Search Results (49)

Background: Kai-Bi-Bu-Fei Decoction (KBD) is derived from the canonical Traditional Chinese Medicine formulas Xuan-Bai-Cheng-Qi and Ma-Xing-Shi-Gan. It has been employed for decades in the treatment of severe pneumonia with significant clinical efficacy. This study aimed to evaluate the protective effects of KBD against influenza virus-induced severe pneumonia in a murine model and to elucidate the underlying molecular mechanisms. Methods: The chemical profile of KBD was characterized using UPLC-Q-TOF-MS. A severe pneumonia model was established in C57BL/6J mice via intranasal infection with influenza A/Puerto Rico/8/34 (H1N1, PR8). Multiple parameters, including 14-day survival rate, body weight, lung index, histopathological changes, viral load, and pulmonary cytokine/chemokine levels, were assessed. Furthermore, multi-omics analyses were integrated to characterize the gut microbiota and metabolic profiles. Fecal microbiota transplantation (FMT) was subsequently performed to validate the functional role of the gut microbiota and its metabolites. Results: KBD treatment significantly improved the survival rate by 40%, reduced the lung index by 27.85%, and alleviated lung injury. It also markedly lowered the viral load by 80.88%, suppressed pro-inflammatory cytokine levels, and restored intestinal barrier integrity. Mechanistically, KBD restored gut microbiota diversity by increasing the abundance of Firmicutes and Bacteroidetes, enriching beneficial genera such as Bifidobacterium and Faecalibaculum, and reducing Verrucomicrobiota. Integrated transcriptomic and metabolomic analyses revealed that KBD enhanced short-chain fatty acid (SCFA) metabolism and up-regulated pyruvate metabolism. Finally, FMT confirmed that the therapeutic benefits of KBD were transferable via the microbiota to microbiota-depleted mice. Conclusions: KBD exerts robust protection against severe influenza pneumonia, a process primarily mediated by the gut microbiota–SCFA axis. The enhancement of mitochondrial energy metabolism also appears to play a critical role in its therapeutic mechanism. Full article

In the context of the circular bioeconomy and environmental protection trends, the efficient use of renewable resources has become a driving force for industry, and lignin represents precisely a renewable carbon resource, abundant in terrestrial biomass that could become a sustainable substitute for fossil resources, under conditions of full exploitation. This study systematically evaluates the biosorption of Manganese (Mn(II)) from aqueous media using unmodified Tripidium bengalense (Sarkanda grass) lignin. Under optimal operating conditions (adsorbent dosage of 5 g/L, pH 6.5, and 20 °C), a highly competitive experimental adsorption capacity of 12.52 mg/g was achieved. Kinetic studies revealed exceptionally rapid uptake rates, with thermodynamic equilibrium established within the first 30 min, fitting perfectly with the pseudo-second-order (Ho-McKay) model (R² ≥ 0.9998). Equilibrium data were best described by the Freundlich isotherm (R² ≥ 0.9886), confirming chemisorption via preferential inner-sphere complexation on a heterogeneous surface. Thermodynamic analysis verified that the process is spontaneous (ΔG ranging from −13.24 to −26.19 kJ/mol) and endothermic (ΔH from 11.21 to 14.83 kJ/mol). FTIR, SEM-EDX, and TG/DTG analyses confirmed successful Mn–O coordination involving phenolic hydroxyl and carboxylic groups. Furthermore, the lignin showed excellent recyclability, maintaining a retention efficiency over 70% (70.7–85.8%) after three desorption-resorption cycles using 1N HCl. Ecotoxicological validation via Sorghum bicolor L. germination tests confirmed the complete detoxification of the post-adsorption filtrates (up to 100% germination capacity), while the Mn(II)-loaded lignin completely suppressed seed germination (0%), proving secure metal immobilization. These findings establish raw Sarkanda grass lignin as an efficient, scalable, and ecologically sustainable biosorbent for heavy metal remediation. Full article

