Search Results (6,597)

Large language models (LLMs) often fail to correctly associate biomedical terms with their standardized ontology identifiers, posing challenges for downstream applications that rely on accurate, machine-readable codes. These linking failures can compromise the integrity of data used in precision medicine, clinical decision support, and population health. Fine-tuning can partially remedy these issues, but the degree of improvement varies across terms and terminologies. Focusing on the Human Phenotype Ontology (HPO), we show that a model’s prior knowledge of term–identifier pairs, acquired during pre-training, strongly predicts whether fine-tuning will enhance its linking accuracy. We evaluate prior knowledge in three complementary ways: (1) latent probabilistic knowledge, revealed through stochastic prompting, captures hidden associations not evident in deterministic output; (2) partial subtoken knowledge, reflected in incomplete but non-random generation of identifier components; and (3) term familiarity, inferred from annotation frequencies in the biomedical literature, which serve as a proxy for training exposure. We then assess how these forms of prior knowledge influence the accuracy of deterministic identifier linking. Fine-tuning performance varies most for terms in what we call the reactive middle zone of the ontology—terms with intermediate levels of prior knowledge that are neither absent nor fully consolidated. Fine-tuning was most successful when prior knowledge as measured by partial subtoken knowledge, was ‘weak’ or ‘medium’ or when prior knowledge as measured by latent probabilistic knowledge was ‘unknown’ or ‘weak’ ($p<0.001$). These terms from the ‘reactive middle’ exhibited the largest gains or losses in accuracy during fine-tuning, suggesting that the success of knowledge injection critically depends on the level of term–identifier pair knowledge in the LLM before fine-tuning. Full article

This study reviews the status and direction of Short Food Supply Chains (SFSCs) in Africa, examining their potential to enhance food security, resilience, and smallholder empowerment. Synthesising 69 peer-reviewed studies from 25 African countries, the analysis identifies nine recurring themes consolidated into four clusters: governance, resilience and sustainability; urbanisation and participation; innovation and logistics; and inclusion and equity. The findings show that research is concentrated in South Africa, Ghana, and Kenya, but also highlight emerging diversity across the continent. SFSCs strengthen local resilience and urban food system integration, yet remain limited by weak digital infrastructure, policy fragmentation, and underdeveloped equity measures, especially regarding youth and gender. The review contributes to debates on food sovereignty, political ecology, and sustainability transitions by situating African SFSCs within broader food system transformations. It proposes a policy roadmap prioritising participatory governance, digital market innovation, and inclusive procurement as pathways for institutionalising SFSCs in Africa. Full article

The rapid evolution of unmanned aerial vehicle (UAV) technology and low-altitude economic development have propelled drone applications in critical infrastructure monitoring, particularly in intelligent transportation systems where real-time aerial image processing has emerged as a pressing requirement. We address the pivotal challenge of highway lane extraction from low-altitude UAV perspectives by applying a novel three-stage framework. This framework consists of (1) a deep learning-based semantic segmentation module that employs an enhanced STDC network with boundary-aware loss for precise detection of roads and lane markings; (2) an optimized polynomial fitting algorithm incorporating iterative classification and adaptive error thresholds to achieve robust lane marking consolidation; and (3) a global optimization module designed for context-aware lane generation. Our methodology demonstrates superior performance with 94.11% F1-score and 93.84% IoU, effectively bridging the technical gap in UAV-based lane extraction while establishing a reliable foundation for advanced traffic monitoring applications. Full article

This study investigates long-term changes in thermal comfort across the Italian and Iberian Peninsulas from 1940 to 2024, using the Universal Thermal Climate Index (UTCI) derived from ERA5-HEAT reanalysis. We apply a dual analytical framework combining structural break detection to identify regime shifts and Sen’s slope estimation with confidence intervals to quantify monotonic trends. Results reveal pronounced seasonal asymmetries. Summer exhibits abrupt regime shifts in both regions: in 1980 for Italy (slope shifting from −0.039 °C/year before 1980 to +0.06 °C/year after) and 1978 for Iberia (from −0.054 °C/year to +0.050 °C/year). Winter, by contrast, shows no structural breaks but a persistent, spatially uniform warming trend of ~0.030–0.033 °C/year across the 1940–2024 period, consistent with a gradual erosion of cold stress. Transitional seasons display more nuanced responses. Spring reveals detectable breakpoints in 1987 for Italy (shifting from −0.028 °C/year to +0.027 °C/year) and 1986 for Iberia (from −0.047 °C/year to +0.024 °C/year), indicating the early acceleration of warming. Autumn shows a breakpoint in 1970 for Italy, with trends intensifying from +0.011 °C/year before to +0.052 °C/year after, while Iberia exhibits no clear breakpoint but a consistent positive slope. These findings highlight spring as an early-warning season, where warming acceleration first emerges, and autumn as a consolidating phase that extends summer-like heat into later months. Overall, the results demonstrate that Mediterranean thermal regimes evolve through both abrupt and gradual processes, with summer defined by non-linear regime shifts, winter by steady accumulation of warming, and spring and autumn by transitional dynamics that bridge these extremes. The methodological integration of breakpoint detection with Sen’s slope estimation provides a transferable framework for detecting climate regime transitions in other vulnerable regions under accelerated global warming. Full article

