Search Results (7,503)

Hazelnut (Corylus avellana L.) is a major crop in the Caucasus region, but its safety is often threatened by Aspergillus flavus colonization and aflatoxin (AF) contamination. Although AFs are strictly regulated in the EU, the influence of post-harvest practices on fungal persistence and AF accumulation remains poorly defined. A three-year study was conducted to evaluate the effects of drying protocols, storage temperature, and conservation practices on fungal growth and AF occurrence in hazelnuts from three producing regions of Azerbaijan. Freshly harvested nuts were subjected to two drying regimes: good drying (sun-exposed, mixed, protected from rewetting) and bad drying (shaded, piled, rewetted). After drying, samples were stored at cold (8–10 °C) or room temperature (18–22 °C). Fungal prevalence was determined by CFU counts with morphological and qPCR identification of Aspergillus section Flavi. AFs were quantified by HPLC, and water activity (a_w) was monitored during storage. Drying emerged as the decisive factor: bad drying consistently resulted in markedly higher fungal loads for A. section Flavi, with mean counts up to 1.5 × 10² CFU/g, compared with 2.1 × 10¹ CFU/g under good drying, representing a 7-fold increase. In contrast, storage temperature and shell condition had negligible effects when nuts were properly dried. Aflatoxins were consistently below the 5 µg/kg EU limit for AFB₁ in traced and well-dried samples, whereas market samples occasionally exhibited AFB₁ concentrations >450 µg/kg. These findings highlight drying efficiency as the key determinant of fungal persistence and AF risk in hazelnut post-harvest management. Full article

As a core node of the air transportation network, airports rely on aircraft model identification as a key link to support the development of smart aviation. Synthetic Aperture Radar (SAR), with its strong-penetration imaging capabilities, provides high-quality data support for this task. However, the field of SAR image interpretation faces numerous challenges. To address the core challenges in SAR image-based aircraft recognition, including insufficient dataset samples, single-dimensional target features, significant variations in target sizes, and high missed-detection rates for small targets, this study proposed an improved network architecture, SAR-YOLOv8l-ADE. Four modules achieve collaborative optimization: SAR-ACGAN integrates a self-attention mechanism to expand the dataset; SAR-DFE, a parameter-learnable dual-residual module, extracts multidimensional, detailed features; SAR-C2f, a residual module with multi-receptive field fusion, adapts to multi-scale targets; and 4SDC, a four-branch module with adaptive weights, enhances small-target recognition. Experimental results on the fused dataset SAR-Aircraft-EXT show that the mAP₅₀ of the SAR-YOLOv8l-ADE network is 6.1% higher than that of the baseline network YOLOv8l, reaching 96.5%. Notably, its recognition accuracy for small aircraft targets shows a greater improvement, reaching 95.2%. The proposed network outperforms existing methods in terms of recognition accuracy and generalization under complex scenarios, providing technical support for airport management and control, as well as for emergency rescue in smart aviation. Full article

Background/Objectives: Anemia is a multifactorial condition influenced by nutritional deficiencies, chronic diseases, and inflammatory processes. These factors not only contribute to anemia but may also exacerbate oral conditions such as Oral Lichen Planus (OLP) by impairing epithelial integrity and immune function. By synthesizing published studies, this review seeks to clarify whether anemia is associated with OLP and to highlight biological mechanisms common to both conditions that could be relevant for future therapeutic development. Methods: A comprehensive literature search was conducted across the selected electronic databases: Medline/Pubmed, Scopus, and Cochrane. Methodological quality and potential bias of the included studies were evaluated using the Newcastle–Ottawa Scale (NOS), while the overall certainty of the evidence was appraised according to the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) framework. Forest plots were generated using the Cochrane RevMan software to evaluate and visually summarize the results of the included studies. Results: Application of the search strategy resulted in the identification of 549 articles; after applying exclusion and inclusion criteria, 11 papers were selected. The prevalence of anemia, iron deficiency, and folic acid deficiency was significantly increased in the study population (p < 0.05); whereas hemoglobin deficiency was observed exclusively in women with statistical significance (p < 0.00001), driven by a single large study. Conclusions: Patients with OLP show a higher prevalence of anemia and deficiencies in key hematologic micronutrients such as vitamin B12, folic acid, and iron. Routine laboratory evaluation is recommended to detect and manage these systemic alterations. In addition to corticosteroid therapy, micronutrient supplementation may serve as a useful complementary treatment approach. Full article

