Obesity and periodontitis: A systematic review and updated meta-analysis.

Background: A previous 2014 meta-analysis reported a positive association between obesity and periodontitis. It was considered necessary to update the recently published papers and to analyse subgroups on important clinical variables that could affect the association between obesity and periodontitis. Therefore, we updated the latest studies and attempted to derive more refined results. Methods: All observational studies were eligible for inclusion. The Newcastle-Ottawa scale was used to qualitatively evaluate the risk of bias. Subgroup analyses were conducted for patients aged 18-34, 35-54, and 55+ years and the countries (European countries, USA, Brazil, Japan, Korea, and other Asian countries). Results: Thirty-seven full-text articles were included. Obesity conferred increased odds of periodontal disease with an odds ratio (1.35, 95% CI: 1.05-1.75). In the subgroup analysis by age, the odds ratio was the highest in the 18-34 years group (2.21, 95% CI: 1.26-3.89). In the subgroup analysis by country, European countries had the highest odds ratio (2.46, 95% CI: 1.11-5.46). Conclusion: Despite the differences in degree, a positive association between obesity and periodontitis was found regardless of country or age. Therefore, medical professionals should try to prevent periodontitis by controlling patient weights, and more studies should be conducted to determine the association between obesity and oral health. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022301343.

A Dual-Responsive Magnetoactive and Electro-Ionic Soft Actuator Derived from a Nickel-Based Metal-Organic Framework.

There is growing demand for multiresponsive soft actuators for the realization of natural, safe, and complex motions in robotic interactions. In particular, soft actuators simultaneously stimulated by electrical and magnetic fields are always under development owing to their simple controllability and reliability during operation. Herein, magnetically and electrically driven dual-responsive soft actuators (MESAs) derived from novel nickel-based metal-organic frameworks (Ni-MOFs-700C), are reported. Nanoscale Ni-MOFs-700C has excellent electrochemical and magnetic properties that allow it to be used as a multifunctional material under both magnetoactive and electro-ionic actuations. The dual-responsive MESA exhibits a bending displacement of 30 mm and an ultrafast rising time of 1.5 s under a very low input voltage of 1 V and also exerts a bending deflection of 12.5 mm at 50 mT under a high excitation frequency of 5 Hz. By utilizing a dual-responsive MESA, the hovering motion of a hummingbird robot is demonstrated under magnetic and electrical stimuli.

Colored Point Cloud Registration by Depth Filtering.

In the last stage of colored point cloud registration, depth measurement errors hinder the achievement of accurate and visually plausible alignments. Recently, an algorithm has been proposed to extend the Iterative Closest Point (ICP) algorithm to refine the measured depth values instead of the pose between point clouds. However, the algorithm suffers from numerical instability, so a postprocessing step is needed to restrict erroneous output depth values. In this paper, we present a new algorithm with improved numerical stability. Unlike the previous algorithm heavily relying on point-to-plane distances, our algorithm constructs a cost function based on an adaptive combination of two different projected distances to prevent numerical instability. We address the problem of registering a source point cloud to the union of the source and reference point clouds. This extension allows all source points to be processed in a unified filtering framework, irrespective of the existence of their corresponding points in the reference point cloud. The extension also improves the numerical stability of using the point-to-plane distances. The experiments show that the proposed algorithm improves the registration accuracy and provides high-quality alignments of colored point clouds.

CTF-based soft touch actuator for playing electronic piano.

In the field of bioinspired soft robotics, to accomplish sophisticated tasks in human fingers, electroactive artificial muscles are under development. However, most existing actuators show a lack of high bending displacement and irregular response characteristics under low input voltages. Here, based on metal free covalent triazine frameworks (CTFs), we report an electro-ionic soft actuator that shows high bending deformation under ultralow input voltages that can be implemented as a soft robotic touch finger on fragile displays. The as-synthesized CTFs, derived from a polymer of intrinsic microporosity (PIM-1), were combined with poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) to make a flexible electrode for a high-performance electro-ionic soft actuator. The proposed soft touch finger showed high peak-to-peak displacement of 17.0 mm under ultralow square voltage of ±0.5 V, with 0.1 Hz frequency and 4 times reduced phase delay in harmonic response compared with that of a pure PEDOT-PSS-based actuator. The significant actuation performance is mainly due to the unique physical and chemical configurations of CTFs electrode with highly porous and electrically conjugated networks. On a fragile display, the developed soft robotic touch finger array was successfully used to perform soft touching, similar to that of a real human finger; device was used to accomplish a precise task, playing electronic piano.

An Efficient Three-Dimensional Convolutional Neural Network for Inferring Physical Interaction Force from Video.

