Search Results (72)

Background/Objectives: Glioblastoma (GBM) remains the most aggressive primary brain tumor, characterized by high malignancy, recurrence rate, and dismal prognosis, thereby demanding innovative therapeutic strategies. In this study, we report a novel in situ targeting inclusion complex hydrogel hybrid system (DOX/RGD-CD@Gel) that integrates doxorubicin (DOX) with RGD-conjugated cyclodextrin (RGD-CD) and a thermosensitive hydrogel for enhanced GBM therapy. Methods: The DOX/RGD-CD@Gel system was prepared by conjugating doxorubicin (DOX) with RGD-modified cyclodextrin (RGD-CD) and embedding it into a thermosensitive hydrogel. The drug delivery and antitumor efficacy of this system were evaluated in vitro and in vivo. Results: In vitro and in vivo evaluations demonstrated that DOX/RGD-CD@Gel significantly enhanced cytotoxicity compared to free DOX or DOX/CD formulations. The targeted delivery system effectively promoted apoptosis and inhibited cell proliferation and metastasis in GBM cells. Moreover, the hydrogel-based system exhibited prolonged drug retention in the brain, as evidenced by its temperature- and pH-responsive release characteristics. In a GBM mouse model, DOX/RGD-CD@Gel significantly suppressed tumor growth and improved survival rates. Conclusions: This study presents a paradigm of integrating a targeted inclusion complex with a thermosensitive hydrogel, offering a safe and efficacious strategy for localized GBM therapy with potential translational value. Full article

Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection and early warning by utilizing the in situ monitoring data from a wind farm. This comprehensive architecture involves five modules: data preprocessing, multi-dimensional spatial feature extraction, temporal dependency modeling, global relationship learning, and hyperparameter optimization. It was achieved by using real-time monitoring data to predict the GHSS temperature in 10 min, with an accuracy of 1 °C. Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. Furthermore, the architecture establishes a normal operating condition model and provides benchmark eigenvalues for subsequent fault warnings. The model was validated to issue early warnings up to seven hours before the fault alert is triggered by the supervisory control and data acquisition system of the wind turbine. By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention. Full article

Pneumonia is a severe lower respiratory tract infection. This study demonstrates that phospholipase A2 (PLA2), a potential biomarker for pneumonia, contributes to alveoli damage by hydrolyzing pulmonary surfactant phospholipids. This process impairs gas exchange and generates hemolytic phospholipids that disrupt cellular membranes, exacerbating pulmonary injury. Experimental evidence demonstrates that PLA2 inhibitors significantly alleviate cellular damage in lipopolysaccharide (LPS)-induced pulmonary inflammation. These findings reveal a key mechanistic role of PLA2 in pneumonia pathogenesis and suggest novel therapeutic strategies. The results may provide more effective clinical interventions and guide further research in related fields. Full article

Anthropogenic revegetation allows effective and timely soil development in mine restoration areas. The evaluation of soil quality is one of the most important criteria for measuring reclamation effectiveness, providing scientific reference for the subsequent management of ecological restoration projects. The aim of this research was to further investigate the influence of revegetation on mine-reclaimed soils in a semi-arid region. Thus, a coal-gangue dump within the afforestation chronosequence of 1 and 19 years in Shanxi Province, China, was selected as the study area. We assessed the physicochemical properties and nutrient stock of topsoils under four revegetation species, i.e., Pinus tabuliformis (PT), Medicago sativa (MS), Styphnolobium japonicum (SJ), and Robinia pseudoacacia ‘Idaho’ (RP). A two-way ANOVA revealed that reclamation age significantly affected SOC, TN, EC, moisture, and BD (p < 0.05), while the interaction effects of revegetation type and age were also significant for TN and moisture. In addition, SOC and TN stocks at 0–30 cm topsoil at the RP site performed the best among 19-year reclaimed sites, with an accumulation of 62.09 t ha⁻¹ and 4.23 t ha⁻¹, respectively. After one year of restoration, the MS site showed the highest level of SOC and TN accumulation, which increased by 186.8% and 88.5%, respectively, compared to bare soil in the 0–30 cm interval, but exhibited declining stocks during the 19-year restoration, possibly due to species invasion and water stress. In addition, an integrated soil quality index (ISQI) and the partial least squares structural equation model (PLS-SEM) were used to estimate comprehensive soil quality along with the interrelationship among influencing factors. The reclaimed sites with an ISQI value > 0 were 19-RP (3.906) and 19-SJ (0.165). In conclusion, the restoration effect of the PR site after 19 years of remediation was the most pronounced, with soil quality approaching that of the undisturbed site, especially in terms of soil carbon and nitrogen accumulation. These findings clearly revealed the soil dynamics after afforestation, further providing a scientific basis for choosing mining reclamation species in the semi-arid regions. Full article

