Search Results (169)

The development of bioactive materials in endodontics has advanced tissue regeneration by enhancing the biological responses of periradicular tissues. Recently, calcium silicate-based sealers have gained attention for their superior biological properties, including biocompatibility, osteoconductivity, and cementogenic potential. This study aimed to evaluate the cytotoxicity, biocompatibility, and bioactivity of EndoSequence BC Sealer (ES BC) and AH Plus Bioceramic Sealer (AHP BC) using human periodontal ligament stromal cells (hPDLSCs). Biocompatibility was assessed using MTT, Live/Dead, and wound healing assays. ES BC and AHP BC demonstrated significantly higher cell viability and proliferation compared to AH Plus used as a control. Gene expression analysis via real-time quantitative PCR demonstrated that ES BC, especially in set form, significantly upregulated osteogenic markers—alkaline phosphatase (2.49 ± 0.10, p < 0.01), runt-related transcription factor 2 (2.33 ± 0.13), and collagen type I alpha 1 chain (2.85 ± 0.40, p < 0.001)—more than cementogenic markers (cementum protein 1, cementum attachment protein, and cementum protein 23). This differential response may reflect the fibroblast-dominant nature of hPDLSCs, which contain limited cementoblast-like cells. This study supports the superior biocompatibility and regenerative capacity of ES BC and AHP BC compared to AH Plus. While in vitro models provide foundational insights, advanced ex vivo approaches are crucial for translating findings to clinical practice. Full article

Chrysanthemum has a long history of medicinal use with rich germplasm resources and extensive cultivation. Traditional chrysanthemum cultivation involves complex patterns and long flowering periods, with the ongoing expansion of planting areas complicating statistical surveys. Currently, reliable, timely, and universally applicable standardized monitoring methods for chrysanthemum cultivation areas remain underdeveloped. This research employed 16 m resolution satellite imagery spanning 2021 to 2023 alongside 2 m resolution data acquired in 2022 to quantify chrysanthemum cultivation extent across Sheyang County, Jiangsu Province, China. After evaluating multiple classifiers, Maximum Likelihood Classification was selected as the optimal method. Subsequently, time-series-based post-classification processing was implemented: initial cultivation information extraction was performed through feature comparison, supervised classification, and temporal analysis. Accuracy validation via Overall Accuracy, Kappa coefficient, Producer’s Accuracy, and User’s Accuracy identified critical issues, followed by targeted refinement of spectrally confused features to obtain precise area estimates. The chrysanthemum cultivation area in 2022 was quantified as 46,950,343 m² for 2 m resolution and 46,332,538 m² for 16 m resolution. Finally, the conversion ratio characteristics between resolutions were analyzed, yielding adjusted results of 38,466,192 m² for 2021 and 47,546,718 m² for 2023, respectively. These outcomes demonstrate strong alignment with local agricultural statistics, confirming method viability for chrysanthemum cultivation area computation. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article

The only vaccine against Haemonchus contortus is limited by short-lived antibody persistence and the need for frequent booster immunizations. This study leveraged the advantages of nano-adjuvants in enhancing antigen presentation and immune regulation to evaluate the efficacy of novel adjuvants (IMX, AddaS03) and the conventional QuilA combined with H11 protein. Goats were divided into four groups (IMX + H11, AddaS03 + H11, QuilA + H11, and infected control). They were immunized three times and challenged with 6000 infective third-stage larvae (iL3s) of H. contortus on the day of the third immunization, with the experiment lasting for 98 days. The results showed that vaccination with IMX + H11 conferred the strongest protection, demonstrating 88.3% efficacy in fecal egg count (FEC) reduction and 75.8% efficacy against worm burden, followed by QuilA + H11 (85.2% FEC reduction and 68% worm burden reduction) and AddaS03 + H11 (79.4% FEC reduction and 61.3% worm burden reduction). Serum IgG analysis revealed high antibody levels in all immunized groups. Cytokine detection found that IMX + H11 significantly upregulated IL-2 and IFN-γ expression in PBMCs and TNF-α expression in splenocytes, activating Th1-type responses and immune memory. QuilA + H11 showed weaker Th1 activation, and AddaS03 + H11 faced limitations due to insufficient antibody persistence for long-term protection. These findings suggest that IMX can induce highly efficient humoral and cellular immunity, providing a new direction for the optimization of H. contortus vaccines and suggesting the importance of nano-adjuvants for precise regulation of immune patterns. Full article