Bird strikes are a key threat to aircraft wing leading edges. This investigation evaluates a honeycomb block reinforcement concept to improve bird strike resistance while maintaining structural efficiency. A validated simulation was developed using an explicit dynamic finite element approach, in which the bird was modeled as a soft body using smoothed particle hydrodynamics, and the wing leading edge was represented with a honeycomb block reinforcement concept. A design of experiments based on McKay Latin hypercube sampling was applied to comprehensively examine the effects of the geometric parameters on the maximum von Mises stress and maximum deformation. Response surface regression models were then constructed to approximate the impact responses and analyze the model correctness. These models were subsequently integrated into a constrained optimization methodology using sequential quadratic programming and population-based integrated learning to minimize deformation while limiting stress below the material yield threshold. The optimized honeycomb and skin configuration demonstrated a noticeable optimization of the maximum deformation within the yield stress limit compared with the baseline design. The results confirm that the proposed honeycomb block reinforcement concept, combined with a regression-based optimization strategy, constitutes a practical, computationally effective approach to improving bird strike resistance and provides a feasible design option for future impact-resistant wing leading-edge designs. Full article

Sarcopenia and glucocorticoid-induced myopathy are significant forms of muscle atrophy that pose considerable public health challenges. In this regard, preventing muscle atrophy is crucial for enhancing quality of life and increasing life expectancy. In this study, we investigated the effect of recombinant human KAI1 (rhKAI1) on myogenic differentiation and its protective effect against dexamethasone-induced muscle atrophy. rhKAI1 enhanced myogenic differentiation in both murine C2C12 myoblasts and primary human endometrial stromal cells, as evidenced by upregulation of myogenic regulatory factors and increased myotube formation. These effects were accompanied by increased phosphorylation of Akt and AMPK. In a dexamethasone (Dex)-induced atrophy model, rhKAI1 increased myotube diameter, restored MyHC expression, and reduced the expression of the E3 ligase atrogin-1, accompanied by increased phosphorylation of Akt and AMPK. In addition, rhKAI1 administration improved Dex-induced functional impairment in mice, as reflected by increased grip strength and improved rotarod performance. Molecular analyses further showed that rhKAI1 modulated Dex-induced fiber-type-related gene expression by restoring MYH7 (type I) and reducing MYH4 (type IIb) expression. Collectively, our findings demonstrate that rhKAI1 promotes myogenic differentiation and alleviates several functional and molecular features associated with glucocorticoid-induced muscle deterioration. These results support the potential of rhKAI1 as a candidate molecule for further investigation in steroid-induced muscle dysfunction. Full article

Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara Kai^TM wearable controller and a convolutional neural network (CNN). Unlike the system based on fixed, predefined gestures, the proposed approach enables users to define and train their own gesture sets. During gesture recording, users may either select a gesture pattern from a predefined prompt set or create their own natural, unprompted gestures. A dedicated software framework was developed for data acquisition, preprocessing, model training, and real-time recognition. The developed system was evaluated by optimizing the parameters of a lightweight CNN and examining the influence of sequentially applied changes to the input and network pipelines, including resizing the input layer, applying data augmentation, experimenting with different dropout ratios, and varying the number of learning samples. The performance of the resulting network setup was assessed using confusion matrices, accuracy, and precision metrics for both original gestures and gestures smoothed using the cubic Bézier function. The resulting validation accuracy ranged from 0.88 to 0.94, with an average test-set accuracy of 0.92 and macro precision of 0.92. The system’s resilience to rapid or casual gestures was also evaluated using the receiver operating characteristic (ROC) method, achieving an Area Under the Curve (AUC) of 0.97. The results demonstrate that the proposed approach achieves high recognition accuracy, indicating its potential for a range of practical applications. Full article

In the present study three composite materials based on iron in combination with bismuth, copper or lithium carbonates FeNO₃@Li₂CO₃ (SFL), FeNO₃@CuCO₃ (SFC), and FeNO₃@(BiO)₂CO₃ (SFB) were synthesized by coprecipitation. The purpose was to obtain materials that possess targeted adsorbent properties for the recovery of silver ions from aqueous solutions. After synthesis, to emphasize the adsorptive qualities of materials for the recovery of silver ions, the synthesized composite materials, as well as those doped with silver ions following the adsorption process (SFL-Ag, SFC-Ag, and SFB-Ag), were characterized and several adsorption-specific parameters were examined, including temperature, contact time, pH, adsorbent dose, and the initial concentration of silver ions in solution. Subsequently, the ideal adsorption conditions were determined to be as follows: pH > 4, contact time 60 min, temperature 298 K, and solid–liquid ratio (S–L) of 0.1 g of adsorbent to 25 mL of Ag (I) solution for all three materials. The Langmuir model properly fits the experimental equilibrium data of the adsorption process; however, the Ho–McKay model closely represents the adsorption kinetics. The maximum adsorption capacities of the materials, 19.7 mg Ag(I)/g for SFC, 19.3 mg Ag(I)/g for SFB, and 19.9 mg Ag(I)/g for SFL, are comparable. The adsorption mechanism is physical in nature, as evidenced by the activation energies of 1.6 kJ/mol for SFC, 4.15 kJ/mol for SFB, and 1.32 kJ/mol for SFL. The highest Ag(I) concentration used for doping all three materials in the study was 150 mg Ag(I)/L. The process is endothermic, spontaneous, and takes place at the interface between the adsorbent and the adsorbate, according to thermodynamic theory. Subsequently, the antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans microorganisms was evaluated by rate of inhibition assessment. The SFC-Ag material showed a percentage of 100% inhibition with respect to the positive control for each microorganism. All synthetized materials have better efficiency as antifungal agents. Full article