This research presents the calibration of a constitutive model to replicate the cyclic performance of soils using a Bayesian framework. This study uses data from laboratory-conducted consolidated undrained isotropic cyclic triaxial tests and numerical tools to estimate optimal parameters by the application of Slice Sampling in a Bayesian analysis and to determinate the uncertainty of the model. For each calibrated parameter in the model, a probability distribution was obtained from the Markov chain. The means and the standard deviations from the distributions are compared with the laboratory results by the simulation of a series of consolidated undrained isotropic cyclic triaxial tests and a numerical model for a deposit that replicates the Wildlife’s stratigraphic characteristics. The calibrated model response offers a good approximation of the recorded data and the uncertainty due to the model is evaluated. The results of this study demonstrate that Bayesian calibration can reliably quantify parameter uncertainty, reveal parameter correlations that deterministic methods overlook, and improve confidence in liquefaction assessments. This probabilistic framework provides a robust basis for extending calibration to other soil types and site conditions. Full article

Synchronous motors are pivotal to modern industrial systems, particularly those aligned with Industry 4.0 initiatives, due to their high precision, reliability, and energy efficiency. This review systematically examines fault detection and diagnostic techniques for synchronous motors from 2021 to 2025, emphasizing recent methodological innovations. A PRISMA-guided literature survey combined with scientometric analysis via VOSviewer 1.6.20 highlights growing reliance on data-driven approaches, especially deep learning models such as CNNs, RNNs, and hybrid ensembles. Model-based and hybrid techniques are also explored for their interpretability and robustness. Cross-domain methods, including acoustic and flux-based diagnostics, offer non-invasive alternatives with promising diagnostic accuracy. Key challenges persist, including data imbalance, non-stationary operating conditions, and limited real-world generalization. Emerging trends in sensor fusion, digital twins, and explainable AI suggest a shift toward scalable, real-time fault monitoring. This review consolidates theoretical frameworks, comparative analyses, and application-oriented insights, ultimately contributing to the advancement of predictive maintenance and fault-tolerant control in synchronous motor systems. Full article

Blockchain technology is now a useful tool that FinTech organizations use to increase transparency, optimize activities, and seize new possibilities. This research explores blockchain applications within the FinTech sector. This study systematically explores blockchain applications within the FinTech sector by 164 peer-reviewed articles, utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The review identifies eleven applications, such as smart contracts, financial inclusion, crowdfunding, digital identity, trade finance, regulatory compliance, insurance, asset management, investment, banking, and lending. A mixed-method strategy, combining quantitative and qualitative content analysis, was applied to examine the adoption and impact of blockchain across these subdomains. It further discusses current challenges such as regulatory ambiguity, interoperability limitations, and cybersecurity threats. This paper provides a consolidated framework of blockchain’s actual application in FinTech subdomains and identifies the main gaps in the existing literature. These results have practical implications for practitioners, researchers, and policymakers who seek to harness blockchain for achieving financial innovation and inclusive growth. Full article

Background: Older adults with malnutrition (≥65 years) require coordinated nutrition care during hospital-to-home transitions. A key purpose of integrated electronic medical record (iEMR) systems is to support clinicians in ensuring continuity of care across settings, yet little is known about their use in nutrition care discharge practices. This study explored how clinical dietitians use the iEMR to support nutrition care discharge practices for older adults with malnutrition and identified opportunities for optimisation to enhance care continuity. Methods: Semi-structured interviews were conducted with 16 clinical dietitians (11 frontline clinicians, 5 senior leaders) from 10 public hospitals across Queensland, Australia. Analysis combined deductive coding using the Consolidated Framework for Implementation Research 2.0 with inductive thematic analysis to identify system-level, organisational and behavioural influences on iEMR use and optimisation opportunities. Results: Four themes and ten subthemes were identified. System fragmentation, policy constraints and documentation burden limited dietitians’ ability to coordinate discharge care. Workarounds were common and reflected both practical adaptation and conditional trust in iEMR. Discharge practices were also shaped by local culture, professional norms and variable expectations for iEMR use. Despite these constraints, participants expressed aspirations for an optimised iEMR with embedded referral tools, real-time alerts and analytics to support improved service delivery. Conclusions: This study identified key factors influencing iEMR use by clinical dietitians to support nutrition care transitions for older adults with malnutrition. While current systems present significant challenges, optimising iEMR alongside organisational and policy enablers holds potential to strengthen nutrition care discharge practices and care continuity. Full article

