Evidence shows that the font size of study items significantly influences judgments of learning (JOLs) and that people’s JOLs are generally higher for larger words than for smaller words. Previous studies have suggested that font size influences JOLs in a belief-based way. However, few studies have directly examined how much people’s beliefs contribute to the font-size effect in JOLs. This study investigated the degree to which font size influenced JOLs in a belief-based way. In Experiment 1, one group of participants (learners) studied words with different font sizes and made JOLs, whereas another group of participants (observers) viewed the learners' study phase and made JOLs for the learners. In Experiment 2, participants made both JOLs and belief-based recall predictions for large and small words. Our results suggest that metamemory beliefs play an important role in the font-size effect in JOLs.

Periodontitis is an immune inflammatory disease that leads to progressive destruction of bone and connective tissue, accompanied by the dysfunction and even loss of periodontal ligament stem cells (PDLSCs). Pyroptosis mediated by gasdermin-D (GSDMD) participates in the pathogenesis of inflammatory diseases. However, whether pyroptosis mediates PDLSC loss, and inflammation triggered by pyroptosis is involved in the pathological progression of periodontitis remain unclear. Here, we found that PDLSCs suffered GSDMD-dependent pyroptosis to release interleukin-1β (IL-1β) during human periodontitis. Importantly, the increased IL-1β level in gingival crevicular fluid was significantly correlated with periodontitis severity. The caspase-4/GSDMD-mediated pyroptosis caused by periodontal bacteria and cytoplasmic lipopolysaccharide (LPS) dominantly contributed to PDLSC loss. By releasing IL-1β into the tissue microenvironment, pyroptotic PDLSCs inhibited osteoblastogenesis and promoted osteoclastogenesis, which exacerbated the pathological damage of periodontitis. Pharmacological inhibition of caspase-4 or IL-1β antibody blockade in a rat periodontitis model lead to the significantly reduced loss of alveolar bone and periodontal ligament damage. Furthermore, Gsdmd deficiency alleviated periodontal inflammation and bone loss in mouse experimental periodontitis. These findings indicate that GSDMD-driven PDLSC pyroptosis and loss plays a pivotal role in the pathogenesis of periodontitis by increasing IL-1β release, enhancing inflammation, and promoting osteoclastogenesis.

It is unclear whether vascular endothelial growth factor (VEGF) can initiate osteoarthritis (OA) in the temporomandibular joint (TMJ). In this study we evaluated the effects of intra-articular injection of exogenous VEGF in the TMJ in mice on the early stage. Forty-eight male Sprague-Dawley mice were equally divided into 3 groups. In the vegf group, the mice received an injection of VEGF solution (50 μL) in the TMJ once a week over a period of 4 weeks. In the sham group, the mice received an injection of saline (50 μL). The control group did not receive any injection. Four mice from each group were sacrificed at 1, 2, 4, and 8 weeks. Gradual prominent cartilage degeneration was observed in the vegf group. Additionally, this group showed higher expressions of metalloproteinase (MMP)-9, MMP-13, receptor activator of nuclear factor-kappa-B ligand (RANKL), and a higher number of apoptotic chondrocytes and VEGF receptor 2 (VEGFR2)-positive chondrocytes. Micro-computed tomography (CT) revealed prominent subchondral bone resorption in the vegf group, with a high number of osteoclasts in the subchondral bone. In vitro study demonstrated that VEGF can promote osteoclast differentiation. In conclusion, our study found that VEGF can initiate TMJ OA by destroying cartilage and subchondral bone.

Numerous studies have provided experience-based or theory-based frameworks for the basis of judgment of learning (JOL). However, few studies have directly measured processing experience and beliefs related to the same cue in one experiment and examined their joint contribution to JOLs. The present study focused on font-size effects and aimed to examine the simultaneous contribution of processing fluency and beliefs to the effect of font size on JOLs. We directly measured processing fluency via self-paced study time. We also directly measured participants’ beliefs via two approaches: pre-study global differentiated predictions (GPREDs) as an indicator of preexisting beliefs about font size and memory and ease of learning judgments (EORs) as online generated item-specific beliefs about fluency. In Experiment 1, EORs partially mediated the font-size effect, whereas self-paced study time did not. In Experiments 2a and 2b, EORs mediated the font-size effect; at the same time, beliefs about font size and memory moderated the font-size effect. In summary, the present study demonstrates a major role of beliefs underlying the font-size effect.

