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This study is aimed at performing a systematic review and a network meta-analysis of the effects of several membranes on vertical bone regeneration and clinical complications in guided bone regeneration (GBR) or guided tissue regeneration (GTR). We compared the effects of the following membranes: high-density polytetrafluoroethylene (d-PTFE), expanded polytetrafluoroethylene (e-PTFE), crosslinked collagen membrane (CCM), noncrosslinked collagen membrane (CM), titanium mesh (TM), titanium mesh plus noncrosslinked (TM + CM), titanium mesh plus crosslinked (TM + CCM), titanium-reinforced d-PTFE, titanium-reinforced e-PTFE, polylactic acid (PLA), polyethylene glycol (PEG), and polylactic acid 910 (PLA910). Using the PICOS principles to help determine inclusion criteria, articles are collected using PubMed, Web of Science, and other databases. Assess the risk of deviation and the quality of evidence using the Cochrane Evaluation Manual, and GRADE. 27 articles were finally included. 19 articles were included in a network meta-analysis with vertical bone increment as an outcome measure. The network meta-analysis includes network diagrams, paired-comparison forest diagrams, funnel diagrams, surface under the cumulative ranking curve (SUCRA) diagrams, and sensitivity analysis diagrams. SUCRA indicated that titanium-reinforced d-PTFE exhibited the highest vertical bone increment effect. Meanwhile, we analyzed the complications of 19 studies and found that soft tissue injury and membrane exposure were the most common complications.

Operating experience has shown that vibration can cause pressure fluctuations and initiate bubbles at the oil gap, which seriously threatens the insulation properties of oil-immersed power equipment. The main aim of this study was to determine the characteristics and mechanism of bubble initiation at the transformer oil gap under vibration in this paper. The results show the existence of the vibration-dependent bubbling phenomenon at the oil gap. Interestingly, bubbles initiate during the process of gap expansion, and the evolution of bubble morphology was further analyzed. The threshold characteristic of the bubble initiation process was found, and the relevant influencing factors were experimentally obtained. In the range considered in this study, higher gas content, higher temperature, and lower moisture content are conducive to bubble initiation at the oil gap under vibration, while vibrational frequency does not affect the threshold characteristic of bubble initiation process. The vibration-dependent bubbling phenomenon can be attributed to the vibration-induced cavitation process caused by the decrease in pressure at the oil gap. The influence mechanism of the above factors on the threshold characteristics of bubble initiation was further discussed. This study contributes to providing a reference for risk assessment of abnormal vibration of oil-immersed power equipment.

Background Rho-associated protein kinase(ROCK) plays important roles in cell proliferation and migration, depending on cancer types. ROCK inhibitor Y-27632 was referred as a potential immunotherapy strategy for cancer treatment. Oral squamous cell carcinoma (OSCC) remains one of the most aggressive oral carcinoma types. Y-27632 was shown to block the growth, migration and invasion of Cal27 cells, but no detailed studies of underlying mechanisms have been reported. Methods In this study we explored the effect of Y27632 on OSCC (Cal27, SCC4, SCC9) by comparing with and without Y27632 treatment in vitro and in vivo. And further investigations were done to reveal the expression of AKT/mTOR pathway by Biochemical assays, additionally AKT activator (SC79) or mTOR activator (3BDO) was utilized to evaluate the roles of AKT/mTOR pathway in Y27632-induced tumor suppression. Results Our data showed Y-27632 had a strong inhibitory activity against OSCC (Cal27, SCC4, SCC9). In vivo assays confirmed that Y-27632 suppressed OSCC growth by reducing cell proliferation, and importantly Y-27632 did not have any evident toxicity on normal tissues. Biochemical assays demonstrated that Y-27632 inactivated the AKT/mTOR pathway, and treatment with SC79 or 3BDO, respectively AKT and mTOR activator rescued the cell growth and migration inhibition elicited by Y-27632. Further investigations revealed that Y-27632 could enhance cell autophagy by suppressing AKT/mTOR pathway. Conclusions Our study demonstrated that Y-27632 significantly suppressed the AKT/mTOR pathway to promote cell autophagy to inhibit OSCC growth, providing a potential therapeutic drug for OSCC treatment in the future.

With the development of ceramic technology, prosthodontic ceramics are becoming a useful option for improving esthetic outcomes in dentistry. In this paper, various ceramic materials were reviewed and evaluated, and their advantages and disadvantages and indications in oral prosthodontics were analyzed objectively. The properties of resin-based ceramics, polycrystalline ceramics, and silicate ceramics were compared and analyzed. Resin-based ceramics may replace other ceramic materials in the CAD/CAM field.

