Based on the BERTopic model, this study analyzes 15,744 scientific papers in the field of functional agriculture from 1995 to 2024 to uncover core themes and evolutionary trends in global functional agriculture, and particularly focuses on revealing the developmental trajectory in China. The results indicate that global functional agriculture research is characterized by diverse themes and intensive study, forming a multi-topic cross-network centered on plant chemical extraction and agricultural soil research, with a focus on food nutrition, human health, and environmental protection. By contrast, China’s functional agriculture research demonstrates a more focused and in-depth approach, concentrating on functional food development and agricultural environmental protection themes, with notable growth trends in areas such as selenium-enriched products and resistant starch. Combined with China’s agricultural development environment, this study makes the following suggestions for the development of functional agriculture in China: (1) Promoting interdisciplinary cooperation between functional agriculture and other technologies. (2) Developing agricultural products with Chinese characteristics and forming Chinese functional agricultural product brands. (3) Utilizing smart farming technology to boost functional agriculture.

Polyphenols are a diverse group of plant-derived bioactives that have been investigated as multi-target candidates for the potential prevention and management of neurodegenerative diseases (NDDs). We conducted an integrated bibliometric and mechanistic scoping review covering 12 polyphenol classes and seven major NDDs using records from PubMed, Embase, the Cochrane Library, and Web of Science (1940–2024). Research landscapes and emerging themes were mapped using keyword co-occurrence, clustering analyses, and BERTopic modeling. Mechanistic evidence was synthesized across core pathways, including oxidative stress, neuroinflammation, proteostasis (amyloid/tau and α-synuclein), mitochondrial dysfunction, and cholinergic modulation, to link preclinical findings with clinical outcomes. Publication output increased markedly after 2000, with China and the United States contributing the most records. Four persistent hotspots were identified: (1) antioxidant and neuroprotective effects (e.g., resveratrol, curcumin); (2) anti-inflammatory activity and intracellular signaling; (3) cognition, aging, and sex-specific responses in clinical research; and (4) animal models of memory impairment. Clinically investigated interventions include epigallocatechin gallate, Ginkgo biloba extracts, olive/cocoa polyphenols, and flavonoid-rich mixtures; however, limited bioavailability and heterogeneous trial designs constrain the strength of effect estimates. Advances in delivery systems, computational screening, and precision nutrition may improve translation. Overall, polyphenols show multi-target neuroprotective potential, but larger and more standardized clinical trials are needed to support evidence-based nutritional strategies for NDD prevention and management.

This paper gives brief introduction about the big data, linked data and knowledge organization systems (KOS) and their relationships. As the authors mainly focus on the variety and value characteristics of big data, the linked data and KOS technologies are used to link and consume the large amounts of literature and scientific data in agricultural research community. The results show that it is a good way to describe, connect, organize, represent, visualize and access to big data effectively and semantically based on the linked data and KOS technologies.

Information theory, originally rooted in thermodynamics, is utilized in scientometrics to quantify the diversity and heterogeneity of knowledge combinations. This study analyzes a large-scale journal citation network to introduce and empirically validate a fundamental principle of reference behavior, which we term "Mediating Similarity." We posit that a journal's reference distribution (the knowledge it cites) acts as a cognitive bridge between its own citation distribution (its identity in the scientific landscape) and the overall scientific content distribution (the broader knowledge environment). This phenomenon is captured by the Kullback-Leibler (KL) divergence inequality: the sum of the distances from a journal to its references and from its references to the global landscape is less than the direct distance from the journal to the global landscape. Our experimental findings provide robust, multi-level evidence for this principle. First, we demonstrate the universality of the phenomenon, showing that for all 19,129 journals in our dataset, the mediated KL divergence path is consistently shorter than the direct path. Second, we conducted two perturbation experiments on the top 500 journals ranked by the SJR indicator. Based on our finding that real references are mostly contained within the closest journals as measured by KL divergence, we created a high-relevance candidate pool for each journal, consisting of its real references plus twice the actual number of citations closest un-cited journals. In a global resampling test, we found that the actual reference portfolio exhibited a lower "cognitive energy" (sum of KL divergences) than 99% of 1,000 randomly assembled portfolios from the candidate pool averagely.This indicates that citation is a holistic process that selects for a synergistically optimal combination of references. In a local perturbation test where 10% of real references were swapped, the actual portfolio still outperformed the majority of 1,000 perturbed variations. This suggests that the real-world reference selection process, while driven by an optimization principle, operates as a robust "satisficing" strategy within the constraints of the scientific discovery process. Collectively, these findings reveal that reference behavior is a strategic process. Journals selectively curate references to construct an optimal cognitive path, efficiently shortening the distance between their field and the broader scientific environment.

A large number of potential knowledge associations in scientific and technological innovation activities have not yet become explicit. How to identify potential valuable innovation opportunity clues from complex knowledge structures has therefore become an important issue in intelligence analysis research. This study takes the field of rice drought-tolerant breeding as an empirical case. Based on PMC full-text literature data, the LightRAG model was employed to extract innovation-related entities and their semantic relationships, including varieties, genes, proteins, phenotypes, and technological methods. A technology-data heterogeneous network for rice drought-tolerant breeding was then constructed, and the HetGNN link prediction method was introduced to predict potential relationships within the network. To evaluate the effectiveness of the proposed model, literature published from 2006 to 2020 was used to construct the training network, while newly emerging relationships extracted from literature published between 2021 and 2023 were used as a future validation set. Adamic-Adar and Node2Vec were further selected as baseline models for comparison. The experimental results show that the proposed method achieved an AUC of 0.8901, an AP of 0.9190, and an F1@0.5 of 0.8322 on the internal testing set, outperforming the baseline models in overall performance. In the temporal holdout validation, the model was able to identify some newly emerging knowledge associations that subsequently appeared in the 2021–2023 literature. The prediction results based on the full dataset indicate that the potential relationships are mainly concentrated in influence relationships between data elements and drought-tolerant phenotypes, as well as support relationships between technological methods and drought-tolerant phenotype research. This study constructs an analytical framework consisting of “innovation element extraction, heterogeneous network modeling, temporal holdout validation, and potential relationship interpretation,” thereby providing a methodological reference for identifying potential innovation opportunity clues from complex scientific knowledge structures.

While information theory is widely used to quantify knowledge combinations, the fundamental principles guiding reference selection in science remain largely unexplored. This study analyzes a large-scale journal citation network to introduce and empirically validate a principle we term “Mediating Similarity”. We posit that a journal’s reference list acts as a strategic cognitive bridge, creating a more efficient informational path from its specific research identity to the broader scientific landscape. Using information-theoretic measures and computational experiments, we tested this principle and its underlying mechanisms. Our findings provide robust, multi-level evidence. First, we confirm the universality of the principle, showing that the mediated path through references is consistently more efficient than the direct path for thousands of journals. Second, perturbation experiments reveal a dual mechanism guiding reference selection: real-world reference portfolios are not merely collections of relevant works, but are synergistically optimal combinations that vastly outperform randomly assembled alternatives. This global optimization, however, operates as a robust “satisficing” strategy, balancing the search for an ideal cognitive path with the practical constraints of scientific discovery. Collectively, these findings reframe reference behavior as a strategic process of navigating a cognitive energy landscape, where journals selectively curate references to enhance their integrative capacity and innovative potential.