Search Results (2,784)

Insertion sequence (IS) elements are key drivers of bacterial genome plasticity, yet the overall regulation of their transposition remains poorly understood. This is especially true for the multiple-layer regulation at the donor site, which has been largely overlooked. Using multiple mutation assays, genetic manipulations and reporter genes, this study focuses on characterizing how endogenous DNA sequences, transcriptional and translational factors, and genomic context regulate IS1 transposition from its donor site. Out of six elements within the chromosome of E. coli strain BW25113, IS1A and IS1E (both with the consensus sequence) contribute to over 99.9% of the overall IS1 transposition within the genome while the other four elements without the non-consensus sequence are essentially incapable of transposing. Inducing a ribosomal -1 frameshift at the A₆C motif increases transposition over 1000-fold, but this enhancement is largely reversed by restoring InsA-mediated transcriptional regulation. Strikingly, genomic sequences flanking IS1 elements appreciably modulate transposition by promoting transcription or facilitating formation of transpososomes, a phenomenon that remains under-studied. Finally, IS1 was confirmed to undergo replicative transposition intramolecularly, a mechanism shown here to be independent of transposase levels in the cell. These findings contribute to our understanding of mobile genetic element regulation and potentially offer strategies for mitigating their potentially harmful effects. Full article

Biological-sequence representation methods are pivotal for advancing machine learning in computational biology, transforming nucleotide and protein sequences into formats that enhance predictive modeling and downstream task performance. This review categorizes these methods into three developmental stages: computational-based, word embedding-based, and large language model (LLM)-based, detailing their principles, applications, and limitations. Computational-based methods, such as k-mer counting and position-specific scoring matrices (PSSM), extract statistical and evolutionary patterns to support tasks like motif discovery and protein–protein interaction prediction. Word embedding-based approaches, including Word2Vec and GloVe, capture contextual relationships, enabling robust sequence classification and regulatory element identification. Advanced LLM-based methods, leveraging Transformer architectures like ESM3 and RNAErnie, model long-range dependencies for RNA structure prediction and cross-modal analysis, achieving superior accuracy. However, challenges persist, including computational complexity, sensitivity to data quality, and limited interpretability of high-dimensional embeddings. Future directions prioritize integrating multimodal data (e.g., sequences, structures, and functional annotations), employing sparse attention mechanisms to enhance efficiency, and leveraging explainable AI to bridge embeddings with biological insights. These advancements promise transformative applications in drug discovery, disease prediction, and genomics, empowering computational biology with robust, interpretable tools. Full article

The GRF (Growth-Regulating Factor) gene family has indispensable regulatory functions in the morphological and physiological development of plants. Nonetheless, comprehensive investigations of GRF gene family members and their functional roles in Cymbidium goeringii, Cymbidium ensifolium, and Cymbidium sinense are still lacking. Therefore, the GRF gene family members in three Cymbidium species were systematically identified, and their expression profiles and potential biological functions were comprehensively evaluated in the study. The results provided evidence that eleven, eleven, and nine GRF genes were identified in C. goeringii, C. ensifolium, and C. sinense, respectively. These genes encode proteins considered as 153–584 amino acids and have been postulated to be located in the cell nucleus. The promoter contains cis-acting elements associated with hormone response regulation, tissue-specific expression, modulation of organismal growth and development, and environmental signal response. The analyses of gene architecture and motif composition demonstrated that introns and motifs within each evolutionary branch are highly similar, whereas significant differences exist between evolutionary branches. The results of chromosome localization and collinearity analysis showed that only a pair of segmental duplication genes was identified in C. goeringii. Moreover, transcriptome data and qRT-PCR results indicated that GRF genes are involved in various organs of C. goeringii. In conclusion, these findings may establish a foundation for theoretical inquiry into the future functional analysis of GRF genes in orchids. Full article

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals. Full article

In Directed Energy Deposition (DED), modeling the molten pool temperature field is crucial for precise temperature control, process optimization, and quality improvement. However, conventional numerical methods suffer from limitations such as high computational costs and poor transferability. This study proposes a physics-informed neural network with dynamic learning rate (DLR-PINN) model, which integrates transfer learning to enable rapid prediction of 3D temperature fields and dimensions of molten pools across process parameters. Its validity is verified by a finite element method (FEM) calibrated via single-track DED experiments. Results show that DLR-PINN exhibits superior convergence and stability compared to traditional PINN. Combined with transfer learning, training efficiency is significantly enhanced, with a single prediction taking only 10 s. Using the FEM as the benchmark, it achieves a mean absolute percentage error (MAPE) of 0.53% for temperature prediction, and MAPE of 3.69%, 2.48%, and 6.96% for molten pool dimension predictions, respectively. Sensitivity analysis of process parameters reveals that scanning speed has a significantly greater regulatory effect on molten pool characteristics than laser power. Additionally, the temperature field of the flat-top heat source is more uniform than that of the Gaussian heat source, which is more conducive to improving printing quality and efficiency. Full article

