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Clec7a-targeted Res@GelMA hydrogels regulate macrophage polarization to reduce neuroinflammation and promote spinal cord repair
No full textAbstract Excessive inflammation driven by macrophage phenotype imbalance is a key pathological barrier hindering neural repair after spinal cord injury (SCI). Here, methacryloyl gelatin hydrogel (GelMA) loaded with natural anti-inflammatory agent resveratrol (Res) was designed and synthesized. Scanning electron microscopy (SEM), x-ray diffraction (XRD), fourier-transform infrared spectroscopy (FT-IR), Ultraviolet (UV) and rheological characterization confirmed that Res-loaded GelMA hydrogel (Res@GelMA) was successfully synthesized. High-Performance Liquid Chromatography (HPLC) analysis demonstrated sustained Res release. CCK8 and cell adhesion experiments confirmed that Res@GelMA treatment did not affect the cell function of RAW264.7 and had good cell compatibility. Flow cytometry, Enzyme-Linked Immunosorbent Assay (ELISA) and western blot assays revealed that Res@GelMA treatment promoted RAW264.7 to M2 polarization, while reducing the levels of inflammatory factors (IL-1β, IL-6, TNF-α) and down-regulating the expression of IL-1R1/MyD88/TNFR1 inflammatory signaling proteins. Transcriptome sequencing combined with functional screening identified C-type lectin receptor Clec7a as a key target gene regulated by Res@GelMA. Importantly, knockdown of Clec7a and Res@GelMA were both anti-inflammatory, promoted M2 polarization, and blocked the activation of the TLR2/TLR4-p38 MAPK signaling axis. In the SCI mouse model, local implantation of Res@GelMA significantly improved tissue pathological damage and enhanced motor function recovery compared with free Res or blank GelMA. In addition, Res@GelMA achieved systemic anti-inflammation by downregulating the Clec7a-TLR-p38 pathway in the injured area and promoting M2 polarization. This study developed an anti-inflammatory hydrogel material that can regulate the phenotype of macrophages, laying a theoretical and technical foundation for the development of neural repair strategies targeting the inflammatory microenvironment
The implementation of the femoral neck surgical central axis guide pins to establish the stable implanting-screws spatial coordinate system
No full textAbstract Background For internal-fixation of femoral neck fractures, determining the femoral neck central axis intraoperatively and calculating screw coordinates accurately remain crucial but extremely challenging. Previous research has shown that the flat anterior cortex of the femoral neck base (AC-FNB) can be used to establish a constant spatial coordinate system parallel to the surgical central axis (SCA) of the femoral neck theoretically, promising for solving the above conundrums. In this study, based on osseous marker AC-FNB, three guide pins of femoral neck surgical central axis (GPs-SCA) were implanted to build the stable implanting-screws spatial coordinate system, for providing convenience for controllable inserting femoral neck screws accurately during surgery. Methods After three-dimensional-reconstruction of forty sawbone synthetic femoral necks by CT scanning, the axial safety target areas (ASTA) for drilling femoral neck screw channels were determined by the intersection method to measure parameters such as superior-inferior diameter (D-SI) and anterior–posterior diameter (D-AP), verifying sawbones meet the inclusion criteria. Utilizing the robot-assisted patented technologies, the GPs-SCA were drilled with reference to the AC-FNB. Postoperatively, the ratio coordinates of each GP-SCA and the angle deviations between the GPs-SCA and the SCA of the corresponding femoral neck were measured using orthogonal X-rays and CT three-dimensional-reconstructions. The parameters of each GP-SCA on both X-rays and CT scans were then calculated, and paired t-tests were conducted. Results All preoperative parameters were within normal ranges for the femoral neck. Postoperatively, angular deviations between GPs-SCA and SCA were less than 5° on both orthogonal X-rays and CT scans. The Y-axis ratio coordinates of GP-SCA were − 0.41% ± 2.32% on orthogonal X-rays and − 0.32% ± 2.29% on axial CT, while the Z-axis ratio coordinates of GP-SCA were 0.74% ± 2.76% on orthogonal X-rays and 0.64% ± 2.87% on axial CT, respectively. There were no significant differences found (Py = 0.245, Pz = 0.185). Conclusions By utilizing the AC-FNB as a landmark, GPs-SCA could be successfully implanted within the allowable error ranges to facilitate building a reliable spatial rectangular coordinate system for controllable implanting screws. This coordinate system offers convenience in determining the directions of implanting screws, standardizing orthogonal fluoroscopies, registering X-rays and CT images, and converting X-ray data to axial coordinates
Text mining-assisted machine learning prediction and experimental validation of emission wavelengths
No full textAbstract The data-driven discovery of luminescent materials is hindered by a critical paradox: while decades of research have generated extensive data, the unstructured textual format of such data in literature precludes its systematic reuse. While text mining enables conversion to structured datasets, general natural language processing (NLP) tools fail at domain-specific challenges, such as fragmented host-dopant syntax and incomplete representations of property relationships. To address these limitations, we developed a novel NLP pipeline specifically tailored to the phosphor domain, integrating three key algorithms: chemically-aware parsing, frequency-driven subject recovery, and context-aware disambiguation algorithms. Using this approach, we successfully extracted 6,400 material-emission relationships from 16,659 scientific papers, with F1 scores of 0.956 for phosphor formula extraction and 0.851 for relation extraction. Additionally, a machine learning model trained on the extracted dataset predicted Eu2+ emission wavelengths, with a coefficient of determination (R2) of 0.91. Guided by predictions, we synthesized novel phosphors (e.g., CaGd2S4: Eu2+), the experimental emission spectra of these materials deviated by around 10 nm from predictions, verifying the robustness of the “data extraction-model prediction-experimental validation” workflow. This study bridges computational discovery and experimental validation, offering an open framework and tools to accelerate luminescent materials innovation
