Search Results (651)

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas. Full article

This study investigated the protective effect of fermented milk by Lacticaseibacillus rhamnosus D1 in a murine model of Typhoid fever, focusing on cytokines, antimicrobial peptides and microbiota modulation. BALB/c mice were pre-treated with milk fermented by L. rhamnosus D1 prior to Salmonella Typhimurium challenge. Outcomes assessed included survival, weight change, bacterial translocation, mRNA expression of cytokines and antimicrobial peptides, in addition to gut microbiota modulation. Mice receiving fermented milk exhibited higher survival rates, reduced bacterial translocation and attenuated weight loss compared to controls. mRNA expression analyses revealed that L. rhamnosus D1 pre-treatment suppressed the expression of pro-inflammatory cytokines (IFN-γ, IL-6 and IL-12) and upregulated anti-inflammatory cytokines (IL-5, IL-10 and TGF-β), as well as antimicrobial peptides (Reg3β, Reg3γ and Lcn2). Furthermore, we observed that the consumption of fermented milk changed the gut microbiota of infected mice, not only by modulating the existing taxa, but also by facilitating the emergence of unique, potentially beneficial microbial lineages, such as Muribaculum, Roseburia, Intestinimonas, Bdellovibrio and Facklamia. These findings indicate that L. rhamnosus D1 protected mice against S. Typhimurium infection through immunomodulatory and microbiota-mediated mechanisms, changing mucosal immunity and strengthening the intestinal barrier by modulating gut microbiota and immune responses, in addition to promoting host antimicrobial defenses. Full article

The Upper Blue Nile Basin (UBNB), which contributes about 60% to the annual Nile flow, plays a critical role in the Nile water management. However, its complex terrain and climate create significant challenges for accurate regional climate simulations, which are essential for climate impact assessments. This study aims to address the challenges of climate simulation over the UBNB by enhancing the Regional Climate Model system (RegCM5) with its new non-hydrostatic dynamical core (MOLOCH) to simulate precipitation and temperature. The model is driven by ERA5 reanalysis for the period (2000–2009), and two scenarios are simulated using two different schemes of the Planetary Boundary Layer (PBL): Holtslag (Hol) and University of Washington (UW). The two scenarios, noted as (MOLOCH-Hol and MOLOCH-UW), are compared to the previously best-performing hydrostatic configuration. The MOLOCH-UW scenario showed the best precipitation performance relative to observations, with an accepted dry Bias% up to 22%, and a high annual cycle correlation >0.85. However, MOLOCH-Hol showed a very good performance only in the wet season with a wet bias of 4% and moderate correlation of ≈0.6. For temperature, MOLOCH-UW also outperformed, achieving the lowest cold/warm bias range of −2% to +3%, and high correlations of ≈0.9 through the year and the wet season. This study concluded that the MOLOCH-UW is the most reliable configuration for reproducing the climate variability over the UBNB. This developed configuration is a promising tool for the basin’s hydroclimate applications, such as dynamical downscaling of the seasonal forecasts and future climate change scenarios produced by global circulation models. Future improvements could be achieved through convective-permitting simulation at ≤4 km resolution, especially in the application of assessing the land use change impact. Full article

Osseodensification (OD) has emerged as a favorable osteotomy preparation technique that preserves and compacts autogenous bone along the osteotomy walls during site preparation, enhancing primary stability and implant osseointegration. While OD has demonstrated promising results in low-density trabecular bone, especially when used in conjunction with acid-etched (AE) implant surfaces, its efficacy in high-density cortical bone remains unclear—particularly in the context of varying implant surface characteristics. In this study, Grade V titanium alloy implants (Ti-6Al-4V, 4 mm × 10 mm) with deep threads, designated bone chambers and either as-machined (Mach) or AE surfaces were placed in 3.8 mm diameter osteotomies in the submandibular region of 16 adult sheep using either OD or conventional (Reg) drilling protocols. Insertion torque values (N·cm) were measured at the time of implant placement to evaluate primary stability. Mandibles were harvested at 3-, 6-, 12-, or 24-weeks post-implantation (n = 4 sheep/time point), and histologic sections were analyzed to quantify bone-to-implant contact (BIC) and bone area fractional occupancy (BAFO). Qualitative histological analysis confirmed successful osseointegration among all groups at each of the healing time points. No statistically significant differences were observed between OD and conventional drilling techniques in insertion torque (p > 0.628), BIC (p > 0.135), or BAFO (p > 0.060) values, regardless of implant surface type or healing interval. The findings indicate that neither drilling technique nor implant surface treatment significantly influences osseointegration in high density cortical bone. Furthermore, as the osteotomy was not considerably undersized, the use of OD instrumentation showed no signs of necrosis, inflammation, microfractures, or impaired osseointegration in dense cortical bone. Both OD and Reg techniques appear to be suitable for implant placement in dense bone, allowing flexibility based on surgeon preference and clinical circumstances. Full article