Fritillaria thunbergii Miq., a medicinal plant rich in steroidal alkaloids, produces bulbs that clear heat, resolve phlegm, and detoxify. However, excessive yield-oriented cultivation has reduced the number of F. thunbergii plants that meet commercial standards. This study explored the effects of potassium application and shading on the bulb biomass and medicinal substance content of F. thunbergii. Shading increased the active ingredient content in bulbs by approximately 20.71% but reduced biomass by approximately 17.24%. Fertilization with different potassium concentrations under shading (K1S–K3S) alleviated shading-induced biomass reduction and increased active ingredient accumulation, with the K2S and K3S groups yielding significantly better results than the K1S group. Pharmacological experiments showed that the K2S group exerted the best antitussive, expectorant, and anti-inflammatory effects. Metabolome analysis showed that compared with those in the controls, peiminine, peimine, imperialine, solasodine, and cyclopamine were the most abundant steroidal alkaloids under K2S treatment. Transcriptome analysis identified key genes and biosynthetic pathways for major steroidal alkaloids, namely, farnesyl pyrophosphate synthase (FtFPS) involved in steroidal alkaloid biosynthesis. Transcription factor analysis revealed that nine transcription factors predominantly expressed under the K2S treatment might regulate steroidal alkaloid biosynthesis. Furthermore, FtFPS was identified as a hub gene in the co-expression network and was verified to catalyze the biosynthesis of farnesyl pyrophosphate. The interaction between FtFPS and FtAP2/ERF was verified through yeast two-hybrid experiments. These findings offer new insights into the steroidal alkaloid biosynthesis mechanism triggered in F. thunbergii by potassium application and shading, supporting ecological strategies to enhance steroidal alkaloid levels in this species. Full article

Ensuring secure and efficient water level monitoring is critical for the intelligent management of hydropower plants, especially in challenging indoor environments. Existing methods, which are tailored for open areas with optimal conditions (adequate lighting, absence of debris interference, etc.), frequently falter in scenarios characterized by poor lighting, water vapor, and confined spaces. To address this challenge, this study introduces a robust indoor water level monitoring framework specifically for hydropower plants. This framework integrates a temporal super-resolution technique with an improved Yolov5 model. Specifically, to enhance the quality of indoor monitoring images, we propose a temporal super-resolution enhancement module. This module processes low-resolution water-level images to generate high-resolution outputs, thereby enabling reliable detection even in suboptimal conditions. Furthermore, unlike existing complex model-based approaches, our enhanced, lightweight Yolov5 model, featuring a small-scale feature mapping branch, ensures real-time monitoring and accurate detection across a variety of conditions, including daytime, nighttime, misty conditions, and wet surfaces. Experimental evaluations demonstrate the framework’s high accuracy, reliability, and operational efficiency, with recognition speeds reaching O(n). This approach is suitable for deployment in emerging intelligent systems, such as HT-for-Web analysis software 0.2.3 and warning platforms, providing vital support for hydropower plant security and emergency management. Full article

