Search Results (1,499)

This study aimed to develop a fermented mango-based beverage using Lactiplantibacillus plantarum strains Lp6 and Lp32, focusing on enhancing its functional properties, ensuring microbiological safety, improving nutritional value, and achieving sensory acceptability. A central composite design (CCD) was employed to assess the effects of two factors (fermentation time and inoculum concentration) on several response variables: viable cell concentration (CC), total phenolic compounds (TPCs), total flavonoid compounds (TFCs), and concentrations of L-lactic acid and D-lactic acid. The optimized formulation was achieved using L. plantarum Lp6, with an inoculum concentration of 9.89 Log (7.76 × 10⁹) CFU/mL and a fermentation time of 20.47 h. Under these conditions, the beverage reached the highest values for CC, TPC, TF, and L-lactic acid while minimizing the production of D-lactic acid. Following optimization, the fermented beverage underwent further characterization, including physicochemical analysis, microbiological evaluation, proximate composition analysis, and sensory evaluation. The final product exhibited a viable cell count of 13.01 Log (10.23 × 10¹²) CFU/mL, demonstrated functional potential, complied with microbiological safety standards, and showed adequate nutritional content. Sensory analysis revealed high consumer acceptability, attributed to its distinctive mango aroma and flavor. These findings highlight the potential of this fermented mango-based beverage as a novel functional food with promising market appeal. Full article

Background/Objectives: Moso bamboo (Phyllostachys edulis), the most widely distributed bamboo species in China, is valued for both its shoots and timber. This species often faces challenges from high-temperature stress. To cope with this stress, Moso bamboo has evolved various adaptive mechanisms at the physiological and molecular levels. Although numerous studies have revealed that a large number of transcription factors (TFs) and genes play important roles in the regulatory network of plant heat stress responses, the regulatory network involved in heat responses remains incompletely understood. Methods: In this study, Moso bamboo was placed in a high-temperature environment of 42 °C for 1 h and 24 h, and transcriptome sequencing was carried out to accurately identify key molecules affected by high temperature and their related biological pathways. Results: Through a differential expression analysis, we successfully identified a series of key candidate genes and transcription factors involved in heat stress responses, including members of the ethylene response factor, HSF, WRKY, MYB, and bHLH families. Notably, in addition to traditional heat shock proteins/factors, multiple genes related to lipid metabolism, antioxidant enzymes, dehydration responses, and hormone signal transduction were found to play significant roles in heat stress responses. To further verify the changes in the expression of these genes, we used qRT-PCR technology for detection, and the results strongly supported their key roles in cellular physiological processes and heat stress responses. Conclusions: This study not only deepens our understanding of plant strategies for coping with and defending against extreme abiotic stresses but also provides valuable insights for future research on heat tolerance in Moso bamboo and other plants. Full article

The main objective of this study was to reveal the molecular mechanism of the albinism in Schima superba and to identify the related functional genes to provide theoretical support for the optimization of S. superba seedling nursery technology. Combining third-generation SMRT sequencing with second-generation high-throughput sequencing technology, the transcriptomes of normal seedlings and albinism seedlings of S. superba were analyzed and the sequencing data were functionally annotated and deeply resolved. The results showed that 270 differentially expressed transcripts were screened by analyzing second-generation sequencing data. KEGG enrichment analysis of the annotation information revealed that, among the photosynthesis-antenna protein-related pathways, the expression of LHCA3 and LHCB6 was found to be down-regulated in S. superba albinism seedlings, suggesting that the down-regulation of photosynthesis-related proteins may affect the development of chloroplasts in leaves. Down-regulated expression of VDE in the carotenoid biosynthesis leads to impaired chlorophyll cycling. In addition, transcription factors (TFs), such as bHLH, MYB, GLK and NAC, were closely associated with chloroplast development in S. superba seedlings. In summary, the present study systematically explored the transcriptomic features of S. superba albinism seedlings, screened out key genes with significant differential expression and provide a reference for further localization and cloning of the key genes for S. superba albinism, in addition to laying an essential theoretical foundation for an in-depth understanding of the molecular mechanism of the S. superba albinism. The genes identified in this study that are associated with S. superba albinism will be important targets for genetic modification or molecular marker development, which is essential for improving the cultivation efficiency of S. superba. Full article

