Search Results (278)

Pulmonary fibrosis (PF) is a progressive and fatal lung disease characterized by irreversible alveolar destruction and pathological extracellular matrix (ECM) deposition. Currently approved agents (pirfenidone and nintedanib) slow functional decline but do not reverse established fibrosis or restore functional alveoli. Multifunctional bioscaffolds present a promising therapeutic strategy through targeted modulation of critical cellular processes, including proliferation, migration, and differentiation. This review synthesizes recent advances in scaffold-based interventions for PF, with a focus on their dual mechano-epigenetic regulatory functions. We delineate how scaffold properties (elastic modulus, stiffness gradients, dynamic mechanical cues) direct cell fate decisions via mechanotransduction pathways, exemplified by focal adhesion–cytoskeleton coupling. Critically, we highlight how pathological mechanical inputs establish and perpetuate self-reinforcing epigenetic barriers to regeneration through aberrant chromatin states. Furthermore, we examine scaffolds as platforms for precision epigenetic drug delivery, particularly controlled release of inhibitors targeting DNA methyltransferases (DNMTi) and histone deacetylases (HDACi) to disrupt this mechano-reinforced barrier. Evidence from PF murine models and ex vivo lung slice cultures demonstrate scaffold-mediated remodeling of the fibrotic niche, with key studies reporting substantial reductions in collagen deposition and significant increases in alveolar epithelial cell markers following intervention. These quantitative outcomes highlight enhanced alveolar epithelial plasticity and upregulating antifibrotic gene networks. Emerging integration of stimuli-responsive biomaterials, CRISPR/dCas9-based epigenetic editors, and AI-driven design to enhance scaffold functionality is discussed. Collectively, multifunctional bioscaffolds hold significant potential for clinical translation by uniquely co-targeting mechanotransduction and epigenetic reprogramming. Future work will need to resolve persistent challenges, including the erasure of pathological mechanical memory and precise spatiotemporal control of epigenetic modifiers in vivo, to unlock their full therapeutic potential. Full article

The structural safety monitoring of industrial heritage is of great significance for global urban renewal and the preservation of cultural heritage. However, traditional tilt monitoring methods suffer from limited accuracy, low efficiency, poor global perception, and a lack of intelligence, making them inadequate for meeting the tilt monitoring requirements of super-high-rise industrial heritage chimneys. To address these issues, this study proposes a tilt monitoring method for super-high-rise industrial heritage chimneys based on LiDAR point clouds. Firstly, LiDAR point cloud data were acquired using a ground-based LiDAR measurement system. This system captures high-density point clouds and precise spatial attitude data, synchronizes multi-source timestamps, and transmits data remotely in real time via 5G, where a data preprocessing program generates valid high-precision point cloud data. Secondly, multiple cross-section slicing segmentation strategies are designed, and an automated tilt monitoring algorithm framework with adaptive slicing and collaborative optimization is constructed. This algorithm framework can adaptively extract slice contours and fit the central axes. By integrating adaptive slicing, residual feedback adjustment, and dynamic weight updating mechanisms, the intelligent extraction of the unit direction vector of the central axis is enabled. Finally, the unit direction vector is operated with the x- and z-axes through vector calculations to obtain the tilt-azimuth, tilt-angle, verticality, and verticality deviation of the central axis, followed by an accuracy evaluation. On-site experimental validation was conducted on a super-high-rise industrial heritage chimney. The results show that, compared with the results from the traditional method, the relative errors of the tilt angle, verticality, and verticality deviation of the industrial heritage chimney obtained by the proposed method are only 9.45%, while the relative error of the corresponding tilt-azimuth is only 0.004%. The proposed method enables high-precision, non-contact, and globally perceptive tilt monitoring of super-high-rise industrial heritage chimneys, providing a feasible technical approach for structural safety assessment and preservation. Full article

