Search Results (7,671)

Objectives: To synthesize current evidence regarding advances in periodontal diagnosis and therapy, with emphasis on molecular biomarkers, omics technologies, microbiome profiling, digital imaging, and artificial intelligence-based analytical models that support the transition toward precision periodontology. Methods: This narrative review examines contemporary evidence on emerging molecular, microbiological, and digital technologies applied to periodontal diagnosis, prognostic assessment, and therapeutic planning. The review includes studies addressing salivary and gingival crevicular fluid biomarkers, microbiome characterization, omics approaches, cone-beam computed tomography, three-dimensional imaging, machine-learning algorithms, and personalized periodontal therapies. Relevant literature was identified through searches in major biomedical databases, including PubMed/MEDLINE, Scopus, and Web of Science, focusing on studies published on periodontal diagnostics, biomarkers, digital technologies, artificial intelligence, and precision medicine approaches in periodontology. Results: Peer-reviewed articles addressing innovative diagnostic and therapeutic approaches in periodontology were considered. Priority was given to studies evaluating clinical applicability, diagnostic performance, prognostic utility, and personalized treatment strategies integrating molecular and digital technologies. Conclusions: Emerging molecular and digital technologies are reshaping periodontal diagnosis and therapy by improving disease detection, risk prediction, and individualized treatment planning. Biomarkers, omics technologies, microbiome profiling, and artificial intelligence-assisted imaging may enhance diagnostic precision and clinical decision-making. These developments support the implementation of precision periodontology; however, challenges related to biomarker validation, algorithm standardization, cost, and accessibility remain barriers to routine clinical adoption. Further research is necessary to validate these approaches and facilitate their integration into periodontal practice. The integration of biomarkers, omics technologies, advanced imaging, and artificial intelligence may improve early periodontal diagnosis, prognostic assessment, and personalized treatment planning. These innovations support the transition toward precision periodontology and have the potential to enhance clinical decision-making, treatment outcomes, and long-term periodontal health in routine dental practice. Full article

Crop production is continually exposed to a wide range of abiotic stresses that negatively affect growth and yield, posing a severe threat to global food security. Plant growth-promoting bacteria (PGPB) promote nutrient assimilation, activate antioxidant enzymes, and stimulate phytohormone production to mitigate abiotic stress. However, the effective application of PGPB in the field depends on host colonization, soil specificity, and susceptibility to competitive microbial communities. Recently, non-thermal plasma (NTP) has emerged as a revolutionary tool for sustainable agriculture, making it a priority to develop efficient, low-cost, and eco-friendly strategies to enhance seed vitality and manage abiotic stress. Plasma-generated reactive oxygen and nitrogen species (RONS) have been shown to mediate intracellular redox homeostasis and the antioxidant defense signaling network. Furthermore, plasma stimulates MAPK cascades and stress-responsive genes such as LEA1, SnRK2, P5C, and the SOS pathway, ionic balance, and membrane stability, ultimately supporting plant stress adaptation to drought, salinity, and heavy metals. Plasma-induced RONS signaling activates PGPB functional traits such as root colonization, biofilm formation, nutrient mobilization, and plant growth-promoting activities. However, the molecular mechanisms underlying NTP-PGPB microbial multiple stress adaptation and the long-term ecological stability and biosafety of microbial communities remain inadequately resolved. Consequently, future integration of multi-omics approaches, synthetic microbial communities, and field-scale validation is required to explore the mechanistic advances of plasma-modulated microbiome interactions to enable agricultural applications. Full article

Policies designed to enhance agricultural resilience may produce unintended trade-offs among ecological, industrial, and social subsystems due to resource competition, structural constraints, and cross-subsystem feedback. This study conceptualizes these unintended effects as a “resilience compensation trap.” Taking Nenjiang City, Heilongjiang Province, China, as a case study, we integrate social–ecological systems theory with system dynamics modeling to examine the evolution of agricultural resilience from 2015 to 2035 under five scenarios: baseline development, ecological priority, industrial upgrading, talent revitalization, and comprehensive optimization. The results show that overall agricultural resilience increases slowly under the baseline scenario. Industrial upgrading raises overall resilience to 16.43 in 2035, representing an increase of 60.92% relative to the baseline, but reduces ecological resilience by 26.94%, thereby producing a clear cross-subsystem compensation effect. By contrast, the comprehensive optimization scenario increases overall resilience to 18.07, 76.98% above the baseline, while reducing conflicts among the three subsystems. These results indicate that single-objective interventions may activate negative feedback that offset their intended benefits. By operationalizing absorptive, adaptive, and transformative capacities as interacting variables and feedback loops, this study provides a process-based explanation of agricultural resilience trade-offs. The findings further highlight the importance of coordinated ecological, industrial, and social policies. Full article