Fe deficiency in apple trees can lead to leaf chlorosis and impede root development, resulting in significant alterations in signaling, metabolism, and genetic functions, which severely restricts fruit yield and quality. It is well established that WRKY transcription factors (TFs) are of vital significance in mediating plant responses to abiotic stress. Real-time quantitative fluorescence (RT-qPCR) analysis displayed that Fe deficiency stress can significantly induce WRKY69 TF gene expression. However, the potential mechanisms by which the WRKY69 gene involved in Fe deficiency stress remains to be investigated. To address this limitations, the WRKY69 gene (MD09G1235100) was successfully isolated from apple rootstock Malus halliana and performed both homologous and heterologous expression analyses in apple calli and tobacco to elucidate its functional role in response to Fe deficiency stress. The findings indicated that transgenic tobacco plants exhibited enhanced growth vigor and reduced chlorosis when subjected to Fe deficiency stress compared to the wild type (WT). Additionally, the apple calli that were overexpressed WRKY69 also exhibited superior growth and quality. Furthermore, the overexpression of the WRKY69 gene enhanced the ability of tobacco to Fe deficiency stress tolerance by stimulating the synthesis of photosynthetic pigments, increasing antioxidant enzyme activity, and facilitating Fe reduction. Additionally, it increased the resistance of apple calli to Fe deficiency stress by enhancing Fe reduction and elevating the activity of antioxidant enzymes. For example, under Fe deficiency stress, the proline (Pro) contents of the overexpression lines (OE-2, OE-5, OE-6) were 26.18 mg·g⁻¹, 26.13 mg·g⁻¹, and 26.27 mg·g⁻¹, respectively, which were 16.98%, 16.76%, and 17.38% higher than the proline content of 22.38 mg·g⁻¹ in the wild-type lines, respectively. To summarize, a functional analysis of tobacco plants and apple calli displayed that WRKY69 TF serves as a positive regulator under Fe deficiency stress, which provides candidate genetic resources for cultivating apple rootstocks or varieties with strong stress (Fe deficiency) resistance. Full article

Accurate estimation of rice leaf nitrogen content (LNC) is essential for optimizing nitrogen management in precision agriculture. However, challenges such as spectral saturation and canopy structural variations across different growth stages complicate this task. This study proposes a robust framework for LNC estimation that integrates both spectral and texture features extracted from UAV-based multispectral imagery through the development of novel Spectral–Texture Fusion Indices (STFIs). Field data were collected under nitrogen gradient treatments across three critical growth stages: heading, early filling, and late filling. A total of 18 vegetation indices (VIs), 40 texture features (TFs), and 27 STFIs were derived from UAV images. To optimize the feature set, a two-stage feature selection strategy was employed, combining Pearson correlation analysis with model-specific embedded selection methods: Recursive Feature Elimination with Cross-Validation (RFECV) for Random Forest (RF) and Extreme Gradient Boosting (XGBoost), and Sequential Forward Selection (SFS) for Support Vector Regression (SVR) and Deep Neural Networks (DNNs). The models—RFECV-RF, RFECV-XGBoost, SFS-SVR, and SFS-DNN—were evaluated using four feature configurations. The SFS-DNN model with STFIs achieved the highest prediction accuracy (R² = 0.874, RMSE = 2.621 mg/g). SHAP analysis revealed the significant contribution of STFIs to model predictions, underscoring the effectiveness of integrating spectral and texture information. The proposed STFI-based framework demonstrates strong generalization across phenological stages and offers a scalable, interpretable approach for UAV-based nitrogen monitoring in rice production systems. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

The role of G-quadruplexes (G4s) in gene regulation has been widely documented, especially in gene promoters. However, the transcriptional mechanisms involving G4s in other regulatory regions remain largely unexplored. In this study, we integrated the G4-DNA data derived from 22 breast cancer patient-derived tumor xenograft (PDTX) models and MCF7 cell line as potential breast cancer-associated G4s (BC-G4s). Genome-wide analysis showed that BC-G4s are more prevalent in gene promoters and the first introns. The genes accommodating promoter or intronic BC-G4s show significantly higher transcriptional output than their non-G4 counterparts. The biased distribution of BC-G4s in close proximity to the transcription start site (TSS) is associated with an enrichment of transcription factor (TF) interactions. A significant negative correlation was detected between the G4–TF interactions within the first introns and their cognate promoters. These different interactions are complementary rather than redundant. Furthermore, the differentially expressed genes (DEGs) harboring promoter and first intron BC-G4s are significantly enriched in the cell cycle pathway. Notably, promoter BC-G4s of DEGs could be a central hub for TF–TF co-occurrence. Our analysis also revealed that G4-related single nucleotide variants (SNVs) affect the stability of G4 structures and the transcription of disease-related genes. Collectively, our results shed light on how BC-G4s within promoters and first introns regulate gene expression and reinforce the critical role of G4s and G4-related genes in breast cancer-associated processes. Full article