The cheese microbiome is a complex ecosystem strongly influenced by both technological practices and the processing environment. Moving beyond traditional cultured-based methods, the integration of shotgun metagenomics into cheese microbiology has enabled in-depth resolution of microbial communities at the species and strain levels. The aim of the present study was to review recent applications of shotgun metagenomics in cheese research, underscoring its role in tracking microbial dynamics during production and in discovering genes of technological importance. In addition, the review highlights how shotgun metagenomics enables the identification of key metabolic pathways, including amino acid catabolism, lipid metabolism, and citrate degradation, among others, which are central to flavor formation and ripening. Results of the discussed literature demonstrate how microbial composition, functional traits, and overall quality of cheese are determined by factors such as raw materials, the cheesemaking environment, and artisanal practices. Moreover, it highlights the analytical potentials of shotgun metagenomics, including metagenome-assembled genomes (MAGs) reconstruction, characterization of various genes contributing to flavor-related biosynthetic pathways, bacteriocin production, antimicrobial resistance, and virulence, as well as the identification of phages and CRISPR-Cas systems. These insights obtained are crucial for ensuring product’s authenticity, enabling traceability, and improving the assessment of safety and quality. Despite shotgun metagenomics’ advantages, there are still analytical restrictions concerning data handling and interpretation, which need to be addressed by importing standardization steps and moving towards integrating multi-omics approaches. Such strategies will lead to more accurate and reproducible results across studies and improved resolution of active ecosystems. Ultimately, shotgun metagenomics has shifted the field from descriptive surveys to a more detailed understanding of the underlying mechanisms shaping the overall quality and safety of cheese, thus bringing innovation in modern dairy microbiology. Full article

Actinidia deliciosa is a globally important economic fruit crop, and its fruit quality and yield are profoundly influenced by light and environmental conditions. Sucrose phosphate synthase (SPS), a key rate-limiting enzyme in the sucrose biosynthesis pathway, plays a central role in regulating carbon metabolism and sucrose accumulation in plants. However, comprehensive studies of the SPS gene family in A. deliciosa are still lacking, particularly regarding its expression in response to different light qualities. In this study, genome-wide identification of the SPS gene family in A. deliciosa was conducted using bioinformatics approaches. A total of 31 SPS genes were identified and named AdSPS1 to AdSPS31 on the basis of their chromosomal positions. The encoded proteins were predicted to be acidic, hydrophilic, and primarily localized in the chloroplast. All the AdSPS proteins contained the conserved domains Sucrose_synth, Glyco_trans_1, and S6PP, indicating potential roles in sucrose metabolism. Phylogenetic analysis classified the 31 AdSPS members into three subfamilies, A, B, and C, comprising 20, 5, and 6 members, respectively. Collinearity analysis revealed extensive syntenic relationships among AdSPS genes across different chromosomes, suggesting that gene duplication events contributed to the expansion of this gene family. Promoter cis-acting element analysis revealed that light-responsive elements were the most abundant among all the detected elements in the upstream regions of the AdSPS genes, implying potential regulation by light signals. Different light qualities significantly affected the contents of sucrose, glucose, and fructose, as well as SPS activity in kiwifruit leaves, with the highest activity observed under the R3B1 (red–blue light 3:1) treatment. Spearman’s correlation analysis indicated that AdSPS3 was significantly negatively correlated with sucrose, fructose, glucose, and SPS activity, suggesting a potential role in negatively regulating sugar accumulation in kiwifruit leaves, whereas AdSPS12 showed positive correlations with these parameters, implying a role in promoting sucrose synthesis. To further explore the light response of the AdSPS genes, eight representative members were selected for qRT‒PCR analysis under red light, blue light, and combined red‒blue light treatments. These results demonstrated that light quality significantly influenced SPS gene expression. Specifically, AdSPS6 and AdSPS24 were highly responsive to R1B1 (1:1 red‒blue light), AdSPS9 was significantly upregulated under R6B1 (6:1 red‒blue light), AdSPS21 was strongly induced by blue light, and AdSPS12 expression was suppressed. This study systematically identified and analyzed the SPS gene family in A. deliciosa, revealing its structural characteristics and light-responsive expression patterns. These findings suggest that AdSPS genes may play important roles in light-regulated carbon metabolism. These results provide a theoretical foundation and valuable genetic resources for further elucidating the molecular mechanisms of sucrose metabolism and light signal transduction in kiwifruit. Full article

