Search Results (4,219)

Smart Specialization Strategy (S3) has become a central instrument of European Union Cohesion Policy, yet its implementation has revealed recurring limitations, including formalistic Entrepreneurial Discovery Processes, weak multilevel coordination, generic priorities, and evaluation systems focused mainly on innovation outputs. This paper examines how shared purpose can be incorporated into S3 in ways that improve both developmental direction and implementation quality across levels. The study adopts a conceptual research design based on a critical synthesis of literature and a model-building procedure, complemented by an illustrative regional application. The main result is the Purpose-Driven Smart Specialization (S3+P) framework, a multilevel model linking individual, organizational, territorial, and macro-policy dimensions through five catalytic mechanisms: plasticity, temporality, identity, memory, and relational networks. The paper also proposes a six-step policy cycle and an indicator logic that broadens evaluation beyond conventional innovation metrics. The analysis suggests that purpose can strengthen directionality, coherence, and legitimacy in regional strategy while preserving the place-based and discovery-oriented rationale of S3. The framework contributes to current debates on the renewal of smart specialization for more sustainable and coordinated regional development. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data reuse have grown accordingly. Nevertheless, data availability has not been translated into straightforward reuse: differences in experimental design, acquisition strategies, quantification workflows and metadata quality still limit the reproducibility and cross-study comparability. In this review, proteomics data reuse is defined as the systematic reanalysis and integration of publicly available datasets to support precision oncology applications such as biomarker assessment and antibody–drug conjugate target prioritization. We discuss reuse as an end-to-end analytical process, focusing on data analysis workflows, harmonization strategies, and the impact of heterogeneous experimental and analytical choices on interoperability. The increased application of artificial intelligence in proteomics data integration and reuse is also addressed, highlighting its analytical potential while underscoring the risks of overinterpretation when biological context and data structure are not adequately considered. Using colorectal and prostate cancer as representative examples, we illustrate how proteomics data reuse can support biological discovery and translational research, while critically examining the factors that limit robustness and clinical relevance. Full article

Whey is a major by-product of the dairy industry and represents a valuable source of proteins that can be enzymatically converted into bioactive peptides with diverse health-related functions. In recent years, increasing attention has been given to whey-derived peptides due to their antioxidant, antihypertensive, antimicrobial, anti-inflammatory, antithrombotic, immunomodulatory, and anticancer activities, highlighting their potential use as functional ingredients and nutraceutical compounds. The generation and biological functionality of these peptides are strongly influenced by the protein source, processing conditions, enzymatic or microbial hydrolysis strategies, and peptide structure. Unlike the existing literature, this review provides an analysis of individual peptide sequences, meticulously linking their specific chemical structures to their diverse biological activities, such as antioxidants, antihypertensive, and immunomodulatory effects. By moving beyond general protein hydrolysis, this work offers a unique comparative framework that evaluates how these distinct peptide fractions perform under industrial conditions. Furthermore, it bridges the gap between laboratory discovery and commercial implementation, focusing on critical parameters for large-scale production, stability in functional food matrices, and the regulatory pathways required for market-ready nutraceuticals. This integrated approach provides a strategic roadmap for translating molecular bioactivity into high-value industrial applications. This review provides an applied overview of recent advances in the production of whey bioactive peptides, emphasizing enzymatic generation methods, structure–activity relationships, and underlying mechanisms of action associated with their biological effects. In addition, current and emerging applications of whey-derived peptides in functional foods, nutraceuticals, and health-oriented formulations are critically discussed. Finally, key challenges related to peptide stability, bioavailability, industrial scalability, and regulatory aspects are addressed to identify future perspectives for the effective translation of whey bioactive peptides from research to practical applications. Full article