Pear (Pyrus pyrifolia) is an important deciduous fruit tree that requires a specific period of low-temperature accumulation to trigger spring flowering. The warmer winter caused by global warming has led to insufficient winter chilling, disrupting floral initiation and significantly reducing pear yields in Southern China. In this study, we integrated targeted phytohormone metabolomics, full-length transcriptomics, and proteomics to explore the regulatory mechanisms of dormancy in ‘Mixue’, a pear cultivar with an extremely low chilling requirement. Comparative analyses across the multi-omics datasets revealed 30 differentially abundant phytohormone metabolites (DPMs), 2597 differentially expressed proteins (DEPs), and 7722 differentially expressed genes (DEGs). Integrated proteomic and transcriptomic expression clustering analysis identified five members of the dormancy-associated MADS-box (DAM) gene family among dormancy-specific differentially expressed proteins (DEPs) and differentially expressed genes (DEGs). Phytohormone correlation analysis and cis-regulatory element analysis suggest that DAM genes may mediate dormancy progression by responding to abscisic acid (ABA), gibberellin (GA), and salicylic acid (SA). A dormancy-associated transcriptional regulatory network centered on DAM genes and phytohormone signaling revealed 35 transcription factors (TFs): 19 TFs appear to directly regulate the expression of DAM genes, 18 TFs are transcriptionally regulated by DAM genes, and two TFs exhibit bidirectional regulatory interactions with DAM. Within this regulatory network, we identified a novel pathway involving REVEILLE 6 (RVE6), DAM, and CONSTANS-LIKE 8 (COL8), which might play a critical role in regulating bud dormancy in the ‘Mixue’ low-chilling pear cultivar. Furthermore, lncRNAs ONT.19912.1 and ONT.20662.7 exhibit potential cis-regulatory interactions with DAM1/2/3. This study expands the DAM-mediated transcriptional regulatory network associated with bud dormancy, providing new insights into its molecular regulatory mechanisms in pear and establishing a theoretical framework for future investigations into bud dormancy control. Full article

Background: Tumor-associated macrophages (TAMs) are critical regulators of the hepatocellular carcinoma (HCC) microenvironment, yet their epigenetic heterogeneity and regulatory programs remain poorly defined. Methods: We performed integrative analysis on single-cell RNA-seq and ATAC-seq profiling of HCC patients to dissect TAM subtypes at high resolution. By correlating chromatin accessibility with gene expression, we identified cell-type-specific candidate cis-regulatory elements (CREs). TAM subsets with prognostic significance were determined through integration with HCC clinical cohorts. Pseudotime and multi-regional analyses were used to uncover regulatory trajectories underlying macrophage phenotypic transitions. The identification framework of a super-enhancer (SE) was constructed, and potential therapeutic targets were prioritized using drug–gene interaction data. Results: We delineated the regulatory landscape of TAMs in HCC, revealing cell-type-specific chromatin accessibility patterns underlying TAM heterogeneity. The 65,342 CREs linked to gene expression were identified, with distal CREs contributing most to cell-type-specific regulation. Notably, SPP1⁺ TAMs were found to be enriched in tumor cores and associated with poor prognosis in HCC. Liver-resident Kupffer cells showed progressive loss of the core transcription factors SPIC and MAFB, suggesting a potential transition into SPP1⁺ TAMs under tumor pressure. We identified 133 SPP1⁺ TAM-specific SEs and constructed a TF–SE–target gene regulatory network. Notably, 13 target genes showed higher drug–gene interaction effects, highlighting their therapeutic potential. Conclusions: This study provides the chromatin accessibility map of TAMs in HCC and reveals how distal CRE-driven transcriptional programs shape TAM states. Our findings lay the foundation for understanding the epigenetic regulation of TAM heterogeneity and nominate potential targets for TAM-directed immunotherapy in HCC. Full article

NAC (NAM, ATAF1/2, CUC1/2) is a plant-specific transcription factor (TF) family that plays important roles in various physiological and biochemical processes of plants. However, the NAC gene family in Lagerstroemia indica and its role in anthocyanin metabolism are still unexplored. In our study, a total of 167 NACs were identified in the L. indica genome via genome-wide analysis and bioinformatics techniques. Amino acid sequence analysis showed that all 167 NAC proteins contained a conserved NAM domain. This domain primarily comprised random coils, extended strands, and alpha helices. Most NACs were found on the nucleus and dispersed over 23 of the 24 plant chromosomes. Based on phylogenetic analysis, the NACs can be categorized into ten subgroups. Furthermore, the promoter homeotropic elements predicted the cis-acting elements in the promoters of these genes related to hormones, development, environmental stress response, and other related responses, demonstrating the diverse regulatory mechanisms underlying gene functions. In addition, a co-expression network was established through RNA sequencing. This network helped identify seven key LiNACs, genes related to anthocyanin expression (CHS) and transcription factors (MYB and bHLH). To identify potential anthocyanin regulatory factors present in L. indica petals, protein interaction prediction was performed, which revealed that LiNACs might participate in anthocyanin regulation by interacting with other proteins, such as MYB, ABF, ABI, bZIP, MYC, etc. Our results provided novel insights and could help in the functional identification of LiNACs in L. indica and the regulation of anthocyanin synthesis. Full article