Music teaching evaluation using multimodal deep reinforcement learning
No full textAbstract This paper presents a Multi-modal Music Teaching Evaluation System (MMTES) that combines Deep Reinforcement Learning (DRL) with Retrieval-Augmented Generation (RAG) to provide personalized, accurate, and adaptive feedback in music education. Conventional evaluation approaches often rely on unimodal data, limiting their ability to capture the complexity of multimodal teaching environments. In contrast, MMTES integrates synchronized audio, video, symbolic music notation, and textual feedback, offering a holistic assessment of both teaching effectiveness and student performance. Within the system, a DRL agent adaptively refines evaluation strategies by maximizing cumulative rewards aligned with learning progress, while an RAG-enhanced language model retrieves pedagogical knowledge to generate context-aware evaluation and feedback. To ensure transparency and trust, a blockchain module is incorporated for secure data management. Experimental results on both benchmark and custom multimodal datasets demonstrate that MMTES surpasses state-of-the-art baselines in evaluation accuracy, contextual relevance, and adaptability. These results highlight the system’s potential as an intelligent, scalable, and reliable tool for advancing music education in real-world settings
A unified framework with U-Net + + and CNN-RNN-BiGRU architectures for automated weed detection in precision agriculture using AI
No full textAbstract The automated weed identification is necessary to enhance crop yield and the sustainable precision agriculture. The use of manual labour and chemical herbicides in traditional practices makes them very expensive, harmful to the environment, and causes more herbicide resistance. To overcome, this paper presents an AI-driven model AgroWeedX-Ensemble, which combines enhanced preprocessing, state-of-the-art segmentation, multi-scale feature extraction, feature selection, and efficient weed detection. An enhanced WeedNet-Adaptive Pre-Processing Optimizer (WN-APO) improves the quality of data by dynamically changing bilateral filtering, augmentation, and normalization to accommodate lighting changes and shadows. The proposed Attention-ASPP Enhanced Hybrid Dilated Network (AA-HDN) is based on the U-Net + + , attention, ASPP, and hybrid dilated convolutions to precisely differentiate between overlapping weed-crop areas. Multi-Scale Residual Spatial Feature Extractor (MS-RSFE) uses HOG, Gabor filters, and ResNet-50 with Feature Pyramid Networks to identify a variety of morphologies of weeds. The Parrot-Wheel Feature Selector (PWFS) is used to reduce feature redundancy by combining Parrot Optimizer and Binary Waterwheel Plant Optimization. Lastly, WeedAttnX-Net is a CNN-based model that adds RNN, Bi-GRU, attention layers, and the Bi-GRU-Attention to lower the false positives and enhance the reliability of the detection. The experimental findings show that AgroWeedX-Ensemble is highly accurate (0.99), precise (0.98), and robust in different field conditions with a lower false positives (0.02) and false negatives (0.009). The suggested system provides a scalable and useful solution to automated weed detection in precision agriculture
An intelligent recognition method for irregular movements of sports players based on improved fish swarm algorithm
No full textAbstract This paper proposes an intelligent recognition method based on an improved fish swarm algorithm to address complex issues such as noise, occlusion, and viewpoint changes in sports athlete movement data. The aim is to achieve stable and accurate recognition of non-standard movements in complex scenes. Firstly, the preprocessing module is used to denoise multi view action videos. Subsequently, the 3D ResNet module was used to extract spatiotemporal local features of non-standard actions, and their temporal features were further extracted through multi kernel convolution LSTM. The core innovation of this article lies in the introduction of an improved fish swarm algorithm, which combines a clustering based initialization strategy with an escape mechanism that introduces crossover and mutation operations, effectively optimizing the parameters of the multi kernel convolutional LSTM and significantly enhancing the robustness and recognition accuracy of the model in complex situations such as noise, occlusion, and viewpoint changes. Finally, combined with the SoftMax classifier, the recognition results of non-standard actions are outputted. The experimental results show that when the target occlusion rate is 5% and 30%, the maximum recognition errors of this method are only 2% and 4%, respectively; In tests covering 5 types of sports, the highest recognition probability was 0.91, successfully identifying various typical non-standard movements such as trunk misalignment, walking violations, and wrist bending
BizAssistBot: an artificial intelligence assistant for small business operations
