Search Results (1,563)

Inflammatory bowel disease (IBD), a chronic inflammatory disorder with relapsing/remitting characteristics, lacks reliable non-invasive biomarkers for accurate diagnosis and longitudinal monitoring. This study explored salivary exosomal miRNAs as potential biomarkers to address this unmet clinical need. Using discovery (24 IBD patients [11 active, 13 remission] and 6 healthy controls [HCs]) and validation cohorts (102 IBD patients [53 active, 49 remission] and 18 HCs), we analyzed miRNA profiles via reverse transcription quantitative PCR (RT-qPCR). Receiver operating characteristic (ROC) curves evaluated diagnostic performance, with area under the curve (AUC) quantifying discriminatory capacity. Initial screening revealed 23 miRNAs significantly upregulated in IBD salivary exosomes. An 8-miRNA signature distinguished IBD patients from HCs in validation analyses, with five miRNAs (hsa-miR-1246, hsa-miR-142-3p, hsa-miR-16-5p, hsa-miR-301a-3p, and hsa-miR-4516) showing strong correlations with disease activity. The combination of hsa-miR-16-5p and hsa-miR-4516 achieved robust discrimination (AUC = 0.925 for IBD vs. HCs; AUC = 0.82 for active disease vs. remission). A composite model integrating all five miRNAs demonstrated superior performance (AUC = 1.00 for IBD/HC differentiation; AUC = 0.86 for disease activity assessment). These findings reveal dynamic associations between salivary exosomal miRNA signatures and IBD progression, underscoring their utility as non-invasive diagnostic tools. This approach enables serial sampling, enhances patient compliance, and provides actionable insights for personalized disease management, establishing salivary exosomal miRNAs as promising candidates for clinical translation in IBD care. Full article

Objectives: Deoxynivalenol (DON) is a ubiquitous mycotoxin detected in the environment and foodstuffs. DON exposure can lead to chronic intestinal inflammation. Therefore, intervention strategy needs to be established to prevent the intestinal inflammation caused by DON. Methods: The structure of Mesona chinensis Benth polysaccharide-3 (MCP-3), a major component isolated and purified from crude MCP, was analyzed using spectroscopic and chromatographic methods. In vitro assays were conducted on the potential antioxidant bioactivities of MCP-3 and its ameliorative effects on deoxynivalenol-induced oxidative stress in intestinal epithelial cells. Results: Saline-eluted MCP-3 was identified as an acidic heterogeneous polysaccharide with an average molecular weight of 16.014 kDa. Its major monosaccharide components were glucose (20.19%), galactose (11.82%), rhamnose (17.23%), galacturonic acid (29.72%), arabinose (7.11%), xylose (8.09%), mannose (2.79%), and glucuronic acid (3.04%). The main backbone of MCP-3 was composed of the following sequence: →4)-α-D-GalpA-6-(1→4)-α-GalpA-(1→4)-α-D-GalpA-6-(1→2)-α-L-Rhap-(1→4)-α-D-GalpA-6-(1→2,4)-α-L-Rhap-(1→. MCP-3 showed strong antioxidant ability in in vitro assays. It effectively prevented redox imbalance induced by the mycotoxin deoxynivalenol in intestinal epithelial cell models based on Caco-2 and NCM460 cells. MCP-3 significantly increased (p < 0.05) the activities of superoxide dismutase, glutathione peroxidase, and catalase, and significantly decreased (p < 0.05) the levels of malondialdehyde and reactive oxygen species, thereby improving redox homeostasis. Conclusions: MCP-3 has potential as a natural antioxidant for use in functional food and nutraceutical industries to help regulate intestinal oxidative stress caused by mycotoxin DON. Full article

The natural resistance-associated macrophage proteins (NRAMPs) gene family represents a group of membrane transporter proteins with wide distribution in plants. This family of membrane transporters plays a pivotal role in mediating plant responses to metal stress by coordinating ion transport processes and maintaining cellular metal homeostasis, thereby effectively mitigating the detrimental effects of metal ion stress on plant growth and development. This study conducted a comprehensive genome-wide analysis of the NRAMP gene family in A. tauschii using integrated bioinformatics approaches, as well as the expression pattern when exposed to heavy metal-induced stress. By means of phylogenetic investigation, eleven AetNRAMP proteins were categorized into five distinct subgroups. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis revealed that the majority of NRAMP genes exhibited marked differential expression patterns under specific stress treatments. Subsequently, yeast cells were employed to validate the functions of AetNRAMP1 and AetNRAMP3. It was confirmed that AetNRAMP1 functioned in copper transport, and AetNRAMP3 showed an increase in its expression level under manganese stress. These findings establish a molecular foundation for elucidating the functional specialization of NRAMP gene family members in A. tauschii’s heavy metal detoxification pathways, providing critical genetic evidence for their stress-responsive regulatory networks. Nevertheless, significant knowledge gaps persist regarding its functions in A. tauschii. Research on metal stress resistance in this wheat progenitor species may establish a theoretical foundation for enhancing wheat tolerance and developing improved cultivars. Full article

