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Investigation of the mechanism and application of pressure relief roadway in preventing rock burst
Age-related variation in hemoglobin glycation index and stroke mortality: mediation and machine learning in a cohort study
AMF30a promotes survival and function of human corneal endothelial cells by regulating TGF-β/ROCK/HIPPO pathway
Evaluating protein complexes between human aquaporin and calmodulin using biomolecular fluorescence complementation
Abstract Aquaporins (AQPs) are a family of integral membrane proteins crucial for the flow of water and other small molecules across cellular membranes. The involvement of calmodulin (CaM), a multifunctional calcium-binding protein, has emerged as a central regulator for specific aquaporin homologues from eukaryotes. Using a systematic approach, applying advanced high throughput screening methods in vivo, combining flow cytometry with microscopy, we have evaluated the putative interaction between CaM and the 13 human AQP homologues recombinantly produced in the yeast Saccharomyces cerevisiae. This comprehensive approach is complemented by a theoretical validation of potential CaM binding sites and a review of confirmed CaM binding locations from previous research. Our investigation is based on the established interaction of hAQP0 and CaM and we have successfully validated the binding of hAQP1 and hAQP4 to CaM. Noteworthy, discernibly high fluorescence frequency signals were observed for hAQP8 and hAQP9, which did not correlate with a particularly high production level, supporting protein complex formation with CaM for those AQP homologues. Overall, we present a systematic approach to screen novel membrane protein interactions in vivo, relying on co-expression in yeast of Bimolecular Fluorescence Complementation (BiFC) complexes providing new insights into the regulation of the hAQPs.
High-risk human papillomavirus genotype distribution and attribution to cervical lesions in a Shanxi Province screening population
Long-term water types and satellite chlorophyll a variability in a river discharge environment around Ieodo Ocean Research Station
Removal of reactive red 45 dye from aqueous solution using activated carbon developed from Catha edulis stem as a potential biosorbent
Multi-level data fusion enables collaborative dynamics analysis in team sports using wearable sensor networks
Abstract This research proposes a novel multi-level data fusion method for analyzing collaborative dynamics in team sports using wearable sensor networks. We developed and validated this approach through controlled experiments with 40 semi-professional athletes across basketball and soccer scenarios. The multi-level fusion architecture integrates IMU, GPS, physiological, and positioning data through adaptive weight allocation and asynchronous alignment algorithms. Experimental validation demonstrated 8.6 dB improvement in signal quality and 42.3% enhancement in positional accuracy compared to single-source approaches. Cross-sport testing across basketball, soccer, volleyball, and handball showed consistent performance (84.2–91.4% accuracy) with real-time response times of 192-312ms. The developed collaborative dynamics indicator system revealed that temporal coordination parameters strongly correlate with team performance (r = 0.73), while four key metrics predict match outcomes with 73.6% accuracy. This methodology provides coaches and analysts with objective tools for quantifying previously subjective aspects of team coordination.
Postoperative functional training program for vascularised Iliac flap donor site in jaw defect reconstruction based on the Delphi method
Abstract Vascularised iliac flaps (VIFs) are widely used for the reconstruction of jawbone defects; however, postoperative donor-site complications, such as gait disturbances, with an incidence of 13.9–50%, significantly impede patient recovery. Despite this, evidence-based rehabilitation protocols specific to VIFs remain lacking. Existing rehabilitation guidelines for hip surgeries are unsuitable owing to differences in surgical mechanisms. This study employed the Delphi method, engaging 20 multidisciplinary experts (oral and maxillofacial surgery: 5; orthopaedics: 7; rehabilitation: 6; nursing: 2). Through three rounds of anonymous consultations, and by integrating literature evidence with postoperative mobility assessments, we developed a phased, individualised progressive functional training (PFT) protocol featuring dynamic evaluation, coordinated activation of abdominal and hip muscle groups, and safe exercise strategies during head and neck immobilisation, while overcoming conventional hip rehabilitation limitations (e.g., restrictions on flexion < 90°, and bans on squatting or cross-legged sitting). PFT is structured into six progressive phases, with exercise intensity tailored to assessment outcomes. A single-centre randomised controlled trial (n = 62) demonstrated that PFT significantly accelerated lower limb functional recovery, improved hip mobility and balance, reduced donor-site pain, and enhanced quality of life (University of Washington Quality of Life questionnaire: F (1,60) = 17.262, P < 0.001), without increasing the risk of flap vascular compromise or iliac hematoma. The limitations of the study include the single-centre design and lack of cross-cultural validation. Future multicentre studies are required to enhance adaptability. This study establishes a foundational yet effective framework for post-VIF rehabilitation, guiding clinical practice and research advancements.