The current study presents a multi-class, image-based classification of eight morphologically similar macroscopic Earthstar fungal species (Astraeus hygrometricus, Geastrum coronatum, G. elegans, G. fimbriatum, G. quadrifidum, G. rufescens, G. triplex, and Myriostoma coliforme) using deep learning and explainable artificial intelligence (XAI) techniques. For the first time in the literature, these species are evaluated together, providing a highly challenging dataset due to significant visual overlap. Eight different convolutional neural network (CNN) and transformer-based architectures were employed, including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet. The accuracy scores of these models ranged from 86.16% to 96.23%, with EfficientNet-B3 achieving the best individual performance. To enhance interpretability, Grad-CAM and Score-CAM methods were utilised to visualise the rationale behind each classification decision. A key novelty of this study is the design of two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S. These ensembles further improved classification stability, reaching 93.71% and 93.08% accuracy, respectively. Based on metric-based evaluation, the EfficientNet-B3 + DeiT model delivered the most balanced performance, with 93.83% precision, 93.72% recall, 93.73% F1-score, 99.10% specificity, a log loss of 0.2292, and an MCC of 0.9282. Moreover, this modeling approach holds potential for monitoring symbiotic fungal species in agricultural ecosystems and supporting sustainable production strategies. This research contributes to the literature by introducing a novel framework that simultaneously emphasises classification accuracy and model interpretability in fungal taxonomy. The proposed method successfully classified morphologically similar puffball species with high accuracy, while explainable AI techniques revealed biologically meaningful insights. All evaluation metrics were computed exclusively on a 10% independent test set that was entirely separate from the training and validation phases. Future work will focus on expanding the dataset with samples from diverse ecological regions and testing the method under field conditions. Full article

Cerebral autoregulation refers to the ability of cerebral vasculature to maintain stable blood flow by adjusting vascular resistance in response to changes in perfusion pressure. With advancing age, this regulatory capacity gradually declines, and its early, real-time, and dynamic monitoring holds potential as a promising approach for the prevention and treatment of cerebrovascular diseases. Given the absence of an established “gold standard” for assessing cerebral autoregulation, this study aimed to develop a non-invasive, continuous method for assessing cerebral autoregulation based on bioelectrical impedance technology. Using a wearable headband in combination with a Finapres device, blood pressure and cerebral blood flow were continuously monitored. A novel impedance recovery curve method was developed and, together with systemic blood pressure data, used to construct a hierarchical cerebral autoregulation assessment model via system identification. Moreover, the utility of this method in differentiating autoregulatory capacity across age groups (young adult and middle-aged) was assessed. The results demonstrated that the time constant (τ_REG), which characterizes the speed of cerebral blood flow recovery, differed significantly between the young adult and middle-aged groups (p < 0.001). These findings suggest the potential of τ_REG as a quantitative indicator for distinguishing cerebral autoregulatory function between healthy age cohorts. Full article

