Experimental study on penetration of dental implants into the maxillary sinus in different depths

The exposing of dental implant into the maxillary sinus combined with membrane perforation might increase risks of implant failure and sinus complications. Objective: The purpose of this study was to investigate the effects of the dental implant penetration into the maxillary sinus cavity in different depths on osseointegration and sinus health in a dog model. Material and Methods: Sixteen titanium implants were placed in the bilateral maxillary molar areas of eight adult mongrel dogs, which were randomly divided into four groups according to the different penetrating extents of implants into the sinus cavities (group A: 0 mm; group B: 1 mm; group C: 2 mm; group D: 3 mm). The block biopsies were harvested five months after surgery and evaluated by radiographic observation and histological analysis. Results: No signs of inflammatory reactions were observed in any maxillary sinus of the eight dogs. The tips of the implants with penetrating depth of 1 mm and 2 mm were found to be fully covered with newly formed membrane and partially with new bone. The tips of the implants with penetrating depth over 3 mm were exposed in the sinus cavity and showed no membrane or bone coverage. No significant differences were found among groups regarding implant stability, bone-to-implant contact (BIC) and bone area in the implant threads (BA). Conclusions: Despite the protrusion extents, penetration of dental implant into the maxillary sinus with membrane perforation does not compromise the sinus health and the implant osseointegration in canine

High Precision Multi-parameter Weak Measurement with Hermite-Gaussian Pointer

The weak value amplification technique has been proved useful for precision metrology in both theory and experiment. To explore the ultimate performance of weak value amplification for multi-parameter estimation, we investigate a general weak measurement formalism with assistance of high-order Hermite-Gaussian pointer and quantum Fisher information matrix. Theoretical analysis shows that the ultimate precision of our scheme is improved by a factor of square root of 2n+1, where n is the order of Hermite-Gaussian mode. Moreover, the parameters' estimation precision can approach the precision limit with maximum likelihood estimation method and homodyne method. We have also given a proof-of-principle experimental setup to validate the H-G pointer theory and explore its potential applications in precision metrology

Ultrasensitive Measurement of Angular Rotations via Hermite-Gaussian Pointer

Exploring high sensitivity on the measurement of angular rotations is an outstanding challenge in optics and metrology. In this work, we employ the mn-order Hermite-Gaussian beam in the weak measurement scheme with an angular rotation interaction, where the rotation information is taken by another HG mode state completely after the post-selection. By taking a projective measurement on the final light beam, the precision of angular rotation is improved by a factor of 2mn+m+n. For verification, we perform an optical experiment where the minimum detectable angular rotation improves $\sqrt{15}$-fold with HG55 mode over that of HG11 mode, and achieves a sub-microradian scale of the measurement precision. Our theoretical framework and experimental results not only provide a more practical and convenient scheme for ultrasensitive measurement of angular rotations, but also contribute to a wide range of applications in quantum metrology.Comment: 21 pages, 8 figures, 3 tables. Published in Photonics Researc

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.</p

Exploring the Cascading Failure in Taxi Transportation Networks

To explore the ability of taxi transportation service capacity in unexpected conditions, based on the taxi GPS trajectory data, this paper presented a taxi transportation network and explored a cascading failure model with the non-linear function of traffic intensity as the initial load. Moreover, the cascading failure conditions for different initial loads with different parameter settings were derived by combining the complex network theory. We verified the ability of taxi transportation networks to withstand unexpected conditions and analyzed the differences and features of taxi transportation service capacity for different areas of Lanzhou city. Three sets of comparative simulation experiments were implemented. The results show that when the initial load regulation factor α1/θ, the failure of nodes with smaller initial loads in the network is more likely to cause cascading failure phenomena. When α>1/θ, the failure of nodes with larger initial loads in the network is more likely to cause cascading failure phenomena. Additionally, when α=1/θ, there is no significant correlation between whether cascading failure phenomena occur in the network and node loads. This study can provide a prior basis for decision-making in the management of urban taxi operations under different passenger flow intensities