Addressing technical challenges such as inaccurate appearance detection, inaccurate weight estimation, and low automation levels in passion fruit sorting under postharvest conveyor-line conditions, this study proposes an intelligent detection and grading model, YOLOv11n-ACH, based on an improved YOLOv11n. By integrating the Hybrid Inverted Block (HIB) module, the ASF-YOLO scale fusion mechanism, and the Convolutional Attention Fusion Mechanism (CAFM), the model effectively mitigates severe fruit occlusion and background interference caused by conveyor surfaces, residual plant material, and illumination variation. Consequently, the high-precision metric mAP@50-95 reaches 99.4%, representing an increase of 3.9 percentage points over the baseline model. Building upon this foundation, a real-time grading and counting system incorporating a confidence-priority frame-selection mechanism was constructed by combining the ByteTrack multi-object tracking algorithm with horizontal dynamic scale calibration technology. The study establishes a multivariate linear regression mass-estimation model based on morphological features (R² = 0.9617). The regression model was developed using 500 fruits, and its performance was independently evaluated using a second, non-overlapping cohort of 500 fruits collected from the same orchard. Using 12 horizontal calibration points, a cubic spline interpolation function was constructed to compensate for horizontal position-dependent variation in the pixel-to-physical scale under the tested fixed imaging configuration. In the independent mass-validation cohort, the system achieved an MAE of 2.54 g, an RMSE of 3.21 g, and an MARE of 5.65%. A third, non-overlapping cohort of 1568 fruits was used for end-to-end passage-level counting and operational grading evaluation. This lightweight solution provides an engineering approach for passion-fruit sorting under the tested postharvest conveyor-line conditions. Full article

Sodium chloride (NaCl) serves multiple functions in meat product processing, including flavor enhancement, preservation, and texture regulation. However, excessive sodium intake significantly increases the risks of chronic diseases, including cardiovascular disease, hypertension, and gastric cancer. Globally, sodium intake among the general population consistently exceeds the daily upper limit recommended by the World Health Organization (less than 2000 mg sodium per day, equivalent to <5 g salt per day). In certain regions, processed meat products are an important source of dietary sodium intake. Consequently, the development of low-sodium meat products has emerged as a critical priority in both the food industry and public health. This review reviews and summarizes the multifunctional roles of sodium chloride in meat products and the underlying mechanisms of these functions, and evaluates mainstream sodium reduction strategies, namely direct sodium reduction, physical modification, salt substitutes, flavor enhancement, odor-induced saltiness enhancement (OISE), and non-thermal processing. The analysis indicates that individual strategies exhibit limitations in terms of sensory quality, safety, or cost. Future efforts should focus on achieving effective sodium reduction through the synergistic application of multiple strategies, without compromising product quality or safety. This review further proposes a product-type-oriented strategy matrix, and multi-strategy synergy combined with AI optimization which is put forward as a promising potential pathway for the industrialization of sodium reduction in meat products, which remains to be validated by further research. Full article

To effectively address the challenges of complex spatiotemporal conflicts, dynamic obstacle avoidance, and coordinated multi-objective optimization in preemptive multi-UAV logistics delivery within complex airspace, this study proposes a Hybrid Improved Multi-Objective Cuckoo Search algorithm (HI-MOCS) for preemptive multi-UAV cooperative logistics scheduling and planning. To overcome the limitations of conventional MOCS, including a low proportion of feasible solutions under complex constraints, susceptibility to local optima, and uneven distribution of multi-objective solution sets, a multi-constraint physical model and a multidimensional evaluation framework are established for preemptive scheduling. A positive knowledge-transfer mechanism based on the co-evolution of primary and auxiliary populations is developed, in which constraint-violation information is used to guide infeasible solutions toward the feasible region. A hybrid heuristic population initialization strategy combining emergency-order priority and spatial scanning rules is introduced to increase the proportion of high-quality feasible solutions in the initial population. In addition, a nonlinear dynamic adaptive parameter-adjustment strategy is designed to balance global exploration and local exploitation, while an iterative truncation-based environmental selection mechanism using the shortest-distance criterion is employed to improve the distribution quality of the Pareto solution set. The experimental results show that, in the benchmark scenario, HI-MOCS achieves an average increase of 33.26% in the total order completion rate and an average reduction of 15.34% in emergency response time compared with 11 multi-objective optimization algorithms, while also exhibiting favorable performance in terms of flight distance per completed order. The fleet-size analysis shows that the 15-UAV configuration achieves the lowest best mean fitness. The safety-distance analysis indicates that, compared with the other safety-distance settings, the 30 m setting increases the total order completion rate by an average of 26.55%, while reducing emergency response time and flight distance per completed order by 27.36% and 33.72%, respectively. The task-scale analysis shows that the 50-order scenario achieves the lowest best mean fitness. Further ablation experiments demonstrate that, compared with the average performance of MOCS and the four single-strategy variants, the complete HI-MOCS improves the total order completion rate by 20.27%, while reducing emergency response time and flight distance per completed order by 20.71% and 36.18%, respectively. The HV, IGD, and Pareto-front results further confirm that the synergistic effects of the four improvement mechanisms effectively enhance the multi-objective optimization performance and the quality of the nondominated solution set. The current study is still validated under simulation conditions assuming reliable GNSS positioning and communication links, without explicitly considering communication delays. Full article

