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Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students' acquisition of skills and knowledge and positively mediated dental technology students' CAD performance.

Aiming at the problem of high fault similarity in the transmission chain of wind turbines, this study proposes a single-sample wind turbine bearing fault diagnosis method based on empirical mode decomposition and signal equalization processing. In this method, the signal components of different modes are obtained by empirical mode decomposition of the actual monitoring fault signal. The energy value and peak value of each modal component are calculated, and some modal components with large energy and high peak value are selected for signal reconstruction to obtain a new fault signal. The new fault signal decomposed by wavelet packet, and the wavelet packet is decomposed into the third layer component for signal reconstruction. The variance of the reconstructed signal is used as the eigenvalue of the fault diagnosis. Non-linear equalization is performed on the eigenvalues, which solves the problem of signal mutual submergence. The concept of discrimination is introduced to quantify the difference between different fault signals. The experimental results show that the fault diagnosis method proposed is effective, and the discrimination between the four faults on the transmission chain of the wind turbine before and after the treatment is significantly increased, which shows that the experimental method has a strong robustness. Compared with the improved fuzzy clustering method and the deep learning method based on the improved AlxeNet network, the method performs better. The fault diagnosis method only uses a single sample to realize the fault diagnosis of the transmission chain of wind turbines, which is in line with the characteristic of the low failure rate of wind turbines, and is of great significance for improving the troubleshooting efficiency of wind turbines in actual projects.

Abstract Glioblastoma stem cells (GSCs) play a key role in glioblastoma (GBM) resistance to temozolomide (TMZ) chemotherapy. With the increase in research on the tumour microenvironment, exosomes secreted by GSCs have become a new focus in GBM research. However, the molecular mechanism by which GSCs affect drug resistance in GBM cells via exosomes remains unclear. Using bioinformatics analysis, we identified the specific expression of ABCB4 in GSCs. Subsequently, we established GSC cell lines and used ultracentrifugation to extract secreted exosomes. We conducted in vitro and in vivo investigations to validate the promoting effect of ABCB4 and ABCB4-containing exosomes on TMZ resistance. Finally, to identify the transcription factors regulating the transcription of ABCB4, we performed luciferase assays and chromatin immunoprecipitation-quantitative PCR. Our results indicated that ABCB4 is highly expressed in GSCs. Moreover, high expression of ABCB4 promoted the resistance of GSCs to TMZ. Our study found that GSCs can also transmit their highly expressed ABCB4 to differentiated glioma cells (DGCs) through exosomes, leading to high expression of ABCB4 in these cells and promoting their resistance to TMZ. Mechanistic studies have shown that the overexpression of ABCB4 in GSCs is mediated by the transcription factor ATF3. In conclusion, our results indicate that GSCs can confer resistance to TMZ in GBM by transmitting ABCB4, which is transcribed by ATF3, through exosomes. This mechanism may lead to drug resistance and recurrence of GBM. These findings contribute to a deeper understanding of the mechanisms underlying drug resistance in GBM and provide novel insights into its treatment.

Abstract BackgroundRemovable partial denture (RPD) design is crucial to long-term success in dental treatment, but shortcomings in RPD design training and competency acquisition among dental students have persisted for decades. Digital production is increasing in prevalence in stomatology, and a digital RPD (D-RPD) module, under the framework of the certified Objective Manipulative Skill Examination of Dental Technicians (OMEDT) system reported in our previous work, may improve on existing RPD training models for students. ObjectiveWe aimed to determine the efficacy of a virtual 3D simulation–based progressive digital training module for RPD design compared to traditional training. MethodsWe developed a prospective cohort study including dental technology students at the Stomatology College of Chongqing Medical University. Cohort 1 received traditional RPD design training (7 wk). Cohort 2 received D-RPD module training based on text and 2D sketches (7 wk). Cohort 3 received D-RPD module pilot training based on text and 2D sketches (4 wk) and continued to receive training based on 3D virtual casts of real patients (3 wk). RPD design tests based on virtual casts were conducted at 1 month and 1 year after training. We collected RPD design scores and the time spent to perform each assessment. ResultsWe collected the RPD design scores and the time spent to perform each assessment at 1 month and 1 year after training. The study recruited 109 students, including 58 (53.2%) female and 51 male (56.8%) students. Cohort 1 scored the lowest and cohort 3 scored the highest in both tests (cohorts 1-3 at 1 mo: mean score 65.8, SD 21.5; mean score 81.9, SD 6.88; and mean score 85.3, SD 8.55, respectively; PPPPPPP ConclusionsCohort 3 obtained the highest score at both time points with retention of competency at 1 year, indicating that progressive D-RPD training including virtual 3D simulation facilitated improved competency in RPD design. The adoption of D-RPD training may benefit learning outcomes.

Due to the low-complexity implementation, direction-of-arrival (DOA) estimation-based one-bit quantized data are of interest, but also, signal processing struggles to obtain the demanded estimation accuracy. In this study, we injected a number of noise components into the receiving data before the uniform linear array (ULA) composed of one-bit quantizers. Then, based on this designed noise-boosted quantizer unit (NBQU), we propose an efficient one-bit multiple signal classification (MUSIC) method for estimating the DOA. Benefiting from the injected noise, the numerical results show that the proposed NBQU-based MUSIC method outperforms existing one-bit MUSIC methods in terms of estimation accuracy and resolution. Furthermore, with the optimal root mean square (RMS) of the injected noise, the estimation accuracy of the proposed method for estimating DOA can approach that of the MUSIC method based on the complete analog data.

Developing an efficiently supported Cu-based catalyst with promoters to substitute the existing non-supported Cu-based catalysts is highly desirable to the Rochow-Müller reaction. Using a simple ball-milling method and CeO2 support, we prepared a high-performance CuO-ZnO-P-Sn/CeO2 catalyst by integrating highly dispersed multicomponent promoters of ZnO, Sn, and P with the active component CuO. This catalyst shows a significantly enhanced dimethyldichlorosilane selectivity because these promoters can substantially increase the Cu+ content and the formation of an active CuxSi phase. This work provides a new approach to efficiently designing Cu-based catalysts for the Rochow-Müller reaction.