This research is an in-depth qualitative case study of a historic homestead in the town of Čekiškė, located in Lithuania, through the lens of sustainability aesthetics and cultural ecology. The research addresses a gap in the literature where aesthetic expressions of sustainability are predominantly examined in urban settings, while rural hybrid environments, intertwining urban and traditional features, remain underexplored. The homestead, with architectural and landscape features dating back to the early 20th century, was studied across four temporal stages: the interwar period (1922–1946), the early Soviet period (1946–1976), late Soviet to post-independence (1976–2021), and the period of a proposed vision for its sustainable development (2025 and beyond). The theoretical framework developed and applied in this research combines four complementary approaches: (1) the cultural ecology model by J. Steward, (2) environmental ethics trends (egocentrism, homocentrism, biocentrism, ecocentrism), (3) the principles of biophilic design, and (4) the ecological aesthetics framework by M. DeKay. Data collection included continuous qualitative in-depth on-site observations, analysis of the relevant literature sources, archival documents and photographs, and the recording of information in photographs and drawings. The findings reveal nuanced and evolving aesthetic expressions of sustainability tied to cultural practices, land use, ownership attitudes, and environmental perception. While earlier periods of development of the analyzed homestead reflected utilitarian and homocentric relations with the environment, later stages showed increased detachment from ecological sensitivity, resulting in the degradation of both material and intangible heritage; future perspectives of the homestead being transformed into a private museum, actualizing heritage through sustainability aesthetics, were also presented. The study highlights the role of tacit knowledge and lived experience in shaping hybrid sustainable aesthetics and provides insights for design and landscape planning in rural and small town heritage contexts. The research reveals that sustainability aesthetics in rural hybrid spaces is shaped by a confluence of environmental adaptation, socio-cultural transitions, and embedded values. It argues for a more context-sensitive and historically aware approach to sustainability discourse, particularly in heritage conservation and rural development. Full article

10-Hydroxy-2-decenoic acid (10-HDA), a major fatty acid (FA) component of royal jelly, is synthesized in the mandibular glands (MGs) of worker honeybees. Despite its well-documented nutritional and therapeutic significance, the biosynthetic pathway and regulatory mechanisms of 10-HDA production remain largely unresolved. In this study, the molecular basis of 10-HDA biosynthesis and regulation in the MGs of newly emerged bees (NEBs), nurse bees (NBs), and forager bees (FBs) were investigated using RNA sequencing and weighted gene co-expression network analysis (WGCNA). A five-step biosynthetic pathway for 10-HDA was proposed, and cross-species analysis of Apis mellifera and A. cerana revealed the conserved expression patterns of 15 key enzymes involved. Functional validation via RNA interference (RNAi) demonstrated that knockdown of acyl-CoA Delta(11) desaturase (d11ds, LOC551527), a key enzyme in FA desaturation, led to a 50% reduction in 10-HDA levels. Protein–protein interaction (PPI) network analysis further identified transcriptional regulators Kay and Drep-2 as potential modulators of 10-HDA metabolism. This study provides the first comprehensive mechanistic model of 10-HDA biosynthesis in honeybee MGs and highlights the labor-specific regulation of FA metabolism. These findings offer promising genetic targets for improving the royal jelly quality through genetic technology. Full article