Experimental study on penetration of dental implants into the maxillary sinus in different depths

The exposing of dental implant into the maxillary sinus combined with membrane perforation might increase risks of implant failure and sinus complications. Objective: The purpose of this study was to investigate the effects of the dental implant penetration into the maxillary sinus cavity in different depths on osseointegration and sinus health in a dog model. Material and Methods: Sixteen titanium implants were placed in the bilateral maxillary molar areas of eight adult mongrel dogs, which were randomly divided into four groups according to the different penetrating extents of implants into the sinus cavities (group A: 0 mm; group B: 1 mm; group C: 2 mm; group D: 3 mm). The block biopsies were harvested five months after surgery and evaluated by radiographic observation and histological analysis. Results: No signs of inflammatory reactions were observed in any maxillary sinus of the eight dogs. The tips of the implants with penetrating depth of 1 mm and 2 mm were found to be fully covered with newly formed membrane and partially with new bone. The tips of the implants with penetrating depth over 3 mm were exposed in the sinus cavity and showed no membrane or bone coverage. No significant differences were found among groups regarding implant stability, bone-to-implant contact (BIC) and bone area in the implant threads (BA). Conclusions: Despite the protrusion extents, penetration of dental implant into the maxillary sinus with membrane perforation does not compromise the sinus health and the implant osseointegration in canine

Experimental Study on Tunnel Bottom Deformation Trend in Gently Inclined Layered Shale Based on the Energy Index

Influenced by the anisotropy and water-softening characteristics of gently inclined layered shale, many tunnels have encountered bottom deformation issues during construction and operation, which severely impact the safety of tunnel structures. The energy evolution law during rock deformation and damage can provide support for the assessment and prediction of structure deformation. However, most studies have been conducted on enstatite, granite, and sandstone with limited research on shale. In this study, both conventional and single-cyclic loading-and-unloading uniaxial compression tests were conducted on shale specimens with varying dip angles of the structural plane (Dφ) and water content (Wc) in addressing the most typical layered shale in the Chaoyang Tunnel. The energy evolution features of rock samples at each stage of the tests were analyzed to determine the discriminating indicator (SC) for tunnel bottom deformation tendency. The indicator was based on the elastic strain energy (Uei) and the post-peak dissipation energy (Udi). The results demonstrated that the Dφ and Wc directly affected the energy storage and dissipation process of rock specimens, which in turn enabled them to exhibit different damage evolution features. The Uei and the total input energy (Uli) satisfied a linear relationship, which was determined by the Dφ and Wc of rock specimens. The energy evolution-based indicator SC can accurately characterize the bottom deformation of the tunnel constructed in a gently inclined layered shale stratum. The findings can offer a scientific foundation for rational evaluation of the structure deformation of tunnels under construction

Experiment Study on Damage Properties and Acoustic Emission Characteristics of Layered Shale under Uniaxial Compression

The gently tilt-layered shale displays anisotropic behavior and includes structural planes that cause the rock to exhibit weakened features. As a result, the load-bearing capacity and failure mechanisms of this type of rock differ significantly from those of other rock types. A series of uniaxial compression tests were performed on shale samples from the Chaoyang Tunnel to investigate damage evolution patterns and typical failure characteristics of gently tilt-layered shale. An acoustic emission testing system was incorporated to analyze the acoustic emission parameters of the shale samples during the loading process. The results indicate that the failure modes of the gently tilt-layered shale are significantly correlated with the structural plane angles and water content. The shale samples gradually transition from tension failure to tension-shear compound failure as the structural plane angles and water content increase, with an increasing level of damage. The maximum values of AE ringing counts and AE energy for shale samples with diverse structural plane angles and water content are reached near the peak stress and serve as precursors to rock failure. The primary factor influencing the failure modes of the rock samples is the structural plane angle. The precise correspondence between the structural plane angle, water content, crack propagation patterns, and failure modes of gently tilted layered shale can be captured by the distribution of the RA-AF values

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic “meta-network” framework to support flexible functional segregation and integration during language and speech processing