Search Results (6,618)

Excessive application of chemical fertilisers has led to soil phosphorus immobilisation and aquatic eutrophication, making the development of highly efficient acid/neutral phosphatases crucial for sustainable phosphorus utilisation. In this study, we systematically investigated strain J2, which was isolated from phosphate-contaminated soil in Laoshan, Nanjing, China. 16S rRNA gene sequence analysis identified this strain as Acinetobacter nosocomialis J2, with 99.78% sequence similarity. Whole-genome sequencing generated a 3.83 Mb genome with a GC content of 38.59%, revealing multiple phospho-metabolism-related enzyme genes, including phospholipase C and α/β-hydrolases. A large language model–based protein representation learning strategy was employed to mine acid/neutral phosphatase genes from the genome, in which the model learned contextual and functional features from known phosphatase sequences and was used to identify semantically similar genes within the J2 genome. This approach predicted nine phosphatase candidate sequences, including AnACPase, a putative acid/neutral phosphatase. Biochemical characterisation showed that AnACPase exhibits optimal activity at pH 6.0 and 50 °C, with a K_m value of 0.2454 mmol/L for the p-NPP substrate, indicating high substrate affinity. Mn²⁺ and Ni²⁺ significantly enhanced enzyme activity, whereas Cu²⁺ and Zn²⁺ strongly inhibited it. Soil remediation experiments further validated the application potential of AnACPase, which solubilised 171.56 mg/kg of phosphate within seven days. Overall, this study highlights the advantages of deep learning-assisted genome mining for functional enzyme discovery and provides a novel technological pathway for the bioremediation of phosphorus-polluted soils. Full article

Effective monitoring of maize phenology under stress conditions is crucial for optimizing agricultural management and mitigating yield losses. Crop prediction models constructed from Convolutional Neural Network (CNN) have been widely applied. However, CNNs often struggle to capture long-range temporal dependencies in phenological data, which are crucial for modeling seasonal and cyclic patterns. The Transformer model complements this by leveraging self-attention mechanisms to effectively handle global contexts and extended sequences in phenology-related tasks. The Transformer model has the global understanding ability that CNN does not have due to its multi-head attention. This study, proposes a synergistic framework, in combining CNN with Transformer model to realize global-local feature synergy using two models, proposes an innovative phenological monitoring model utilizing near-ground remote sensing technology. High-resolution imagery of maize fields was collected using unmanned aerial vehicles (UAVs) equipped with multispectral and thermal infrared cameras. By integrating this data with CNN and Transformer architectures, the proposed model enables accurate inversion and quantitative analysis of maize phenological traits. In the experiment, a network was constructed adopting multispectral and thermal infrared images from maize fields, and the model was validated using the collected experimental data. The results showed that the integration of multispectral imagery and accumulated temperature achieved an accuracy of 92.9%, while the inclusion of thermal infrared imagery further improved the accuracy to 97.5%. This study highlights the potential of UAV-based remote sensing, combined with CNN and Transformer as a transformative approach for precision agriculture. Full article

Skin development undergoes significant molecular changes during early life stages in mammals. This study investigated transcriptomic differences in skin tissues between newborn (Y0) and one-year-old (Y1) Dezhou donkey foals using RNA-sequencing technology. Skin samples were collected from 13 Dezhou donkeys (7 newborns and 6 one-year-olds) and subjected to transcriptome analysis using the Illumina NovaSeq 6000 platform. A total of 133.66 Gb of clean data was obtained, yielding 252,342 transcripts and 204,683 unigenes. Differential expression analysis revealed 9878 significantly differentially expressed genes (DEGs) between age groups, with 4252 up-regulated and 5626 down-regulated genes in Y1 compared to Y0. Functional enrichment analysis identified key pathways, including ECM–receptor interaction, PI3K-Akt signaling, WNT signaling, and TGF-β signaling pathways. Notable findings included up-regulation of keratin genes (KRT1) and WNT family genes (WNT3, WNT4, WNT5, WNT6, WNT7, WNT10) in one-year-old foals, while collagen genes (COL1A, COL4A, COL5AS) and TGF-β signaling components (TGFB2, TGFB3, BMP5) were down-regulated. These results suggest that skin maturation involves enhanced barrier function, hair follicle development, and reduced collagen synthesis rates, providing insights into mammalian skin development mechanisms and potential applications in veterinary medicine and comparative biology. Full article