Accurate prediction of jacking force is crucial for long-distance pipe jacking construction. This study establishes a theoretical jacking force model considering the soil pressure arch effect, where the Persson contact model and the hydrodynamic parallel plate model are incorporated to accurately characterize pipe–soil friction and lubrication resistance, resulting in more reliable friction force estimation as confirmed by field measurements. To enhance prediction accuracy, a Physics-Informed Domain Adversarial Training Neural Network (PIDANN) is proposed, integrating physical constraints into neural network training and expanding the dataset with theoretical values. A case study on the Zheng-Kai city water supply project demonstrates that (1) the theoretical model improves friction force predictions by incorporating the pressure arch effect; (2) PIDANN achieves superior prediction accuracy compared to models without adversarial training; (3) the two fusion methods of physical information integration reduce the mean squared error (MSE) by 36.9% and 20.2%, enhancing generalization and reducing overfitting risks. These findings provide valuable guidance for jacking force control in pipe jacking construction. Full article

Effective pretreatment is essential for achieving long-term stable operation and high water recovery during the desalination of alternative waters. This study developed a process modeling approach for technical, economic, energetic, and environmental assessments of pretreatment technologies to identify the impacts of each technology treating brackish water desalination brine with high scaling propensity. The model simulations evaluated individual pretreatment technologies, including chemical softening (CS), chemical coagulation (CC), electrocoagulation (EC), and ion exchange (IX). In addition, combinations of these pretreatment technologies aiming at the effective reduction of key scaling constituents such as hardness and silica were investigated. The three evaluation parameters in this assessment consist of levelized cost of water (LCOW, $/m³), specific energy consumption and cumulative energy demand (SEC|CED, kWh/m³), and carbon dioxide emissions (CO₂, kg CO_2-eq/m³). The case study evaluated in this work was the desalination brine from the Kay Bailey Hutchison Desalination Plant (KBHDP) with a total dissolved solids (TDS) concentration of 11,000 mg/L and rich in hardness and silica. The evaluation of individual pretreatment units from the highest to lowest LCOW, SEC|CED, and CO₂ emissions in the KBHDP brine was IX > CS > EC > CC, CS > IX > EC > CC, and CC > CS > EC > IX, respectively. In the case of pretreatment combinations for the KBHDP, the EC + IX treatment combination was shown to be the best in terms of the LCOW and CO₂ emissions. The modeling and evaluation of these pretreatment units provide valuable guidance on the selection of cost-effective, energy-efficient, and environmentally sustainable pretreatment technologies tailored to desalination brine applications for minimal- or zero-liquid discharge. Full article

In response to the growing demand for advanced nuclear reactor technologies, this study addresses significant gaps in thermal-hydraulic modelling for dual fluid reactors (DFRs) by integrating Kays correlation to implement a variable turbulent Prandtl number in the Reynolds-averaged Navier–Stokes (RANS) simulations. Traditional approaches employing a constant value of the turbulent Prandtl number have proven inadequate, leading to inaccurate heat transfer predictions for low Prandtl number liquids. The study carefully selects the appropriate formula for the turbulent Prandtl number in the DFR context, enhancing the accuracy of thermal-hydraulic modelling. The simulations consider Reynolds numbers between 15,000 and 250,000, calculated based on the hydraulic diameters at different diameter pipes of the fuel and coolant loops. The molecular Prandtl number is equal to 0.025. Key findings reveal that uneven flow distributions within the fuel pipes result in variable temperature distribution throughout the reactor core, confirming earlier observations while highlighting significant differences in parameter values. These insights underscore the importance of model selection in CFD analysis for DFRs, revealing potential hotspots and high turbulence areas that necessitate further investigation into vibration and structural safety. The results provide a framework for improving reactor design and operational strategies, ensuring enhanced safety and efficiency in next-generation nuclear systems. Future work will apply this modelling approach to more complex geometries and flow scenarios to optimise thermal-hydraulic performance. Full article

Survival in desert ecosystems poses significant challenges for plants due to harsh conditions. Plant microbiomes are thought to promote resilience; however, whether plant hormones, specifically strigolactones (SLs) and karrikins (KARs), shape plant microbiomes remains unknown. The recruitment of root-associated microbiomes in Nicotiana attenuata, a model desert plant, silenced in specific genes associated with SL biosynthesis (CCD7) and perception (D14), karrikin perception (KAI2), and in the shared receptor (MAX2), required for both pathways, was studied. SL and KAR signaling, with MAX2 as a co-regulator, fine-tuned the assembly of root-associated microbiomes, with unique and shared regulatory functions on bacterial microbiome recruitment, particularly in taproot. Significant variation among the different plant genotypes in bacterial diversity and composition in taproot and lateral roots provides a foundation for future research to explore how microbiomes function in plant resilience in these harsh environments. Full article

