that highlights greater diversity than previously estimated among the iguanodontians of the Late Jurassic and highlights the importance of Europe in diversification and dispersal events of this clade. A series of smaller, isolated femora from the same sub-basin as SHN.JJS.015 may represent the same taxon, presenting evidence of thriving communities of ankylopollexians during the Kimmeridgian–Tithonian interval in Portugal.

Incorporating technologies with Generative Artificial Intelligence (GenAI) into education requires a shift in teaching methodologies. However, little is known about how pre-service teachers perceive the relevance and challenges of this incorporation, particularly in mathematics education. This study investigates pre-service teachers’ interactions with GenAI, addressing the relevance and challenges of integrating it in mathematics teaching and discussing possible implications for their knowledge and professional development. Specifically, it intends to understand: How does the pre-service teachers’ interaction with GenAI during the design and implementation of teaching activities relate to their professional knowledge? And how does this relation impact the relevance they ascribe to GenAI? In this qualitative and interpretative study, involving seven pre-service mathematics teachers, we analyse the interplay between participants’ knowledge and use of a GenAI (in this case, ChatGPT), guided by KTMT – Knowledge for Teaching Mathematics with Technology model. The main conclusions revealed a landscape characterised by promise and challenge, where GenAI can be a valuable educational
tool when used to facilitate discussion and promote critical thinking, highlighting the relevance and development of KTMT. The ability to evaluate and reflect on AI-generated responses can promote professional development, preparing pre-service teachers for an increasing presence of technology in educational environments.