Search Results (8,028)

The growing global demand for clean and sustainable energy has reignited interest in nuclear power as a carbon-free alternative to fossil fuels, driving an increase in uranium mining. However, uranium extraction releases radioactive elements along with toxic and heavy metals, posing serious environmental risks. A combined narrative and systematic review was employed to evaluate remediation mechanisms, performance trends, sustainability, and emerging technological advancements. The results indicate that phytoremediation remains the most extensively studied and field-applicable technique, while phycoremediation offers rapid uptake in aqueous systems and mycoremediation demonstrates higher tolerance to extreme conditions. However, limitations such as slow remediation rates, site-specific performance, and scalability challenges restrict their widespread implementation. This study also highlights the emerging role of artificial intelligence and machine learning in optimizing remediation processes, although their application remains limited, particularly in fungal systems. Furthermore, the integration of nature-based solutions into nuclear waste management frameworks, aligned with international safety standards, presents a promising pathway for sustainable remediation. Future research should focus on developing hybrid remediation strategies, establishing performance thresholds under high contamination conditions, and advancing AI-driven, site-specific optimization models to enhance efficiency and scalability. Full article

Enhancing cereal grain quality while maintaining yield stability represents a pressing global challenge for sustainable agricultural development. Optimizing grain quality in cereal crops, which account for more than 60% of global dietary energy, relies heavily on managing nitrogen dynamics during the heading and grain-filling stages. Late-stage nitrogen application (from heading to early grain-filling stages) optimizes the temporal dynamics of nitrogen supply and exhibits substantial regulatory potential in mediating the yield–quality trade-off. Nitrogen availability can profoundly influence source–sink dynamics, carbon–nitrogen metabolic coordination, and the biosynthesis of storage reserves. This systematic review consolidates current understanding of the molecular and physiological mechanisms by which late-stage nitrogen application affects grain development and final quality in cereals, with a particular focus on major cereal crops including wheat, rice, and malting barley, which represent contrasting quality objectives and nitrogen management requirements. At the physiological level, late-stage nitrogen application delays functional leaf senescence, sustains photosynthetic carbon assimilation capacity, facilitates assimilate transport and partition to developing grains, and optimizes the accumulation dynamics and compositional profiles of starch and protein. At the molecular level, this review elucidates the sequential regulatory cascades governing nitrogen signal perception and transduction, the coordinated transcriptional networks underlying carbon–nitrogen metabolic crosstalk, and the expression dynamics of genes encoding starch biosynthetic enzymes and storage proteins. Integrating those recent research advances, this review also highlights several critical challenges currently facing the field. To address these challenges, we delineate promising avenues for future research including constructing time-series multi-omics frameworks, employing genome-editing technologies to functionally validate key regulatory genes and integrating artificial intelligence and big data analytics. The goal of this review is to establish a theoretical basis for precision nitrogen management strategies designed to optimize cereal crop production, targeting high yield, superior quality, and improved nitrogen use efficiency concurrently. Full article

Little is known about the fungal pathogens responsible for fig (Ficus carica L.) branch canker in the Kashi region of Xinjiang, China. Using a combination of morphological characterization and multilocus sequence analyses of ITS, TEF1-α, and RPB2, we identified fungal isolates obtained from cankered fig branches collected in commercial orchards in this region. The pathogenicity of representative isolates was evaluated by artificial inoculation of fig branches under natural field conditions. Two dominant fungal species, Fusarium proliferatum and Alternaria alternata, were consistently isolated from diseased tissues. In inoculation assays, both species induced typical branch canker lesions similar to those observed in the field. Lesions caused by F. proliferatum were generally larger than those induced by A. alternata. The original pathogens were successfully re-isolated from the inoculated branches, thereby fulfilling Koch’s postulates. This study represents the first report of F. proliferatum and A. alternata as causal agents of fig branch canker in Xinjiang and expands the known spectrum of pathogens associated with fig branch diseases. These findings provide a scientific basis for improved disease monitoring and the development of sustainable management strategies in local fig orchards. Full article

BioMedInformatics is an international, peer-reviewed, open access journal that covers all areas of biomedical informatics, computational biology, and medicine. Established in 2021, the journal is now five years old and reflects the evolution of the field through its consistent thematic focus on Artificial Intelligence (AI)-driven diagnosis and prediction, with a particular emphasis on translational clinical decision support and biomedical signal and imaging analysis. Despite the predominance of AI-related topics, classical bioinformatics remains a major focus, with a particular emphasis on the discovery of biomarkers and the development of data resources. This editorial summarises this evolution, which accurately reflects the field as a whole. Full article