Body color is the most prominent phenotypic trait in animals. To investigate the molecular regulatory mechanisms underlying skin pigmentation and body color in Channa argus, single-nucleus RNA sequencing technology was employed to analyze cell diversity and functional changes in the skin of normal and albino C. argus. Three pigment-related cell types, seven immune-related cell types, and nine other skin-related structural and functional cell types were identified. The skin of albino C. argus, which appears white to the naked eye, contains numerous melanocytes and iridophores with reflective silver properties. Compared to normal C. argus, melanocytes in albino individuals contained fewer melanin granules, while iridophores exhibited increased chromogenic substances. Melanocyte-specific genes—kitlg, myo5a, and scarb1—were significantly downregulated in albino melanocytes (p < 0.05). Conversely, iridophore-specific genes alk, pnp, and gpnmb were significantly upregulated in albino skin, whereas mlph was significantly downregulated (p < 0.05). Weighted gene co-expression network analysis revealed that scarb1 was associated with the melanocyte module, alk was identified as a core gene, and pnp was linked to the iridophore module. Functionally, scarb1 is involved in pigment transport, pnp in purine synthesis, and alk is essential for iridophore development. Therefore, scarb1, pnp, and alk may be correlated to albinism in C. argus. Overall, this study constructed a single-cell transcriptional atlas of C. argus skin, providing valuable reference data for further research into the regulatory mechanisms governing body color formation and maintenance in this species. Full article

Micronutrients, particularly boron (B), iron (Fe), manganese (Mn), and zinc (Zn), are pivotal for cotton (Gossypium spp.) growth, reproductive success, and fiber quality. However, their critical roles are often overlooked in fertility programs focused primarily on macronutrients. This review synthesizes recent advances in the physiological, molecular, and agronomic understanding of B, Fe, Mn, and Zn in cotton production. The overarching goal is to elucidate their impact on cotton nutrient use efficiency (NUE). Drawing from the peer-reviewed literature, we highlight how these micronutrients regulate essential processes, including photosynthesis, cell wall integrity, hormone signaling, and stress remediation. These processes directly influence root development, boll retention, and fiber quality. As a result, deficiencies in these micronutrients contribute to significant yield gaps even when macronutrients are sufficiently supplied. Key genes, including Boron Transporter 1 (BOR1), Iron-Regulated Transporter 1 (IRT1), Natural Resistance-Associated Macrophage Protein 1 (NRAMP1), Zinc-Regulated Transporter/Iron-Regulated Transporter-like Protein (ZIP), and Gossypium hirsutum Zinc/Iron-regulated transporter-like Protein 3 (GhZIP3), are crucial for mediating micronutrient uptake and homeostasis. These genes can be leveraged in breeding for high-yielding, nutrient-efficient cotton varieties. In addition to molecular hacks, advanced phenotyping technologies, such as unmanned aerial vehicles (UAVs) and single-cell RNA sequencing (scRNA-seq; a technology that measures gene expression at single-cell level, enabling the high-resolution analysis of cellular diversity and the identification of rare cell types), provide novel avenues for identifying nutrient-efficient genotypes and elucidating regulatory networks. Future research directions should include leveraging microRNAs, CRISPR-based gene editing, and precision nutrient management to enhance the use efficiency of B, Fe, Mn, and Zn. These approaches are essential for addressing environmental challenges and closing persistent yield gaps within sustainable cotton production systems. Full article

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

In recent years, the development of new diagnostic technologies, such as tandem mass spectrometry (MS/MS) and next-generation sequencing (NGS), has caused a veritable revolution in the diagnosis of genetic diseases, reducing time, cost, and invasiveness associated with prior diagnostic techniques. While MS/MS laid the foundation for the development of numerous, usually institutionally based, neonatal screening programs, NGS has gained traction in newborn screening (NBS), primarily through pilot projects and private funding across different countries. As a result, the traditional Wilson and Jungner criteria have been supplemented by additional criteria, including considerations of equity and access, in response to emerging technologies. This review aims to provide an up-to-date overview of the global landscape of metabolic screening panels, highlight the major ongoing genomic screening projects, and outline the current models for integrating these two screening systems. Substantial differences exist across countries in the numbers and types of diseases included in national NBS programmes. In this context, Italy represents a prominent case, as its neonatal screening framework has seen significant expansion and development in recent years, reaching a particularly comprehensive metabolic screening panel. Nonetheless, a number of initiatives to incorporate genomic technologies into the NBS pathway are currently underway, primarily involving high-income countries. Nonetheless, unlike metabolomic-based NBS programs, no country has a government-mandated NGS program as first-tier testing for newborns. New evidence is emerging from ongoing models of integration of multi-omics approaches into NBS, including the use of AI and machine learning. Identifying the most appropriate system for this integration to reduce the false-positive and false-negative rates associated with both screening types, ensure more equitable access to screening, and facilitate faster access to treatment may represent a useful and foresightful way to conceptualize NBS in the future. This transitional phase should promote rigorous improvements before full-scale adoption. Full article