Combined radiation exposures—pairings of ionizing and non-ionizing radiation—are increasingly relevant in medical, spaceflight, and environmental contexts. This systematic review evaluates their radiobiological effects and therapeutic applications, focusing on synergistic interactions and underlying biological mechanisms. A comprehensive search of PubMed, Google Scholar, Semantic Scholar, bioRxiv, and Europe PMC identified studies published from the 1960s through 2025. Eligible studies assessed biological responses to different radiation types applied either simultaneously or within 24 h, with minor exceptions. A total of 172 studies were included and categorized into radiobiological, therapeutic, and space radiation domains. Due to the predominance of mechanistic research, no formal risk-of-bias tool was applied; methodological limitations were assessed qualitatively. Findings were synthesized narratively by radiation type and domain. Synergistic and additive effects were frequently observed, with responses influenced by dose, sequence, radiation type, and DNA repair dynamics. Therapeutic combinations often enhanced efficacy, while space radiation studies revealed multifaceted biological damage. This review provides a consolidated reference for advancing research and applications involving combined radiation exposures, emphasizing the need for mechanistic insight and standardized protocols in therapy, radiation protection, and spaceflight. This study was funded by project 21GRD02 BIOSPHERE (European Partnership on Metrology, Horizon Europe) and reported per PRISMA 2020 guidelines; no protocol was registered. Full article

Although cannabinoids have proven therapeutic benefits, they are increasingly known for their capacity to disturb cardiac electrophysiology, particularly in individuals with hidden genetic issues such as channelopathies. This review consolidates molecular, clinical, epidemiological, and forensic findings linking cannabinoid exposure to arrhythmias and sudden cardiac death. It examines how phytocannabinoids, synthetic analogs, and endocannabinoids influence calcium and potassium currents through cannabinoid receptor-dependent and -independent pathways, affect autonomic regulation, and contribute to adverse conditions such as oxidative stress and inflammation in heart tissue. Genetic variants in key genes linked to SCD (SCN5A, KCNH2, KCNQ1, RYR2, and NOS1AP) can reduce repolarization reserve, transforming otherwise subclinical mutations into lethal substrates when combined with cannabinoid-induced electrical disruptions. Forensic research highlights the importance of comprehensive toxicological testing and postmortem genetic analysis in distinguishing between actual causes and incidental findings. There is an urgent need to re-evaluate the cardiovascular safety of cannabinoids, and this is underscored by the findings presented. The merging of molecular, clinical, and forensic evidence reveals that cannabinoid exposure—especially from high-potency synthetic analogs—can reveal latent channelopathies and precipitate fatal arrhythmias. Accordingly, this review advocates for a paradigm shift toward personalized risk stratification. If genetic screening is integrated with ECG surveillance and controlled cannabinoid dosing, risk assessment can be personalized. Ultimately, forensic and epidemiological data highlight the heart’s vulnerability, emphasizing its role as a target of cannabinoid toxicity and as a crucial aspect of public health monitoring. Full article

Thanks to their multiple outstanding features—such as high fluorescence quantum yield, good photostability, and excellent sensitivity—conjugated polymers (CPs) have emerged as a pioneering class of fluorescent materials for sensing applications, particularly in environmental and biological fields, for the detection of a wide range of environmental pollutants and bioactive compounds. The presence of delocalized π-electrons in the CP backbone significantly enhances sensing performance through a unique phenomenon known as the “molecular wire effect.” As a result, CP-based fluorescent sensors have been extensively developed and employed as exceptional tools for monitoring various analytes in environmental and biological contexts. A deep understanding of their unique properties, fabrication techniques, and recent innovations is essential for guiding the strategic development of advanced CP-based fluorescent sensors, particularly for future point-of-care applications. This study presents a critical review of the key characteristics of fluorescent sensors and highlights several common types of conjugated polymers (CPs) used in their design and fabrication. It summarizes and discusses the main sensing mechanisms, state-of-the-art applications, and recent innovations of CP-based fluorescent sensors for detecting target compounds in environmental and biological fields. Furthermore, potential strategies and future perspectives for designing and developing high-performance CP-based fluorescent sensors are emphasized. By consolidating current scientific evidence, this review aims to support the advancement of highly sensitive fluorescent sensors based on various CP nanoparticles for environmental and biological applications. Full article