In order to study the influence of multi-cycle stress sensitivity on the injection–production effect, it is necessary to conduct multi-cycle stress sensitivity experiments on reservoir permeability and fracture conductivity first and then calculate the impact on the injection–production effect after the occurrence of the stress sensitivity effect by using the CMG software. After stress sensitivity occurs and the production rate decreases, the constraints of the well should be adjusted. The results showed that the conductivity of the 30–50 mesh ceramsite decreased by 15.94% after 100 cycles, while the conductivity of the 20–40 mesh quartz sand decreased by 51.17%. Under alternating stress, the reservoir permeability decreased significantly during the first 50 cycles, with an average decrease of 20.8%, but remained relatively stable in the later stages. When stress sensitivity was disregarded, the gas production rate of the ceramic and quartz sand stabilized at approximately 3700 m³/h and 2600 m³/h, respectively. When stress sensitivity was considered, the secondary gas cushion for ceramsite had to reach at least 500,000 m³ to maintain a gas production rate of over 3700 m³/h within 40 cycles after the gas cushion. When stress sensitivity was considered, the secondary gas cushion for quartz sand had to exceed 800,000 cubic meters to maintain the gas production rate of over 2600 m³/h within the first 30 cycles after the gas cushion. To sustain the gas production rate over the long term, it was necessary to increase the injection pressure per cycle. The gas injection pressure for ceramsite should be adjusted to more than 17 MPa, and the gas injection pressure for quartz sand should be adjusted to more than 19.3 MPa. Full article

In urban gas network leakage monitoring, the optimized placement of sensors plays a pivotal role in ensuring public safety and minimizing system maintenance costs. This study introduces an innovative approach that integrates hierarchical clustering with ant colony optimization (ACO) to optimize sensor layouts in urban gas networks. The hierarchical clustering technique is first employed to evaluate the strategic importance of each monitoring node, which subsequently influences the pheromone importance parameter in the ACO algorithm. Furthermore, the proposed method accounts for soil types and gas diffusion characteristics, which affect the pheromone concentration gradient, as well as the physical distances between nodes, which determine the heuristic factors in the algorithm. By finely tuning these parameters, the method achieves a significant reduction in the number of sensors required while ensuring comprehensive network coverage, thereby improving economic and operational efficiency. The optimized sensor layout not only accelerates the response to gas leaks but also enhances the system’s adaptability to complex urban environments. Simulation and field test results validate the effectiveness of this optimization approach, demonstrating its practical value in advancing the safety management of urban gas networks. Full article

During gas leak detection and risk monitoring of commercial sealed areas, different types of sensors are deployed to monitor leak signals. The arrangement of a limited number of sensors in the most strategic positions, solving the problem of optimal sensor placement, is key to improving detection efficiency. Aiming at gas leak detection in commercial areas, this paper proposes a sensor placement methodology based on a particle swarm optimization algorithm to determine the optimal number and position of sensors. First, Bayesian networks assess the gas leak risk levels. Second, a discrete optimization model for sensor placement is established. Finally, the particle swarm optimization algorithm is applied to calculate the optimal sensor placement solution. In the iterative process, partial differential equation models simulate gas diffusion paths to verify the effectiveness of the sensor layout, followed by computational fluid dynamics simulations for further validation of the optimization results. The simulation case of a commercial gas system demonstrates that the proposed method achieves fast convergence and significant optimization results. A real case study shows that the method reduces the number of sensors and data redundancy. Compared with traditional methods, the robustness and efficiency of the system under the optimal solution are significantly improved. Full article

This study investigated the structural response characteristics of a novel single-point mooring gravity-type deep-water (SPM-GDW) net cage under irregular waves and currents. A hydrodynamic numerical model of the cage was created and validated through model experiments. Based on the validated cage model, the structural response characteristics such as cage motion response, mooring line forces, and floating collar stress were studied, considering the actual operating conditions in the target sea area. The response time history curves, wave height time history, and spectral density statistics were studied and compared. The results showed that the heave motion of the cage was consistent with wave elevation in the vertical direction and mainly influenced by wave conditions. The surge motion of the cage was closely related to the current, with a significant lag effect compared to wave elevation motion. Low-frequency loads under the combined action of waves and currents had a significant impact on the surge motion of the cage. In addition, the mooring line tension and pontoon stress were closely related to the wave elevation, with peak values of tension and stress occurring almost simultaneously with the peak wave elevation. However, the pontoon stress exhibited high-frequency response characteristics while satisfying the wave frequency response trend. It was found that the flow velocity had a significant impact on the spectral density of mooring line tension and pontoon stress in the low-frequency range, with an increase in spectral density values as the flow velocity increased. The structural response characteristics identified in this study provide a computational basis for the optimized design and analysis of single-point mooring gravity-type deep-water cages. Full article