With the rapid advancement of new energy electric vehicles, high-capacity nickel-rich layered oxides have emerged as predominant cathode materials in lithium-ion battery systems. However, their widespread implementation necessitates rigorous investigation into cycling stability. We synthesized nickel-manganese-aluminum hydroxide precursors as raw materials by co-precipitation method, and synthesized ultrathin Al₂O₃-coated LiNi_0.9Mn_0.05Al_0.05O₂ cathode materials by hydrolysis reaction. The cathode material was uniformly covered by an Al₂O₃ layer with an average thickness of 5–10 nm by high resolution transmission electron microscopy (HRTEM). Electrochemical performance tests showed that the modified cathode material exhibited significantly enhanced reversible capacity, cycling stability, and rate performance, and a more favorable differential capacity curve. In particular, the LNMA-2 samples were able to maintain 90.6% and 88.3% of their initial capacity after 100 cycle tests (with cutoff voltages of 4.3 and 4.5 V, respectively) at 0.5 C charge/discharge rate. These improved electrochemical properties are mainly attributed to the advantages offered by the unique Al₂O₃ coating structure. This study provides significant theoretical value for designing and optimizing the production of high-nickel cobalt-free cathode materials with high cycling performance. Full article

Type 2 diabetes mellitus (T2DM) is recognized as a multifactorial health disorder associated with various complications. This paper presents a bibliometric analysis of type 2 diabetes mellitus and natural active substances. Currently, the research field in this area is on an upward trajectory, with major research hotspots focusing on pathogenesis, pharmacological activities, the gut microbiota, and lipid metabolism. Algae-derived natural active substances, namely astaxanthin, extracellular polysaccharide from Porphyridium cruentum (EPS-P), and β-carotene, all exhibit high antioxidant properties and safety, along with favorable hypoglycemic effects. Therefore, their therapeutic intervention effects on type 2 diabetes mellitus were evaluated through in vitro experiments. Compared with the model group, astaxanthin, β-carotene, and Porphyridium cruentum polysaccharide (EPS-P) improved various indicators by at least 24.17%, 7.7%, and 6.7%, respectively. All three substances could, to a certain extent, enhance glucose consumption, glycogen content, and pyruvate activity, as well as improve and restore the condition of IR-HepG2 cells. The order of intervention efficacy was astaxanthin, followed by β-carotene, and then Porphyridium cruentum polysaccharide (EPS-P). These findings provide a scientific basis for the biomedical applications of algae-derived natural products. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Image processing has been widely adopted as an effective technology for analyzing weld forming quality which is greatly affected by the welding process parameters. In this paper, an L₂₅(5³) orthogonal experiment is designed to investigate the effects of welding process parameters on the weld forming quality in laser welding of aluminum alloy. The weld characteristics including the weld width (WW), weld penetration (PD), weld area (WA) and weld porosity (WP) under the conditions of the different welding process parameters consisting of the laser power (LP), welding speed (WS) and defocus distance (DD) are extracted from the laser welding experiment based on image processing. The effectiveness of the weld characteristics extraction method is verified by comparing the extracted results with the measured results. It is found that the WW, PD and WA are all significantly influenced by the LP among the three welding process parameters while the influences of the three process parameters on the WP are insignificant. The DD has a significant influence on the PD and the WS has a significant influence on the WA. The corresponding significance of influence is lower than the significance of influence of LP. The analysis results are conducive to the optimization of laser welding process parameters and improvement of welding quality. Full article

Background: Gossypium hirsutum L. is one of the main economic crops worldwide, and increasing the size/weight of its seeds is a potential strategy to improve its seed-related yield. AINTEGUMENTA (ANT) is an organogenesis transcription factor mediating cell proliferation and expansion in Arabidopsis, but little is known about its candidate function in upland cotton seed. Results: In this study, functional characterization of GhANT in the cotton seed development stage was performed. The expression pattern analysis showed that GhANT was predominantly expressed in the ovules, and its expression was consistent with the ovules’ development stage. Heterologous expression of GhANT in Arabidopsis promoted plant organ growth and led to larger seeds. Importantly, specific expression of GhANT by the TFM7 promoter in the cotton ovules enlarged the seeds and increased the cotton seed yield, as compared with the wild-type in a three-year field trial. Furthermore, transcription level analysis showed that numerous genes involved in cell division were up-regulated in the ovules of TFM7::GhANT lines in comparison to the wild-type. These results indicate that GhANT is a potential genetic resource for improving cotton seed yield through its molecular links with cell cycle controllers. Full article