Bovine milk is a complex biological fluid whose lipid fraction plays essential roles in nutrition, processing, and product quality. While conventional analyses have traditionally focused on total fat content and fatty acid composition, recent advances in liquid chromatography–mass spectrometry (LC–MS) have unveiled the molecular diversity of polar lipids, particularly phospholipids and sphingolipids. These compounds, largely associated with the milk fat globule membrane (MFGM), include key molecular species such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), ceramides (Cer), and lysophospholipids, which collectively contribute to emulsion stability, flavor development, and bioactive functionality. This review summarizes current progress in the determination of sphingolipids and phospholipids in bovine milk, with a specific focus on analytical strategies enabling their accurate detection, identification, and quantification. We discuss how advanced LC–MS platforms have been applied to investigate factors shaping the milk polar lipidome, including lactation stage, animal diet, metabolic and inflammatory stress, and technological processing. Accumulating evidence indicates that specific lipid species and ratios, such as PC/PE balance, SM and ceramide profiles, and Lyso-PC enrichment, act as sensitive molecular indicators of membrane integrity, oxidative status, heat stress, and processing history. From an applied perspective, these lipidomic markers hold strong potential for dairy quality control, shelf-life assessment, and authenticity verification. Overall, advanced lipidomics provides a robust analytical framework to translate molecular-level lipid signatures into actionable tools for monitoring cow health, technological performance, and the nutritional valorization of bovine milk. Full article

With the growing prominence of skin photodamage caused by ultraviolet (UV) radiation, the development of efficient and safe natural photoprotectants has become a major research focus. Ginsenoside Re (G-Re), a primary active component of ginseng (Panax ginseng C. A. Mey.), has attracted much attention due to its significant antioxidant and anti-inflammatory activities; however, its systemic role and mechanism in protecting against photodamage remain unclear. In this study, a UVB-induced rat photodamage model was established to evaluate the protective effect of ginsenoside Re through histopathological staining, biochemical assay, and immunohistochemical analysis. Furthermore, an integrated transcriptomic and metabolomic approach was applied to elucidate the molecular mechanism of G-Re protection and to establish the association between the photodamage phenotype, metabolic pathways, and gene functions. Following their identification via integrated multi-omics analysis, the key targets were subjected to verification via Western blotting. The results showed that G-Re could effectively alleviate UVB-induced pathological injury and reduce the level of oxidative stress and inflammatory factors, which could reverse regulate the abnormal expression of 265 differential genes and 30 metabolites. The glutathione metabolism pathway was proven as a key pathway mediating the protective effects of ginsenoside Re against skin photodamage via integrated analysis, WB verification, and molecular docking. The current study indicated that G-Re could be a promising natural sunscreen additive in cosmetical products. Full article

Habitat micromodification poses significant challenges to wildlife, necessitating adaptive responses. This study aimed to investigate how such habitat alterations affect the dietary intake and gut microbiota of Père David’s deer (Elaphurus davidianus). A total of 25 fresh fecal samples were collected from Père David’s deer through non-invasive sampling in Tianjin Qilihai Wetland across three distinct phases of habitat micromodification: pre-change (N = 10), under-change (N = 8), and post-change (N = 7). Dietary composition was analyzed via microscopic identification of plant residues, and gut microbiota structure was characterized using 16S rRNA high-throughput sequencing. Results showed that the diet shifted significantly across phases, with 33 plant species from 20 families identified. Meanwhile, the core gut microbiota remained structurally stable. The phyla Firmicutes and Bacteroidota consistently dominated, despite fluctuations in some specific bacterial genera. Functional prediction indicated minimal change in core microbial metabolic pathways. Correlation analysis suggested that key dietary plants were linked to the abundance of specific, functionally relevant microbial taxa. In conclusion, this study demonstrates that the gut microbiota of Père David’s deer exhibits marked resilience to dietary shifts induced by habitat micromodification. This stability is underpinned by functional redundancy within the microbial community and the consistent intake of fibrous plants, representing a key adaptive mechanism. Our findings highlight that integrating non-invasive monitoring of diet and microbiota can effectively assess the adaptive capacity of endangered ungulates to managed habitat change, thereby informing more resilient conservation strategies. Full article