Climate change and water scarcity are two global challenges. Coal mining is the main source of carbon emissions. The utilization of mine water resources and its carbon footprint calculation are of paramount significance in promoting water conservation and carbon reduction in mining areas. However, research on the carbon footprint and other environmental indicators across the life cycle of mine water in developing countries, such as China, remains limited. This study focuses on a representative mine water resource utilization system in China and describes the method used to calculate carbon emissions associated with mine water resource utilization throughout its life cycle. Based on life cycle assessment (LCA) and using on-site investigations and analysis of environmental indicators, the study evaluates the environmental impacts at different stages of mine water resource utilization, identifies key processes, and provides some improvement suggestions. The research results indicate that the life cycle carbon emissions of mine water amount to 2.35 kg CO₂ eq per 1 m³. The water extraction stage highlights the potential environmental impact, including water use (WU) and ozone depletion potential (ODP). By substituting traditional power generation methods and incorporating intelligent dosing equipment to optimize chemical usage, the global warming potential (GWP) has been decreased by over 90%, and the GWP of chemical consumption has also witnessed respective reductions of 21.5% and 10.1%. This study can serve as a basis for calculating carbon emissions in mining areas and formulating strategies to reduce their environmental impact. Full article

The infrared image colorization technique overcomes the limitation of grayscale characteristics of infrared images and achieves cross-modal conversion between infrared and visible images. Aiming at the problem of lack of infrared-visible pairing data, existing studies usually adopt unsupervised learning methods based on contrastive loss. Due to significant differences between modalities, reliance on contrastive loss alone hampers the learning of accurate semantic features. In this paper, we propose DC-Net, which is a dual-branch contrastive learning network that combines perceptual features and multiscale residual attention for the unsupervised cross-modal transformation of infrared to visible images. The network comprises a patch-wise contrastive guidance branch (PwCGB) and a perceptual contrastive guidance branch (PCGB). PwCGB focuses on discerning feature similarities and variances across image patches, synergizing patch-wise contrastive loss with adversarial loss to adaptively learn local structure and texture. In addition, we design a multiscale residual attention generator to capture richer features and adaptively integrate multiscale information. PCGB introduces a novel perceptual contrastive loss that uses perceptual features from pre-trained VGG16 models as positive and negative samples. This helps the network align colorized infrared images with visible images in the high-level feature space, improving the semantic accuracy of the colorized infrared images. Our unsupervised infrared image colorization method achieves a PSNR of 16.833 and an SSIM of 0.584 on the thermal infrared dataset and a PSNR of 18.828 and an SSIM of 0.685 on the near-infrared dataset. Compared to existing algorithms, it demonstrates substantial improvements across all metrics, validating its effectiveness. Full article