Gastrointestinal nematode (GIN) infections are the most prevalent parasitic diseases in grazing sheep worldwide, causing significant productivity losses, high mortality and, as a result, economic losses and emerging animal welfare concerns. Conventional control strategies, primarily relying on anthelmintic treatments, face limitations due to rising drug resistance and environmental concerns, underscoring the need for sustainable alternatives. Selective breeding for host genetic resistance has emerged as a promising strategy, while recent advances in transcriptomics and integrative omics research are providing deeper insights into the immune pathways and molecular and genetic mechanisms that underpin host–parasite interactions. This review summarizes current evidence on transcriptomic signatures associated with resistance and susceptibility to H. contortus and T. circumcincta GIN infections, highlighting candidate genes, functional genetic markers, key immune pathways, and regulatory networks. Furthermore, we discuss how other omics approaches, including genomics, proteomics, metabolomics, microbiome, and multi-omics integrations, provide perspectives that enhance the understanding of the complexity of the GIN resistance trait. Transcriptomic studies, particularly using RNA-Sequencing technology, have revealed differential gene expression, functional genetic variants, such as SNPs and INDELs, in expressed regions and splice junctions, and regulatory long non-coding RNAs that distinguish resistance from susceptible sheep, highlighting pathways related to Th2 immunity, antigen presentation, tissue repair, and stress signaling. Genomic analyses have identified SNPs, QTL, and candidate genes linked to immune regulation and parasite resistance. Proteomic and metabolomic profiling further elucidates breed- and tissue-specific alterations in protein abundance and metabolic pathways, while microbiome studies demonstrate distinct microbial signatures in resistant sheep, suggesting a role in modulating host immunity. In conclusion, emerging multi-omics approaches and their integration strategies provide a comprehensive framework for understanding the complex host–parasite interactions that govern GIN resistance, offering potential candidate biomarkers for genomic selection and breeding programs aimed at developing sustainable, parasite-resistant sheep populations. Full article

Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article

Scutellaria baicalensis is an important medicinal plant, and the diversity of its rhizosphere microbiota may influence its growth, development, and yield. Numerous studies have reported that warming associated with global climate change significantly altered plant-associated soil microbial diversity. To reveal the effects of night-time warming on the rhizosphere microbial community of S. baicalensis, soil microbial diversity in the rhizosphere (RS) and bulk soil (BS) of S. baicalensis were analyzed by employing bacterial 16S rRNA and fungal ITS sequencing technology. Warming significantly altered both bacterial and fungal communities in the rhizosphere and bulk soils of S. baicalensis, with pronounced changes in OTU composition, relative abundances at both phylum and species levels. The analysis of alpha and beta diversity showed that warming significantly altered the fungal community structure in the rhizosphere soil (R² = 0.423, p < 0.05) and significantly reduced the species richness in the bulk soil of S. baicalensis (Shannon and Simpson index, p < 0.05). LEfSe and functional prediction analyses revealed that warming altered the taxonomic composition of both bacterial (35 taxa, LDA > 3) and fungal (24 taxa, LDA > 4) communities in rhizosphere and bulk soils of S. baicalensis, with multiple bacterial and fungal taxa serving as treatment-specific biomarkers. Functional predictions indicated that fungal functional groups, including saprotrophic and mycorrhizal guilds, were more strongly affected by warming than bacteria. Overall, warming has a significantly stronger impact on fungal communities in the rhizosphere and bulk soils of S. baicalensis than on bacteria, and has a significantly greater effect on the diversity of microbial communities in bulk soils than that in rhizosphere soils. This study provides important data for understanding the impact of global climate change on the rhizosphere microbial communities of cultivated plants. Full article