The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems. Critical insights are embedded within these large-scale spatiotemporal datasets. Spatiotemporal Data Science provides a conceptual and methodological framework for analyzing such data by integrating spatiotemporal thinking, computational infrastructure, artificial intelligence, and domain knowledge. The field advances methods for data acquisition, harmonization, modeling, visualization, and decision support, enabling applications in natural disaster response, public health, climate adaptation, infrastructure resilience, and geopolitical analysis. By leveraging emerging technologies—including generative Artificial Intelligence (AI), large-scale cloud platforms, Graphics Processing Unit (GPU) acceleration, and digital twin systems—Spatiotemporal Data Science enables scalable, interoperable, and solution-oriented research and innovation. It represents a critical frontier for scientific discovery, engineering advancement, technological innovation, education, and societal benefit. Spatiotemporal Data Science is a transdisciplinary field that studies and models dynamic phenomena across space and time by integrating spatial theory, temporal reasoning, artificial intelligence, and scalable computational infrastructure. It enables the development of adaptive, predictive, and increasingly autonomous systems for understanding and managing complex real-world processes. Full article

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies. Full article

Purpose: Clinical presentation of anterior uveitis affecting the iris is nearly identical in patients with juvenile idiopathic-associated uveitis (JIAU) and patients with antinuclear antibody-positive anterior uveitis (ANA-U). This study investigates whether the iris transcriptomes of patients with JIAU or ANA-U differ or reflect the clinical homogeneity. Methods: Iris biopsies were obtained from patients with JIAU (n = 31) or ANA-U (n = 9) during trabeculectomy, iridectomy, or complex cataract surgery. Frozen iris tissues were homogenized, the RNA was isolated and analyzed using a bulk RNA sequencing approach. Genes with a Benjamini–Hochberg false discovery rate (FDR) p ≤ 0.05 and a log₂ fold change (FC) of >1 or <−1 were defined as significantly differentially expressed genes (DEGs). Pathway enrichment analysis was subsequently performed to characterize these DEGs. Results: The JIAU group included nine male and 22 female patients (median age 10.2 [IQR 7.2, 13.7] years), while the ANA-U group consisted of one male and eight female patients (median age 10.2 years (IQR [6.1, 11.7]) years). No DEGs were identified when comparing the iris transcriptomes of JIAU and ANA-U directly. Subgroup analyses based on disease activity revealed 35 DEGs in ANA-U, one DEG in JIAU. Inter-group comparisons based on uveitis activity status yielded no DEGs. In the merged JIAU/ANA-U cohort, 28 DEGs were upregulated in the active vs. inactive uveitis. Regarding secondary glaucoma, 12 DEGs were identified in JIAU, and 15 DEGs were identified in the corresponding ANA-U subgroup. Inter-group comparisons based on glaucoma status yielded no DEGs. In the merged JIAU/ANA-U cohort, 20 DEGs were identified between patients with and without glaucoma. Conclusions: Our findings reveal only marginal differences in gene expression patterns in iris tissues from JIAU and ANA-U patients. These results suggest a high degree of similarity in the underlying inflammatory processes of both uveitis subsets. Full article

Interest in further developments of the classical Fischer–Tropsch technology has increased in recent years. The development of processes capable of producing synthetic fuels has become a highly attractive research area due to the continuous global growth in energy demand. An extensive review covering the full development chain (from laboratory-scale experiments to pilot-scale studies and plant-level implementations) is therefore of significant relevance. Consequently, this review aims to be a reference by integrating findings across different development levels of Fischer–Tropsch synthesis technologies, thereby enabling a holistic perspective of the pathway toward industrial-scale deployment. The present work thus critically reviews recent advances in catalyst development, including the role of active phases, particle size effects, supports, and promoters, as well as the growing contribution of in situ and operando characterization techniques. In parallel, progress in kinetic and mechanistic modeling is discussed, highlighting both classical approaches and emerging data-driven and optimization-based methods. Different reactor technologies, from classical to novel technologies, are also analyzed with respect to hydrodynamics, heat and mass transfer limitations, and reactor intensification strategies. At the process level, the review assesses integrated and intensified Fischer–Tropsch-based routes, with particular emphasis on CO₂ utilization pathways, process integration, polygeneration schemes, and optimization frameworks. The potential of artificial intelligence and machine learning tools to accelerate catalyst discovery, reactor optimization, and process design is also addressed. Overall, this review identifies key technological advances, remaining challenges, and research gaps that must be addressed to enable economically viable and environmentally sustainable, and scalable Fischer–Tropsch processes to meet future energy demands. Full article

