Search Results (23,445)

In complex agricultural production environments, small-target pests—characterized by tiny scales, strong background confusion, and close dependence on environmental conditions—pose major challenges to precise monitoring and green pest control. To facilitate the transition from experience-driven to data-driven pest management, a multi-scale vision–sensor collaborative recognition method is proposed for field and protected agriculture scenarios to improve the accuracy and stability of small-target pest recognition under complex conditions. The method jointly models multi-scale visual representations and pest ecological mechanisms: a multi-scale visual feature module enhances fine-grained texture and morphological cues of small targets in deep networks, alleviating feature sparsity and scale mismatch, while environmental sensor data, including temperature, humidity, and illumination, are introduced as priors to modulate visual features and explicitly incorporate ecological constraints into the discrimination process. Stable multimodal fusion and pest category prediction are then achieved through a vision–sensor collaborative discrimination module. Experiments on a multimodal dataset collected from real farmland and greenhouse environments in Linhe District, Bayannur City, Inner Mongolia, demonstrate that the proposed method achieves approximately 93.1% accuracy, 92.0% precision, 91.2% recall, and a 91.6% $F_1$-score on the test set, significantly outperforming traditional machine learning approaches, single-scale deep learning models, and multi-scale vision baselines without environmental priors. Category-level evaluations show balanced performance across multiple small-target pests, including aphids, thrips, whiteflies, leafhoppers, spider mites, and leaf beetles, while ablation studies confirm the critical contributions of multi-scale visual modeling, environmental prior modulation, and vision–sensor collaborative discrimination. Full article

Periodontitis is a highly prevalent chronic inflammatory disease initiated by dysbiotic biofilms and sustained by an exaggerated host immune response, for which scaling and root planing (SRP) remains the cornerstone of therapy. However, mechanical debridement alone may be insufficient to fully resolve inflammation in complex cases and in susceptible patients. In this context, natural products and host modulatory strategies have emerged as potential adjunctive therapies owing to their antimicrobial, anti-inflammatory, antioxidant, and immunomodulatory properties. This systematic review and meta-analysis aimed to evaluate the effectiveness of natural products used as adjuncts to SRP on periodontal clinical outcomes. Comprehensive electronic searches were conducted in MEDLINE/PubMed, Scopus, Web of Science, Cochrane CENTRAL, Embase, SciELO, and Google Scholar through December 2025, and randomized controlled clinical trials were included. Ninety studies were eligible for qualitative synthesis, and thirty-three were incorporated into the meta-analysis. The interventions encompassed a broad spectrum of plant-derived, host-modulatory and nutraceutical compounds, including curcumin, resveratrol, propolis, Aloe vera, green tea catechins, and omega-3 fatty acids, administered via local, systemic, or rinse-based approaches. Meta-analytic findings demonstrated that adjunctive natural products significantly enhanced probing pocket depth (PPD) reduction and clinical attachment level (CAL) gain compared with SRP alone, with additional improvements in gingival inflammation and bleeding outcomes; however, substantial heterogeneity was observed among studies. Overall, natural products provide measurable adjunctive benefits to SRP in the management of periodontitis, although further well-designed, standardized, and long-term randomized trials are necessary to support their routine clinical implementation. Full article

Flap reconstruction remains a cornerstone after oncologic resection, trauma, and complex wounds, yet partial necrosis, venous congestion, and delayed healing continue to drive morbidity and unplanned re-exploration. Even when macroscopic inflow and outflow are re-established, distal and border-zone tissue may remain constrained by microcirculatory dysfunction. This review frames flap compromise as a biomimetics-relevant failure of a hierarchical transport network and summarizes the vascular repair mechanisms that regenerative interventions aim to replicate. We outline key concepts governing flap perfusion, including angiosomes, choke vessels, endothelial barrier failure, mural cell support, and immune regulation within the angiogenic niche, and relate these to no-reflow, thrombo-inflammation, and impaired vascular regeneration. We then synthesize regenerative strategies aimed at durable reperfusion, spanning recombinant factors, gene and nucleic acid delivery, cell-based therapies, cell-free biologics, including extracellular vesicles and platelet-derived products, pharmacologic modulators, and biomaterial platforms that localize and sustain bioactivity. Translation will require functional perfusion endpoints, standardized reporting of delivery parameters, and safety-conscious designs that minimize aberrant angiogenesis and vector-related risks in post-resection settings. Full article

