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Early orthodontic correction of skeletal malocclusion takes advantage of mechanical force to stimulate unclosed suture remodeling and to promote bone reconstruction; however, the underlying mechanisms remain largely unclear. Gli1+ cells in maxillofacial sutures have been shown to participate in maxillofacial bone development and damage repair. Nevertheless, it remains to be investigated whether these cells participate in mechanical force-induced bone remodeling during orthodontic treatment of skeletal malocclusion. In this study, rapid maxillary expansion (RME) mouse models and mechanical stretch loading cell models were established using two types of transgenic mice which are able to label Gli1+ cells, and we found that Gli1+ cells participated in mechanical force-induced osteogenesis both in vivo and in vitro. Besides, we found mechanical force-induced osteogenesis through inositol 1,4,5-trisphosphate receptor (IP3R), and we observed for the first time that inhibition of Gli1 suppressed an increase in mechanical force-induced IP3R overexpression, suggesting that Gli1+ cells participate in mechanical force-induced osteogenesis through IP3R. Taken together, this study is the first to demonstrate that Gli1+ cells in maxillofacial sutures are involved in mechanical force-induced bone formation through IP3R during orthodontic treatment of skeletal malocclusion. Furthermore, our results provide novel insights regarding the mechanism of orthodontic treatments of skeletal malocclusion. [ABSTRACT FROM AUTHOR]

Early orthodontic correction of skeletal malocclusion takes advantage of mechanical force to stimulate unclosed suture remodeling and to promote bone reconstruction; however, the underlying mechanisms remain largely unclear. Gli1+ cells in maxillofacial sutures have been shown to participate in maxillofacial bone development and damage repair. Nevertheless, it remains to be investigated whether these cells participate in mechanical force-induced bone remodeling during orthodontic treatment of skeletal malocclusion. In this study, rapid maxillary expansion (RME) mouse models and mechanical stretch loading cell models were established using two types of transgenic mice which are able to label Gli1+ cells, and we found that Gli1+ cells participated in mechanical force-induced osteogenesis both in vivo and in vitro. Besides, we found mechanical force-induced osteogenesis through inositol 1,4,5-trisphosphate receptor (IP3R), and we observed for the first time that inhibition of Gli1 suppressed an increase in mechanical force-induced IP3R overexpression, suggesting that Gli1+ cells participate in mechanical force-induced osteogenesis through IP3R. Taken together, this study is the first to demonstrate that Gli1+ cells in maxillofacial sutures are involved in mechanical force-induced bone formation through IP3R during orthodontic treatment of skeletal malocclusion. Furthermore, our results provide novel insights regarding the mechanism of orthodontic treatments of skeletal malocclusion. [ABSTRACT FROM AUTHOR]

In the article titled, "Gli1+ Cells Residing in Bone Sutures Respond to Mechanical Force via IP3R to Mediate Osteogenesis" [[1]], there is an error in Figure 3(d) which was introduced during production process. Graph: Figure 3 REFERENCES 1 Huang X., Li Z., Liu P., Wu M., Liu A.-q., Hu C., Liu X., Guo H., Yang X., Guo X., Li B., He X., Xuan K., Jin Y. Gli1+ cells residing in bone sutures respond to mechanical force via IP3R to mediate osteogenesis. [Extracted from the article]

In this study, a data-driven interior noise prediction model is developed for vehicles on an urban rail transit system based on random forest (RF) and a vehicle/track coupling dynamic model (VTCDM). The proposed prediction model can evaluate and optimize the sustainability of railway alignment from the perspective of interior noise. First, a data collection framework via embedded sensors of onboard smartphones was developed. Then, for establishing the mapping relationship between the dynamic responses of the car body and interior noise, the collected dataset was fed to the RF. Parameter, error distribution, and feature importance analyses were conducted for evaluating and optimizing the performance of the RF. With the optimized parameters, the probability of prediction errors being within 5 dB was 86.9%. Next, the VTCDM was established using an existing industry multibody simulation tool and verified through a comparison between the simulated and field dynamic responses. Finally, a case study that extends the application of this interior noise prediction model to railway alignment design is presented. [ABSTRACT FROM AUTHOR]

Background. The extensive burns devastate trauma. The research was designed to analyse the predictive value of early platelet (PLT) indices on the development of acute kidney injury (AKI) after severe burns. Methods and Results. 186 patients with extensive burns (burn area ≥30%) were eventually involved. Multivariate analyses pointed out that platelet distribution width (PDW) in the first 24 h after admission was an independent risk factor for AKI, severe AKI, and RRT requirement in patients with severe burns, and AKI risk showed an increase of 30.9% per increase of 1% in PDW (OR = 1.309, CI, 1.075–1.594, and P = 0.007). It was found that the area under the ROC curve (AUC) of PDW predicting AKI was 0.735 and that the AUC value was 0.81 for AKI after combining PDW and blood urea nitrogen (BUN). Based on the cut-off value PDW = 17.7%, patients were divided into high- (PDW ≥17.7%) and low-risk (PDW <17.7%) groups. In the KM analysis, there was a higher cumulative incidence of AKI if patients were in a high-risk group (in 30 days); and the stages of AKI showed a linear upward trend (chi-square test for linear trend P < 0.001) as there was an increase in the risk level. Conclusion. The PDW level in the early stage serves as an important risk factor for AKI, severe AKI, and RRT requirement in extensive burns. When PDW >17.7%, burn patients are not only at a higher risk for AKI but may also have higher AKI severity. Due to low cost and wide availability, PDW has the potential to be the tool that can predict AKI in extensive burn patients. [ABSTRACT FROM AUTHOR]

A dual‐function switchable and frequency tunable active frequency selective surface (AFSS) is proposed in this article. The proposed AFSS consists of a dielectric substrate and metal patches on the top and bottom surfaces of the substrate, while varactor diodes and PIN diodes are soldered on the top and bottom layers, respectively. A simple bias network is designed to facilitate the function adjustment of the AFSS. By controlling the OFF and ON states of the PIN diode, the AFSS can switch between filtering and wave absorbing functions. Furthermore, by adjusting the bias voltage of the top varactor diode, the AFSS can achieve continuous frequency tunability. Simulation and experimental results confirm the dual‐function switchable and frequency tunable performance of the proposed AFSS. [ABSTRACT FROM AUTHOR]

To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, "mU-Net-B3", with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field. [ABSTRACT FROM AUTHOR]