Search Results (10,504)

Mass spectrometry has become an indispensable tool for the identification and quantification of epigenetic modifications, offering both high sensitivity and structural specificity. The two major classes of epigenetic modifications identified—DNA methylation and histone post-translational modifications—play fundamental roles in cancer development, underscoring the relevance of their precise quantification for understanding tumorigenesis and potential therapeutic targeting. In this scoping review, we included 89 studies that met the inclusion criteria for detailed methodological assessment. Among these, we compared pre-treatment workflows, analytical platforms, and acquisition modes employed to characterize epigenetic modifications in human samples and model systems. Our synthesis highlights the predominance of bottom-up strategies combined with Orbitrap-based platforms and data-dependent acquisition for histone post-translational modifications, whereas triple quadrupole mass spectrometers were predominant for DNA methylation quantification. We critically evaluate current limitations, including heterogeneity in validation reporting, insufficient coverage of combinatorial post-translational modifications, and variability in derivatization efficiency. Full article

The durability of concrete in immersed tunnels is critically influenced by the coupled effects of stress, seepage, and chemical erosion, particularly in inland water environments. However, the spatio-temporal evolution of mechanical property degradation under such multi-field coupling remains insufficiently quantified. Unlike previous studies focused on “load-ion” or “hydraulic pressure-ion” dual coupling, this work introduces a complete stress–seepage–chemical tri-coupling that incorporates the critical seepage effect, representing a fundamental expansion of the experimental scope to better simulate real-world conditions. This study investigates the degradation mechanisms of concrete in the Shunde Lungui Road inland immersed tunnel subjected to such coupled erosion. A novel aspect of our approach is the application of the micro-indentation technique to quantitatively characterize the spatio-temporal evolution of the local elastic modulus at an unprecedented spatial resolution (0.5 mm intervals), a dimension of analysis not achievable by conventional macro-scale testing. Key findings reveal that the mechanical properties of concrete exhibit an initial enhancement followed by deterioration. This behavior is attributed to the filling of pores by reaction products (gypsum, ettringite, and Friedel’s salt) in the short term, which subsequently induces microcracking as the volume of products exceeds the pore capacity. Furthermore, increasing hydro-mechanical loading significantly accelerates the erosion process. When the load increases from 1.596 kN to 3.718 kN, the influence range of elastic modulus variation expands by 9.2% (from 5.186 mm to 5.661 mm). To quantitatively describe this acceleration effect, a novel load-acceleration erosion coefficient is proposed. The erosion rate increases from 0.0688 mm/d to 0.0778 mm/d, yielding acceleration coefficients between 1.100 and 1.165, quantifying a 10–16.5% acceleration effect beyond what is typically captured in dual-coupling models. These quantitative results provide critical parameters for employing laboratory accelerated tests to evaluate the ionic erosion durability of concrete structures under various loading conditions, thereby contributing to more accurate service life predictions for engineering structures. Full article

Non-linear systems of equations play a fundamental role in various engineering and data science models, where accurate solutions are essential for both theoretical research and practical applications. However, solving such systems is highly challenging due to their inherent non-linearity and computational complexity. This study proposes a novel hybrid iterative method with fourth-order convergence. The foundation of the proposed scheme combines the Walrus Optimization Algorithm and a fourth-order iterative technique. The objective of this hybrid approach is to enhance global search capability, reduce the likelihood of convergence to local optima, accelerate convergence, and improve solution accuracy in solving non-linear problems. The effectiveness of the proposed method is checked on standard benchmark problems and two real-world case studies, hydrocarbon combustion and electronic circuit design, and one non-linear boundary value problem. In addition, a comparative analysis is conducted with several well-established optimization algorithms, based on the optimal solution, average fitness value, and convergence rate. Furthermore, the proposed scheme effectively addresses key limitations of traditional iterative techniques, such as sensitivity to initial point selection, divergence issues, and premature convergence. These findings demonstrate that the proposed hybrid method is a robust and efficient approach for solving non-linear problems. Full article