Plant heat shock proteins (Hsps) are from a diverse and ancient protein family, with small Hsps of ~20 kDa molecular weight classified as Hsp20s. As a key transcription factor in the jasmonic acid (JA) pathway, myelocytomatosis protein 2 (MYC2) plays a vital role in stamen development. In this study, we identified six genes with significantly altered expression levels using previous RNA-Seq data from PhMYC2a-overexpressing and methyl jasmonate (MeJA)-treated petunia. Interestingly, five of these are Hsp20 family members (PhHsp16.0A, PhHsp16.1, PhHsp16.8, PhHsp21.9, and PhHsp40.8). Yeast one-hybrid (Y1H) and dual-luciferase assays demonstrated that PhMYC2a directly binds their promoters, indicating a collective effect. Thus, a genome-wide analysis was conducted and a total of 38 genes encoding Hsp20s were identified in the reference genome of Petunia axillaris. Phylogenetic analysis revealed that 38 members of Hsp20s were irregularly distributed on 34 chromosome scaffolds and separated into 13 subfamilies, with only PaHsp16.0A and 16.1, among the five selected Hsp20s, being in the same Cytosol IV (CIV) subfamily. Conserved motif analysis suggested that the PaHsp20 gene family members may have a high degree of conservation. The promoter sequence analysis suggested that the promoter regions of PaHsp20 genes contained multiple light- and hormone-related cis-regulatory elements. Subsequently, spatiotemporal expression patterns, analyzed by qRT-PCR, showed that PhHsp16.0A and PhHsp16.1 had relatively high expression levels in flowers, with similar expression patterns at various stages of flower bud and anther development. Furthermore, virus-induced gene silencing (VIGS) of PhHsp16.0A and PhHsp16.1 resulted in significantly reduced pollen fertility, indicating their regulation in the process of flower development and echoing the role of PhMYC2a. This study highlights the pivotal role of Hsp20s in MYC2a-mediated regulatory mechanisms during petunia pollen development. Full article

The European Sustainability Reporting Standards (ESRS), a set of standards to be used by companies to meet the requirements of the Corporate Sustainability Reporting Directive (CSRD), create a paradigm shift in sustainability reporting. Nonetheless, their implementation poses significant challenges, especially due to their complexity and extensive reporting requirements. This paper proposes a multi-tiered information architecture to assist organizations in addressing those challenges, presenting a comprehensive framework that links the ESRS regulatory requirements with the informational requirements for sustainability reporting. The architecture considers the ESRS datapoints as its foundational elements, emphasizing on systematic data collection, analysis, and reporting. Additionally, it presents a conceptual model that describes the ESRS principles, outlines the main ideas and connections, and establishes a clear foundation for the management of sustainability-related information. The model enables the practical implementation of the ESRS, as it bridges the gap between abstract standards and operational processes. The proposed framework supports digital transformation, enhances transparency, and promotes a strategic approach to sustainability reporting. The paper addresses a significant academic and conceptual gap regarding the transformation of the ESRS regulatory requirements into suggestions for information management. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

This study investigates the concentrations of essential and trace elements (Ni, Cu, Fe, Zn, Mn, and Al) in packaged ice cream samples collected from markets in Kırşehir province, located in Central Anatolia, Turkey, aiming to assess potential health risks associated with their consumption. Among the detected trace elements, Al (3.21–16.6 mg/kg) and Fe (2.03–24.0 mg/kg) had the highest concentrations, followed by Zn (0.56–3.00 mg/kg), Ni (0.84–4.84 mg/kg), Cu (1.15–3.46 mg/kg), and Mn (0.18–1.56 mg/kg). To explore the relationships between trace elements and identify possible contamination sources, chemometric approaches including principal component analysis, correlation matrices, and hierarchical cluster analysis (Ward’s method) were applied. Human health risk assessment was conducted by calculating Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR), with uncertainty evaluated through Monte Carlo Simulation (10,000 iterations). HI values above 1 in children and adults indicate that trace element exposure through ice cream consumption may pose a health risk. High Al-THQ and Ni-CR values in children may require stricter monitoring and regulatory measures in case of long-term and regular consumption. Full article

TIFY transcription factors play crucial regulatory roles in secondary metabolism and stress response. However, the expression patterns of the Cannabis sativa L. TIFY gene family under alkali stress, their involvement in cannabinoid metabolism, and their underlying genetic evolutionary mechanisms remain largely unexplored. In this study, we used bioinformatics approaches to conduct genome-wide identification and functional characterization of the C. sativa TIFY gene family. Fourteen TIFY genes were identified and mapped onto seven chromosomes. These genes were classified into four subfamilies: TIFY, JAZ, ZML, and PPD, with the JAZ subfamily further subdivided into five distinct branches. Collinearity analysis suggested that gene duplication events contributed to the expansion of the TIFY gene family in C. sativa. Weighted gene coexpression network analysis (WGCNA) revealed that CsJAZ2, CsJAZ3, and CsJAZ6 participated in the cannabinoid regulatory network. Cis-element analysis indicated that the promoter regions of TIFY genes were enriched in hormone- and stress-responsive elements. Furthermore, transcriptome and RT-qPCR analyses were conducted to examine gene expression patterns under alkaline stress (the RNA employed in RT-qPCR was extracted from the apical leaves of samples subjected to short-duration alkaline stress treatment). The results showed that CsJAZ5 and CsJAZ6 were downregulated, whereas CsPPD1, CsTIFY1, and CsZML1 were upregulated in response to alkali stress. In summary, CsJAZ5, CsPPD1, and CsTIFY1 may serve as candidate genes for the development of alkali-tolerant cultivars, while CsJAZ2 and CsJAZ3 may be valuable targets for enhancing cannabinoid production. This study provides important molecular insights and a theoretical basis for future research on the evolutionary dynamics and functional roles of TIFY transcription factors, particularly in stress adaptation and cannabinoid metabolism. Full article

Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics^® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

Glutaredoxins (GRXs) are important proteins in plant development and environmental adaptation. Despite extensive characterization of GRX gene family members in various plant species, limited research has been conducted on the identification and functional analysis of GRXs in the economically important Solanaceae family pepper (Capsicum annuum). This study identified 35 typical GRX genes in pepper and categorized them into three distinct groups: CC-, CGFS-, and CPYC-type, based on the phylogenetic topology, which was consistent with motif or domain arrangement, and gene structures. Furthermore, the determination of ω values indicated that purifying selection was a significant factor in the evolutionary diversification of GRX genes in the eudicot family. Intra-genome investigations demonstrated that both segmental and tandem duplications were involved in the expansion of CaGRX genes. Moreover, examination of collinearity within the Solanaceae family revealed 53 orthologous pairs of GRX genes. Additionally, prediction of cis-regulatory elements and analysis of expression profiles revealed the significant involvement of GRX genes in plant stress response, specifically in relation to hypoxia and submergence. Subsequent subcellular localization examination suggested CaGRX may be involved in the endomembrane system and regulation of oxidative balance in plants. Collectively, these findings enhance our comprehension of the structural and functional properties of GRX in pepper, and establish a groundwork for subsequent functional characterization of the CaGRX genes. Full article

This study evaluated the feasibility of reusing abandoned copper mine tailings (Cu tailings) as a precursor in the production of fly-ash-based alkali-activated materials (FA-AAMs). Two formulations were developed by combining FA and Cu tailings with a mixture of sodium silicate and sodium hydroxide as alkaline activators at room temperature (20 °C). Formulation G1 consisted of 70% Cu tailings and 30% fly ash (FA), whereas G2 included the same composition with an additional 15% ordinary Portland cement (OPC). The materials were characterized using X-ray fluorescence (XRF), -X-ray diffraction (XRD), field emission scanning electron microscopy with energy-dispersive spectroscopy (FESEM-EDS), and particle size analysis. While FA exhibited a high amorphous content (64.4%), Cu tailings were largely crystalline and acted as inert fillers. After 120 days of curing, average compressive strength reached 24 MPa for G1 and 41 MPa for G2, with the latter showing improved performance due to synergistic effects of geopolymerization and OPC hydration. Porosity measurements revealed a denser microstructure in G2 (35%) compared to G1 (52%). Leaching tests confirmed the immobilization of hazardous elements, with arsenic concentrations decreasing over time and remaining below regulatory limits. Despite extended setting times (24 h for G1 and 18 h for G2) and the appearance of surface efflorescence, both systems demonstrated good chemical stability and long-term performance. The results support the use of Cu tailings in FA-AAMs as a sustainable strategy for waste valorization, enabling their application in non-structural and moderate-load-bearing construction components or waste encapsulation units. This approach contributes to circular economy goals while reducing the environmental footprint associated with traditional cementitious systems. Full article

For 40 years, Intron Retention (IR) was dismissed as splicing noise and is now recognized as a dynamic and evolutionarily conserved mechanism of post-transcriptional gene regulation. Unlike canonical splicing, which excises all introns from pre-mRNAs, IR selectively retains intronic sequences, albeit at seemingly random places; however, current research now reveals that this process is strategic in its retention. IR influences mRNA stability, localization, and translational potential. Retained introns can lead to nonsense-mediated decay, promote nuclear retention, or give rise to novel protein isoforms that contribute to expanding proteomic and transcriptomic profiles. IR is finely regulated by splice site strength, splicing regulatory elements, chromatin structure, methylation patterns, RNA polymerase II elongation rates, and the availability of co-transcriptional splicing factors. IR plays critical roles in cell-type and tissue-specific gene expression with observed patterns, particularly during neuronal, cardiac, hematopoietic, and immune development. It also functions as a molecular switch during cellular responses to environmental and physiological stressors such as hypoxia, heat shock, and infection. Dysregulated IR is increasingly associated with cancer, neurodegeneration, aging, and immune dysfunction, where it may alter protein function, suppress tumor suppressor genes, or generate immunogenic neoepitopes. Experimental and computational tools like RNA-seq, RT-PCR, IRFinder, and IntEREst have enabled transcriptome-wide detection and validation of IR events, uncovering their widespread functional roles. This review will examine current knowledge on the function, regulation, and detection of IR, and also summarize recent advances in understanding its role in both normal and pathophysiological settings. Full article