Cell-wall polysaccharide O-acetylation contributes to cell-wall assembly, organ development, and plant–pathogen interactions, but the cucumber TBL gene family remains poorly characterized. Here, 37 CsTBL genes were identified genome-wide and analyzed using phylogenetic, syntenic, conserved-motif, gene-structure, promoter, protein-structure, Gene Ontology, and transcriptome approaches, followed by RT-qPCR analysis after powdery mildew inoculation. All CsTBL proteins contained the conserved GDS and DxxH motifs, whereas accessory motifs and predicted structural features varied among clades. Intraspecific analysis identified dispersed, WGD/segmental, and tandem duplication categories, and cross-species synteny was more extensive with melon than with Arabidopsis. Homology-derived annotations associated CsTBL genes with cell-wall polysaccharide metabolism, Golgi/endomembrane compartments, and O-acetyltransferase activity, including six genes assigned to xylan O-acetyltransferase-related annotations. Expression profiling revealed tissue- and developmental-stage-dependent patterns, whereas the publicly available powdery mildew RNA-seq dataset provided descriptive temporal expression profiles in Podosphaera xanthii-inoculated samples. Independent RT-qPCR analysis using time-matched mock controls revealed distinct post-inoculation responses among six selected genes. Relative to the corresponding mock controls, CsTBL2 was consistently repressed; CsTBL15 showed transient induction at 1 dpi followed by repression; CsTBL24 exhibited a biphasic response; CsTBL25 was induced at all sampled post-inoculation time points; CsTBL26 showed progressive induction; and CsTBL30 reached its highest observed expression level at 3 dpi. Integrated functional annotation and expression evidence highlighted CsTBL26 as a priority candidate for further functional characterization, while CsTBL24 and CsTBL25 represented fruit-associated candidates with distinct powdery mildew responses; CsTBL30 remained an additional strongly infection-responsive candidate. These findings provide an evolutionary and expression-based framework for the functional characterization of the cucumber TBL gene family. Full article

With immune checkpoint inhibitors becoming the mainstay of systemic therapy in both metastatic and early-stage settings across many cancer types, the management of immune-related adverse events has emerged as a central priority of modern oncological supportive care. These toxicities can substantially impair the quality of life of cancer patients, including those who achieve long-term survival. Consequently, there is a pressing need to develop reliable predictive biomarkers to better tailor immune checkpoint inhibitor treatment and optimize patient selection. This review summarizes several of the most promising predictive biomarkers currently under investigation, including genetic factors; peripheral blood parameters and their ratios; autoantibodies; cytokines and chemokines; cytomegalovirus serostatus; gut microbiome characteristics; body composition metrics; molecular imaging features; and tumour- and patient-related factors such as cancer type, gender, and physical activity. Because single biomarkers have limited predictive value, multi-omics prediction models and composite immune-cell scores are increasingly demonstrating greater potential. However, none of these candidate biomarkers have yet undergone sufficient validation to support their incorporation into routine clinical practice. Full article