The Xenopus oocyte has long served as a versatile and powerful model for dissecting the molecular underpinnings of reproductive and developmental processes. Its large size, manipulability, and well-characterized cell cycle states have enabled generations of researchers to illuminate key aspects of oocyte maturation, fertilization, and early embryogenesis. This review provides an integrated overview of the cellular and molecular events that define the Xenopus oocyte’s transition from meiotic arrest to embryonic activation—or alternatively, to programmed demise if fertilization fails. We begin by exploring the architectural and biochemical landscape of the oocyte, including polarity, cytoskeletal organization, and nuclear dynamics. The regulatory networks governing meiotic resumption are then examined, with a focus on MPF (Cdk1/Cyclin B), MAPK cascades, and translational control via CPEB-mediated cytoplasmic polyadenylation. Fertilization is highlighted as a calcium-dependent trigger for oocyte activation. During fertilization in vertebrates, sperm-delivered phospholipase C zeta (PLCζ) is a key activator of Ca²⁺ signaling in mammals. In contrast, amphibian species such as Xenopus lack a PLCZ1 ortholog and instead appear to rely on alternative protease-mediated signaling mechanisms, including the uroplakin III–Src tyrosine kinase pathway and matrix metalloproteinase (MMP)-2 activity, to achieve egg activation. The review also addresses the molecular fate of unfertilized eggs, comparing apoptotic and necrotic mechanisms and their relevance to reproductive health. Finally, we discuss recent innovations in Xenopus-based technologies such as mRNA microinjection, genome editing, and in vitro ovulation systems, which are opening new avenues in developmental biology and translational medicine. By integrating classic findings with emerging frontiers, this review underscores the continued value of the Xenopus model in elucidating the fundamental processes of life’s origin. We conclude with perspectives on unresolved questions and future directions in oocyte and early embryonic research. Full article

The interfacial polymerization of fluorine-containing polyarylates (F-PAR) represents an important synthetic route for advanced polymeric materials. This work presents a comprehensive mechanistic investigation through integrated kinetic analysis and macromolecular characterization. The polymerization for both F-PAR and its non-fluorinated analogue (M-PAR) follows a two-stage, second-order kinetic profile, with the F-PAR system exhibiting a lower initial rate constant. Kinetic modeling revealed a dynamic reaction locus, transitioning from the bulk organic phase to an indistinguishable regime. The fluorinated system exhibits distinct stage-dependent behavior: initial retardation due to fluorine-induced “nucleophilicity penalty” on bisphenol monomer followed by a kinetic crossover where the growth rate of F-PAR surpasses M-PAR through enhanced oligomer electrophilicity. The terminal stage reveals fundamental divergence, while flexible M-PAR chains sustain accelerated growth via efficient chain-chain coupling, rigid F-PAR chains reach a molecular weight plateau. The incorporation of fluorine enhances thermal stability and optical transparency due to the low polarizability of C-F bonds. This study provides a complete mechanistic roadmap of fluorine’s dynamic role in polymer architecture control. Full article

Drought is a natural phenomenon that has significant environmental and socioeconomic impacts. Drought indices are fundamental tools for quantifying and monitoring this hazard. In regions where ground data are scarce, hydrological modeling offers an alternative for drought monitoring and developing early warning systems. This study conducted a systematic literature review, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, to analyze the integrated application of the SWAT (Soil and Water Assessment Tool) model and the use of drought indices. A total of 803 articles published between 2011 and 2025 were identified in the Scopus and Web of Science databases, of which 115 met the eligibility criteria and were included in the review. The analysis revealed significant advances in the use of SWAT for drought monitoring and prediction, including the development of indices and forecasting systems. However, notable gaps remain, particularly the limited use of advanced statistical methodologies (e.g., machine learning and non-stationarity analyses) and the lack of harmonization and standardization across indices. Overall, this review establishes SWAT as a robust tool to support drought management strategies, while highlighting substantial untapped potential. Future research addressing these gaps is essential to strengthen drought indices and improve operational warning systems. Full article

Quantum materials exhibit a rich dynamic of physical parameters, which, when combined, can lead to entirely different behaviors. These parameters constantly compete with each other, with the most influential parameters determining the state of the system. For example, in the case of metal–insulator transitions, electron–electron interactions compete with the kinetic energy of the electrons and disorder. Understanding these complex dynamics is crucial for both fundamental physics and the development of novel technological applications, particularly given the role of disorder in tuning critical temperatures, a property with significant potential benefit in the fabrication of new devices where temperature requirements are still the bottleneck. In this article, properties of the Mott metal–insulator transition within disordered electron systems are explored using the disordered Hubbard model, the simplest Hamiltonian for capturing the metal–insulator transition. The model solutions are obtained using the self-consistent statistical dynamical mean-field theory (statDMFT). statDMFT incorporates local electronic correlation effects while allowing for Anderson localization due to disorder. Full article