Nitrogen oxides (NO_x) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NO_x cycling across the Tibetan Plateau (TP), which plays a crucial role in regional and global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data to examine the tropospheric NO₂ vertical column density (Ω_NO2) spatiotemporal variability over TP, a pristine environment marked with natural sources. GEMS observations revealed that the Ω_NO2 over TP is generally low compared with surrounding regions with significant surface emissions, such as India and the Sichuan basin. A spatial decreasing trend of Ω_NO2 is observed from the south and center to the north over Tibet. Unlike the surrounding regions, the TP exhibits opposing seasonal patterns and a negative correlation between the surface NO₂ and Ω_NO2. In the Lhasa and Nam Co areas within Xizang, the highest Ω_NO2 in spring contrasts with the lowest surface concentration. Diurnally, a midday increase in Ω_NO2 in the warm season reflects some external sources affecting the remote area. Trajectory analysis suggests strong convection lifted air mass from India and Southeast Asia into the upper troposphere over the TP. These findings highlight the mixing interplay of nonlocal and local NO_x sources in shaping NO₂ variability in a high-altitude environment. Future research should explore these transport mechanisms and their implications for atmospheric chemistry and climate dynamics over the TP. Full article

To better understand the physiological mechanisms underlying the variation of Cadmium (Cd) accumulation in wheat, Cd absorption, translocation, and distribution in five low grain-Cd-accumulating wheat (LCA) and five high grain-Cd-accumulating wheat (HCA) were studied at four growth stages under three soil Cd concentrations. Grain Cd concentration of HCA was 2.92 times, 1.61 times, and 1.40 times more than that of LCA under the soil with 0.3 mg/kg,1.5 mg/kg, and 7.5 mg/kg Cd concentrations, respectively. LCA was more tolerant of Cd pollution than HCA. Consequently, dry matter in LCA roots, stems + leaves, glumes, grains, and the entire plant was significantly higher than that of HCA at all growth stages under all three soil Cd concentrations, and the most pronounced difference was observed during the maturity stage. The critical period governing the disparity in Cd uptake between LCA and HCA primarily occurred before jointing and the maturity stage. LCA absorbed more Cd than HCA under the three Cd soil concentrations before the jointing stage, during which Cd uptake of LCA was 1.92 times, 1.86 times, and 1.46 times that of HCA under 0.3, 1.5 and 7.5 Cd soil concentrations. But LCA absorbed less Cd than HCA at the maturity stage, during which Cd uptake of LCA was 50%, 50%, and 49% of HCA under 0.3,1.5 and 7.5 mg/kg soil Cd concentrations, respectively. Cd uptake or accumulation per plant in LCA was significantly lower than that of HCA throughout the entire growth period, but the difference between them becomes increasingly smaller as the concentration of Cd contamination increases. Early absorption and accumulation of Cd played a limited role in grain Cd accumulation, and Cd transport played a critical role in determining grain Cd content at maturity. In addition, tolerance to Cd was higher, and grain Cd concentration was lower. Full article

Background/Objectives: Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed models, such as Genomic Best Linear Unbiased Prediction (GBLUP), and conventional machine learning methods like Support Vector Regression (SVR). Traditional methods are limited in handling high-dimensional data and nonlinear relationships. Thus, deep learning methods have also been applied to genomic prediction in recent years. Methods: We proposed iADEP, Integrated Additive, Dominant, and Epistatic Prediction model based on deep learning. Specifically, single nucleotide polymorphism (SNP) data integrating latent genetic interactions and genome-wide association study results as biological prior knowledge are fused to an SNP embedding block, which is then input to a local encoder. The local encoder is fused with an omic-data-incorporated global decoder through a multi-head attention mechanism, followed by multilayer perceptrons. Results: Firstly, we demonstrated through experiments on four datasets that iADEP outperforms existing methods in genotype-to-phenotype prediction. Secondly, we validated the effectiveness of SNP embedding through ablation experiments. Third, we provided an available module for combining other omics data in iADEP and propose a novel method for fusing them. Fourthly, we explored the impact of feature selection on iADEP performance and conclude that utilizing the full set of SNPs generally provides optimal results. Finally, by altering the partition of training and testing sets, we investigated the differences between transductive learning and inductive learning. Conclusions: iADEP provides a new approach for AI breeding, a promising method that integrates biological prior knowledge and enables combination with other omics data. Full article