Microorganisms provide critical lead compounds for drug development, yet most biosynthetic gene clusters remain silent under standard culture conditions. The OSMAC strategy activates these clusters by adjusting cultivation parameters, thereby enabling the discovery of novel compounds from a single strain. Here, we applied OSMAC to explore the metabolic potential of the soft coral-derived fungus Aspergillus sclerotiorum SCSIO 41031. Three different culture media were employed for the large-scale fermentation process. After isolation by chromatography, the compounds were structurally characterized using NMR, MS, and X-ray single-crystal diffraction, and their absolute configurations were determined by electronic circular dichroism (ECD) calculations. In total, three new compounds, named 6,6′-diacetyl-1,1′-dihydroxy-3,3′-dimethoxydibenzyl ether (1), esterwortmannolol (17) and pestalpolyol I (20), along with 19 known compounds (2–16, 18–19 and 21–22) were obtained. This study validates the efficacy of the OSMAC strategy and underscores that A. sclerotiorum SCSIO 41031 serves as a valuable resource for producing structurally diverse natural products with potent biological activities. Full article

The widespread availability of generative artificial intelligence has increased the volume of images and videos shared online, while making it difficult to verify origin and integrity after routine post-processing such as re-encoding, resizing, and transcoding. This research proposes a broker-assisted trust chain architecture that treats authenticity verification as an evidence registration and validation workflow rather than a single-signal decision. A trust chain broker seals submitted media by embedding a robust hidden watermark, deriving an embedding-based semantic fingerprint, and producing standardized provenance metadata, then stores the sealed media off-chain using content-addressed storage and anchors only compact evidence on an immutable ledger. The anchored evidence binds the content identifier of the sealed artifact with semantic and provenance hashes, timestamps, and the broker signature, while scalable candidate discovery is supported through an off-chain Facebook AI Similarity Search (FAISS)-based nearest-neighbor similarity index. We evaluate the retrieval stage on a COCO 2017 validation subset (N = 200) under representative post-processing transformations (JPEG compression, resizing, and center cropping), and observe near-perfect candidate identification performance with Recall@1 = 0.9988 and Recall@5/10 = 1.000. During verification, the broker retrieves candidates by embedding similarity, validates ledger inclusion and broker signatures, applies consistency checks across evidence fields, and issues an operational verdict with a signed verification report that is independently checkable. We also implement an EVM-based proof-of-concept for on-chain anchoring and report low ledger-side overhead for a representative registration transaction (gasUsed = 25,380) when recording fixed-size compact evidence fields. The proposed architecture does not prevent copying itself, but improves traceability and auditability under realistic transformation and redistribution conditions by combining watermarking, semantic association, provenance binding, and tamper-evident evidence anchoring within a clear service accountability boundary. Full article

Fungal pigments have gained attention as eco-friendly and versatile materials for green nanotechnology because of their varied chemical structures, inherent redox properties, and strong metal ion-binding capabilities. These pigments, such as polyketides, azaphilones, melanins, and carotenoids, can function simultaneously as reducing, capping, and surface-functionalizing agents, facilitating the environmentally friendly production of metallic nanoparticles without the use of harmful chemicals. This review provides a critical overview of recent progress in the production, extraction, and application of fungal pigments for nanoparticle synthesis, focusing on the mechanistic roles of pigment functional groups in metal ion reduction, nanoparticle nucleation, growth, and stabilization. The impact of pigment chemistry and reaction conditions on the nanoparticle size, shape, crystallinity, and colloidal stability was thoroughly examined. Additionally, this review highlights the emerging biomedical, environmental, and industrial applications of pigment-mediated nanoparticles, emphasizing their biocompatibility and functional adaptability. Key challenges, such as variability in pigment yield and composition, limited mechanistic validation, lack of standardized synthesis protocols, and insufficient toxicity assessment, are critically analyzed in this review. Finally, future directions are outlined, emphasizing the importance of process optimization, omics-guided pigment discovery, and comprehensive safety evaluations as crucial steps toward the scalable and reliable use of fungal pigment-mediated nanoparticle synthesis in sustainable nanotechnology. Full article