Radio Frequency Identification (RFID) systems operating in the 860–960 MHz frequency range are widely used in applications such as supply chain management, retail, access control, healthcare, and transportation. This study presents the design, modeling, and fabrication of two antennas for this frequency range, followed by a comparative analysis to identify the antenna with superior gain. Key parameters, including corner fillets and chamfering, as well as antenna length, were varied to evaluate their impact on gain and S-parameters for the initial antenna considered the best from the two structures analyzed, aiming to optimize performance while minimizing size and keeping the frequency unchanged. Additionally, the antennas’ interaction with the human body was assessed through numerical modeling by evaluating the electric and magnetic fields and calculating the specific absorption rate for a human leg and hand in order to analyze the impact of these types of antennas on the human body. The dimensions of the initial structure were minimized while the antenna operated in the same frequency range, leading to a small decrease in the gain. It was discovered that when analyzing the values of the parameters of interest regarding the interaction with a human body, the RFID will not exceed them when considering the human hand, but it will harm a human foot when not placed at a specific distance from it. Full article

Urban surface subsidence is primarily induced by intensive above-ground and underground construction activities and excessive groundwater extraction. Integrating InSAR techniques for safety monitoring of urban subway infrastructure is therefore of great significance for urban safety and sustainable development. However, single-track high-spatial-resolution SAR imagery is insufficient to achieve full coverage over large urban areas, and direct mosaicking of inter-track InSAR results may introduce systematic biases, thereby compromising the continuity and consistency of deformation fields at the regional scale. To address this issue, this study proposes an inter-track InSAR correction and mosaicking approach based on the mean vertical deformation difference within overlapping areas, aiming to mitigate the overall offset between deformation results derived from different tracks and to construct a spatially continuous urban surface deformation field. Based on the fused deformation results, subsidence characteristics along subway lines and in key urban infrastructures were further analyzed. The main urban area and the eastern and western new districts of Zhengzhou, a national central city in China, were selected as the study area. A total of 16 Radarsat-2 SAR scenes acquired from two tracks during 2022–2024, with a spatial resolution of 3 m, were processed using the SBAS-InSAR technique to retrieve surface deformation. The results indicate that the mean deformation rate difference in the overlapping areas between the two SAR tracks is approximately −5.54 mm/a. After applying the difference-constrained correction, the coefficient of determination (R²) between the mosaicked InSAR results and leveling observations increased to 0.739, while the MAE and RMSE decreased to 4.706 and 5.538 mm, respectively, demonstrating good stability in achieving inter-track consistency and continuous regional deformation representation. Analysis of the corrected InSAR results reveals that, during 2022–2024, areas exhibiting uplift and subsidence trends accounted for 37.6% and 62.4% of the study area, respectively, while the proportions of cumulative subsidence and uplift areas were 66.45% and 33.55%. In the main urban area, surface deformation rates are generally stable and predominantly within ±5 mm/a, whereas subsidence rates in the eastern new district are significantly higher than those in the main urban area and the western new district. Along subway lines, deformation rates are mainly within ±5 mm/a, with relatively larger deformation observed only in localized sections of the eastern segment of Line 1. Further analysis of typical zones along the subway corridors shows that densely built areas in the western part of the main urban area remain relatively stable, while building-concentrated areas in the eastern region exhibit a persistent relative subsidence trend. Overall, the results demonstrate that the proposed inter-track InSAR mosaicking method based on the mean deformation difference in overlapping areas can effectively support subsidence monitoring and spatial pattern identification along urban subway lines and key regions under relative calibration conditions, providing reliable remote sensing information for refined urban management and infrastructure risk assessment. Full article