Uranium is a critical strategic resource. However, uranium mining can cause severe contamination of surrounding soils by potentially toxic elements (PTEs). Conventional approaches that separately perform source apportionment and risk assessment fail to establish a direct linkage between pollution sources and their associated environmental and health risks. Herein, we develop an integrated framework that combines the absolute principal component score–multiple linear regression (APCS-MLR) model, the potential ecological risk index, and Monte Carlo simulation. The established framework was used to try quantifying the potential sources of soil PTEs and their corresponding ecological and human risks in a uranium mining area. The results showed that the concentrations of U, Th, Cd, and Cr significantly exceeded local background levels, with Cd and U exhibiting higher spatial variability than the other PTEs. The APCS-MLR model identified three potential sources of soil PTE contamination: mining activities, mixed anthropogenic-natural, and natural sources. Mining and mixed sources were the dominant contributors to ecological risk, jointly accounting for 88.7% of the total ecological risk, with Cd identified as the primary ecological risk pollutant. The mixed sources also contributed the largest proportions of non-carcinogenic and carcinogenic health risks, accounting for 52.56% and 67.5%, respectively. Furthermore, children were found to face greater health risks than adults due to higher exposure levels. Priority factor analysis indicated that pollution management should continuously monitor Cd derived from mining activities based on statistical inference. Overall, the proposed integrated framework successfully established a quantitative linkage between pollution sources and associated risks, providing a scientific basis for source-specific and zone-specific soil pollution management in uranium mining areas. Full article

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations ($N=50$) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of $3.60\pm 0.18 \mathrm{s}$ and a path success rate of $95.8\pm 1.2\%$, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections. Full article

Biomass-derived nanoengineered carbon materials have emerged as key platforms in environmental technologies due to their high surface area, electrical conductivity, chemical stability, and sustainable synthetic route starting from renewable feedstock. This broad family comprises dimensionally nanoscale materials, such as carbon dots, carbon nanofibers, and graphene-based structures, as well as biochars, hydrochars, activated carbons, and related porous carbonaceous materials whose pore architecture, surface chemistry, or defects are deliberately engineered at the nanometer scale. Beyond their traditional role as passive supports, these materials can actively regulate adsorption phenomena, charge transport, and catalytic microenvironments through precise control of heteroatom doping, graphitic domains, and hierarchical porosity. Among current environmental priorities, carbon dioxide (CO₂) management represents one of the most pressing challenges. Biomass-derived nanocarbons offer tunable adsorption sites for selective CO₂ capture while simultaneously serving as active matrices for catalytic conversion. Tailored doped-carbon frameworks can stabilize key reaction intermediates, suppress competing pathways such as hydrogen evolution, and promote selective transformation into fuels and high-value chemicals. In addition, these materials are excellent hosts for atomically dispersed metals, dual-site catalysts, and semiconductor hybrids used in electrochemical and photocatalytic CO₂ reduction. By combining renewable sourcing with nanoscale control of reactivity, carbon materials create a bridge between environmental remediation and carbon valorization. This review critically examines recent progress in biomass-derived nanoengineered carbon materials for integrated CO₂ capture and conversion, with emphasis on structure-property-performance relationships, mechanistic roles, scalability, and sustainability. Particular attention is also devoted to catalytic conversion and electrochemical CO₂ sensing, where carbon-based and hybrid interfaces enable the transduction of CO₂ recognition into measurable electrical responses. These materials represent a promising yet underexplored pathway toward circular carbon management and the development of next-generation low-carbon chemical technologies. Full article

Clinical decision making in Acute Myeloid Leukemia (AML) critically relies on rapid genomic characterization. To better understand the AML diagnostic landscape in Canada, the Canadian Leukemia Study Group (CLSG) conducted a survey of laboratory hematology leadership (n = 18) at 16 laboratories across 10 provinces, administered using Google Forms in September 2024. Nearly all surveyed sites were equipped to deliver a full suite of testing platforms through existing on-site infrastructure or laboratory partnerships. Reporting practices varied in terms of genomic integration into bone marrow results and the use of AML classification systems. Turn-around-time (TAT) targets were predominantly determined through internal institutional consensus (62%) or recommendations by provincial cancer agencies/international groups (44%). TAT reduction was a top priority for 56% of laboratories, suggesting timely biomarker results to be an active area for improvement. Various treatment-determining biomarkers were frequently assessed as rapid-tests (defined as a 5-day TAT), including FLT3-ITD (69%), FLT3-TKD (56%), and NPM1 (56%), while others such as IDH1 and TP53 were rapid at a limited number of laboratories. Respondents demonstrated a strong shared interest in joint projects such as the validation of AML measurable residual disease (MRD) assays (56%). There was also unanimous support for establishing CLSG AML laboratory consensus guidelines. This survey documents the current state of Canadian AML laboratories and provides a foundation for future shared development projects. Full article