Dynamic derivatives are critical for evaluating an aircraft’s aerodynamic characteristics, dynamic modeling, and control system design during the design phase. However, due to the multiple iterations of the design phase, a method for calculating dynamic derivatives that balances computational efficiency and accuracy is required. This work presents a CFD-enhanced engineering calculation method (CEHM) for calculating tandem-wing UAVs’ dynamic derivatives. A coupling-effect-driven estimation strategy is proposed to incorporate the contribution of the rear wing to the longitudinal dynamic derivatives, and it accounts for the aerodynamic coupling effects between the front and rear wings. To enhance the accuracy of the dynamic derivative calculations, we put forward a dynamic derivative-correction mechanism based on the CFD method. It achieves three types of parameters from the static derivative CFD simulations to enhance accuracy, including parameters for aerodynamic force coefficient fitting, the dynamic pressure ratio, and the upwash and downwash gradients. The CEHM method is applied to compute the dynamic derivatives of the SULA90 tandem-wing UAV, with results compared to those obtained from the traditional engineering estimation tools (XFLR5 and OpenVSP). The simulation experiment results show that the proposed method not only calculates the acceleration derivatives but also provides higher calculation accuracy. To further validate the method’s effectiveness, open-loop model verifications were conducted using field flight test data of the SULA90. The field flight test results show that the CEHM method’s predicted results align closely with the measured flight data. The proposed method calculates dynamic derivatives in seconds, balancing accuracy and computational cost, making it highly suitable for tandem-wing aircraft during the design phase. Furthermore, this approach is generalizable and can be applied to other aircraft configurations. Full article

Nck-associated protein 1 (NCKAP1) is critical for cytoskeletal functions and various cellular activities, and deregulation of NCKAP1 in many cancers significantly influences the outcomes of malignant diseases. However, the functions of NCKAP1 in the progression of renal cancer are yet unknown. To investigate the specific roles of NCKAP1 in the immune regulation and tumor progression of renal cancer, the expression of NCKAP1 and genetic variations were analyzed across cancer types at different pathological stages via UALCAN and cBioPortal. Immune cell infiltration in renal cancer was also assessed by ssGSEA and single-cell gene expression data from the GEO. RNA sequencing of NCKAP1-overexpressing 769P cells further examined the impact of NCKAP1 on kidney cancer. Our pancancer analyses revealed a complex NCKAP1 expression profile across various cancer types, with reduced levels in renal cancer patients linked to patient prognosis. CIBERSORT and single-cell RNA sequencing revealed the expression patterns of NCKAP1 in different cell lineages in renal cancer and a significant correlation between NCKAP1 and immune cell infiltration in the kidney tumor microenvironment. We further verified that NCKAP1 suppressed cancer cell growth and affected tumor development in renal cancer via the PI3K/AKT/mTOR signaling pathway. Our results indicate that NCKAP1 is a potential predictive marker and treatment target for renal cancer. Full article

This study addresses stability challenges in oil shale reservoirs during the in situ conversion process by developing a thermo-hydro-mechanical damage (THMD) coupling model. The THMD model integrates thermo-poroelasticity theory with a localized gradient damage approach, accounting for thermal expansion and pore pressure effects on stress evolution and avoiding mesh dependency issues present in conventional local damage models. To capture tensile–compressive asymmetry in geotechnical materials, an equivalent strain based on strain energy density is introduced, which regularizes the tensile component of the elastic strain energy density. Additionally, the model simulates the multi-layer wellbore structure and the dynamic heating and extraction processes, recreating the in situ environment. Validation through a comparison of numerical solutions with both experimental and analytical results confirms the accuracy and reliability of the proposed model. Wellbore stability analysis reveals that damage tends to propagate in the horizontal direction due to the disparity between horizontal and vertical in situ stresses, and the damaged area at a heating temperature of 600 °C is nearly three times that at a heating temperature of 400 °C. In addition, a cement sheath thickness of approximately 50 mm is recommended to optimize heat transfer efficiency and wellbore integrity to improve economic returns. Our study shows that high extraction pressure (−4 MPa) nearly doubles the reservoir’s damage area and increases subsidence from −3.6 cm to −6.5 cm within six months. These results demonstrate the model’s ability to guide improved extraction efficiency and mitigate environmental risks, offering valuable insights for optimizing in situ conversion strategies. Full article

Flexible pressure sensors play an extremely important role in the fields of intelligent medical treatment, humanoid robots, and so on. However, the low sensitivity and the small initial capacitance still limit its application and development. At present, the method of constructing the microstructure of the dielectric layer is commonly used to improve the sensitivity of the sensor, but there are some problems, such as the complex process and inaccurate control of the microstructure. In this work, an ion composite photosensitive resin based on polyurethane acrylate and ionic liquids (ILs) was prepared. The high compatibility of the photosensitive resin and ILs was achieved by adding a chitooligosaccharide (COS) chain extender. The microstructure of the dielectric layer was optimized by digital light processing (DLP) 3D-printing. Due to the introduction of ILs to construct an electric double layer (EDL), the flexible pressure sensor exhibits a high sensitivity of 32.62 kPa⁻¹, which is 12.2 times higher than that without ILs. It also has a wide range of 100 kPa and a fast response time of 51 ms. It has a good pressure response under different pressures and can realize the demonstration application of human health. Full article