Zinc oxide nanoparticles (ZnO NPs) are increasingly applied in medicine, cosmetics, and environmental technologies, yet their interactions with blood cells remain poorly understood, raising cross-species safety concerns. Using frog (nucleated) and human (anucleate) erythrocytes as comparative models, we show that cellular architecture fundamentally shapes responses to ZnO NPs exposure. Human erythrocytes exhibited a dose-dependent progression from membrane deformation to eryptosis and hemolysis, reflecting the pronounced vulnerability of anucleate cells. In contrast, frog erythrocytes sustained nuclear DNA damage while largely preserving membrane integrity, highlighting the protective or reparative role of the nucleus. Molecular docking revealed energetically favorable interactions of ZnO NPs with ERα-LBD and DNA (ΔG = −4.28 and −5.68 kcal/mol, respectively), while quantum chemical analyses indicated electron-accepting properties and a narrow HOMO–LUMO gap, suggesting efficient macromolecular interactions and intracellular ROS generation. Together, these findings demonstrate that the presence of a nucleus shifts the primary target of nanoparticle toxicity from membrane to genome, providing novel mechanistic insights. This comparative study offers a robust framework for understanding nanomaterial reactivity across taxa and informs One Health-oriented risk assessments. Full article

Industrial development is a fundamental driver of socio-economic progress, and industrial structure upgrading plays a vital role in advancing rural revitalization. Based on county-level panel data from Fujian Province from 2017 to 2022, this study employs Ordinary Least Squares (OLS) and spatial econometric models—including the Spatial Lag Model (SLM) and Spatial Error Model (SEM)—to empirically assess the impact of county-level industrial structure upgrading on rural revitalization, as well as its spatial transmission mechanisms. The findings reveal that: (1) an increase in the proportion of secondary and tertiary industries significantly enhances the rural revitalization development index at the 1% level of significance; (2) rural revitalization development exhibits strong spatial dependence and positive spatial spillover effects, indicating a “local club convergence” pattern among neighboring counties; and (3) the SEM outperforms OLS and SLM, suggesting that inter-county disparities in rural revitalization primarily result from spatial heterogeneities such as infrastructure and public service quality. Additionally, factors such as transportation accessibility, social public services, and per capita GDP have significant positive effects, while the impact of fiscal agricultural investment appears limited. This study provides empirical evidence to support coordinated development between industrial upgrading and rural revitalization strategies and offers policy insights for constructing an integrated and regionally synergistic framework for rural development in China. Full article

The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality. Full article

This paper discusses the evolution of convolutional (CNN) and recurrent (RNN) artificial neural networks in applications for Building Information Modeling (BIM). The paper outlines the milestones reached in the last two decades. The article organizes the current state of knowledge and technology in terms of three aspects: (1) computer visualization coupled with BIM models (detection, segmentation, and quality verification in images, videos, and point clouds), (2) sequence and time series modeling (prediction of costs, energy, work progress, risk), and (3) integration of deep learning results with the semantics and topology of Industry Foundation Class (IFC) models. The paper identifies the most used architectures, typical data pipelines (synthetic data from BIM models, transfer learning, mapping results to IFC elements) and practical limitations: lack of standardized benchmarks, high annotation costs, a domain gap between synthetic and real data, and discontinuous interoperability. We indicate directions for development: combining CNN/RNN with graph models and transformers for wider use of synthetic data and semi-/supervised learning, as well as explainability methods that increase trust in AECOO (Architecture, Engineering, Construction, Owners & Operators) processes. A practical case study presents a new application, Bimetria, which uses a hybrid CNN/OCR (Optical Character Recognition) solution to generate 3D models with estimates based on two-dimensional drawings. A deep review shows that although the importance of attention-based and graph-based architectures is growing, CNNs and RNNs remain an important part of the BIM process, especially in engineering tasks, where, in our experience and in the Bimetria case study, mature convolutional architectures offer a good balance between accuracy, stability and low latency. The paper also raises some fundamental questions to which we are still seeking answers. Thus, the article not only presents the innovative new Bimetria tool but also aims to stimulate discussion about the dynamic development of AI (Artificial Intelligence) in BIM. Full article