Traditional drug discovery processes are typically expensive, time-consuming, and have a very high failure rate. Artificial intelligence (AI) is currently reshaping this field in unprecedented ways, promising to significantly improve the efficiency and success rate of drug development. This article systematically compares and analyzes the application of AI for two major drug types: small molecule vs. peptide drugs. It explores their applications in several key stages of drug development, including virtual screening, lead compound optimization, de novo drug design, ADMET (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and chemical synthesis planning. While both drug types benefit from AI-driven approaches, fundamental differences exist in molecular representation, data availability, key challenges, and model adaptability. For small molecule drugs, AI focuses on drug efficacy, synthetic feasibility, and accurate structure–activity relationship prediction. In contrast, for peptide drugs, AI faces more unique biological challenges, such as inherent flexibility, complex biological functions, stability, and immunogenicity. Finally, this article provides a forward-looking perspective on the future of AI-driven drug discovery, highlighting the immense potential of basic models, multimodal integrated systems, and autonomous discovery platforms, which will collectively drive the next wave of precision drug development. Full article

Natural product research has experienced substantial growth over the past two decades, driven by a renewed appreciation for the structural complexity and biological relevance of compounds derived from nature. Technological advances in separation science, spectroscopic characterization, and high-sensitivity bioassays have collectively restored natural products to a position of prominence in modern drug discovery efforts. Nature remains the most prolific source of bioactive molecular diversity, drawing from microorganisms, plants, and marine life to offer a vast reservoir of structurally novel scaffolds whose pharmacological potential remains largely unexplored. Effective extraction and isolation remain foundational to natural product research, as the quality and purity of isolated compounds directly govern the reliability of downstream biological evaluation. Recent years have witnessed remarkable innovation in this space, spanning green and designer solvent systems, pressurized and ultrasound-assisted extraction platforms, supercritical fluid techniques, and integrated purification workflows that dramatically reduce processing time while improving compound recovery and analytical throughput. Particularly noteworthy is the growing application of artificial intelligence and machine learning tools for solvent selection, extraction optimization, and metabolite dereplication, which in combination with advanced phase-separation strategies and informatic platforms have substantially expanded the scope of detectable and characterizable metabolites within complex biological matrices. This review summarizes recent progress in extraction and isolation methodologies supporting natural product research, with particular emphasis on combinatorial extraction strategies, next-generation solvent systems, and AI-driven applications that have collectively improved operational efficiency, selectivity, and analytical output over the past five years. Full article

Polycystic ovary syndrome (PCOS) is associated with reproductive, metabolic, and inflammatory disturbances. Alterations in T-cell subpopulations—particularly increased T helper 17 cells (Th17) and decreased regulatory T cells (Treg)—have been reported in PCOS; however, data on normoandrogenic phenotype D remain limited. Hypoxia-inducible factor 1α (HIF-1α), a key regulator of hypoxic response, also influences immune and metabolic processes and may affect the Treg/Th17 balance. To assess Treg and Th17 abundance, HIF-1α expression within these cells, and their ratios in women with phenotype D PCOS compared with healthy controls. The study included 49 women with phenotype D PCOS and 40 controls comparable in terms of age and BMI. Anthropometric, hormonal, metabolic, and inflammatory parameters were evaluated. Peripheral T-cell subsets and intracellular HIF-1α expression were analyzed by multiparameter flow cytometry. Absolute numbers of Treg and Th17 cells did not differ between groups. However, PCOS patients showed significantly higher Treg/Th17 and HIF-1α-positive Treg/HIF-1α-positive Th17 ratios. HIF-1α-positive Treg cells correlated positively with adiposity and insulin resistance markers; however, after False Discovery Rate (FDR) correction, correlations no longer remained statistically significant. Despite normoandrogenemia, PCOS patients exhibited higher hs-CRP levels. Phenotype D PCOS is characterized by altered immune cell ratios rather than absolute T-cell differences, suggesting distinct immunological features and persistent low-grade inflammation. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article