The goal of this review is to expand our understanding of how the cellular organization of the normal colonic crypt is maintained and elucidate how this intricate architecture is disrupted during tumorigenesis. Additionally, it will focus on implications for new therapeutic strategies targeting Epithelial–Mesenchymal Transition (EMT). The colonic crypt is a highly structured epithelial unit that functions in maintaining homeostasis through a complex physiological function of diverse cell types: SCs, transit-amplifying (TA) progenitors, goblet cells, absorptive colonocytes, Paneth-like cells, M cells, tuft cells, and enteroendocrine cells. These cellular subpopulations are spatially organized and regulated by multiple crucial signaling pathways, including WNT, Notch, Bone Morphogenetic Protein (BMP), and Fibroblast Growth Factor (FGF). Specifically, we discuss how these regulatory networks control the precise locations and functions of crypt cell types that are necessary to achieve cellular organization and homeostasis in the normal colon crypt. In addition, we detail how the crypt’s hierarchical structure is profoundly perturbed in colorectal cancer (CRC) development. Tumorigenesis appears to be driven by LGR5+ cancer stem cells (CSCs) and the hyperproliferation of TA cells as colonocytes undergo metabolic reprogramming. Goblet cells lose their secretory phenotype, while REG4+ Paneth-like cells foster SC niches. Tumor microenvironment is also disrupted by upregulation of M cells and by tumor-immune crosstalk that is promoted by tuft cell expansion. Moreover, the presence of enteroendocrine cells in CRC has been implicated in treatment resistance due to its contribution to tumor heterogeneity. These cellular changes are caused by the disruption of homeostasis signaling whereby: overactivation of WNT/β-catenin promotes stemness, dysregulation of Notch inhibits differentiation, suppression of BMP promotes hyperproliferation, and imbalance of FGF/WNT/BMP/NOTCH enhances cellular plasticity and invasion. Further discussion of emerging therapies targeting epithelial markers and regulatory factors, emphasizing current development in novel, precision-based approaches in CRC treatment is also included. Full article

Camellia oleifera C. Abel is an economically important oilseed crop. This study aimed to investigate the chemical composition and bioactive potential of its leaf extracts, an underutilized by-product, for cosmetic and pharmaceutical applications. Extracts of C. oleifera leaves were prepared using three solvents (water, 50% ethanol, 95% ethanol) via ultrasonication. The total polyphenol and flavonoid contents were quantified, and key bioactivities, including antioxidant capacity, tyrosinase inhibition, and effects on cell proliferation, were evaluated. The 50% ethanolic extract exhibited the highest total polyphenol (337.24 ± 1.94 GAE/g extract) and total flavonoid (189.23 ± 1.12 mg RE/g extract) contents. This extract also demonstrated superior antioxidant activity, with an IC₅₀ of 28.10 ± 0.46 μg/mL for DPPH scavenging and an ORAC value of 2651.54 ± 112.41 μmol/g. Nine compounds were isolated and identified, comprising flavonoids (1–3) and polyphenols (4–9). Compound 1 showed the strongest DPPH scavenging activity with IC₅₀ of 24.19 ± 0.07 μM. Compound 9 significantly stimulated HaCaT cell proliferation (169.30 ± 2.17%), while compound 2 promoted the growth of HFF-1 cells (129.36 ± 2.81%). These results highlight the potential of C. oleifera leaves as a valuable source of bioactive compounds for cosmetic and pharmaceutical applications. Full article

Regulation of the phenylpropanoid pathway is critical for plant development and defense. This research investigates the transcriptional control of six Phenylalanine Ammonia-Lyase (PAL) gene homologs identified in the mulberry genome. A comprehensive in silico pipeline was employed to analyze the promoter architecture of these genes. Using the MEME suite, we identified three statistically significant conserved motifs within the 2000 bp upstream region. Subsequent TF binding prediction with FootprintDB for these motifs implicated the TCP, NAC, AP2/ERF, B3, and BBR-BPC families as potential regulators. A parallel analysis with PlantRegMap highlighted a high density of binding sites for the BBR-BPC and AP2/ERF families in the core promoter regions. A comparative analysis showed a weak correlation between the databases, underscoring the necessity of a multi-faceted predictive approach. Transcriptomic profiling under chitosan-induced conditions validated our in silico framework, suggesting the involvement of these TF families. Specifically, the data support NAC083 as a putative transcriptional activator and suggest a repressive function for members of the AP2/ERF and BBR-BPC families, providing a robust, experimentally supported model of PAL regulation. Full article