The integration of artificial intelligence (AI) into industrial processes is a promising method for enhancing operational efficiency and quality control. In particular, contact lens manufacturing requires specialized artificial intelligence technologies owing to stringent safety requirements. This study introduces a novel approach that employs polar coordinate transformation and a customized deep learning model, the Polar Sequence-guided Attention Network (PSgANet), to improve the accuracy of defect detection in the rim-connected zone (RCZ) of contact lenses. PSgANet is specifically designed to process polar coordinate-transformed image data by integrating sequence learning and attention mechanisms to maximise the capability for detecting and classifying defective patterns. This model converts irregularities along the edges of contact lenses into linear arrays via polar coordinate transformation, enabling a clearer and more consistent identification of defective regions. To achieve this, we applied sequence learning architectures such as GRU, LSTM, and Transformer within PSgANet and compared their performances with those of conventional models, including GoogleNetv4, EfficientNet, and Vision Transformer. The experimental results demonstrated that the PSgANet models outperformed the existing CNN-based models. In particular, the LSTM-based PSgANet achieved the highest accuracy and balanced precision and recall metrics, showing up to a 7.75% improvement in accuracy compared with the traditional GoogleNetv4 model. These results suggest that the proposed method is an effective tool for detecting and classifying defects within the RCZ during contact lens manufacturing processes. Full article

Choy sum (Brassica rapa var. parachinensis) is an important vegetable crop in Brassicaceae. However, its mitochondrial genome has not been well studied. In this study, Illumina and Nanopore sequencing technologies were combined to assemble the complete mitochondrial genome of choy sum. The mitochondrial genome is a circular molecule of 219,775 bp, with a GC content of 45.23%. A total of 60 genes were annotated, including 33 protein-coding genes (PCGs), 23 transfer RNA (tRNA) genes, 3 ribosomal RNA (rRNA) genes, and one pseudogene. A total of 466 RNA editing sites were identified in the PCGs. Codon usage analysis revealed that leucine (leu) was the most frequently used amino acid. Twenty-nine codons showed a relative synonymous codon usage (RSCU) value greater than 1. Most of these preferred codons ended with A or U. A total of 308 repetitive sequences were detected, including 136 dispersed repeats, 17 tandem repeats, and 55 simple sequence repeats (SSRs). Evolutionary analysis indicated that most mitochondrial genes are under negative selection. The highest nucleotide diversity detected in the cox2 gene suggests that this gene could serve as a valuable molecular marker for mitochondrial research in the species. Homology analysis found 22 homologous fragments between the mitochondrial and chloroplast genomes of choy sum. These fragments total 13,325 bp, representing 6.06% of the mitochondrial genome. Phylogenetic analysis showed that choy sum is most closely related to B. rapa var. purpuraria. This study offers a genomic resource for genetic improvement and breeding of choy sum. It also provides molecular insights into the evolution of Brassica species. Full article

The tumor immune microenvironment (TIME) is closely involved in tumor initiation, malignant progression, immune escape, and response to immunotherapy. With the continued development of high-throughput sequencing technologies, transcriptomic approaches have become essential for examining the cellular and molecular features of the TIME. Bulk RNA sequencing offers tissue-level gene expression profiles and allows the estimation of immune cell composition through computational deconvolution. Single-cell RNA sequencing provides finer resolution, revealing cellular heterogeneity, lineage relationships, and functional states. Spatial transcriptomics (ST) retains the native anatomical context, making it possible to localize gene expression patterns and cell–cell interactions within intact tissues. These approaches, when considered together, have shifted TIME research from averaged measurements toward a more detailed and mechanistic understanding. This review summarizes the principles, applications and limitations of bulk, single-cell and spatial transcriptomic methods, highlighting emerging strategies for integrative analysis. Such multi-scale frameworks are increasingly important for studying immune dynamics and may contribute to the development of more precise biotechnological and immunotherapeutic strategies. Full article

Silver nanoparticles (AgNPs) are increasingly applied in agriculture and related technologies due to their antimicrobial properties, yet their interactions with soil-associated organisms and microbial communities remain insufficiently characterized. This study examined the effects of AgNP exposure (10.85 mg/L) on trace element accumulation and gut bacterial communities of the earthworm Eisenia fetida under two substrate conditions (horticultural substrate and compost). High-throughput 16S rRNA gene sequencing revealed substrate-dependent shifts in microbial community structure following AgNP exposure. Several bacterial taxa, including Proteobacteria, Gammaproteobacteria, Bacilli, Streptococcus sp., and Staphylococcus sp., exhibited pronounced numerical declines, indicating sensitivity to AgNPs, whereas Actinobacteria and Bacteroidetes showed comparatively higher relative abundances, suggesting greater tolerance. Compost partially mitigated the inhibitory effects of AgNPs on gut microbiota. Concurrently, AgNP exposure altered trace element accumulation patterns in earthworm tissues, highlighting interactions between silver uptake and elemental homeostasis. Collectively, these findings demonstrate that AgNPs can induce taxon- and substrate-specific responses in earthworm-associated microbial communities and metal accumulation, providing insight into potential ecological consequences of nanoparticle use in agricultural systems. Full article