This study aimed to investigate the effect of superheated steam treatment on the cooking and eating quality of rice, and further explore the effect of superheated steam treatment on the structure, gel properties, and rheological behavior of rice starch. After superheated steam treatment, the optimal cooking time of rice was effectively reduced by 26.9%, and the taste value of rice was significantly improved, from 78.45 to 84.20, when treated at 155 °C for 10 s. Superheated steam treatment significantly reduced the amylose and protein content, and increased the average particle size of rice starch. Compared with the control, the enthalpy change (ΔH) in the superheated steam treatment rice starch decreased notably from 6.53 to 5.28 after treatment, the relative crystallinity of the starch was significantly reduced from 21.20 to 10.89, and the short-term order of the starch was enhanced owing to the rearrangement of starch molecules after gelatinization. The starch structure was more compact and orderly after the superheated steam treatment, which significantly improved the hardness, viscoelasticity, and strength of the gel. These results indicate that superheated steam treatment improves the quality of rice by changing the structure of rice starch. Full article

The security of ultra-high-voltage (UHV) overhead transmission lines is frequently threatened by diverse external-force damages. As real-world transmission line scenarios are complex and external-force-damage objects exhibit varying scales, deep learning-based object detection methods necessitate the capture of multi-scale information. However, the downsampling and upsampling operations employed to learn multi-scale features work locally, resulting in the loss of details and boundaries, which makes it difficult to accurately locate external-force-damage objects. To address this issue, this paper proposes a content-aware method based on the generalized efficient layer aggregation network (GELAN) framework. A newly designed content-aware downsampling module (CADM) and content-aware upsampling module (CAUM) were integrated to optimize the operations with global receptive information. CADM and CAUM were embedded into the GELAN detection framework, providing a new content-aware method with improved cost accuracy trade-off. To validate the method, a large-scale dataset of external-force damages on transmission lines with complex backgrounds and diverse lighting was constructed. The experimental results demonstrate the proposed method’s superior performance, achieving 96.50% mean average precision (mAP) on the transmission line dataset and 91.20% mAP on the pattern analysis, statical modeling and computational learning visual object classes (PASCAL VOC) dataset. Full article

Conotoxins, a diverse family of disulfide-rich peptides derived from the venom of Conus species, have gained prominence in biomedical research due to their highly specific interactions with ion channels, receptors, and neurotransmitter systems. Their pharmacological properties make them valuable molecular tools and promising candidates for therapeutic development. However, traditional conotoxin classification and functional characterization remain labor-intensive, necessitating the increasing adoption of computational approaches. In particular, machine learning (ML) techniques have facilitated advancements in sequence-based classification, functional prediction, and de novo peptide design. This review explores recent progress in applying ML and deep learning (DL) to conotoxin research, comparing key databases, feature extraction techniques, and classification models. Additionally, we discuss future research directions, emphasizing the integration of multimodal data and the refinement of predictive frameworks to enhance therapeutic discovery. Full article

Metastatic cancer poses significant clinical challenges, necessitating effective immunotherapies with minimal systemic toxicity. Building on prior research demonstrating the rWTC-MBTA vaccine’s ability to inhibit tumor metastasis and growth, this study focuses on its clinical translation by optimizing vaccine composition, dosing regimens, and freezing techniques. The vaccine formula components included three TLR ligands (LTA, Poly I:C, and Resiquimod) and an anti-CD40 antibody, which were tested in melanoma and triple-negative breast cancer (TNBC) models. The formulations were categorized as rWTC-MBT (Mannan-BAM with LTA, Poly I:C, Resiquimod), rWTC-MBL (LTA), rWTC-MBP (Mannan-BAM with Poly I:C), and rWTC-MBR (Resiquimod). In the melanoma models, all the formulations exhibited efficacy that was comparable to that of the full vaccine, while in the “colder” TNBC models, the formulations with multiple TLR ligands or Resiquimod alone performed the best. Vaccine-induced activation of dendritic cell (DC) subsets, including conventional DCs (cDCs), myeloid DCs (mDCs), and plasmacytoid DCs (pDCs), was accompanied by significant CD80+CD86+ population induction, suggesting robust innate immune stimulation. An initial three-dose schedule followed by booster doses (3-1-1-1 or 3-3-3-3) reduced the metastatic burden effectively. Gradual freezing (DMSO-based preservation) maintained vaccine efficacy, underscoring the importance of intact cell structure. These findings highlight the potential of simplified formulations, optimized dosing, and freezing techniques in developing practical, scalable immunotherapies for metastatic cancers. Full article