Cellular senescence (CSEN) is caused by a variety of factors that trigger complex molecular pathways. These include telomere shortening, oncogene activation and replicative stress, as well as DNA damage caused by genotoxic anticancer drugs and endogenous and exogenous genotoxins. Here, we review the induction of CSEN by exogenous genotoxic insults resulting from food and environmental exposures. The available data show that genotoxins/carcinogens in tobacco smoke and smokeless tobacco, in the environment, in food, beverages and life-style products induce CNS. The exposures include N-nitroso compounds, polycyclic aromatic hydrocarbons, heterocyclic aromatic amines, acrylamide, heavy metals, fine dust, mycotoxins, phytotoxins, and phycotoxins. Also, heme in red meat contributes to CSEN as it catalyzes the formation of genotoxic species in the colon. Induction of CSEN by external genotoxins/carcinogens is bound on the DNA damage response pathway (DDR), which relies on activation of the ATM/ATR-CHK2/CHK1-p53-p21 axis and the p53-independent p16/p14 axis, eliciting cyclin-dependent kinase inhibition and permanent cell cycle arrest. Other factors that can be involved are DREAM, MAPK, cGAS/Sting, and NF-κB. The accumulation of non-repaired DNA damage triggering CSEN following external genotoxic exposures may contribute significantly to the amelioration of senescent cells and organ failure with age in humans. Senescent cells drive, via the senescence-associated secretory phenotype (SASP), inflammation that is involved in many diseases, including cancer. Although most of the studies were performed with in vitro cell systems, the consequences of CSEN induction by genotoxic nutritional components and environmental exposures seem to be underestimated. Since CSEN correlates with aging, it is reasonable to conclude that exogenous genotoxic pollutants contribute significantly to the aging process through CSEN induction. In light of these findings, it is deduced that reducing genotoxin exposures and using “rejuvenation” supplements (senotherapeutics) are reasonable strategies to counteract cellular senescence and the aging process. Full article

Rheumatoid arthritis is a chronic systemic autoimmune disease that arises from complex interactions among genetic susceptibility, environmental factors, and immune dysregulation. Growing evidence indicates that microorganisms residing in the oral cavity and gastrointestinal tract, together with dietary factors, play a central role in shaping inflammatory and autoimmune responses in rheumatoid arthritis, forming an interconnected microbiome–immune–nutrition axis. Alterations in the composition and function of oral and intestinal microbial communities are associated with disruption of mucosal barrier integrity, activation of innate and adaptive immune pathways, increased differentiation of proinflammatory T lymphocyte subsets, and loss of immune tolerance that promotes autoantibody production. In addition, microbially derived metabolites, particularly short-chain fatty acids, provide a mechanistic link between microbial ecology, immune regulation, and bone metabolism. Diet represents a key upstream modulator of this axis. Dietary patterns rich in anti-inflammatory nutrients support microbial diversity and immunoregulatory metabolite production, whereas diets high in processed foods and saturated fats favor proinflammatory microbial profiles. Accumulating clinical evidence suggests that nutritional strategies and microbiome-targeted dietary interventions may reduce systemic inflammation and disease-related comorbidities when used alongside standard pharmacological treatments. Taken together, the microbiome–immune–nutrition axis represents a modifiable and clinically meaningful target in rheumatoid arthritis, emphasizing the need for interdisciplinary research and well-designed clinical trials to translate these insights into personalized approaches for disease management. The aim of this review is to integrate current mechanistic and clinical evidence on the interactions between the microbiome, immune system, and nutrition in rheumatoid arthritis, with a focus on their pathogenic relevance, therapeutic potential, and implications for personalized, diet-based interventions. Full article