This study presents a comprehensive grayscale texture analysis framework for investigating the microstructural evolution of cement-based materials during hydration. High-resolution X-ray computed tomography (X-CT) slice images were analyzed across five hydration ages (12 h, 1 d, 3 d, 7 d, and 31 d) using three complementary methods: grayscale histogram statistics, fractal dimension calculation via differential box-counting, and texture feature extraction based on the gray-level co-occurrence matrix (GLCM). The average value of the mean grayscale value of slice (Mean_{G_AVE}) shows a trend of increasing and then decreasing. Average fractal dimension values (D_{B_AVE}) decreased logarithmically from 2.48 (12 h) to 2.41 (31 d), quantifying progressive microstructural homogenization. The trend reflects pore refinement and gel network consolidation. GLCM texture parameters—including energy, entropy, contrast, and correlation—captured the directional statistical patterns and phase transitions during hydration. Energy increased with hydration time, reflecting greater spatial homogeneity and phase continuity, while entropy and contrast declined, signaling reduced structural complexity and interfacial sharpness. A quantitative evaluation of parameter performance based on intra-sample stability, inter-sample discrimination, and signal-to-noise ratio (SNR) revealed energy, entropy, and contrast as the most effective descriptors for tracking hydration-induced microstructural evolution. This work demonstrates a novel, integrative, and segmentation-free methodology for texture quantification, offering robust insights into the microstructural mechanisms of cement hydration. The findings provide a scalable basis for performance prediction, material optimization, and intelligent cementitious design. Full article

As intelligent vessel technology moves from the proof-of-concept stage to engineering applications, the performance testing and evaluation of autonomous collision avoidance algorithms have become core issues for safeguarding maritime traffic safety. The International Maritime Organization (IMO)’s Maritime Safety Committee (MSC), at its 109th session, agreed to a revised road map for the development of the Maritime Autonomous Surface Ships (MASS) Code; the field has experienced the development stages of single-vessel collision avoidance validation based on COLREGs, multimodal algorithm collaborative testing, and the current construction of a progressive validation system for the integration of a mix of virtual reality and actual reality. In recent years, relevant studies have achieved research achievements, especially in the compatibility of COLREGs and in accurate collision avoidance in complex situations, and other algorithm tests and evaluations have made great breakthroughs. However, a systematic literature review is still lacking. In this paper, we systematically review the research progress of autonomous collision avoidance performance testing and the evaluation of intelligent vessels; summarize the advantages and disadvantages of virtual testing, model testing, and full-scale vessel testing; and analyze the applicability and limitations of mainstream algorithms such as the velocity obstacle algorithm, the artificial potential field algorithm, and reinforcement learning. It focuses on the key technologies such as diverse scene generation, local scene slicing, and the construction of an evaluation index system. Finally, this paper summarizes the challenges faced by autonomous collision avoidance performance testing and the assessment of intelligent vessels and proposes potential technical solutions and future development directions in terms of virtual–real fusion testing, dynamic evaluation index optimization, and multimodal algorithm co-validation, aiming to provide a reference for the further development of this field. Full article

The increasing demand for plant-based foods has led to significant growth in the availability, at a retail level, of plant-based cheese analogue products. This study presents the first comprehensive benchmarking of commercially available plant-based cheese analogue (PBCA) products in the Irish market, comparing them against conventional cheddar and processed dairy cheeses. A total of 16 cheese products were selected from Irish retail outlets, comprising five block-style plant-based analogues, seven slice-style analogues, two cheddar samples, and two processed cheese samples. Results showed that plant-based cheese analogues had significantly lower protein content (0.1–1.7 g/100 g) than cheddar (25 g/100 g) and processed cheese (12.9–18.2 g/100 g) and lacked a continuous protein matrix, being instead stabilized largely by solid fats, starch, and hydrocolloids. While cheddar showed the highest hardness, some plant-based cheeses achieved comparable hardness using texturizing agents but still demonstrated lower tan δ_max values, indicating inferior melting behaviour. Thermograms of differential scanning calorimetry presented a consistent single peak at ~20 °C across most vegan-based variants, unlike the dual-phase melting transitions observed in dairy cheeses. Sensory analysis further highlighted strong negative associations between PBCAs and consumer-relevant attributes such as flavour, texture, and overall acceptability. By integrating structural, functional, and sensory findings, this study identifies key formulation and performance deficits across cheese formats and provides direction for targeted improvements in next-generation PBCA product development. Full article

