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Background: Cognitive and psychiatric disorders are caused by a complex interplay between genetic predisposition, environmental exposures, and dynamic molecular regulation in the brain. Animal models provide a controlled environment for examining these mechanisms, and advances in transcriptome and epigenomic technologies have greatly expanded our knowledge of disease-relevant pathways. Objective: This scoping review systematically maps and synthesizes the epigenetic and transcriptomic findings from the established animal models of four neuropsychiatric conditions—autism spectrum disorder (ASD), schizophrenia, depression, and Rett syndrome—drawing on a PRISMA-ScR-guided literature search. The review characterizes the breadth of evidence, identifies convergent and divergent molecular pathways, and highlights the translational gaps and therapeutic implications. Methods: Research employing chromatin accessibility testing, genome-wide DNA methylation mapping, single-cell and bulk RNA sequencing, histone modification profiling, and multi-omics integration in mouse and other validated animal models was thoroughly reviewed. A quality appraisal of the primary experimental studies (n = 63) was performed using a modified CAMARADES checklist. Results: Beyond generalized cellular stress responses, multi-omics analysis emphasizes the cell-type- and context-dependent nature of epigenetic changes in animal models, including isoform-specific histone modifications and model-dependent binding of HDAC/MeCP2 complexes to genes involved in synaptic plasticity. Single-cell RNA sequencing analyses have uniformly shown transcriptional changes in parvalbumin-positive (PV+) interneurons. Conclusions: The specific convergence of epigenetic disruptions in neural circuits involved in synaptic structure and inhibitory function could play a role in the generation of neuropsychiatric phenotypes in animal models, highlighting the importance of circuit- and cell-type-specific epigenetics while pointing to potential therapeutic avenues. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

Land use change detection (LUCD) serves as a crucial technical cornerstone for natural resource management and ecological environment monitoring, playing an indispensable role in advancing the modernization of national governance capacities. Nonetheless, severe interference from radiometric variations on feature representation readily induces spurious changes and thus a high false alarm rate. Additionally, the challenge of balancing discriminative feature extraction and fine-grained contextual modeling leads to fragmented change regions and missed detection. To address these issues and eliminate the reliance on annotated samples, a novel framework is proposed for unsupervised LUCD, integrating superpixel-based deep feature analysis with a dense conditional random field (CRF). Firstly, relative radiometric correction and band-wise maximum stacking fusion are performed on the bi-temporal images. A simple non-iterative clustering (SNIC) algorithm is adopted to generate homogeneous superpixels with cross-temporal consistency. Then, a deep feature coupling mining mechanism is introduced to implement spatial–spectral feature extraction and in-depth parsing of invariant semantic information. Meanwhile, the difference confidence map based on dual features is constructed using superpixel-level discriminant vectors to enhance the separability. Finally, leveraging homogeneous units with spatial correspondence, a task-specific redesign of a global optimization model is established to achieve the precise extraction of change regions, which incorporates difference confidence, spatial adjacency relationship, and cross-temporal feature similarity into the dense CRF. The experimental results demonstrate that the proposed method achieves an average overall accuracy of over 90% across all datasets with excellent comprehensive performance, striking a well-balanced trade-off in practical applicability. It can effectively suppress salt-and-pepper noise, significantly improve the recall rate of change regions (maintaining at approximately 90%), and exhibit favorable superiority and robustness in complex land cover scenarios. Full article

Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a critical review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil–structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role that LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future. Lastly, this paper highlights the different LLM capabilities which can be used to empower geotechnical engineers. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

Mosses (Bryophyta) comprises a group of terrestrial plants that colonized land more than 450 million years ago that play fundamental ecological and evolutionary roles, particularly in polar and peatland ecosystems. The sequencing of Physcomitrium patens marked a milestone in bryophyte genomics, establishing mosses as model organisms for evolutionary and functional studies. However, the recent advent of next-generation sequencing technologies has broadened genomic exploration beyond P. patens, unveiling the genetic diversity of additional bryophyte species. Notably, the genomes of Sphagnum fallax, Sphagnum magellanicum, the liverwort Marchantia polymorpha and hornworts from Athoceros genus have provided new insights into carbon fixation mechanisms, ecological adaptations, and lineage-specific evolutionary traits. These advances have enabled large-scale comparative analyses and expanded the understanding of conserved and divergent genomic features among bryophytes. The integration of these datasets into public databases such as Phytozome and NCBI Genome has created a robust framework for investigating plant genome evolution and biotechnological potential. Altogether, the expanding genomic landscape of bryophytes reveals their remarkable evolutionary plasticity and underscores their importance as key models for studying adaptation, metabolism, and genomic innovation in terrestrial plants. Full article