Additive Manufacturing (AM) and printing-based fabrication technologies have emerged as powerful enablers for next-generation electronic integration and packaging, addressing the growing limitations of conventional subtractive manufacturing techniques. As electronic systems continue to scale toward higher operating frequencies (10–110 GHz and beyond) and increased functional density (>10⁴ interconnects/cm²), traditional packaging approaches struggle with rigid design constraints, complex processing steps (>15–25 fabrication steps), high tooling costs ($10,000–$100,000 for mask and molds) and limited compatibility with heterogeneous integration. In this review, a comprehensive and critical overview of major additive manufacturing and printing technologies including aerosol jet printing, inkjet printing, vat polymerization, fused filament fabrication (FFF) and nScrypt printing is presented from the perspective of electronic assembly and packaging. The fundamental working mechanisms, material compatibility, resolution limits, scalability, and reliability considerations of each technique are systematically discussed. From a manufacturing standpoint, AM reduces material waste by 50–90% compared to subtractive PCB processing and eliminates tooling costs, enabling low-volume prototyping with per-unit fabrication costs reduced by 30–70% for small batches (<100 units). Production throughput varies widely, from 1 to 20 cm²/min for high-resolution direct write systems to >100 cm²/min for scalable inkjet systems. Moreover, it is discussed how these technologies enable advanced packaging architectures such as printed signal crossovers, three-dimensional interconnects, ramps, and embedded chip assemblies. Recent research efforts and reported demonstrations are analyzed to highlight the advantages and current limitations of additive manufacturing for high-frequency, RF, and system-on-package (SoP) applications. Finally, future directions and remaining challenges are discussed, including advances in materials, custom and on-demand manufacturing, enhanced design freedom, integration of multifunctionality, cost-effectiveness, and smart packaging solutions. This review aims to serve as a reference for researchers and engineers seeking to leverage additive manufacturing for high-performance electronic integration and next-generation electronic packaging solutions. Full article

The topic of improving the strength and performance properties of secondary polyamide materials as part of their functional modification is a very relevant area of expanding the possibilities of secondary use of plastic waste. The article aims to conduct a systematic study of the combined modification of polyamide waste agglomerate by six different types of carbon materials to improve their technological and strength properties. PA6 waste agglomerate from polyamide clothing items, tights, socks, and various carbon materials were studied: masterbatch for polyamides MW-PA CB10, brown coal humic substances, coke residue from pyrolysis, a mixture of plastic waste, and finely dispersed coal enrichment waste. A sustainable polymer composite based on a modified agglomerate of PA6 waste was obtained by extruding pre-prepared raw materials in a single-screw extruder. The structural and morphological analysis of the studied carbon materials showed that, within the framework of the combined modification of polyamide-6 waste agglomerate, they should perform different functions related to their distinct morphology and chemical composition. Thus, humic substances can act as functional modifiers and compatibilizers due to their nanodispersity and a wide range of active chemical groups. In contrast, coke residue from pyrolysis and coal enrichment waste will act as a functional filler to improve the complex strength properties of sustainable polymer composites. As part of a study on the effect of modifying polyamide-6 waste agglomerate by carbon materials on its complex technological characteristics, it was demonstrated that humic substances enhance sustainable polymer composite’s technological properties by increasing the melting temperature and melt flow index while reducing density. The increase in the functional effect of humic substances is due to the growth of a wide range of active chemical groups (hydroxyl, carboxyl, peptide). During the initial oxidation of brown coal, the coke residue from pyrolysis and coal enrichment waste served as a filler, increasing the sustainable polymer composite’s density and melt flow index. As part of the study of the effect of modification by carbon materials on the complex strength characteristics of polyamide-6 waste agglomerate, it was shown that all carbon materials studied, except for coke residue, improve the strength characteristics of polyamide-6 waste agglomerate. The optimal content of different types of humic substances is 0.5% wt., while the sustainable polymer composite’s impact strength and breaking stress during bending increase with the increase in the functionalization of humic substances during the oxidation of brown coal. It has been shown that the combination of small amounts of oxidized humic substances at the level of 0.5% by weight, as a functional additive with a masterbatch MW-PACB10 in an amount of 2–3.5%wt., provides materials with increased impact strength from 23 to ~48 kJ/m² and bending fracture stress from 115 to ~135 MPa, which allows returning secondary PA6 waste to the “traditional areas of primary PA6” in the manufacture of general technical parts and products. Full article