At present, the construction industry has not yet fully optimized the integration of the potential of big data. Past studies signaled the potential benefits of integrating building information management (BIM) and big data in the field of sustainable building management (SBM). However, these studies have a monotonous perspective in identifying the development of BIM and big data applications in SBM. Therefore, this paper aims to explore BIM and big data from various perspectives in the field of SBM to identify the aspects where additional efforts are required and provide insights into future directions, and it adopts a mixed method of quantitative and qualitative analysis, including bibliometric analysis and knowledge mapping, providing a macro-overview of the research status and development trends of BIM and big data integration for SBM from multiple bibliometric perspectives. The results indicate the following: (1) the current studies on BIM and big data integration (BBi)-aided SBM mainly focused on data integration and interoperability for collaboration, development of information technologies and emerging technologies, data analysis and presentation, and green building and sustainability assessment; (2) the longitudinal analysis of three time-slice phases (2010–2014, 2015–2018, and 2019–2024) over the past 15 years indicates that the studies on BBi-aided SBM have been expanded from the application of BIM in construction projects to the integration and interoperability of BIM with information technology, the integration of virtual models with physical buildings, and sustainable management throughout the building life cycle stages; and (3) key research gaps and emerging directions include data integration and model interoperability across the building life cycle, model transferability in the application of technology, and a comprehensive sustainability assessment framework based on the whole building life cycle stages. Full article

Changes in hepatic lipoprotein metabolism are responsible for the majority of metabolic dysfunction-associated disorders, including familial hypercholesterolemia (FH), metabolic syndrome (MetS), metabolic dysfunction-associated fatty liver disease (MAFLD), and age-related diseases such as atherosclerosis, a major health burden in modern society. This review aims to advance the understanding of state-of-the-art mechanistic concepts in lipoprotein metabolism, with a particular focus on lipoprotein uptake and secretion and their dysregulation in disease, and to provide a comprehensive overview of experimental models used to study these processes. Human lipoprotein research faces several challenges. First, significant differences in lipoprotein metabolism between humans and other species hinder the reliability of non-human model systems. Additionally, ethical constraints often limit studies on human lipoprotein metabolism using tracers. Lastly, while 2D hepatocyte cell culture systems are widely used, they are commonly of cancerous origins, limiting their physiological relevance and necessitating the use of more physiologically representative models. In this review, we will elaborate on key findings in lipoprotein metabolism, as well as limitations and challenges of currently available study tools, highlighting mechanistic insights throughout discussion of these models. These include human tracer studies, animal studies, 2D tissue culture-based systems derived from cancerous tissue as well as from induced pluripotent stem cells (iPSCs)/embryonic stem cells (ESCs). Finally, we will discuss precision-cut liver slices, liver-on-a-chip models, and, particularly, improved 3D models: (i) spheroids generated from either hepatoma cancer cell lines or primary human hepatocytes and (ii) organoids generated from liver tissues or iPSCs/ESCs. In the last section, we will explore future perspectives on liver-in-a-dish models in studying mechanisms of liver diseases, treatment options, and their applicability in precision medicine approaches. By comparing traditional and advanced models, this review will highlight the future directions of lipoprotein metabolism research, with a focus on the growing potential of 3D liver organoid models. Full article

Accurately detecting lane lines is a hot topic in computer vision. How to effectively utilize the relationship between lane features for detection is still an open question. In this paper, we propose a novel lane detection model based on convolutional neural network (CNN), namely, the BSCNNLaneNet (Bidirectional Spatial CNN Lane Detection Network). The proposed model is based on the spatial CNN method and incorporates a bidirectional recurrent neural network (BRNN) block to learn the spatial relationships between slice features. Additionally, a convolutional block attention mechanism is introduced to gain global features, which enhance the global connection between slice features in different directions. We conduct extensive experiments on the TuSimple dataset. The results demonstrate that the proposed method surpasses the original spatial CNN method, achieving an increase in accuracy from 96.53% to 96.86%. Full article

The capillary force driving the water penetration process in the coal pore network is the key factor affecting the effect of coal seam water injection. The resistivity method can be used to determine the migration characteristics of water in coal. In order to study the relationship between the resistivity of gas-bearing coal and the migration of water in the process of imbibition, the self-generated imbibition tests of coal under different external water conditions were carried out by using the self-developed gas-bearing coal imbibition experimental platform and the dynamic response characteristics of coal resistivity with external water were obtained. The results show that the water injected into the coal body migrates from bottom to top under the driving of capillary force, and the resistivity of the wetted coal body shows a sudden decline, slow decline, and gradually stable stage change. Through the slice drying method, it is found that the moisture in the coal body is almost uniform after imbibition, and the resistivity method can be used to accurately and quantitatively characterize the moisture content of the coal body. In the axial direction, as water infiltrates layer by layer, the sudden change time of resistivity is delayed with the deepening of the layer. The resistivity of each layer first drops sharply then slows down and tends to stabilize. The stable value of resistivity increases gradually with the depth of the layer. In the radial direction, within the same plane, water first migrates to the centre of the coal body and then begins to spread outwards. The average mutation time and stable value of coal resistivity during spontaneous imbibition decrease with increasing water content. When the water content reaches 10%, the stable value of resistivity tends to be constant, and the relationship between the stable value of coal resistivity and water content conforms to an exponential function. Full article