Background: Technological innovation increasingly shapes modern sport, influencing performance, athlete safety, and regulatory governance. While new technologies enhance training and monitoring, they also raise concerns regarding fairness, health protection, and ethical legitimacy, commonly described as technological doping. The fragmented nature of the literature in this field requires integrative synthesis. Methods: A structured narrative synthesis was conducted using systematic searches and predefined eligibility criteria to identify studies addressing performance technologies, digital monitoring and detection systems, healthcare compliance, and governance and ethical frameworks. Twenty-four studies spanning empirical, policy, and conceptual domains were included. Results: Mechanical technologies, particularly advanced carbon-plate footwear, were associated with approximately 1–3% faster marathon performances and measurable alterations in lower-limb kinematics and kinetics under fatigue, while running-specific prostheses demonstrated performance-relevant differences in stiffness and energy return properties. Wearable monitoring systems supported training optimization but raised concerns related to surveillance and athlete autonomy. Artificial intelligence-based medication screening tools demonstrated high operational performance, with reported recognition accuracy ranging from approximately 92% to 98%, sensitivity approaching 1.00, and strong specificity for identifying prohibited substances from prescription images. Healthcare studies identified persistent knowledge gaps, medication risks, and the importance of pharmacists and education programs. Governance analyses revealed disparities in laboratory capacity and regulatory ambiguity when addressing emerging technologies, while ethical scholarship questioned the boundaries of legitimate enhancement. Conclusions: Technological doping reflects an interconnected performance–health–governance challenge rather than an isolated equipment issue. The synthesis demonstrates that technological doping is driven by measurable performance gains, digitally mediated compliance systems, and uneven regulatory capacity, indicating that future governance must shift from reactive equipment bans toward integrated, evidence-based oversight of biomechanical, digital, and healthcare technologies. Full article

Transparent wood possesses advantages such as light weight, high strength, translucency, thermal insulation, acoustic performance, and sustainability, demonstrating significant development potential. Its properties are highly compatible with the demands of pop-up commercial spaces, which are characterized by pop-up, low energy consumption, and strong visual expression. Based on Artificial Intelligence-Generated Content (AIGC) technologies, this study takes an urban greenhouse installation as a case study and develops a systematic design methodology for applying transparent wood in modern pop-up exhibition spaces. Through field research, questionnaire surveys, and the integration of design requirements using AIGC, the study employs the Analytic Hierarchy Process (AHP) to construct an evaluation system encompassing esthetic performance, structural safety, sustainability, and exhibition experience. In addition, a Fuzzy Comprehensive Evaluation (FCE) method is adopted for quantitative assessment. The results indicate that transparent wood not only meets the requirements of lightweight structures and full life-cycle environmental performance, but also enhances spatial transparency and immersive atmosphere. This research proposes a standardized evaluation framework and a reproducible design reference for material selection in pop-up exhibition spaces. Full article

Cancer metastasis and chemoresistance are primary reasons for cancer-related mortality. Current therapeutic options rely mostly on single-target drugs, which often fail to exhibit long-lasting remission of the disease progression due to the complexity of metastasis and resistance mechanisms. Natural products (NPs) possess inherent structural diversity, rendering them suitable as multi-target agents. The utilization of NPs is often impeded in treating complex diseases such as cancer, even though approximately 65% of approved anticancer drugs are NP derivatives, or synthetic derivatives containing NP-pharmacophores, due to various factors, including poor aqueous solubility and variable oral bioavailability, structural complexity, synthetic inaccessibility, and stereochemical diversity that confounds structure–activity relationship analyses. This review discusses how integrating artificial intelligence (AI) and machine learning (ML) with chemoinformatics can identify, prioritize, and experimentally validate NPs, potentially paving the way for new drugs that address intricate processes such as metastasis and resistance. We summarize the recent computational advances in the field, including graph neural networks, attention mechanisms, Siamese networks, virtual screening, and network pharmacology. These advancements address ADMET optimization, molecular representation, virtual screening, network pharmacology, and experimental validation. We emphasize how each of these approaches tackles the unique challenges associated with NPs. We contextualize our review within the specific challenges presented by the chemical space of NPs. Additionally, we analyze real-world case studies of successful AI-assisted NP discovery and categorize the quality of evidence into three levels: Level A, which includes in vivo efficacy with mechanistic details; Level B, which consists of in vitro validation of mechanisms and phenotypes; and Level C, which represents computational hypotheses that are awaiting experimental verification. Additionally, we propose an operational framework for selecting suitable AI methodologies based on available data, target characterization, and validation resources. Finally, we emphasize the limitations and future directions in AI-facilitated NP discovery. Full article