We propose an encoder–decoder CNN framework to predict depth from one single image in a self-supervised manner. To this aim, we design three kinds of encoder based on the recent advanced deep neural network and one kind of decoder which can generate multiscale predictions. Eight loss functions are designed based on the proposed encoder–decoder CNN framework to validate the performance. For training, we take rectified stereo image pairs as input of the CNN, which is trained by reconstructing image via learning multiscale disparity maps. For testing, the CNN can estimate the accurate depth information by inputting only one single image. We validate our framework on two public datasets in contrast to the state-of-the-art methods and our designed different variants, and the performance of different encoder–decoder architectures and loss functions is evaluated to obtain the best combination, which proves that our proposed method performs very well for single-image depth estimation without the supervision of ground truth.

To parse images into fine-grained semantic parts, the complex elements will put it in trouble when using off-the-shelf semantic segmentation networks, because it is difficult for them to utilize the contextual information of fine-grained parts. In this paper we propose a progressive decomposition method to parse images in a coarse-to-fine manner with refined semantic classes. It consists of two aspects: stacked networks and progressive supervisions. The stacked network is achieved by stacking some segmentation layers in a segmentation network. The former segmentation module parses images at a coarser-grained level, and the result will be fed to the following one to provide effective contextual clues for the finer-grained parsing. The skip connections from shallow layers of the network to fine-grained parsing modules are also added to recover the details of small structures. For the training of the stacked networks which have coarse-to-fine outputs, a strategy of progressive supervision is proposed to merge classes in ground truth to get coarse-to-fine label maps, and then train the stacked network end-to-end with the hierarchical supervisions. The proposed framework can be injected into many advanced neural networks to improve the parsing results. Extensive evaluations on several public datasets including face parsing and human parsing well demonstrate the superiority of our method.

In this study, density functional theory was employed to calculate the adsorption of polycaprolactone (PCL) by pure hydroxyapatite (HA), Zn-doped HA, and Ag-doped HA, and the interaction of PCL on the surface of HA (001) was simulated. The results show that there was significant electron transfer between the carbonyl O in PCL and the Zn, Ag, and Ca in HA, forming coordinate bonds. The binding energies of Ag-doped HA/PCL and Zn-doped HA/PCL were much higher than those of HA/PCL. HA doped with Ag had the highest binding energy to PCL. Therefore, we believe that when HA is doped with Ag atoms, its adsorption capacity for PCL can be increased. The results obtained in this study can be used as a guide for the development of HA/PCL bone graft composite material doped with appropriate metal ions to improve its adsorption capacity.

At the moment, unserviceable magnesium implants make a good case in point for further responsible study in this field. Whether we are willing to admit it or not, existing methods for corrosion monitoring are exposed to susceptibility and instability. Interdisciplinary theories and the existing corrosion experiments were combined based on their various merits for developing an accurate and precise corroding experiment for Mg/Mg alloys. We used the water-soluble tetrazolium-8 (WST-8) reagent to further complete the immersion experiment. The color change of the solution reflects the rationale of corrosion, followed by monitoring the degree of corrosion. The feasibility of this idea will be demonstrated.

Introduction. This study is aimed at illustrating the bimaxillary basal bone contours, to clarify the match of the basal bone arches of the upper and lower, especially the posterior segments, including the second molar and retromolar region. Methods. Based on 100 cone-beam computed tomography (CBCT) images (50 males and 50 females), we obtained 100 pairs of basal bone arches, which were the horizontal inner cortex contours passing the furcation of the first molar paralleled to the lower occlusal plane. The Generalized Procrustes Analysis (GPA) was applied to depict average contours and calculate the ratio and difference width of both upper and lower dental arches in different positions. Variations of the basal bone morphology among individuals were revealed using Principal Component Analysis (PCA). Results. The width discrepancy occurred at 7-7 segment (male: upper 65.62 mm and lower 68.81 mm and female: upper 62.98 mm and lower 68.38 mm) and the retromolar region (male: upper 64.67 mm and lower 71.96 mm and female: upper 62.34 mm and lower 71.44 mm). The ratio ( p = 0.006 ) and difference value ( p = 0.009 ) of 7-7 segment and the ratio of retromolar region ( p = 0.044 ) differed in genders. Setting 2 mm overjet, the upper basal bone arch was wider than the lower by approximate 2 mm on both sides, except the second molar and retromolar region. According to PCA, the variation of basal bone arches appeared mainly at terminal segments. Conclusions. For both male and female, the bimaxillary basal bone matched except terminal segments. Mismatch of female bimaxillary posterior basal bone was more pronounced than male. The basal bone arches of male were wider and longer than that of female.