Optically pumped magnetometer OPM-MEG has the potential to replace the traditional low-temperature superconducting quantum interference device SQUID-MEG. Coherence analysis can be used to evaluate the functional connectivity and reflect the information transfer process between brain regions. In this paper, a finger tapping movement paradigm based on auditory cues was used to measure the functional signals of the brain using OPM-MEG, and the coherence between the primary motor cortex (M1) and the primary motor area (PM) was calculated and analyzed. The results demonstrated that the coherence of the three frequency bands of Alpha (8–13 Hz), Beta (13–30 Hz), and low Gamma (30–45 Hz) and the selected reference signal showed roughly the same position, the coherence strength and coherence range decreased from Alpha to low Gamma, and the coherence coefficient changed with time. It was inferred that the change in coherence indicated different neural patterns in the contralateral motor cortex, and these neural patterns also changed with time, thus reflecting the changes in the connection between different functional areas in the time-frequency domain. In summary, OPM-MEG has the ability to measure brain coherence during finger movements and can characterize connectivity between brain regions. Full article

This paper concentrates on the valuation of big data assets within the digital transformation of logistics enterprises. As data evolve into a core production factor in the logistics industry, their valuation is essential, not only for enterprises’ resource allocation decisions, but also as a key indicator for measuring the effectiveness of digital transformation. This paper combines the multiperiod excess earnings model with the analytic hierarchy process (AHP), creating an evaluation system through a comprehensive weighting method. Initially, the multiperiod excess earnings model is used to calculate the excess earnings of off-balance-sheet intangible assets. The AHP is subsequently applied to construct a hierarchical structural model of the enterprise, identifying the core factors that influence the excess earnings of off-balance-sheet intangible assets. This allows for precise segmentation and determination of the distribution rate of the value of data assets. The evaluation model fully accounts for the diversity, dynamics, and potential value of big data assets, effectively identifying and quantifying factors that are not easily observable directly. The findings not only provide a novel evaluation tool for data asset management in logistics enterprises but also offer theoretical support and practical guidance for enhancing the industry’s data asset valuation system and facilitating the realization of data asset value. Full article

The Carbon Emissions Trading System (ETS) serves as a market-based mechanism to drive renewable energy (RE) investments, yet its heterogeneous impacts on different stakeholders remain underexplored. This paper treats the carbon market as an exogenous shock and develops a multi-agent equilibrium model incorporating carbon pricing, encompassing power generation enterprises, power transmission enterprises, power consumers, and the government, to analyze how carbon prices reshape RE investment layouts under dual-carbon goals. Using panel data from Zhejiang Province (2017–2022), a high-energy-consumption region with 25% net electricity imports, we simulate heterogeneous responses of agents to carbon price fluctuations (CNY 50–250/ton). The results show that RE on-grid electricity increases (+0.55% to +2.89%), while thermal power declines (–4.98% to −15.39%) on the generation side. Transmission-side RE sales rise (+3.25% to +9.74%), though total electricity sales decrease (−0.49% to −2.22%). On the consumption side, RE self-generation grows (+2.12% to +5.93%), yet higher carbon prices reduce overall utility (−0.44% to −2.05%). Furthermore, external electricity integration (peaking at 28.5% of sales in 2020) alleviates provincial entities’ carbon cost pressure under high carbon prices. This study offers systematic insights for renewable energy investment decisions and policy optimization. Full article

Conventional trenchless pipeline rehabilitation technologies are primarily designed for circular pipelines, with limited applicability to box culvert structures. Even when adapted, these methods often lead to significant reductions in the effective cross-sectional area and fail to enhance the structural load-bearing capacity due to geometric incompatibilities. To overcome these limitations, this study proposes a novel construction approach that employs prefabricated high-strength thin concrete segment liners for the reinforcement of underground box culverts. The feasibility of this method was validated through full-scale (1:1) experimental construction in a purpose-built test culvert, demonstrating rapid and efficient installation. A static stacking load test was subsequently conducted on the reinforced upper section of the culvert. Results indicate that the proposed reinforcement method effectively restores structural integrity and satisfies load-bearing and serviceability requirements, even after removal of the original roof slab. Additionally, a finite element analysis was performed to simulate the stacking load test conditions. The simulation revealed that variations in the mechanical properties of the grout between the existing structure and the new lining had minimal impact on the internal force distribution and deformation behavior of the prefabricated segments. The top segment consistently exhibited semi-rigid fixation behavior. This study offers a promising strategy for the rehabilitation of urban underground box culverts, achieving structural performance recovery while minimizing traffic disruption and enhancing construction efficiency. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article