Background: In actinic keratosis (AK), clinical clearance after field-directed therapies does not necessarily correspond to histological resolution, resulting in subclinical persistence and risk of recurrence. Objective: To provide a practical, up-to-date framework for non-invasive monitoring of treatment response in AK, integrating clinical assessment and dermoscopy with high-resolution imaging techniques, reflectance confocal microscopy (RCM), line-field confocal optical coherence tomography (LC-OCT), and high-frequency ultrasound (HFUS), and to discuss emerging optical biomarkers based on Raman spectroscopy. Results: For each modality, we summarize pre- and post-treatment imaging patterns, proposed response criteria, recommended follow-up timing, and correlations with clinical outcomes (including clearance and AKASI) and, when available, histological findings. The available evidence is derived from a limited number of observational studies, predominantly involving RCM and LC-OCT, whereas data on HFUS and Raman spectroscopy remain comparatively scarce. RCM and LC-OCT allow in vivo assessment of epidermal architectural normalization and reduction of intraepidermal keratinocyte atypia. HFUS captures quantitative trajectories of superficial dermal remodeling, including changes in the subepidermal low-echogenic band (SLEB) and dermal echogenicity after photodynamic therapy and other field treatments. Dermoscopy remains the first-line tool for routine follow-up but may fail to detect minimal subclinical persistence. Finally, we discuss the potential role of in vivo Raman spectroscopy for dynamic molecular endpoints and its possible integration with artificial intelligence–based analytical approaches. Conclusions: A standardized multimodal follow-up strategy improves the accuracy of treatment-response assessment compared with clinical evaluation alone. We propose a technique-specific checklist of minimal response criteria and a pragmatic temporal assessment scheme, and outline a research roadmap to support validation and clinical implementation of non-invasive imaging-guided monitoring in actinic keratosis. Full article

In response to issues such as insufficient bias in random sampling, low convergence efficiency, inadequate path search efficiency, and lack of path smoothness encountered by the traditional RRT* algorithm during path planning, an improved algorithm is proposed. First, a dynamic ellipsoidal sampling strategy is introduced, which accelerates the exploration of the path space by adaptively adjusting the sampling region. Additionally, a bidirectional RRT* algorithm is employed, establishing two alternately growing search trees to perform bidirectional search, thereby effectively enhancing the convergence speed of the algorithm. Second, a dynamic goal-biased strategy is adopted, which greedily guides the random tree to grow rapidly toward the goal point, thereby improving planning efficiency. A heuristic search scheme is integrated with the RRT* algorithm to further increase convergence speed. A random sampling expansion strategy is utilized to guide the tree to expand into unexplored regions, avoiding local minima while ensuring global search capability. Local reconnection optimization is applied to reduce the cumulative path cost of new nodes while balancing path length, smoothness, and safety. To reduce the number of iterations, an improved artificial potential field method is incorporated into the growth process of the bidirectional random search trees, providing directional guidance for their expansion. Finally, path pruning techniques are applied to eliminate redundant nodes from the initial path, and a cubic B-spline interpolation algorithm is used to smooth the pruned path, generating a final trajectory with continuous curvature suitable for tracking. Quantitative analysis of simulation experiments in three-dimensional space shows that in both simple and complex environments, compared with the RRT, GB-RRT, BI-RRT, APF-RRT, and BI-APF-RRT* algorithms, the improved RRT* algorithm reduces planning time by approximately 58–90%, decreases the number of path nodes by about 31–91%, and shortens path length by around 8–20%, demonstrating the superiority of the proposed algorithm. Full article