Accurately predicting building heating and cooling loads is crucial for optimizing HVAC systems and enhancing energy efficiency. However, data-driven models often face overfitting issues due to scarce training data, a common challenge for new constructions or under data privacy constraints. Transfer learning (TL) offers a solution, but its effectiveness heavily depends on selecting an appropriate source domain through effective similarity measurement. This study systematically evaluates the performance of 20 prevalent similarity metrics in TL for building heating load forecasting to identify the most robust metrics for mitigating data scarcity. Experiments were conducted on data from 500 buildings, with seven distinct low-data target scenarios established for a single target building. The Relative Error Gap (REG) was employed to assess the efficacy of transfer learning facilitated by each metric. The results demonstrate that distance-based metrics, particularly Euclidean, normalized Euclidean, and Manhattan distances, consistently yielded lower REG values and higher stability across scenarios. In contrast, probabilistic measures such as the Bhattacharyya coefficient and Bray–Curtis similarity exhibited poorer and less stable performance. This research provides a validated guideline for selecting similarity metrics in TL applications for building energy forecasting. Full article

Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture with an Enhanced Cross-Modal Channel Attention (E-CMCA) module and a LSTM-Based Spatial Deformation Modeling Module. The E-CMCA module efficiently extracts and integrates multi-scale cross-modal features, while the LSTM-Based Spatial Deformation Modeling Module models temporal dynamics by processing depth-sliced 3D deformation fields as sequential data. A VecInt operation ensures smooth, diffeomorphic transformations, and a FuseConv layer enhances feature integration for precise alignment. Experiments on the $\mu$-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model significantly improves registration accuracy and performs robustly in both static 3D and dynamic 4D registration tasks. Experiments on the $\mu$-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model achieves a DSC of 0.865, RDSC of 0.898, TRE of 2.278 mm, and RTRE of 1.293, surpassing state-of-the-art methods and performing robustly in both static 3D and dynamic 4D registration tasks. Full article

Down syndrome (DS) is one of the prevalent chromosomal disorders, representing distinctive craniofacial features and a range of developmental and medical challenges. Due to the lack of clinical expertise and high infrastructure costs, access to genetic testing is restricted to resource-constrained clinical settings. There is a demand for developing a non-invasive and equitable DS screening tool, facilitating DS diagnosis for a wide range of populations. In this study, we develop and validate a robust, interpretable deep learning model for the early detection of DS using facial images of infants. A hybrid feature extraction architecture combining RegNet X–MobileNet V3 and vision transformer (ViT)-Linformer is developed for effective feature representation. We use an adaptive attention-based feature fusion to enhance the proposed model’s focus on diagnostically relevant facial regions. Bayesian optimization with hyperband (BOHB) fine-tuned extremely randomized trees (ExtraTrees) is employed to classify the features. To ensure the model’s generalizability, stratified five-fold cross-validation is performed. Compared to the recent DS classification approaches, the proposed model demonstrates outstanding performance, achieving an accuracy of 99.10%, precision of 98.80%, recall of 98.87%, F1-score of 98.83%, and specificity of 98.81%, on the unseen data. The findings underscore the strengths of the proposed model as a reliable screening tool to identify DS in the early stages using the facial images. This study paves the foundation to build equitable, scalable, and trustworthy digital solution for effective pediatric care across the globe. Full article

Significant efforts have been dedicated to developing controlled-release systems for the effective management of colorectal cancer. In this study, a once-daily, delayed-release regorafenib (REG) tablet was fabricated using 3D printing technology for the treatment of colorectal cancer. For this, a hydrogel containing 80 mg of the drug in a matrix of hyaluronic acid, carboxymethyl cellulose, Pluronic F127, and glycerol was prepared to incorporate into the shell cavity of tablet via a pressure-assisted microsyringe (PAM). The shell was printed from an optimized ink formulation of Soluplus^®, Eudragit^® RS-100, corn starch 1500, propylene glycol 4000, and talc through melt extrusion-based 3D printing. In vitro release assays showed a drug release rate of 91.1% in the phosphate buffer medium at 8 h and only 8.5% in the acidic medium. Drug release kinetics followed a first-order model. The results showed smooth and uniform layers based on scanning electron microscopy (SEM) and drug stability at 135 °C upon TGA. FTIR analysis confirmed the absence of undesired covalent interactions between the materials. Weight variation and assay results complied with USP standards. Mechanical strength testing revealed a Young’s modulus of 5.18 MPa for the tablets. Overall, these findings demonstrate that 3D printing technology enables the precise fabrication of delayed-release REG tablets, offering controlled-release kinetics and accurate dosing tailored for patients in intensive care units. Full article

