Advances in molecular biology, the rapid growth of high-dimensional data, and new computational technologies continue to transform how diseases are studied and treated. Within this landscape, identifying gene signatures from expression data remains challenging because of the high dimensionality and biological complexity of such datasets. Existing tools are often driven primarily by mathematical and statistical criteria, so their outputs may contain genes of uncertain biological or study-specific relevance, requiring biomedical experts to repeatedly consult computational specialists to modify the analysis and interpret its results. To address this, we introduce GeneCurator, a human-in-the-loop web platform for expert-guided gene signature construction and refinement. The platform applies established statistical and machine learning feature-selection methods to expression data and produces an editable ranked list of candidate genes. The expert evaluates this list using two complementary sources of support: biological knowledge, including functional annotations, pathway and ontology enrichment, protein-protein interaction networks, and ligand-receptor relationships; and an interactive AI agent that considers the study objective, current gene list, and downstream outcomes, and proposes genes for addition, removal, or reinstatement with a rationale for each. The expert retains control throughout, modifying the signature and observing the effects in real time. In an experimental evaluation on a real transcriptomic dataset, expert curation reshaped the signature and every downstream result, showing that the tool contributes biologically meaningful, study-relevant refinements beyond automated selection. By uniting computational methods, biological knowledge, and interactive AI in one environment, GeneCurator strengthens the expert's analytical autonomy while keeping human judgement central to signature curation.
Digital Twin (DT) technologies are widely discussed in the context of Industry 4.0 and advanced manufacturing; however, their role in supporting the sustainability and survival of academic spin-offs remains underexplored. This paper argues that, particularly in peripheral and resource-constrained innovation ecosystems, Digital Twins should be understood not merely as optional technological enhancements but as strategic capabilities that support sustainable technology commercialization in early-stage, research-driven ventures. Building on literature on academic entrepreneurship, technology commercialization, digital innovation, and regional innovation systems, the study develops a conceptual framework that positions Digital Twins as entrepreneurial infrastructures linking scientific outputs to market readiness through three interrelated mechanisms: the reduction in technological uncertainty, the acceleration of market validation, and the enhancement of organizational learning and strategic adaptability. Extending beyond conceptual development, the paper proposes a staged Digital Twin adoption roadmap aligned with Technology Readiness Levels, offering a practical pathway for integrating DT capabilities across venture maturation phases while strengthening investor readiness and commercialization outcomes. The analysis further connects DT-enabled experimentation with sustainability objectives by demonstrating how virtual testing, digital validation, and data-driven learning support capital-efficient, resource-conscious, and resilient innovation processes. By integrating theoretical insights with operational guidance, this conceptual study contributes to research on technology transfer, deep-tech entrepreneurship, and sustainability-oriented innovation by proposing a framework that may guide future empirical investigations of Digital Twin adoption in academic spin-offs. The framework also offers actionable implications for spin-off founders, university technology transfer offices, and policymakers seeking to foster resilient and inclusive innovation ecosystems.
This work presents the determination of the different ways of thinking about chemical analysis that comprise the zones of a conceptual profile model of this concept. A deductive-inductive qualitative approach was adopted to analyze primary data obtained from secondary school and university students' responses to interviews related to chemical analysis. The previously suggested expanded categories of ways of thinking about chemical analysis that emerged from the study of historical and epistemological sources (sociocultural domain) as well as students' alternative ideas (ontogenetic domain) were utilized as a coding framework for the analysis of primary data obtained from students' responses (microgenetic domain). Following dialogic interaction among all three data sources, six conceptual profile zones were established: everyday analysis, alchemical analysis, empirical analysis, compositional analysis, mechanistic analysis and automated analysis.
Marine habitats in the Mediterranean Sea play a vital role in supporting biodiversity, ecosystem functioning and services, and climate resilience. However, they are facing escalating threats from anthropogenic pressures. This study, coordinated and financially supported by the Specially Protected Areas Regional Activity Centre (SPA/RAC) under the Barcelona Convention, presents an updated, harmonized assessment of the distribution and protection status of three iconic benthic habitats: Posidonia oceanica meadows, coralligenous assemblages, and marine caves. A standardized methodology was applied to compile, review, harmonize, and aggregate fine-scale data from regional databases, National Focal Points (NFPs), expert networks, and online repositories. Posidonia oceanica meadows were mapped over 22,379 km(2) across 15 Mediterranean countries, with 95% within territorial waters and 37% in Marine Protected Areas (MPAs). Coralligenous assemblages and ma & euml;rl beds covered 2,889.5 km(2) in 13 countries, in line with previous modelling estimates, with 67.1% located within MPAs, particularly in the central and western regions. Approximately 3,000 marine caves were identified across 15 countries, representing the first country-level spatial inventory of this habitat. Nearly 69% of known caves are located in MPAs, although lacking specific conservation measures. Despite significant progress, notable limitations remain. Data quality and availability are uneven across regions, and inconsistencies in classification, spatial resolution, and ecological descriptors-especially for coralligenous assemblages and marine cave typologies-limit detailed habitat representation. This study highlights persistent knowledge gaps in parts of North Africa, the Eastern Mediterranean, and non-EU coastal states, and provides a robust baseline for conservation planning and supports regional monitoring efforts under the Barcelona Convention. Future priorities include addressing data gaps, standardizing classification systems, prioritizing sites for conservation and restoration to strengthen marine habitat protection and recovery and enhance the ecological effectiveness of MPAs.
Islets often host unique and fragile ecosystems, yet the invertebrate biodiversity of the Inner Ionian Sea Archipelago remains poorly documented. During a targeted expedition across ten islets between Lefkada, Kefalonia and mainland Greece, we conducted the first comprehensive survey of their terrestrial invertebrate fauna. Using hand collecting, net sweeping and substrate sieving, we recorded 222 taxa spanning multiple phyla (Arthropoda, Mollusca, Annelida). Most of these records are deemed new for the region, even though several of the taxa are regarded as well-established since they have an extended distribution in western Greece. Species richness varied substantially among islets, with Kalamos hosting the highest number of taxa (72) and the unnamed islet near Kastos the lowest (11). Multivariate analyses using selected arthropod taxa based on presence-absence data revealed several faunal clusters, although low statistical support suggests considerable heterogeneity among islets. Diversity metrics indicated that Alafonisi exhibited the greatest taxonomic distinctness, whereas Mermygkas showed the most homogeneous fauna. Several endemic and regionally significant species were detected, including multiple Ionian endemics and taxa newly recorded for the archipelago. The results highlight the conservation importance of these small landmasses, which harbour distinct assemblages shaped by isolation, geomorphology, human activity and potential species transfers via boats or grazing animals. This study provides a foundational dataset for future biogeographical, ecological and taxonomic research in the Ionian region and underscores the value of islets as natural laboratories of biodiversity. Finally, the study provides the first standardised Darwin Core dataset for the region, establishing a foundational resource for future ecological, taxonomic, and biogeographical research.