For arch dam seismic safety evaluation, the finite element equivalent stress method has been widely used, and existing studies have realized mature equivalent stress calculation along the foundation surface path. However, from the scientific research perspective, there is a lack of a full dam surface equivalent stress characterization method for arch dams under seismic action; from the engineering practice perspective, the traditional path method cannot fully reflect the overall stress distribution of the dam, leading to insufficient comprehensive safety evaluation. To accurately assess the impact of seismic action on the overall structural safety of arch dams and address the above limitations, this study develops a methodology for calculating equivalent stress across the entire dam surface of arch dams under seismic action. Taking a concrete arch dam as the research object, a seismic wave input method based on viscoelastic artificial boundaries is employed. Three-dimensional finite element analysis of the arch dam is performed using ABAQUS, integrated with Python-based secondary development to extract stress along the integration path of each arch ring layer and calculate sectional internal forces. The equivalent stress of each arch ring layer integration path is then processed using the material mechanics method to obtain the equivalent stress distribution across the entire dam surface. A comparative analysis is conducted between the equivalent stress on the entire dam surface and that along paths on the foundation surface regarding the seismic dynamic response and behavioral patterns of the dam. The results demonstrate that the full dam surface equivalent stress approach not only accurately captures the extreme tensile and compressive stress values in the downstream foundation area but also identifies stress extrema in the upstream dam crest region, thereby achieving comprehensive characterization of the dam stress field under seismic action and enhancing both the efficiency and accuracy of equivalent stress calculations for arch dams. This method provides more comprehensive and reliable data support for seismic design optimization and reinforcement of arch dams. Compared with the traditional foundation surface path method, the proposed method achieves 100% identification of the whole dam surface stress extremum areas, with a maximum relative error of only 1.62% in the overlapping calculation area. Full article

In recent years, agriculture has begun to transform thanks to the arrival of robots and autonomous vehicles capable of performing complex operations such as weeding and spraying in an intelligent and targeted manner. In fact, new-generation agricultural robots use artificial intelligence (AI), cameras, and sensors to recognise weeds, analyse crop conditions, and apply plant protection products only where necessary, thus reducing waste and environmental impact. Some systems combine drones and ground vehicles to achieve even more accurate results. This systematic review synthesises recent advances in agricultural robotics for weed and pest management through a PRISMA-based approach. Literature was collected from major scientific databases (Scopus, Web of Science, IEEE Xplore, Google Scholar) and complementary sources, leading to the inclusion of 83 eligible studies. The selected evidence was structured into four application domains: (i) weed detection and mapping, (ii) robotic and non-chemical weed control (mechanical and laser-based approaches), (iii) selective/variable-rate spraying for pest and disease management, and (iv) integrated weeding–spraying solutions, including cooperative Unmanned Aerial Vehicle–Unmanned Ground Vehicle (UAV–UGV) systems. Overall, the reviewed studies confirm rapid progress in real-time perception (deep learning-based detection), navigation/localization (e.g., GNSS/RTK, LiDAR, sensor fusion) and targeted actuation (spot spraying and precision interventions), while also revealing persistent limitations: heterogeneous evaluation protocols, limited system-level comparisons in terms of work rate, scalability, costs and robustness under variable field conditions, and an often unclear distinction between prototype platforms and solutions close to commercialization. However, the large-scale spread of these technologies is still hampered by high costs, technical complexity, and cultural resistance. The review highlights how the integration of automation, sustainability, and accessibility is key to the agriculture of the future. Full article

Background/Objectives: Artificial intelligence (AI) tools for orthodontic diagnosis are increasingly used in clinical practice; however, there is limited evidence regarding their performance in CBCT-based assessments. In this study, we evaluated the diagnostic reliability of the Diagnocat platform for categorical orthodontic diagnoses obtained from CBCT examinations. Methods: Fifty-nine patients who underwent large-field CBCT (13 × 16 cm) and lateral cephalograms within 30 days were included, and CBCT scans were processed using Diagnocat (v1.0). The platform’s categorical outputs—sagittal skeletal class, vertical facial pattern, overbite category, and Dental Angle class—were compared with manual cephalometric analyses performed by an experienced orthodontist (reference standard). Standard thresholds were used to convert reference continuous measurements into categorical variables. Missing or ‘N/A’ index test outputs were treated as diagnostic failures in accordance with STARD recommendations. Agreement was assessed via Cohen’s kappa (κ), and the sensitivity, specificity, PPV, and NPV were calculated for angle classification. Results: The AI platform generated skeletal and vertical classifications in only 3/59 (5%) and 1/59 (1.7%) patients, respectively. Agreement was fair (κ = 0.324) for overbite categorization, and the Dental Angle class was provided for 34/59 (57.6%) patients. When “N/A” results were treated as diagnostic failures, the overall system usability was <10% for skeletal parameters. Conclusions: The platform demonstrated insufficient diagnostic reliability and failed to generate outputs for most patients. While the specificities for generated diagnoses were acceptable, the low data completeness rate renders the tool currently unsuitable for independent clinical decision-making. Full article