The ileum serves as the primary site for nutrient digestion and absorption in the intestine, with villus height representing a critical indicator of intestinal absorptive capacity. To investigate the regulatory mechanisms underlying ileal villus development, we conducted a feeding trial using crossbred pigs (Duroc × Landrace × Yorkshire) with an initial body weight of 27.74 ± 0.28 kg, stratifying them into high-villus and low-villus groups based on ileal villus height (n = 4). The results revealed 849 differentially RNA-edited genes (REGs) between the two groups, including 472 hyper-edited genes in the low-villus group and 377 in the high-villus group. Functional enrichment analysis showed that these REGs were significantly enriched in inflammation-related pathways, particularly the TNF signaling pathway and IL-17 signaling pathway, with TNF pathway genes exhibiting notably higher editing levels in the high-villus group. Additionally, 46 differentially expressed genes (DEGs) were identified, comprising 22 upregulated in the low-villus group and 24 in the high-villus group, which were similarly enriched in TNF and IL-17 signaling pathways. Integrated quadrant analysis of the RNA editing and transcriptomic profiles demonstrated that pro-inflammatory genes CXCL10 (C-X-C motif chemokine 10), CCL2 (C-C motif chemokine ligand 2), CREB3L2 (CAMP-responsive element-binding protein 3-like 2), and PIK3R1 (Phosphoinositide-3-kinase regulatory subunit 1) were highly expressed in the low-villus group but exhibited significantly lower RNA editing levels compared to the high-villus group. Furthermore, the expression of the inflammation-suppressive RNA editing enzyme APOBEC1 (apolipoprotein B mRNA editing enzyme catalytic subunit 1) showed correlation with villus height (R = 0.81, p < 0.05). Collectively, our findings indicate that RNA editing dynamics influence the variation in ileal villus height within inflammation-associated pathways, particularly the TNF signaling pathway. Enhanced RNA editing of this pathway may mitigate intestinal inflammation and promote healthy ileal villus developments. Full article

Thermal impregnation (TI) is a traditional method of sugar infusion, but it has disadvantages such as long processing time and uneven sugar distribution. Therefore, developing sugar impregnation methods to enhance product flavor, nutritional value, and processing efficiency is critical for addressing potential quality loss and efficiency bottlenecks in traditional preserve processing technologies. This study took the TI process widely adopted in Xinjiang over the long term as a reference and systematically compared the effects of vacuum impregnation (VI) and ultrasonic-assisted impregnation (UI) on the flavor characteristics and physicochemical properties of plum preserves. Volatile organic compounds (VOCs) were identified using gas chromatography–ion mobility spectrometry (GC-IMS) coupled with multivariate analysis, while taste attributes were quantified via electronic tongue (E-tongue). Physicochemical parameters, including titratable acidity (TA), browning index (BI), color parameters (L*, a*, b*), total polyphenol content (TPC), total flavonoid content (TFC), and texture profile analysis (TPA), were also evaluated. GC-IMS identified 60 VOCs, predominantly comprising aldehydes (20), alcohols (10), ketones (6), acids (4), esters (3), furans (3), ketols (2), and unidentified compounds (12). The VI-treated samples exhibited distinct aromatic profiles, retaining a higher proportion of key volatile compounds. E-tongue results showed that VI significantly enhanced sourness, umami, and aftertaste complexity compared with UI and TI (p < 0.05). Physicochemical analyses showed that VI maximally preserved bioactive compounds, with a TPC of 1.23 ± 0.07 mg GAE/g and TFC of 17.55 ± 0.81 mg RE/g. Additionally, VI minimized enzymatic browning (BI: 0.37 ± 0.03), maintained color brightness (L*: 31.85 ± 1.56), maintained favorable textural properties (hardness: 187.63 ± 4.04 N), and retained the highest TA content (0.77 ± 0.05%). In contrast, UI and TI led to significant quality degradation, characterized by pronounced browning and texture deterioration: the BI values were 0.61 ± 0.02 (UI) and 0.83 ± 0.03 (TI), and hardness values were 176.53 ± 5.81 N (UI) and 156.25 ± 4.55 N (TI). These findings provide critical references for sugar impregnation techniques and a scientific basis for flavor regulation in prune preserve production. Full article