Invasive Phytophthora species are increasingly impacting UK landscapes. Most recently, cryptic outbreaks of P. pluvialis Reeser, Sutton & E. Hansen have occurred on western hemlock and Douglas fir at several forest sites across Britain. To better understand the ubiquity and life cycle of this pathogen in British forests and the assemblages of co-inhabiting Phytophthora species, metabarcoding and baiting methodologies were applied to soil, stream water, and rainwater samples collected over a full calendar year from seventeen sites across Britain. Thirty-five Phytophthora species were detected across all sites, substrates, and detection methods, with most detections occurring in stream water by metabarcoding. The three most frequently detected species were (1) P. pluvialis, (2) P. gonapodyides H.E. Petersen & Buisman and (3) P. ramorum Werres, De Cock & Man in ‘t’ veld. Other species detected included the regulated pathogens P. austrocedri Greslen & Hansen, P. kernoviae Brasier, Beales & S.A. Kirk and P. lateralis Tucker & Milbraith, as well as P. ornamentata Scanu, Linald & T. Jung, a new species record for the UK. Phytophthora pluvialis was most frequently detected in March, with rainfall trap metabarcoding data suggesting that aerial dissemination occurs predominantly in late winter/early spring. Consistent detections of P. pluvialis in soil by metabarcoding indicate the potential for soilborne transfer of this pathogen by animal or human vectors, including equipment or machinery in forest operations. The study’s findings are discussed in relation to understanding how P. pluvialis spreads and the approaches needed to address key knowledge gaps in relation to inoculum sources. The results provide a baseline for Phytophthora diversity in British commercial forests, facilitating a greater understanding of typical and unusual trends in species assemblages. This study also consolidates the value of metabarcoding as an effective surveillance tool for Phytophthora in commercial forests. Full article

Primary central nervous system lymphoma (PCNSL) is a rare extra-nodal non-Hodgkin lymphoma confined to the central nervous system. The cancer biology of PCNSL remains incomplete and is often associated with genetic aberrations with abnormal signaling pathways, cell differentiation, regulation of epigenetic modification, and the tumor microenvironment. Stereotactic brain biopsy remains the gold standard for the diagnosis of PCNSL. For patients ill-suited for biopsy, MYD88 and IL-10 may be important biomarkers to diagnose PCNSL. High-dose methotrexate-based polychemotherapy is currently the standard induction treatment for PCNSL, followed by consolidation treatments including autologous stem cell transplant and whole-brain radiotherapy. Some studies suggest that low-dose lenalidomide is recommended as a maintenance therapy for PCNSL. Currently, relapse rates of PCNSL range from 25 to 50% with poor prognosis. Insight research is necessary to identify novel targeted treatments to improve outcomes in relapsed/refractory disease, such as immunomodulatory drugs, immune checkpoint inhibitors, signaling pathway inhibitors, and chimeric antigen receptor T-cell therapy. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

Background: Augmented-reality (AR) navigation is emerging as a means of turning pre-operative cone-beam CT data into intuitive, in situ guidance for difficult tooth removal, yet the scattered evidence has never been consolidated nor illustrated with a full clinical workflow. Aims: This study aims to narratively synthesise AR applications limited to dental extractions and to illustrate a full AR-guided clinical workflow. Methods: We performed a PRISMA-informed narrative search (PubMed + Cochrane, January 2015–June 2025) focused exclusively on AR applications in dental extractions and found nine eligible studies. Results: These pilot reports—covering impacted third molars, supernumerary incisors, canines, and cyst-associated teeth—all used marker-less registration on natural dental surfaces and achieved mean target-registration errors below 1 mm with headset set-up times under three minutes; the only translational series (six molars) recorded a mean surgical duration of 21 ± 6 min and a System Usability Scale score of 79. To translate these findings into practice, we describe a case of AR-guided mandibular third-molar extraction. A QR-referenced 3D-printed splint, intra-oral scan, and CBCT were fused to create a colour-coded hologram rendered on a Magic Leap 2 headset. The procedure took 19 min and required only a conservative osteotomy and accurate odontotomy that ended without neurosensory disturbance (VAS pain 2/10 at one week). Conclusions: Collectively, the literature synthesis and clinical demonstration suggest that current AR platforms deliver sub-millimetre accuracy, minimal workflow overhead, and high user acceptance in high-risk extractions while highlighting the need for larger, controlled trials to prove tangible patient benefit. Full article