In the search for new functional materials, strong emphasis is placed on the ecological aspect, which is why thin layers of materials based on deoxyribonucleic acid (DNA) are fascinating from the point of view of applications. Thin layers of DNA–cationic surfactant complexes were created on mica slices using the Langmuir–Blodgett deposition technique. Three cationic surfactants (CTMA, BAC, HDP) and two types of DNA (linear dsDNA and plasmid pDNA) were used to synthesise the complexes. It was shown that the pattern of the obtained layer depended on the lifting conditions, type of DNA, and type of surfactant. The elongated structures that formed along the layer lifting direction were examined by AFM imaging and fast Fourier transform analysis. The main difference between the layers formed by plasmid pDNA-based and linear dsDNA-based complexes was the thickness of the stripes and the minimum surface pressures at which elongated structures were formed. Full article

To address the inefficiencies and limited spatial resolution of traditional single-point monitoring techniques, this study proposes a multi-scale analysis method that integrates the Multi-Scale Model-to-Model Cloud Comparison (M3C2) algorithm with least-squares plane fitting. This approach employs the M3C2 algorithm for qualitative full-field deformation detection and utilizes least-squares plane fitting for quantitative feature extraction. When applied to the approach span of a cross-river bridge in Hubei Province, China, this method leverages dense point clouds (greater than 500 points per square meter) acquired using a Leica RTC360 scanner. Data preprocessing incorporates curvature-adaptive cascade denoising, achieving over 98% noise removal while retaining more than 95% of structural features, along with octree-based simplification. By extracting multi-level slice features from bridge decks and piers, this method enables the simultaneous analysis of global trends and local deformations. The results revealed significant deformation, with an average settlement of 8.2 mm in the left deck area. The bridge deck exhibited a deformation trend characterized by left and higher right in the vertical direction, while the bridge piers displayed noticeable tilting, particularly with the maximum offset of the rear pier columns reaching 182.2 mm, which exceeded the deformation of the front pier. The bridge deck’s micro-settlement error was ±1.2 mm, and the pier inclination error was ±2.8 mm, meeting the Chinese Highway Bridge Maintenance Code (JTG H11-2004) and the American Association of State Highway and Transportation Officials (AASHTO) standards, and the multi-scale algorithm achieved engineering-level accuracy. Utilizing point cloud densities >500 pt/m², the M3C2 algorithm achieved a spatial resolution of 0.5 mm, enabling sub-millimeter full-field analysis for complex scenarios. This method significantly enhances bridge safety monitoring precision, enhances the precision of intelligent systems monitoring, and supports the development of targeted systems as pile foundation reinforcement efforts and as improvements to foundations. Full article

Three-dimensional (3D) photolithography has found wide applications in microelectronics, optoelectronics, biomedicine, etc. Traditionally, it requires repetitive exposure and developing cycles. Meanwhile, a laser direct writing (LDW) system with a digital micromirror device (DMD) enables high-speed maskless lithography with programmable doses. In this paper, we propose a quasi-3D digitized photolithography via LDW with a DMD to remove multiple developing cycles from the process. This approach quantizes the dose of the 3D geometry and stores it in a grayscale image. And the entire dose distribution can be formed by overlapping the exposures with sliced binary dose maps from the above grayscale dose map. In the image slicing algorithm, a modified Golomb coding is introduced to make full use of the highest available exposure intensity. Both 1D multi-step patterns and diffractive optical devices (DOEs) have been fabricated to verify its feasibility. This type of digitized quasi-3D photolithography can be applied to fabricating DOEs, microlens arrays (MLAs), micro-refractive optical elements (μROEs), etc., and 3D molds for micro-embossing/nano-imprinting. Full article