The production of Chinese Baijiu relies on the synergistic metabolism of multi-species microbial communities in an open environment. Its intricate microbial succession and flavor formation mechanisms have long been considered complex systems that are difficult to fully deconstruct. Traditional culture-dependent techniques inherently fail to comprehensively capture the actual functional roles and dynamic regulation of “viable but non-culturable” (VBNC) microorganisms within this complex system. In recent years, the rapid advancement of multi-omics technologies has offered a novel perspective for elucidating the underlying fermentation mechanisms of Baijiu. This paper systematically reviews the recent progress in the application of metagenomics, metatranscriptomics, metaproteomics, and metabolomics in Baijiu research. Specific focus is placed on the unique contributions of these tools to resolving microbial community structural diversity, mining key functional genes and enzymes, uncovering microbial stress response mechanisms under environmental fluctuations, identifying phages and spoilage microorganisms, and tracing the metabolic pathways of flavor substances. Furthermore, the pivotal role of multi-omics integration strategies in constructing “microbe–metabolite” regulatory networks is highlighted. Finally, current challenges regarding standardization and data integration are discussed, with an outlook on leveraging omics big data to promote digital monitoring and intelligent brewing in the Baijiu industry. Full article

Huntington’s disease (HD) is characterized by progressive striatal atrophy and complex proteomic changes in the central nervous system. Using the ultrasensitive Next-Gen Ultra-Sensitive Immunoassay (NULISA) proteomic platform, we analyzed cerebrospinal fluid (CSF) from 88 persons with HD to dissect the biological correlates of gray matter loss. Our findings reveal a distinct “Two-Track” model of pathology. The first track, marked by the axonal damage protein neurofilament light chain (NEFL), showed a strong inverse correlation with putamen volume (Pearson r = −0.53, p < 0.001), reinforcing its utility as a proxy for structural neurodegeneration. The second track was defined by a positive association between the immune regulator TNFRSF8 (CD30) and putamen volume (Pearson r = 0.36, p < 0.001), reflecting a decline in active immune-regulatory signaling as striatal atrophy advances. Given its established role in immune modulation, TNFRSF8 was pre-specified for follow-up to further interrogate this neuro-immune axis. Crucially, TNFRSF8 maintained an independent association with striatal volume (Beta = 0.24, p = 0.008) even after controlling for NEFL, genetic burden (CAG-Age Product score), and sex. Supplementary analyses confirmed that this structural–immune axis is localized specifically to the striatum—showing no association with generic structural control regions—and is driven by CAG repeat length rather than chronological aging. Furthermore, bidirectional mediation analysis supported an atrophy-driven model, where striatal volume statistically mediates the relationship between genetic burden and downstream immune dysregulation (p = 0.010). These results demonstrate that maladaptive immune signaling is a distinct pathological correlate in HD, separable from general cytoskeletal damage. This dual-axis framework warrants evaluation in larger longitudinal and interventional studies to guide future biomarker-driven patient stratification and target engagement. Full article

With the advancement of intelligent manufacturing strategies, Cyber–Physical Production Systems (CPPSs) generate massive amounts of multidimensional, dynamic, and non-stationary data, posing significant challenges to real-time Process Monitoring. Existing anomaly detection methods often suffer from insufficient feature robustness when dealing with complex spatio-temporal dynamics, high computational complexity, and difficulties in effectively capturing incipient faults within deep topological structures. To address these issues, this paper proposes a Spatio-Temporal Variational Graph Statistical Attention Autoencoder (ST-VGSAE). First, the framework performs end-to-end multi-scale temporal decomposition via an Adaptive Lifting Wavelet Module, which enhances feature robustness while effectively suppressing noise. Furthermore, a spatio-temporal Token statistical self-attention mechanism with linear complexity is incorporated. By modulating local features via global statistics, it significantly reduces computational costs while enhancing anomaly discriminability. Experiments on the Tennessee Eastman (TE) process dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods in key metrics such as the Fault Detection Rate and the False Alarm Rate, exhibiting superior noise robustness and real-time performance. Full article

In Industry 4.0 environments, operators and production managers frequently query industrial databases for production monitoring, quality control, and equipment maintenance using natural language. Existing Chinese NL2SQL systems often process semantic, program, and schema information in a single encoder, which leads to semantic-program interference and frequent structural or schema errors in the generated SQL. We present CIR-SQL, a dual-model framework that separates intent recognition from SQL generation via structured intermediate representations, decoupling semantic understanding from program synthesis. CIR-SQL employs a seven-category intent classification system (simple_select, count_query, filter_query, max_min_query, sort_query, join_query, group_by_query) and leverages large language models for intent recognition and structured information extraction. A three-level hierarchical backtracking strategy (SQL, context, intent) further improves robustness by correcting different error types. The architecture is particularly suited to Industry 4.0 scenarios where Chinese-speaking operators interact with complex industrial databases containing production data, quality metrics, and equipment status information. Full article