Transfer learning based deep architecture for lung cancer classification using CT image with pattern and entropy based feature set
Abstract Early detection of lung cancer, which remains one of the leading causes of death worldwide, is important for improved prognosis, and CT scanning is an important diagnostic modality. Lung cancer classification according to CT scan is challenging since the disease is characterized by very variable features. A hybrid deep architecture, ILN-TL-DM, is presented in this paper for precise classification of lung cancer from CT scan images. Initially, an Adaptive Gaussian filtering method is applied during pre-processing to eliminate noise and enhance the quality of the CT image. This is followed by an Improved Attention-based ResU-Net (P-ResU-Net) model being utilized during the segmentation process to accurately isolate the lung and tumor areas from the remaining image. During the process of feature extraction, various features are derived from the segmented images, such as Local Gabor Transitional Pattern (LGTrP), Pyramid of Histograms of Oriented Gradients (PHOG), deep features and improved entropy-based features, all intended to improve the representation of the tumor areas. Finally, classification exploits a hybrid deep learning architecture integrating an improved LeNet structure with Transfer Learning (ILN-TL) and a DeepMaxout (DM) structure. Both model outputs are finally merged with the help of a soft voting strategy, which results in the final classification result that separates cancerous and non-cancerous tissues. The strategy greatly enhances lung cancer detection’s accuracy and strength, showcasing how combining sophisticated neural network structures with feature engineering and ensemble methods could be used to achieve better medical image classification. The ILN-TL-DM model consistently outperforms the conventional methods with greater accuracy (0.962), specificity (0.955) and NPV (0.964).
Quantitative proteomic analysis deciphers mechanisms of sheath blight resistance in novel rice landrace against Rhizoctonia solani
Experimental study on basic mechanical properties of lightweight self-insulation concrete block masonry in Northeast cold region
Association between body roundness index and myopia among US adolescents: a cross-sectional study of NHANES 2001–2006
Predicting academic performance with fuzzy logic in prospective physical education and sports teachers
A mirrored pair of optimal non-decomposable entanglement witnesses for two qudits does exist
Abstract Two approaches can be utilized to handle the separability problem, finding out whether a given bipartite qudit state is separable or not: a direct procedure on the state space or the effective tool of entanglement witnesses (EWs). This contribution studies the structure of EWs. Exploiting the very concept of mirrored EWs, increasing the detection power, we show, in contrast to the conjecture in a recent paper (Bera et al. in Sci Rep 13:10733, 2023), there exist pairs of optimal EWs, which are both non-decomposible, i.e. can detect bound/PPT-entangled states in an optimal way. Since we show that the structure also extends to higher dimensions, our results reveal a further structure of entanglement witnesses.
Tcf21 modulates fibroblast activation and promotes cardiac fibrosis after injury via Pdgfrb signaling
Minigenes enhance heterologous expression and prevent aberrant splicing of mouse Spink1
Abstract The digestive enzyme trypsin is an important driver of pancreatitis development and progression. Inhibition of intrapancreatic trypsin activity is an attractive yet so far unsuccessful approach for the treatment and prevention of pancreatitis. Gene therapy using adeno-associated viral vector containing the human serine protease inhibitor Kazal type 1 ( SPINK1 ) gene has shown efficacy against murine pancreatitis in preclinical experiments. To advance this method to clinical testing, viral vectors with improved inhibitor expression are needed. In this study, we investigated the effect of minigenes with single introns on the expression of the mouse Spink1 gene. We found that minigenes markedly stimulated the expression of mouse Spink1 mRNA and SPINK1 protein in HEK 293T cells transfected with plasmids and in AR42J rat pancreatic acinar cells and isolated mouse pancreatic acini transduced with adenoviral vectors. The optimal intron size for expression enhancement was 100 nucleotides. We also demonstrated that mouse Spink1 mRNA expressed from a cDNA construct underwent spurious mRNA splicing and minigenes prevented this splicing defect. However, enhancement of mouse Spink1 expression by minigenes was unrelated to the rescue of aberrant mRNA splicing. The observations will facilitate the development of advanced viral vectors harboring mouse Spink1 for preclinical therapeutic studies in mouse models of pancreatitis.