No full textAbstract Experiential learning is vital in business education, as it enables students to comprehend intricate concepts and cultivate essential skills. BizAssistBot, an innovative tool, utilises artificial intelligence to improve the examination of small businesses for undergraduate students. This AI-driven platform employs natural language processing, machine learning, and data analytics to generate immersive learning experiences via real-world business simulations. Previous studies have explored how chatbots can assist in teaching programming languages, teach other languages and other subjects such as Mathematics, psychology and engineering but BizAssistBot enables students to explore domains including customer sales, economic management, inventory control, and marketing strategies. Students acquire insights into office operations, data-driven decision-making, and the influence of artificial intelligence on modern business practices through its application. The platform's ability to provide immediate feedback and simulate complex business scenarios effectively connects theoretical knowledge with practical application. This study examines the creation and application of BizAssistBot in educational settings and evaluates its instructional efficacy. Through the analysis of student feedback and learning outcomes, we assess the efficacy of BizAssistBot in improving comprehension of business concepts, engagement, and critical thinking abilities. The results indicate that incorporating AI-driven tools such as BizAssistBot into business curricula is advantageous and stimulating, preparing students to address contemporary business challenges
Harnessing phytochemicals for engineering health solutions
No full textAbstract The interaction between the genome and the exposome is increasingly recognized as central to human health and disease. While exposome research has generally focused on adverse exposures such as pollutants and toxins, the concept of the beneficial exposome—positive environmental exposures that promote health—remains underexplored. Among the most promising beneficial exposures are plant-derived phytochemicals, a rich class of bioactive compounds with therapeutic potential. Phytoncides, a specific subset of volatile organic compounds released by plants, exemplify this beneficial potential through their antimicrobial, anti-inflammatory, antioxidant, and neuroprotective effects. Historically utilized in traditional medicine across cultures, plant-based remedies containing these compounds are now being examined through modern genomics, exposomics, and systems biology approaches to understand the specific contributions of phytoncides and other bioactive constituents. Emerging data suggest that phytochemicals modulate gene expression, immune function, and metabolic pathways across multiple organ systems, contributing to immune, neurological, endocrine, cardiovascular, respiratory, integumentary, and mental health improvements. However, the evidence base is predominantly preclinical, with limited human validation, considerable heterogeneity in plant-extract composition, and incompletely characterized molecular mechanisms. This review synthesizes current evidence on genome-exposome interactions (GxE) related to plant-derived compounds, highlighting recent mechanistic insights and exploring translational applications—including forest bathing, green space integration in urban design, and bioengineering approaches—while addressing the challenges of clinical translation. As environmental change accelerates, understanding beneficial GxE offers new opportunities for preventative and precision public health interventions and calls for integrating nature-based solutions into modern healthcare paradigms
Isolable radical cation and dication of dialumene
No full textAbstract Alkenes are known to undergo successive oxidation to form alkene-derived radical cations and dications, which have found applications across various fields. As the aluminum analogues of alkenes, dialumenes likewise have the potential to lose one or two π-bonding electrons, forming dialumene-derived radical cations or dications. To date, however, these species have remained elusive, most likely due to the intrinsic electron deficiency imposed by both the positive charge and the pronounced electrophilicity of aluminum. Here, we present the synthesis of a stable aluminum-centered radical cation and dication through the combination of bulky silyl substituents and electron-donating carbene ligands. Further studies reveal that these aluminum complexes can switch between their neutral, radical cationic, and dicationic states, thus establishing a redox-reversible system. Furthermore, the dication exhibits multiple modes of reactivity, acting as a Lewis acid while also mediating both deoxygenation reactions and isocyanide homologation
Presence of primordial Mg can explain the seismic low-velocity layer in the Earth’s outermost outer core
No full textAbstract The composition of Earth’s Fe-rich liquid outer core has long been debated. Available models incorporating light elements, such as Si, O, C, S, and H, cannot explain the seismically low velocity layer in the uppermost outer core (E′ layer). Here we employ first-principles molecular dynamics simulations to determine the density and sound velocity ( V P ) of Fe-Mg liquids under outer core conditions, which were unknown previously. Results show that the presence of Mg slightly decreases the V P of liquid Fe, in contrast to the enhancing effects of other light elements. Our modeling suggests that 0.5-1.79 wt% Mg is required to match seismically observed core densities and velocities. Such amount of Mg could have entered the outermost outer core following the Moon-forming giant impact, thereby providing a viable explanation for the formation of the E′ layer and contributing to the slight Mg depletion in the bulk silicate Earth relative to chondritic meteorites
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