Intraoral scanners have been widely used in the application of dentistry. Accuracy includes trueness and precision; they have an important position in the assessment of intraoral scanners. The existing standard models are divided into the inlay and the crown, but the operation is relatively complicated. In this study, in order to simplify the current standard model, we designed a new integration model to compare the accuracy of two intraoral scanners (CEREC and TRIOS) and an extraoral scanner (SHINING). The coordinate measuring machine measured value is the gold standard. Values of the length and angle were analyzed by converting the scanned digital impressions into an STL (standard triangulation language) format to evaluate the accuracy of the intraoral scanner and to verify the feasibility of the designed model. The result shows that the integration model can be successfully scanned and imaged. In the case of the powder-free integration model, intraoral scanner precision, trueness, 3D fitting, and imaging are better than the extraoral scanner. It can be seen straightly from the measurement result and the 3D fitting result that the intraoral scanner can acquire the shape of the standard model integrally with good repeatability. Therefore, it can be concluded that TRIOS is superior to CEREC and SHINING in accuracy, and the integration model is feasible as a reference in the examination of intraoral scanners. The performance of the newly designed integration model that can be scanned is clinically significant, suggesting that this model can be used as a standard reference model.

3D object reconstruction from a single image is a highly ill-posed problem, requiring strong prior knowledge of 3D shapes. Deep learning methods are popular for this task. Especially, most works utilized 3D deconvolution to generate 3D shapes. However, the resolution of results is limited by the high resource consumption of 3D deconvolution. In this paper, we propose SliceNet, sequentially generating 2D slices of 3D shapes with shared 2D deconvolution parameters. To capture relations between slices, the RNN is also introduced. Our model has three main advantages: First, the introduction of RNN allows the CNN to focus more on local geometry details,improving the results’ fine-grained plausibility. Second, replacing 3D deconvolution with 2D deconvolution reducs much consumption of memory, enabling higher resolution of final results. Third, an slice-aware attention mechanism is designed to provide dynamic information for each slice’s generation, which helps modeling the difference between multiple slices, making the learning process easier. Experiments on both synthesized data and real data illustrate the effectiveness of our method.

Lanthanum oxide was organically modified with dopamine and blended with polypropylene, and then lanthanum oxide (La2O3)/polypropylene (PP) nonwovens were prepared by melt blown method. The nonwovens were characterized by FTIR, SEM, TG, mechanical properties and X-ray shielding. The experimental results show that the addition of modified La2O3 makes the fiber arrangement of PP nonwovens more densely arranged and improves the thermal stability of PP nonwovens. The higher the amount of La2O3, the better the X-ray shielding performance of composite nonwovens.

In this paper, lanthanum oxide was modified by silane coupling agent (KH560), titanate coupler NDZ-201 and dopamine hydrochloride (DP). The microstructure and chemical structure of modified La2O3 were characterized by scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), and the dispersion of modified lanthanum oxide in polypropylene (PP) was investigated. The results show that the three modifiers can improve the organic properties of lanthanum oxide, and the interfacial compatibility between dopamine modified La2O3 and PP is better than the other two modifiers.

With the explosive growth of personal photos, an effective classification tool is becoming an urgent need to organize our progressive image collections. Facing the dynamically growing collections, we present a new method to categorize images effectively by integrating image clustering, incremental updating and user feedback together in an online framework. Considering the user burden and the user-specific preference during image classification, we propose several strategies to learn a customized classification model progressively for each user. Firstly, we use a multi-view learning method to learn the preferred classification perspective of the user. Secondly, we cluster similar images into groups according to user’s preference, so that images in a group can be categorized simultaneously with high efficiency. Thirdly, we propose a multi-centroid nearest class mean classifier to online learn the user’s preferred category granularity, and use it to classify the image groups. Unlike offline systems where pre-labeling and batch training often take hours or even days to perform, our approach is fully online. It can learn the classification model and classify newly acquired images alternately in no time. The sufficient experimental results and a user study demonstrate the effectiveness of the proposed method.

Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for robust co-saliency detection. This paper proposes a novel deep learning co-saliency detection approach which simultaneously learns single image properties and robust group feature in a recurrent manner. Specifically, our network first extracts the semantic features of each image. Then, a specially designed Recurrent Co-Attention Unit (RCAU) will explore all images in the group recurrently to generate the final group representation using the co-attention between images, and meanwhile suppresses noisy information. The group feature which contains complementary synergetic information is later merged with the single image features which express the unique properties to infer robust co-saliency. We also propose a novel co-perceptual loss to make full use of interactive relationships of whole images in the training group as the supervision in our end-to-end training process. Extensive experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

Photocatalytic conversion of CO2 into chemicals has been recognized as a promising strategy for combining virtuous carbon cycle and solar energy storage. The realization of this process depends on an efficient catalyst to activate CO2 molecule utilizing solar light. Rational design of hybrid structural photocatalyst comprising multiple components with distinctive functionalities is highly desirable for gas-phase CO2 photocatalytic conversion. Here we report iron oxide species modified zeolite ZSM-5 by function ion preadsorption assistance for the efficient photocatalytic reduction of CO2 to CO and aldehyde. Derived from the synergistic FeII and FeIII functions on ZSM-5, the FeOX/ZSM-5 composite catalysts furnished favorable band gaps and active sites for CO2 adsorption/activation as well as C C coupling. Significantly, the 3 wt% FeOX/ZSM-5 catalyst exhibits a high CO2 conversion rate of 17.6 μmol gcat−1 h−1, with a high selectivity (43.1 %) for aldehyde generation (7.6 μmol gcat−1 h−1).

To parse images into fine-grained semantic parts, the complex fine-grained elements will put it in trouble when using off-the-shelf semantic segmentation networks. In this paper, for image parsing task, we propose to parse images from coarse to fine with progressively refined semantic classes. It is achieved by stacking the segmentation layers in a segmentation network several times. The former segmentation module parses images at a coarser-grained level, and the result will be feed to the following one to provide effective contextual clues for the finer-grained parsing. To recover the details of small structures, we add skip connections from shallow layers of the network to fine-grained parsing modules. As for the network training, we merge classes in groundtruth to get coarse-to-fine label maps, and train the stacked network with these hierarchical supervision end-to-end. Our coarse-to-fine stacked framework can be injected into many advanced neural networks to improve the parsing results. Extensive evaluations on several public datasets including face parsing and human parsing well demonstrate the superiority of our method., Accepted for presentation in an ORAL session at ICME 2018

PURPOSE The aim of this study was to evaluate the effects of relining materials on the flexural strength of relined thermoplastic denture base resins (TDBRs). MATERIALS AND METHODS For shear bond strength testing, 120 specimens were fabricated using four TDBRs (EstheShot-Bright, Acrytone, Valplast, Weldenz) that were bonded with three autopolymerizing denture relining resins (ADRRs: Vertex Self-Curing, Tokuyama Rebase, Ufi Gel Hard) with a bond area of 6.0 mm in diameter and were assigned to each group (n=10). For flexural strength testing, 120 specimens measuring 64.0×10.0×3.3 mm (ISO-1567:1999) were fabricated using four TDBRs and three ADRRs and were assigned to each group (n=10). The thickness of the specimens measured 2.0 mm of TDBR and 1.3 mm of ADRR. Forty specimens using four TDBRs and 30 specimens using ADRRs served as the control. All specimens were tested on a universal testing machine. For statistical analysis, Analysis of variance (ANOVA) with Tukey's test as post hoc and Spearman's correlation coefficient analysis (P=.05) were performed. RESULTS Acry-Tone showed the highest shear bond strength, while Weldenz demonstrated the lowest bond strength between TDBR and ADRRs compared to other groups. EstheShot-Bright exhibited the highest flexural strength, while Weldenz showed the lowest flexural strength. Relined EstheShot-Bright demonstrated the highest flexural strength and relined Weldenz exhibited the lowest flexural strength (P

Red blood cells (RBC) of the patients with primary liver cancer and with cirrhosis were investigated by using Mossbauer spectroscopy. Control measurements were carried out on RBC from normal adults. The Mossbauer spectra of normal RBC are composed of two doublets corresponding to deoxy-Hb and Oxy-Hb. Besides disappearance or a decrease of the doublets corresponding to deoxy-Hb, no additional peak was detected in the samples from the patients.