Hemibagrus guttatus is a large omnivorous fish of significant economic value, listed as a Class II protected species in the National Key Protected Wildlife List in 2021 in China. To provide a theoretical foundation for the artificial breeding of H. guttatus, this study employs high-throughput transcriptome sequencing of testes and ovaries to elucidate the molecular regulatory pathways involved in sex differentiation. Because H. guttatus exhibits no obvious sexual dimorphism even during the breeding season, the distinctive contribution of this study compared with previous gonadal-transcriptomic investigations in other Siluriformes lies not only in documenting sex-biased genes but also in providing a molecular foundation for developing non-lethal sex-identification methods for this morphologically indistinguishable species. A total of 303,192,896 raw reads were obtained, with an effective data rate of 98.4%, indicating high sequencing quality. Differential expression analysis identified 8694 genes, including 6369 upregulated in testes and 2325 upregulated in ovaries. Among these, 88 genes were functionally annotated as sex-related, with 62 testis-biased genes such as spata17, sox9, and dmrt1, and 26 ovary-biased genes including cyp19a, wnt8, and sox12. KEGG pathway enrichment analysis revealed that the TGF-β signaling pathway, insulin secretion, and steroid hormone biosynthesis may play crucial roles in gonadal development and differentiation in H. guttatus. The expression patterns of key genes such as hsd11b1, amh, and insl3 were validated by quantitative real-time PCR, showing consistency with the transcriptome results. These findings lay a molecular foundation for understanding the regulatory mechanisms of sex differentiation in H. guttatus, and provide candidate genes for further investigation into the genetic basis of gonadal development, which is essential for improving artificial reproduction and selective breeding practices. Full article

Measuring and simulating territorial space conflicts (TSCs) for the achievement of carbon neutrality is of critical significance for formulating regional sustainable utilization of territorial resources that are inherently green and low-carbon. This study develops a TSC evaluation framework: “conflict identification–scenario simulation–carbon effect assessment”. Focusing on Jiangsu Province, we clarify the evolutionary mechanism of TSCs under carbon neutrality goals, providing a scientific basis for high-quality regional development and low-carbon spatial governance. Results show that Jiangsu’s average TSC level was categorized as “strong conflict” (0.66) during 2005–2020. For 2030, four scenarios (natural development, economic priority, ecological protection, low-carbon development) project TSCs shifting from scattered to point-like distribution, concentrating in key core areas. Corresponding projected average carbon neutrality indices are 1.10, 1.11, 1.33, and 1.11, respectively. Under the low-carbon scenario, grid units with serious TSCs decreased by 4.53% compared to 2020—higher than natural development and economic priority scenarios, but lower than the ecological protection scenario (12.45%). Consequently, the low-carbon development scenario can optimally mitigate land use conflicts while maintaining carbon balance. This research provides robust data support for Jiangsu’s sustainable coordinated development and informs efficient land use and regional ecological security. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging technologies, visual animal identification based on machine learning offers a more efficient and non-invasive method with high automation potential, accuracy, and practical applicability. However, a common challenge is the limited variability of training datasets, as images are typically captured in controlled environments with uniform backgrounds and fixed poses. This study investigates the impact of foreground segmentation and background removal on the performance of convolutional neural networks (CNNs) for cow identification. A dataset was created in which training images of dairy cows exhibited low variability in pose and background for each individual, whereas the test dataset introduced significant variation in both pose and environment. Both a fine-tuned CNN backbone and a model trained from scratch were evaluated using images with and without background information. The results demonstrate that although training on segmented foregrounds extracts intrinsic biometric features, background cues carry more information for individual recognition. Full article

Gliomas are notoriously difficult to treat owing to their pronounced heterogeneity and highly variable treatment responses. This reality drives the development of precise diagnostic and prognostic methods. This review explores the modern arsenal of bioinformatic tools aimed at refining diagnosis and stratifying glioma patients by different malignancy grades and types. We perform a comparative analysis of software solutions for processing whole-exome sequencing data, analyzing DNA methylation profiles, and interpreting transcriptomic data, highlighting their key advantages and limited applicability in routine clinical practice. Special emphasis is placed on the contribution of bioinformatics to fundamental oncology, as these tools aid in the discovery of new biomarker genes and potential targets for targeted therapy. The ninth section discusses the role of computational models in predicting immunotherapy efficacy. It demonstrates how integrative data analysis—including tumor mutational burden assessment, characterization of the tumor immune microenvironment, and neoantigen identification—can help identify patients who are most likely to respond to immune checkpoint inhibitors and other immunotherapeutic approaches. The obtained data provide compelling justification for including immunotherapy in standard glioma treatment protocols, provided that candidates are accurately selected based on comprehensive bioinformatic analysis. The tools discussed pave the way for transitioning from an empirical to a personalized approach in glioma patient management. However, we also note that this field remains largely in the preclinical research stage and has not yet revolutionized clinical practice. This review is intended for biological scientists and clinicians who find traditional bioinformatic tools difficult to use. Full article