When ships conduct offshore operations in the ocean, they are subject to disturbances from natural factors such as sea breezes and waves. These disturbances lead to movements detrimental to the ship’s stability, especially heave movement in the vertical direction, which profoundly impacts the safety of shipboard facilities and staff. To counter this, the active wave compensation device is widely used on ships to maintain the stability of the working environment. However, the system’s efficiency and accuracy are compromised by the significant delay incurred while obtaining real-time motion signals and driving the actuator for motion compensation. To solve the time delay problem of shipborne wave compensation equipment in motion compensation under complex sea conditions, it is necessary to improve the ship heave motion prediction accuracy in an active wave compensation system. This paper presents a prediction method of ship heave motion based on the particle swarm optimization (PSO) and convolutional neural network–long short-term memory (CNN-LSTM) hybrid prediction model. The paper begins by establishing the ship heave motion model based on the P–M spectrum and slice theory, simulating the ship heave motion curve under different sea conditions on MATLAB. This simulation provides crucial data for the subsequent prediction model. The paper then delves into the realization method of ship heave motion based on PSO-CNN-LSTM, where the convolutional neural network (CNN) is used to extract the features of the input signal, thereby enhancing the multi-source feature fusion ability of the LSTM neural network model. The PSO algorithm is then employed to optimize the network structure and hyperparameters of the convolutional neural network. The experiments demonstrate that the proposed PSO-CNN-LSTM hybrid model effectively addresses the problem of predicting drift and boasts significantly higher prediction accuracy, making it suitable for predicting the short-term heave motion of ships. The data show that the optimized root mean square error (RMSE) value under level 5 sea conditions is 0.01265 compared to 0.01673 before optimization, and the optimized RMSE value under level 6 sea conditions is 0.01140 compared to 0.01479 before optimization, which demonstrates that the error between the predicted value and the actual value of the model decreases. This improved accuracy provides reassurance in the model’s predictive capabilities and lays the foundation for improving the accuracy of the motion compensation system in the future. Full article

Apple fruit is among the most consumed fruits in the world, both in fresh and processed forms (e.g., ready-to-eat fresh slices, juice, jam, cider, and dried slices). During apple consumption/processing, a significant amount of apple residue is discarded. These residues can also be interesting materials to exploit, particularly for direct valorization in the design of added-value foods. In fact, apple waste/by-products are rich in essential components, including sugars, proteins, dietary fibers, and phenolic compounds, as they comprise apple peels, seeds, and pulp (solid residue of juice production). In this sense, the current review paper presents an overview of the nutritional composition of apple waste/by-products, and mainly apple pomace, highlighting their application in producing value-added products through microbial biotechnology. If appropriately managed, apple by-products can generate a variety of useful compounds required in food (as well as in feed, pharmaceutics, and bioenergy). Recent strategies for the synergic use of apple waste/by-products and microbial resources such as lactic acid bacteria and yeasts are discussed. This review contributes to defining a reference framework for valorizing apple waste/by-products from a circular economy perspective through the application of bioprocesses (e.g., fermentation), mainly oriented towards designing foods with improved quality attributes. Full article

Background/Objectives: In recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC), the overall prognosis is poor, and systemic treatment options remain limited. While precision therapy approaches have revolutionized treatment strategies in several tumor types, molecularly informed therapies in R/M HNSCC are rare, primarily due to the low number of actionable genetic alterations identified through next-generation sequencing (NGS) panels. There is an urgent need to establish precision therapy approaches in R/M HNSCC using innovative predictive testing. Methods: We report the case of a 43-year-old patient with recurrent oral cancer who was extensively pretreated and comprehensively characterized using both descriptive and functional testing. Results: NGS revealed no targetable alterations. A tumor tissue slice radiosensitivity assay suggested radioresistance, arguing against re-irradiation. Kinome profiling identified upregulated Src-family kinases (SFK), and SFK inhibition reduced kinase activity in vitro. Most notably, mRNA analysis demonstrated high Trop-2 overexpression, confirmed by immunohistochemistry (3+ in 100% of tumor cells). Following six cycles of the Trop-2-directed antibody–drug conjugate Sacituzumab govitecan (SG), the patient had an impressive clinical response. Conclusions: Tumor characterization beyond genetic profiling can identify novel treatment options in therapy-refractory HNSCC. This is the first report of “real-world” data on promising antitumor efficacy of SG in a heavily pretreated oral cancer patient with Trop-2 overexpression. Consistent with the findings of the Basket TROPiCS-03 study, SG appears to be a promising novel therapy option for R/M HNSCC after failure of immunotherapy and chemotherapy, particularly in patients with Trop-2 overexpression. Full article