This study investigates the application of NDTs for the detection of sub-surface defects in an ancient book, with the aim of improving conservation methods in the field of cultural heritage. A sequence of thermographic images in a solar loading thermography (SLT) scenario was acquired during a diagnostic campaign in Harbin, China, to identify four distinct fabricated dowels made of Wool, Rubber, Teflon^®, and Synthetic material. The images were processed in two ways: the first combined advanced image-processing methods: pre-processing via MdFIF, post-processing, PCT and RPCT, applied both to the original sequence and to the MdFIF-filtered thermograms. The second approach employed numerical simulations in COMSOL Multiphysics^® to develop a predictive thermal model. The comparison of localized thermal anomalies obtained from the two approaches demonstrated the capability of NDTs to reliably reveal artificial defects, confirming their suitability for diagnostic conservation. Overall, the integration of advanced image processing with numerical simulation enhances diagnostic accuracy, particularly for subtle or low-contrast anomalies, thereby enabling more informed condition assessment and supporting rapid, targeted, and preventive conservation strategies. Full article

Accurate inversion of the deep initial in situ stress field is a fundamental prerequisite for stability analysis of surrounding rock in underground engineering, roadway support design, and prevention and control of dynamic disasters. To address the problems of scarce in situ stress measurements in deep mining areas, the inability of conventional regression methods to capture the nonlinear characteristics of complex tectonic stress fields, and the tendency of traditional inversion algorithms to fall into local optima and overfitting, this paper proposes an intelligent inversion method based on support vector regression optimized by the artificial bee colony algorithm (ABC-SVR). The artificial bee colony algorithm is employed to adaptively optimize the core parameters of the SVR model, thereby enabling high-precision inversion of complex deep stress fields. Comparing the results with acoustic emission tests demonstrated that the ABC-SVR model significantly outperforms conventional SVR and backpropagation neural networks across various performance metrics. The inversion results show high consistency with the measured data, achieving a root mean square error (RMSE) of 1.25, a mean absolute percentage error (MAPE) of 4.16%, and a coefficient of determination (R²) of 0.908. This method can rapidly reconstruct high-precision initial in situ stress fields in deep unmined regions, providing highly reliable boundary conditions for numerical simulations and demonstrating significant engineering application potential. Full article

According to the EFSA Report on Zoonoses (2024), yersiniosis was classified as the fourth most commonly reported zoonosis in humans in 2023, with a 13.5% increase in yersiniosis infections compared to 2022. In 2024, the findings were consistent with the 2020–2023 trend. Isolation and identification of enteropathogenic Yersinia is difficult and time consuming, especially when examining food and environmental samples. Among them, Y. pseudoturbeculosis poses a challenge due to the lack of a single selective medium for all bioserotypes. Therefore, faster methods for the detection of Yersinia spp. need to be implemented into the praxis. Rapid identification of pathogens in food or at the time and location of the epidemiological outbreak (point-of-care testing) enables either prevention of the outbreak or early stage diagnosis and prompt decisions. The loop-mediated isothermal amplification (LAMP) is increasingly coming to scientists’ attention as a robust and rapid methodology for pathogen detection in laboratories with limited resources and equipment. The aim of current study is to evaluate, for the first time, the sensitivity of the LAMP protocol based on colorimetric detection in the visible spectrum in comparison with that of the digital droplet PCR (ddPCR). For this aim, a series of decimal logarithmic dilutions of the pathogen Y. pseudotuberculosis in artificially contaminated raw goat milk was used. One commercial LAMP kit with two different dyes (one dsDNA-binding and one Mg²⁺-sensitive) was compared to the sensitivity of the detection to ddPCR. The results obtained revealed a high sensitivity of the kit for detection of DNA isolated from artificially contaminated milk samples in the following range: visible detection based on visible color change—3.1 × 10⁴ mL (violet dye) and 3.4 × 10³/mL (blue dye); detection with gel electrophoresis—2.0 × 10¹/mL (violet dye) and 3.4 × 10²/mL (blue dye). The enumeration of the DNA copies in the same samples was performed with ddPCR, with a detection limit of 2.0 × 10¹/mL. Our results indicate the potential and the possible applicability of the LAMP method for rapid and sensitive visual detection of Y. pseudotuberculosis in raw goat milk. The presented ddPCR protocol can be used for highly sensitive identification and enumeration of Y. pseudtuberculosis in raw goat milk. In conclusion, the conducted comparison is of importance for future implementation of LAMP protocols for on-field analysis near the sampling site and point-of-care or laboratory diagnostics of Y. pseudtuberculosis after the successful validation procedure of an appropriate LAMP protocol. Full article