BackgroundTotal temporomandibular joint (TMJ) prosthesis is an effective and reliable method of joint reconstruction. However, there is still an urgent need to design a new TMJ prosthesis because of no commercially available TMJ prosthesis appropriate for the clinical application on the Chinese population. This study was introduced to prospectively confirm the safety and effectiveness of a new TMJ prosthesis with customized design and 3D printing additive fabrication in clinical application.MethodsPatients with unilateral end-stage TMJ osteoarthrosis were recruited in this study from Nov 2016 to Mar 2017. Computed tomography scans for all patients were obtained and transformed into three-dimensional (3D) reconstruction models. The customized TMJ prosthesis consisted of three components including the fossa, condylar head, and mandibular handle units, which were designed based on the anatomy of the TMJ and were fabricated using the 3D printing technology. The prominent characters of the prosthesis were the customized design of the fossa component with a single ultra-high-molecular-weight polyethylene and the connection mechanism between the condylar head (Co–Cr–Mo alloy) and mandibular handle components (Ti6Al4 V alloy). The clinical follow-up, radiographic evaluation and laboratory indices were all done to analyze the prosthesis’ outcomes in the clinical application.Results12 consecutive patients were included in the study. There were no complications (infection of the surgical wound, damage of liver and kidney, displacement, breakage, or loosening of the prosthesis) found after surgery. Pain, diet, mandibular function, and maximal interincisal opening showed significant improvements after surgery. But the lateral movement was limited to the non-operated side and the mandible deviated towards the operated side on opening mouth following surgery.ConclusionsThe presented TMJ prosthesis is considered an innovative product in TMJ Yang’s system, which is unique compared to other prostheses for the special design and 3D printing additive manufacture. Moreover, the prosthesis is very safe and efficient for clinical use.Trial registration Prospective reports on Chinese customized total temporomandibular joint prosthesis reconstruction cases, ChiCTR-ONC-16009712. Registered 22 Nov 2016, http://www.chictr.org.cn/showproj.aspx?proj=16091

A common technique for source localization is to utilize the time-of-arrival (TOA) measurements between the source and several spatially separated sensors. The TOA information defines a set of circular equations from which the source position can be calculated with the knowledge of the sensor positions. Apart from nonlinear optimization, least squares calibration (LSC) and linear least squares (LLS) are two computationally simple positioning alternatives which reorganize the circular equations into a unique and non-unique set of linear equations, respectively. As the LSC and LLS algorithms employ standard least squares (LS), an obvious improvement is to utilize weighted LS estimation. In this paper, it is proved that the best linear unbiased estimator (BLUE) version of the LLS algorithm will give identical estimation performance as long as the linear equations correspond to the independent set. The equivalence of the BLUE-LLS approach and the BLUE variant of the LSC method is analyzed. Simulation results are also included to show the comparative performance of the BLUE-LSC, BLUE-LLS, LSC, LLS and constrained weighted LSC methods with Cramér-Rao lower bound.

The dual-basis theory of metamemory suggests that people evaluate their memory performance based on both processing experience during the memory process and their prior beliefs about overall memory ability. However, few studies have proposed a formal computational model to quantitatively characterize how processing experience and prior beliefs are integrated during metamemory monitoring. Here, we introduce a Bayesian inference model for metamemory (BIM) which provides a theoretical and computational framework for the metamemory monitoring process. BIM assumes that when people evaluate their memory performance, they integrate processing experience and prior beliefs via Bayesian inference. We show that BIM can be fitted to recall or recognition tasks with confidence ratings on either a continuous or discrete scale. Results from data simulation indicate that BIM can successfully recover a majority of generative parameter values, and demonstrate a systematic relationship between parameters in BIM and previous computational models of metacognition such as the stochastic detection and retrieval model (SDRM) and the meta-d′ model. We also show examples of fitting BIM to empirical data sets from several experiments, which suggest that the predictions of BIM are consistent with previous studies on metamemory. In addition, when compared with SDRM, BIM could more parsimoniously account for the data of judgments of learning (JOLs) and memory performance from recall tasks. Finally, we discuss an extension of BIM which accounts for belief updating, and conclude with a discussion of how BIM may benefit metamemory research.

A popular strategy for source localization is to utilize the measured differences in arrival times of the source signal at multiple pairs of receivers. Most of the time-difference-ofarrival (TDOA) based algorithms in the literature assume that the signal transmission speed is known which is valid for in-air propagation. However, for in-solid scenarios such as seismic and tangible acoustic interface applications, the signal propagation speed is unknown. In this paper, we exploit the ideas in the two-step weighted least squares method [1] to design a three-step algorithm for joint source position and propagation speed estimation. Simulation results are included to contrast the proposed estimator with the linear least squares scheme as well as Cramér-Rao lower bound.