In the post-COVID-19 era, education requires teachers to engage learners across diverse learning environments (at school or other formal institutions, at home, outdoors, or in virtual environments) using innovative learning strategies. To meet these challenges, teachers must upskill their creativity and strengthen their pedagogical digital competencies and knowledge management skills. This study introduces the innovative teacher training and support (TTS-IPCD) model to enhance teachers’ creativity and pedagogical digital competencies. This research involved a sample of 350 teachers from 75 primary and secondary schools over a four-year period. Teachers’ creativity was measured using the Kirton Adaption–Innovation Inventory (KAI), assessing key metrics such as problem-solving flexibility, openness to change, and inclination toward novel approaches. Quantitative analysis was conducted using an independent samples t-test to evaluate teacher creativity changes. The results indicated that the TTS-IPCD model enhanced teacher creativity in the direction of a stronger propensity toward innovative behaviors, including embracing diversity and change in their work, solving problems through novel approaches, and adopting a holistic perspective rather than strictly adhering to established routines. Furthermore, the TTS-IPCD model improved teamwork and collaboration, contributing to the development of more adaptive and innovative learning environments. These findings highlight the importance of continuous professional development of teachers focused on creative pedagogy and digital competencies to equip teachers for the evolving educational landscape. Full article

Demyelination and axonal degeneration are fundamental pathological characteristics of multiple sclerosis (MS), an inflammatory disease of the central nervous system (CNS). Although the molecular mechanisms driving these processes are not fully understood, hepatocyte growth factor (HGF) has emerged as a potential regulator of neuroinflammation and tissue protection in MS. Elevated HGF levels have been reported in MS patients receiving immunomodulatory therapy, indicating its relevance in disease modulation. This study investigated HGF’s neuroprotective effects using transgenic mice that overexpressed HGF. The experimental autoimmune encephalomyelitis (EAE) model, which mimics MS pathology, was employed to assess demyelination and axonal damage in the CNS. HGF transgenic mice showed delayed EAE progression, with reduced CNS inflammation, decreased demyelination, and limited axonal degeneration. Scanning electron microscopy confirmed the preservation of myelin and axonal integrity in these mice. In addition, we explored HGF’s effects using a cuprizone-induced demyelination model, which operates independently of the immune system. HGF transgenic mice exhibited significant protection against demyelination in this model as well. We also investigated the expression of key HGF receptors, particularly c-Met and KAI-1. While c-Met, which is associated with increased inflammation, was upregulated in EAE, its expression was significantly reduced in HGF transgenic mice, correlating with decreased neuroinflammation. Conversely, KAI-1, which has been linked to axonal protection and stability, showed enhanced expression in HGF transgenic mice, suggesting a protective mechanism against axonal degeneration. These findings underscore HGF’s potential in preserving CNS structure and function, suggesting it may be a promising therapeutic target for MS, offering new hope for mitigating disease progression and enhancing neuroprotection. Full article

Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese character stroke extraction and recognition, as well as unclear segmentation. This study builds upon the YOLOv8n-seg model to propose the YOLOv8n-seg-CAA-BiFPN Chinese character stroke fine segmentation model. The proposed Coordinate-Aware Attention mechanism (CAA) divides the backbone network input feature map into four parts, applying different weights for horizontal, vertical, and channel attention to compute and fuse key information, thus capturing the contextual regularity of closely arranged stroke positions. The network’s neck integrates an enhanced weighted bi-directional feature pyramid network (BiFPN), enhancing the fusion effect for features of strokes of various sizes. The Shape-IoU loss function is adopted in place of the traditional CIoU loss function, focusing on the shape and scale of stroke bounding boxes to optimize the bounding box regression process. Finally, the Grad-CAM++ technique is used to generate heatmaps of segmentation predictions, facilitating the visualization of effective features and a deeper understanding of the model’s focus areas. Trained and tested on the public Chinese character stroke datasets CCSE-Kai and CCSE-HW, the model achieves an average accuracy of 84.71%, an average recall rate of 83.65%, and a mean average precision of 80.11%. Compared to the original YOLOv8n-seg and existing mainstream segmentation models like SegFormer, BiSeNetV2, and Mask R-CNN, the average accuracy improved by 3.50%, 4.35%, 10.56%, and 22.05%, respectively; the average recall rates improved by 4.42%, 9.32%, 15.64%, and 24.92%, respectively; and the mean average precision improved by 3.11%, 4.15%, 8.02%, and 19.33%, respectively. The results demonstrate that the YOLOv8n-seg-CAA-BiFPN network can accurately achieve Chinese character stroke segmentation. Full article