Long- or post-COVID-19 syndrome, which is also designated by WHO as Post COVID-19 Condition (PCC), is characterized by the persistent symptoms that remain after recovery from SARS-CoV-2 infection. A worldwide consortium of Long COVID-19 Host Genetics Initiative (Long COVID-19 HGI) identified an SNP rs9367106 (G>C; chr6:41,515,652, GRCh38, p = 1.76 × 10⁻¹⁰, OR = 1.63, 95% CI: 1.40–1.89) that is associated with PCC. Unraveling the functional significance of this SNP is of prime importance to understanding the development of the PCC phenotypes and their therapy. Here, in Silico, I explored how the risk allele of this SNP alters the functional mechanisms and molecular pathways leading to the development of PCC phenotypes. Bioinformatic methods include physical interactions using HI-C and Chia-PET analysis, Transcription Factors (TFs) binding ability, RNA structure modeling, epigenetic, and pathway analysis. This SNP resides within two long RNA genes, LINC01276 and FOXP4-AS1, and is located at ~31 kb upstream of a transcription factor FOXP4. This DNA region, including this SNP, physically interacts with FOXP4-AS1 and FOXP4, implying that this regulatory SNP could alter the normal cellular function of FOXP4-AS1 and FOXP4. Furthermore, rs9367106 is in eQTL with the FOXP4 gene in lung tissue. rs9367106 carrying DNA sequences act as distant enhancers and bind with several transcription factors (TFs) including YY1, PPAR-α, IK-1, GR-α, and AP2αA. The G>C transition extensively modifies the RNA structure that may affect the TF bindings and enhancer functions to alter the interactions and functions of these RNA molecules. This SNP also includes an ALU/SINE sequence and alteration of which by the G>C transition may prevent IFIH1/MDA5 activation, leading to suppression of host innate immune responses. LINC01276 targets the MED20 gene that expresses mostly in brain tissues, associated with sleep disorders and basal ganglia abnormalities similar to some of the symptoms of PCC phenotypes. Taken together, G>C transition of rs9367601 may likely alter the function of all three genes to explain the molecular basis of developing the long-term symptomatic abnormalities in the lungs and brain observed after COVID-19 recovery. Full article

Mystus guttatus, a second-class protected species in China, has undergone severe population decline due to anthropogenic and environmental pressures, yet conservation efforts are hindered by limited genomic resources and a lack of mechanistic insights into its stress response systems. Here, the first full-length transcriptome of M. guttatus was generated via SMRT sequencing. A total of 32,647 full-length transcripts were obtained, with an average length of 1783 bp. After structure and function annotation of full-length transcripts, 30,977 genes, 1670 transcription factors (TF), 918 alternative splicing (AS), and 11,830 simple sequence repeats (SSR) were identified. In order to further explore the stress resistance of M. guttatus, 93 genes belonging to the heat shock protein (HSP) family were identified and categorized into HSP70 and HSP90 subgroups. After phylogenetic analysis and selective stress analysis, it was discovered that the hsp family has suffered purifying selection and gene loss, potentially contributing to a decrease in the stress resilience and population of M. guttatus. Using protein interaction network and molecular docking tools, we observed the intricate interplay among HSPs and discovered HSP70-HOP-HSP90 interaction, which is an essential stress response mechanism. Our study sequenced the first full-length transcriptome of M. guttatus to enhance its genomic resources for its conservation and breeding and provide new insights into the future study of stress response mechanisms on M. guttatus. Full article