High-sensitivity rapid detection of ammonia (NH₃) in environmental monitoring, industrial safety, early diagnosis, and other fields is of great significance. Covalent organic frameworks (COFs) have shown great potential in the field of gas sensing due to their designable porous structure and active sites. However, the traditional solvothermal synthesis method of COFs has problems such as cumbersome steps, high energy consumption and serious environmental pollution. Therefore, it is of great significance to invent a new method for COF synthesis that is green and efficient and makes it easy to conduct flexible ammonia gas sensing. This study first reported a solvent-free synthesis of imine connection 1,3,5-Triformylbenzene (TFB) and p-Phenylenediamine (PDA)—a new strategy for COF. This method innovatively employs zinc trifluoromethyl sulfonate (Zn(OTf)₂) as a bifunctional catalyst. This catalyst not only efficiently catalyzes para-phenylenediamine, but its zinc ions also play a unique structural guiding role, guiding the reactants to be arranged in a directional manner, thereby constructing a highly ordered porous crystal structure. A series of characterizations confirmed that the obtained TFB-PDA-COF had good crystallinity and a high proportion of imine bonds (C=N). The powder material was coated onto a flexible polyimide (PI) substrate, successfully constructing a resistive ammonia gas sensor that operates at room temperature. The test results show that this sensor has a high response value, rapid response/recovery capability, and good selectivity for ammonia gas. More importantly, based on a flexible PI substrate, the device can maintain stable sensing performance even under repeated bending conditions, demonstrating its great potential in practical flexible electronic applications. This work not only provides a brand-new “zinc ion-guided” paradigm for the green and controllable synthesis of COF but also lays a material foundation for their application in the next-generation flexible sensing field. Full article

Tunnels, soundproof screens and other vertical roadside traffic facilities play an important role in isolating the driving environment, maintaining driving safety, and reducing driving noise. As the usage time increases, these facade traffic buildings become polluted and cause traffic safety problems. Obstacles on three-dimensional walls of different shapes, colors, and sizes are the most challenging problem in intelligent cleaning environment perception. This paper proposes an obstacle segmentation method based on a visual language model to overcome these problems. Firstly, in the constructed experimental environment, a visual–language obstacle dataset is collected, named the Road-side General Obstacles Dataset (RGOD), and the collected dataset is labeled with both a segmentation mask and a language description. These preprocessing results are used as the training input of the perception model to obtain the foreground and background separation results. Secondly, a VLM-GOS model was proposed to segmentation special-shaped obstacles, which emphasizes the distinction between background and foreground targets. Finally, the general obstacle is segmented by a vision–language model with a similar loss function, and evaluated with different metrics. Experimental results show that compared with models such as MaskFormer, SegFormer, and ASD-Net, this method improves the model’s perceptual ability and increases accuracy by 3%. More importantly, the model is more interpretable. Full article

Betaine-homocysteine methyltransferase (BHMT) is an enzyme involved in one-carbon metabolism and plays a crucial role in maintaining liver health. In this study, we investigated the impact of liver-specific deletion of BHMT on liver dysfunction using a mouse model. We generated BHMT floxed mice and bred them with albumin Cre to generate liver-specific BHMT knockout (BHMT LKO) mice. Liver tissues harvested from six-month-old chow-fed BHMT floxed and LKO mice were characterized through histological, biochemical, and molecular analyses. BHMT LKO mice displayed a complete loss of hepatic expression of BHMT mRNA, protein and enzyme activity. Histopathological analysis revealed the development of hepatic steatosis in BHMT LKO mice compared to the floxed mice. These morphological changes were supported by biochemical analysis showing elevated levels of hepatic triglycerides in conjunction with a profound decrease in the methylation potential (i.e., reduced S-adenosylmethionine (SAM): S-adenosylhomocysteine (SAH) ratio), which was mainly driven by a six- to sevenfold increase in SAH levels. BHMT LKO mice also exhibited increased lipid peroxidation and lysosomal dysfunction compared to floxed mice. Early signs of inflammation were seen in the livers of BHMT LKO mice of both sexes, as evident from significant increase in CD68-positive cells and interleukin 1β levels. Additionally, there was a moderate increase in fibrosis, as evidenced by the upregulated expression of α-smooth muscle actin and collagen II levels and the histological assessment of picrosirius red-stained liver sections of BHMT LKO mice of both sexes compared to their respective counterparts. These findings demonstrate that hepatic BHMT deficiency promotes lipid accumulation, lysosomal/proteasomal dysfunction, and early inflammatory and fibrotic changes in the liver by reducing the methylation potential. Collectively, our results underscore BHMT as a critical regulator of liver homeostasis and a potential therapeutic target in liver-related disorders. Full article

Microfluidic electrokinetic flows play a central role in applications such as lab-on-a-chip diagnostics, microelectronics cooling, and biomedical sample manipulation. These systems involve intricate heat transfer processes, including Joule heating from ionic currents, temperature-driven flow instabilities, and coupled thermal–fluid interactions, that crucially affect device performance, reliability, and scalability. Current challenges include non-equilibrium charge dynamics, incomplete thermophysical property data for complex fluids, and thermal crosstalk in integrated platforms. This review summarizes the literature published over the past 20 years, with occasional reference to earlier work, covering the fundamental mechanisms of heat generation and dissipation in electrokinetic microflows; analytical, numerical, and experimental approaches for characterizing thermal effects; and discussion on the limitations and application-driven opportunities. It also highlights open questions and future research directions and offers a comprehensive view of design principles and guidelines for developing robust, thermally optimized electrokinetic microfluidic technologies. Full article