Two warm-mix modification routes were examined to determine how additive chemistry affects the service-temperature rheology of rubber-modified asphalt. Wax-based and bio-oil-based additives were incorporated at 1–3%, and the resulting binders were characterized by FTIR, dynamic shear rheology, MSCR, LAS, and BBR testing. These measurements respectively provided physicochemical evidence and quantified the phase-related response, deformation recovery, fatigue-related damage tolerance, and low-temperature relaxation. The wax-based system developed a stiffness-oriented response: an intermediate dosage produced comparatively lower J_nr and higher R, but further addition impaired relaxation, with m(60) decreasing to 0.285 at −18 °C for the 3% formulation. In contrast, the bio-oil-based system favored relaxation; at a 3% dosage, the LAS-predicted N_f at 2% strain was 15,200 cycles, while m(60) reached 0.460 at −12 °C and 0.384 at −18 °C. The binder-level evidence therefore identifies different selection priorities: an intermediate wax dosage is advantageous when deformation recovery is emphasized, whereas the bio-oil-based route is more favorable for relaxation and low-temperature response. Additional mixture and workability testing is required before these binder findings are translated into construction-temperature or field-performance recommendations. Full article

Green hydrogen strategies proliferate across developing economies, yet most assume the planning authority possesses the state capacity, regulatory autonomy, and investment conditions that conflict-affected and fragile economies lack. Whether a strategy logic derived in one such context transfers to others with different institutional capacity remains untested. This study develops a Conflict-Affected Energy Transitions (CAET) framework and empirically examines its barrier gradient and conditional transferability propositions, analyzing at the country level a regional expert survey—data that prior single-case work, including the authors’ own, used only for binary external validation. A total of 137 energy experts across seventeen Arab states are grouped into three institutional clusters—conflict-affected/fragile, stable developing, and Gulf/high-capacity—and examined through cluster barrier and readiness profiles, a perceived-versus-predictive importance comparison, hierarchical clustering, and a Mode-A partial-least-squares path model with measurement invariance (MICOM) testing and permutation-based multi-group analysis. Energy justice and sovereignty are used as analytical and interpretive lenses, not as measured latent constructs in the structural model. Results show that barrier burdens follow a monotonic institutional fragility gradient: regulatory/institutional barriers rise from 3.02 (Gulf) through 3.52 (stable) to 4.06 (fragile), where they are the most severe constraint. Moreover, the perceived–predictive divergence—social barriers are rated least severe yet associate most strongly with sustainability—recurs across the region. In addition, the model satisfies compositional invariance across fragile and stable clusters while equality of regulatory means and variances fails as expected, and no structural path differs detectably between them at the current sample size. The adoption–governance–sustainability mechanism is therefore provisionally comparable rather than definitively invariant. We propose conditional transferability: the mechanism travels, provisionally, while priorities must be re-weighted by institutional context. Policy implications are differentiated—institutions-first, community-scale pathways for fragile states; finance and market formation for stable developing states; export-scale deployment for high-capacity states—and the CAET framework offers a generalizable alternative to techno-economic determinism for hydrogen strategy under constrained sovereignty. In practical terms, energy ministries in fragile states should sequence rule-making, permitting authority, and community-scale pilots before committing deployment capital; stable developing states should concentrate on de-risking finance and forming early offtake markets; and high-capacity states can proceed directly to export-scale projects and regional standard setting. Full article

Against the backdrop of high-density Asian cities shifting from production- to consumption-oriented spaces, commercial pedestrian streets are key to urban public life and vitality. This study selects six major commercial pedestrian streets in Chongqing and employs natural language processing (NLP) and importance–performance analysis (IPA) methods to construct a four-dimensional evaluation framework (spatial, commercial, cultural, location/facility). It analyzes public perception and experience based on user-generated content (UGC). Findings show that: (1) Significant differences across dimensions form three development types: cultural identity, functional hub, and distinctive growth, reflecting structural bottlenecks in transitioning from single- to multi-functional spaces. (2) IPA identifies “business formats,” “cultural activities,” and “consumption experience” as priorities for improvement, while “commercial atmosphere” and “transportation conditions” are current strengths to maintain. (3) Sentiment analysis reveals that negative perceptions focus on basic functions and sense of place, whereas positive sentiments relate to cultural expression and spatial esthetics, highlighting the role of cultural soft power and visual design in street appeal. This study reveals public perception patterns via big data analysis, offering empirical support for the refined renewal, cultural preservation, and sustainable management of commercial pedestrian streets in high-density Asian cities. Full article