The TGACG motif-binding factor (TGA) family is an important group of basic region/leucine zipper (bZIP) transcription factors in plants, playing crucial roles in plant development and stress responses. This study conducted a comprehensive genome-wide analysis of the TGA transcription factor (TF) family in common wheat (Triticum aestivum L.). A total of 48 wheat TGAs were identified and classified into four subgroups. Collinearity analysis of the TGAs between wheat and other species identified multiple duplicated gene pairs and highlighted the presence of highly conserved TGAs in wheat. Whole-genome and segmental duplications were identified as the primary drivers of TaTGA expansion. Expression pattern analysis indicated that TaTGAs are involved in plant development and responses to abiotic stresses, including drought, heat, and cold treatment. Among these, TaTGA16-2D was significantly upregulated under both drought and heat stresses, showing more than a five-fold increase in expression. Subcellular localization confirmed its nucleus localization. Functional validation through ectopic expression in Arabidopsis demonstrated that transgenic lines overexpressing TaTGA16-2D exhibited significantly improved stress tolerance. Under heat stress, the survival rates of transgenic lines exceeded 34%, compared to less than 18% in wild-type plants. Overall, this study provides valuable insights into the evolution and functional roles of TaTGAs and identifies TaTGA16-2D as a promising candidate to enhance abiotic stress tolerance in wheat via molecular breeding. Full article

Developing early-heading wheat cultivars is an important breeding strategy to utilize light and heat resources, facilitate multiple-cropping systems, and enhance annual grain yield. Psathyrostachys huashanica Keng (2n = 2x = 14, NsNs) possesses numerous agronomically beneficial traits for wheat improvement, such as early maturity and resistance to biotic and abiotic stresses. In this study, we found that a cytogenetically stable wheat–P. huashanica 7Ns disomic addition line showed (9–11 days) earlier heading and (8–10 days) earlier maturation than its wheat parents. Morphological observations of spike differentiation revealed that the 7Ns disomic addition line developed distinctly faster than its wheat parents from the double ridge stage. To explore the potential molecular mechanisms underlying the early heading, we performed transcriptome analysis at four different developmental stages of the 7Ns disomic addition line and its wheat parents. A total of 10,043 differentially expressed genes (DEGs) were identified during spike development. Gene Ontology (GO) enrichment analysis showed that these DEGs were linked to the carbohydrate metabolic process, photosynthesis, response to abscisic acid, and the ethylene-activated signaling pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these DEGs were involved in plant hormone signal transduction (ARF, AUX/IAA, SAUR, DELLA, BRI1, and ETR), starch and sucrose metabolism (SUS1 and TPP), photosynthetic antenna proteins (Lhc), and circadian rhythm (PRR37, FT, Hd3a, COL, and CDF) pathways. In addition, several DEGs annotated as transcription factors (TFs), such as bHLH, bZIP, MADS-box, MYB, NAC, SBP, WRKY, and NF-Y, may be related to flowering time. Our findings reveal spike development-specific gene expression and critical regulatory pathways associated with early heading in the wheat–P. huashanica 7Ns addition line, and provide a new genetic resource for further dissection of the molecular mechanisms underlying the heading date in wheat. Full article

This paper proposes a KeyBERT-based method for constructing a knowledge graph of the electronic information curriculum system, aiming to enhance the structured representation and relational analysis of educational content. Electronic Information Engineering curricula encompass diverse and rapidly evolving topics; however, existing knowledge graphs often overlook multi-word concepts and more nuanced semantic relationships. To address this gap, this paper presents a KeyBERT-enhanced method for constructing a knowledge graph of the electronic information curriculum system. Utilizing teaching plans, syllabi, and approximately 500,000 words of course materials from 17 courses, we first extracted 500 knowledge points via the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to build a baseline course–knowledge matrix and visualize the preliminary graph using Graph Convolutional Networks (GCN) and Neo4j. We then applied KeyBERT to extract about 1000 knowledge points—approximately 65% of extracted terms were multi-word phrases—and augment the graph with co-occurrence and semantic-similarity edges. Comparative experiments demonstrate a ~20% increase in non-zero matrix coverage and a ~40% boost in edge count (from 5100 to 7100), significantly enhancing graph connectivity. Moreover, we performed sensitivity analysis on extraction thresholds (co-occurrence ≥ 5, similarity ≥ 0.7), revealing that (5, 0.7) maximizes the F1-score at 0.83. Hyperparameter ablation over n-gram ranges [(1,1),(1,2),(1,3)] and top_n [5, 10, 15] identifies (1,3) + top_n = 10 as optimal (Precision = 0.86, Recall = 0.81, F1 = 0.83). Finally, GCN downstream tests show that, despite higher sparsity (KeyBERT 64% vs. TF-IDF 40%), KeyBERT features achieve Accuracy = 0.78 and F1 = 0.75, outperforming TF-IDF’s 0.66/0.69. This approach offers a novel, rigorously evaluated solution for optimizing the electronic information curriculum system and can be extended through terminology standardization or larger data integration. Full article