Near-natural transformation can convert artificial monoculture forests into mixed forests with diverse ages, multi-layered structures, and enhanced ecological functions. This transformation optimizes stand structure, improves soil physical and chemical properties, and enhances stand productivity and species diversity. This study aimed to explore the relationship between the underground roots and aboveground growth of Pinus tabuliformis and Populus tomentosa under conditions of nutrient heterogeneity, with the goal of advancing plantation transformation. This research focused on 1-year-old Populus tomentosa and 5-year-old Pinus tabuliformis, employing two planting densities (25 cm and 50 cm) and three fertilization levels, low (50 g·m⁻²), medium (100 g·m⁻²), and high (200 g·m⁻²), using Stanley Potassium sulfate complex fertilizer (N:P:K = 15:15:15). Each treatment had three replicates, resulting in a total of nine experimental groups, all planted in circular plots with a radius of 1 m. Standard major axis (SMA) regression was used to analyze the allometric relationship between underground fine root biomass and aboveground organ biomass. This study further explored correlations between fine root length, root surface area, volume, biomass, and aboveground biomass, culminating in a mixed-effects model. The mixed-effects model quantified the relationships between underground roots and aboveground growth in varying soil nutrient environments. The results indicated optimal root growth in Populus tomentosa and Pinus tabuliformis, characterized by maximum root length, surface area, and volume, under conditions of 200 g·m⁻² soil nutrient concentration and 50 cm planting distance; Populus tomentosa fine roots had a vertical center at a depth of 8.5 cm, whereas Pinus tabuliformis roots were centered at depths of 5–7.5 cm, indicating differing competitive strategies. Pinus tabuliformis exhibited competitive superiority in the soil’s surface layer, in contrast to Populus tomentosa, which thrived in deeper layers. The study of the allometric growth model revealed that under conditions where the nutrient gradient was 200 g·m⁻² and the planting distance was 25 cm, Populus tomentosa demonstrated its highest allometric growth index (2.801), indicative of positive allometric growth. Furthermore, there was a notable inclination of resource allocation towards the aboveground, which enhances the accumulation of aboveground biomass. The mixed-effects model equation showed a clear linear relationship between underground roots and aboveground biomass. The final fitting coefficient of the model was high, providing a robust theoretical basis for future management practices. The mixed-effects model revealed the following hierarchy of fixed-effect coefficients for root system characteristics affecting aboveground biomass: fine root volume (132.11) > fine root biomass (6.462) > root surface area (−4.053) > fine root length (0.201). In subsequent plantation reconstruction and forest management, increasing soil fertility and planting distance can promote the growth of underground roots and biomass accumulation. Appropriately increasing soil fertility and reducing planting distance can effectively promote aboveground biomass accumulation, achieving sustainable forest development. Full article

Introduction: The biokinetics of radioiodine (RAI) in thyroid cancer patients are complex. This study aims to develop a practical approach for assessing RAI biokinetics to predict patient discharge time and estimate radiation exposure to caregivers. Methods: We retrospectively reviewed data from patients with differentiated thyroid carcinoma undergoing RAI treatment. Serial radiation dose rates were dynamically collected during hospitalization and fitted to a biexponential model to assess the biokinetic features: RAI uptake fraction of thyroid tissue (F_t) and effective half-life of extra-thyroid tissue (T_et). Correlations with ^99mTc thyroid uptake ratio (TcUR), radiation retention ratio (RR), renal function, and body mass index (BMI) were analyzed. Results: Thirty-five patients were enrolled. The derived F_t was 0.08 ± 0.06 and T_et was 7.57 ± 1.45 h. Pearson’s correlation analysis revealed a significant association between F_t and both TcUR and RR (p < 0.05), while T_et correlated with renal function and BMI (p < 0.05). Conclusion: This novel and practical method assessing RAI biokinetics demonstrates consistency with other parameters and related studies, enhancing the model reliability. It shows promise in predicting an appropriate discharge time and estimating radiation exposure to caregivers, allowing for modifications to radiation protection precautions to follow ALARA principle and minimize the potential risks from radiation exposure. Full article

Biosensors based on allosteric transcription factors have been widely used in synthetic biology. In this study, we utilized the Acinetobacter ADP1 transcription factor PobR to develop a biosensor activating the PpobA promoter when bound to its natural ligand, 4-hydroxybenzoic acid (4HB). To screen for PobR mutants responsive to 4-hydroxyphenylpyruvate(HPP), we developed a dual selection system in E. coli. The positive selection of this system was used to enrich PobR mutants that identified the required ligands. The following negative selection eliminated or weakened PobR mutants that still responded to 4HB. Directed evolution of the PobR library resulted in a variant where PobR^W177R was 5.1 times more reactive to 4-hydroxyphenylpyruvate than PobR^WT. Overall, we developed an efficient dual selection system for directed evolution of biosensors. Full article