3D bioprinting of hydrogel–cell composites requires simultaneous consideration of the biomechanical properties of the printed structures, the construct’s geometric stability, and conditions conducive to cell survival and function. Hydrogel cross-linking techniques and their kinetics play a key role in this process, determining the time of shape fixation, the mechanical strength of the structures, and the mechanical environment in which the cells are located immediately after printing. The relationships between bioprinting parameters, material properties, cross-linking strategies, and the presence of cells are highly nonlinear and often investigated through trial and error, leading to significant time and material costs. This paper proposes an approach based on artificial intelligence-assisted simulation, focusing on computer modeling of the biomechanical and functional parameters of hydrogel–cell composites produced by 3D bioprinting. The methodology is based on data generated from computer simulations and allows for analysis of the impact of printing parameters and different cross-linking strategies on mechanical strength, time-dependent geometric stability, and limitations related to cellular function, including exposure time to non-cross-linked matrices. The use of artificial intelligence methods allows for the integration of simulation results and predictive assessment of material behavior, providing a basis for future optimization of bioprinting parameters and process costs prior to experimental validation. Full article

The detection technology for metal surface defects plays a crucial role in improving metal product quality and production efficiency in various manufacturing and 3-D printing factories. Metal defect detection faces scale variation and irregular shapes, which limit the adaptability of general object detection models in industrial scenarios. Deep learning-based methods are widely used for metal surface defect detection due to their strong adaptability and high automation. Yet, their existing studies pay limited attention to adaptability, evaluation, and recommendations across different detection methods for metal surface defects. This work mainly discusses YOLO, R-CNN, and transformers, as well as FPN, and analyzes their applications in metal surface defect detection according to their respective characteristics, to provide guidance for future research. YOLO has advantages in real-time industrial online detection, while R-CNN and transformer models show potential advantages in handling complex defect cases. Additionally, this work summarizes commonly used datasets and evaluation metrics for metal surface defect detection and analyzes the benchmark performance of different types of detection methods. It also discusses future research directions, including the current status and improvement paths of different models in terms of accuracy, real-time performance, and adaptability. Future models should focus on balancing accuracy and real-time performance, exploring new hybrid architectures, and improving adaptability to different metal surface defects to support further development in this field. Full article

Accurate ecological prediction is critical for sustainable environmental management and carbon cycle assessment, yet model development is often constrained by limited datasets and inconsistent preprocessing practices. Reliable litterfall prediction plays a key role in understanding nutrient cycling and supporting sustainable forest ecosystem management. Although gradient boosting models have shown promising performance in ecological applications, structured evaluations integrating preprocessing strategies with synthetic data augmentation remain limited under data-scarce conditions. This study proposes the Hybrid Preprocessing and Augmented Boosting Framework (HPABF), which combines multi-stage preprocessing—including MICE imputation, log transformation, and feature engineering—with synthetic data augmentation to enhance predictive robustness. The framework was evaluated across eight machine learning models using a 968-sample forest ecological dataset. To mitigate data scarcity, 5000 synthetic samples were generated while preserving the statistical distribution and multivariate structure of the original data (91% fidelity). Fractal dimension analysis was further introduced as a geometric validation metric to assess prediction structure and stability beyond conventional performance measures. Within the HPABF, gradient boosting models achieved a 7% improvement over baseline performance (R² = 0.96, MAE = 0.06) under cross-validation strategies designed to reduce overfitting. Training with synthetic data further improved predictive accuracy (R² = 0.98), demonstrating the framework’s effectiveness for data-scarce ecological applications. By improving prediction reliability under limited data conditions, the proposed framework supports more accurate environmental monitoring, informed decision-making, and sustainable management of forest ecosystems. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

Staphylococcus aureus remains to be one of the leading causes of global mortality. The most common class of antibiotics used to treat S. aureus infections are next-generation β-lactams (NGBs), as they are highly efficacious and have low adverse effects. NGB resistance in S. aureus is classically attributed to penicillin-binding protein-2a (PBP2a), but previous studies from our group have also implicated an altered expression of penicillin-binding protein-4 (PBP4) with NGB resistance. PBP4 is the sole low-molecular-mass (LMM) PBP present in S. aureus; it is also the only known LMM PBP with transpeptidase activity, giving it the unique ability to bring about peptidoglycan cross-linking. In this article, we review some of the recent findings from our group, which reveal that mutations associated with PBP4 lead to altered protein expression and NGB resistance in both methicillin-susceptible S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) backgrounds. We discuss the clinical relevance of PBP4-associated mutations, particularly in methicillin-resistant lacking mec (MRLM) isolates, as well as the synergistic effect of altered PBP4 and GdpP functions. Finally, this review summarizes the potential role played by PBP4 in S. aureus virulence. Together, we highlight the increasing relevance of PBP4 as a mediator of NGB resistance and discuss its potential as an important factor during infection diagnosis and therapy. Full article