The Citrobacter freundii (C. freundii) complex represents an increasingly significant group of opportunistic pathogens within healthcare settings. This bacterial complex demonstrates remarkable genomic plasticity, characterized by extensive horizontal gene transfer capabilities that facilitate rapid acquisition of resistance determinants and virulence factors. Although originally considered environmental organisms with limited pathogenic potential, members of the C. freundii complex have emerged as important nosocomial pathogens responsible for urinary tract infections, bacteremia, wound infections, and neonatal meningitis. Importantly, their clinical significance lies less in unique disease manifestations and more in the moderate risk of resistance emergence during therapy with third-generation cephalosporins, driven by inducible chromosomal AmpC β-lactamase production. Beyond this intrinsic mechanism, the genomic adaptability of the C. freundii complex also enables acquisition of additional resistance determinants, including extended-spectrum β-lactamases (ESBLs) and carbapenemases, further limiting therapeutic options and complicating clinical management. Understanding the molecular mechanisms underlying genomic plasticity, virulence expression, and resistance development in the C. freundii complex is crucial for developing effective diagnostic strategies, infection control measures, and novel therapeutic approaches. This pathogen exemplifies the challenge of emerging multidrug-resistant bacteria in contemporary healthcare and underscores the need for continued surveillance and research. This narrative review provides current insights into the taxonomy, genomic plasticity, virulence, and mechanisms of antibiotic resistance. Full article

China’s coking coal resources are scarce, and maximizing the recycling of these resources is the primary objective of coal processing and utilization. The embedding features of inorganic minerals within coking middling coal resources are an inherent factor influencing their liberation and separation efficiency. However, current research lacks a systematic investigation into how the embedding features of inorganic minerals in coking middling coal affect their liberation characteristics and flotation separation performance. This study examines three Chinese coking middling coal samples with distinct embedding features. Based on quantitative characterization of inorganic mineral embedding, grinding tests with varying durations and flotation separation tests on post-grinding products were conducted. Liberation and separation efficiencies were evaluated to explore the influence of inorganic mineral embedding on liberation degree and the subsequent impact of the liberation degree on flotation performance. The results indicate that the three coking middling coal samples with different embedding features exhibit significant differences in the dissociation behavior between inorganic minerals and organic matter. The particle size (D) of inorganic mineral phases is the primary factor influencing the liberation degree of inorganic minerals, while the complexity of intergrowth between inorganic minerals and organic matter (CIG) is a secondary factor. The CIG is the primary factor affecting the liberation of organic matter. As the liberation of inorganic minerals and organic matter increases, the separation efficiency improves for the Liuwan middling coal samples, whereas it deteriorates for the Shaqu and Changzhi samples. Full article

Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments. Full article

Fruit quality in melon (Cucumis melo L.) is determined by complex traits such as texture and aroma, which are shaped by both genetic factors and environmental conditions. In this study, we applied an integrated physiology–metabolomics–transcriptomics approach to examine the genetic and seasonal regulation of these traits in the near-isogenic line SC10-2, carrying a defined introgression on linkage group X (LG X), in comparison with its recurrent parent ‘Piel de Sapo’ (PS). Fruit firmness, juiciness, respiration, ethylene production, and volatile organic compounds (VOCs) were evaluated over postharvest ripening across two growing seasons. SC10-2 consistently exhibited firmer flesh, reduced juiciness, and distinct VOC profiles relative to PS, although the magnitude of these differences varied between seasons. Transcriptomic analysis identified 2954 differentially expressed genes genome-wide, including 909 genes located within the LG X introgression, among which candidate genes such as CmTrpD, CmHK4-like, and CmNAC18 showed expression patterns associated with texture- and aroma-related traits. Seasonal comparisons indicated that VOC composition was particularly sensitive to environmental variation, underscoring the contribution of genotype × season interactions to fruit quality expression. Together, these results refine the phenotypic and molecular characterization of the LG X introgression in SC10-2 and provide testable candidate genes and hypotheses for understanding the genetic basis of melon texture and aroma under the studied conditions. Full article