The use of geosynthetics in reinforced soil structures (RSSs) requires the experimental and numerical modelling of the soil–geosynthetic interaction to support the design and analysis and deepen the knowledge of RSS systems. Direct shear testing has served as a fundamental laboratory choice for soil–geosynthetic interface testing, with the benefits being its availability, simplicity, and straightforward shear strength acquisition. This review paper pays attention to the direct shear testing and modelling of soil–geosynthetic interfaces. A brief laboratory interface experiment overview is presented, summarising the adopted soil–geosynthetic types, as well as the influences of various factors regarding soil–geosynthetic properties and loading/environmental conditions. Development of the finite element method to model interfaces is introduced, concentrating on the commonly adopted zero-thickness element, the thin-layer element, and continuum elements. As a result, emphasis is given to the comparison of the three element methodologies for the analysis of their advantages and limitations in accuracy, stability, and applicability for interface modelling. Based on the retrospective analysis, a summary and visions for the research progress of soil–geosynthetic interface testing and modelling are proposed to provide suggestions for future research topics. Full article

To effectively mitigate risks in highway construction and thereby ensure the sustainable development of the transportation sector, this study identifies 27 risk factors across five dimensions—human–machine–environment–process–management—through a combination of literature review, construction accident case analyses, and expert interviews. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, combined with the Maximum Mean Deviation Entropy (MMDE) approach for threshold determination, quantifies centrality and causality of these factors. An Interpretive Structural Modeling (ISM) is employed to construct a multi-level hierarchical framework. The research reveals that highway construction safety risks follow a seven-tier structure: “risk characterization-process assurance-source governance-driven”. Safety education and regulatory systems serve as fundamental drivers, while hazard identification and mitigation, extreme weather response protocols, and equipment compliance form critical safeguard mechanisms. Building on this framework, the study proposes a risk control pathway of “source governance–process interruption–terminal response”, offering practical recommendations for safety management and providing new perspectives for engineering risk assessment and method optimization. Full article

The use of drywall as a non-structural infill has grown significantly due to its rapid and economical installation. Despite this widespread use, a common assumption in structural design is that these elements do not significantly affect seismic performance and are often ignored in analysis. This assumption, however, is increasingly questioned. This study presents a full-scale experimental evaluation of the influence of drywall infill on the seismic response of reinforced concrete frames under cyclic loading. The results quantify how the inclusion of these non-structural elements alters the dynamic properties and structural response of the frame. The infill increased the initial lateral stiffness by approximately three times with respect to the bare frame, thus modifying the structure’s fundamental period. The infill also altered the failure mechanism, initiating with a transient compression strut action at very low drifts, which rapidly and concurrently transitioned into a dominant membrane behavior. This membrane contribution ceased abruptly at a drift of 0.89%, prior to the life-safety limits specified by Eurocode 8. The study’s findings demonstrate the necessity of incorporating the non-linear stiffness and energy dissipation of drywall into structural models to ensure reliable and accurate predictions in seismic design methodologies. Full article

This study analyses the operational efficiency of urban loading and unloading zones (LUZs) by applying queuing theory without waiting (Erlang B model) and incorporating weighted occupancy time as a fundamental metric. Six scenarios were evaluated in an urban block in Zaragoza, Spain: three using field data obtained through real world observation and three simulated. The system’s performance was compared under conditions of free access with a model that strictly enforces the municipal ordinance for Urban Goods Distribution, restricting access to authorized vehicles and maximum dwell times. The objective of this study is to evaluate the operational performance of different LUZ configurations, assessing how real versus regulation-compliant usage affects system capacity, estimated loss rates, and the spatial temporal productivity of the zones. The M/M/1/1 model in Kendall notation is suitable for representing this type of queuing-free urban environment, and weighted occupancy time proves to be a robust indicator for evaluating the performance of heterogeneous zones. The scenario assessment confirms that the sizing of these zones is correct if their proper use is guaranteed. The study concludes with recommendations and best practices for city governance in formulating urban policies aimed at developing more efficient and sustainable logistics to control land use in the LUZ. Full article