Interaction forces are traditionally predicted by a contact type haptic sensor. In this paper, we propose a novel and practical method for inferring the interaction forces between two objects based only on video data-one of the non-contact type camera sensors-without the use of common haptic sensors. In detail, we could predict the interaction force by observing the texture changes of the target object by an external force. For this purpose, our hypothesis is that a three-dimensional (3D) convolutional neural network (CNN) can be made to predict the physical interaction forces from video images. In this paper, we proposed a bottleneck-based 3D depthwise separable CNN architecture where the video is disentangled into spatial and temporal information. By applying the basic depthwise convolution concept to each video frame, spatial information can be efficiently learned; for temporal information, the 3D pointwise convolution can be used to learn the linear combination among sequential frames. To validate and train the proposed model, we collected large quantities of datasets, which are video clips of the physical interactions between two objects under different conditions (illumination and angle variations) and the corresponding interaction forces measured by the haptic sensor (as the ground truth). Our experimental results confirmed our hypothesis; when compared with previous models, the proposed model was more accurate and efficient, and although its model size was 10 times smaller, the 3D convolutional neural network architecture exhibited better accuracy. The experiments demonstrate that the proposed model remains robust under different conditions and can successfully estimate the interaction force between objects.

Real-Time Gas Mixture Analysis Using Mid-Infrared Membrane Microcavities.

Real-time gas analysis on-a-chip was demonstrated using a mid-infrared (mid-IR) microcavity. Optical apertures for the microcavity were made of ultrathin silicate membranes embedded in a silicon chip using the complementary metal-oxide-semiconductor (CMOS) process. Fourier transform infrared spectroscopy (FTIR) shows that the silicate membrane is transparent in the range of 2.5-6.0 µm, a region that overlaps with multiple characteristic gas absorption lines and therefore enables gas detection applications. A test station integrating a mid-IR tunable laser, a microgas delivery system, and a mid-IR camera was assembled to evaluate the gas detection performance. CH4, CO2, and N2O were selected as analytes due to their strong absorption bands at λ = 3.25-3.50, 4.20-4.35, and 4.40-4.65 µm, which correspond to C-H, C-O, and O-N stretching, respectively. A short subsecond response time and high gas identification accuracy were achieved. Therefore, our chip-scale mid-IR sensor provides a new platform for an in situ, remote, and embedded gas monitoring system.

Inferring Interaction Force from Visual Information without Using Physical Force Sensors.

In this paper, we present an interaction force estimation method that uses visual information rather than that of a force sensor. Specifically, we propose a novel deep learning-based method utilizing only sequential images for estimating the interaction force against a target object, where the shape of the object is changed by an external force. The force applied to the target can be estimated by means of the visual shape changes. However, the shape differences in the images are not very clear. To address this problem, we formulate a recurrent neural network-based deep model with fully-connected layers, which models complex temporal dynamics from the visual representations. Extensive evaluations show that the proposed learning models successfully estimate the interaction forces using only the corresponding sequential images, in particular in the case of three objects made of different materials, a sponge, a PET bottle, a human arm, and a tube. The forces predicted by the proposed method are very similar to those measured by force sensors.

In vitro performance of a shape memory polymer foam-coated coil embolization device.

Intracranial saccular aneurysm treatment using endovascular embolization devices are limited by aneurysm recurrence that can lead to aneurysm rupture. A shape memory polymer (SMP) foam-coated coil (FCC) embolization device was designed to increase packing density and improve tissue healing compared to current commercial devices. FCC devices were fabricated and tested using in vitro models to assess feasibility for clinical treatment of intracranial saccular aneurysms. FCC devices demonstrated smooth delivery through tortuous pathways similar to control devices as well as greater than 10 min working time for clinical repositioning during deployment. Furthermore, the devices passed pilot verification tests for particulates, chemical leachables, and cytocompatibility. Finally, devices were successfully implanted in an in vitro saccular aneurysm model with large packing density. Though improvements and future studies evaluating device stiffness were identified as a necessity, the FCC device demonstrates effective delivery and packing performance that provides great promise for clinical application of the device in treatment of intracranial saccular aneurysms.

Particulate Release From Nanoparticle-Loaded Shape Memory Polymer Foams.

Highly porous, open-celled shape memory polymer (SMP) foams are being developed for a number of vascular occlusion devices. Applications include abdominal aortic and neurovascular aneurysm or peripheral vascular occlusion. A major concern with implanting these high surface area materials in the vasculature is the potential to generate unacceptable particulate burden, in terms of number, size, and composition. This study demonstrates that particulate numbers and sizes in SMP foams are in compliance with limits stated by the most relevant standard and guidance documents. Particulates were quantified in SMP foams as made, postreticulation, and after incorporating nanoparticles intended to increase material toughness and improve radiopacity. When concentrated particulate treatments were administered to fibroblasts, they exhibited high cell viability (100%). These results demonstrate that the SMP foams do not induce an unacceptable level of risk to potential vascular occlusion devices due to particulate generation.

Comparison of shape memory polymer foam versus bare metal coil treatments in an in vivo porcine sidewall aneurysm model.

The endovascular delivery of platinum alloy bare metal coils has been widely adapted to treat intracranial aneurysms. Despite the widespread clinical use of this technique, numerous suboptimal outcomes are possible. These may include chronic inflammation, low volume filling, coil compaction, and recanalization, all of which can lead to aneurysm recurrence, need for retreatment, and/or potential rupture. This study evaluates a treatment alternative in which polyurethane shape memory polymer (SMP) foam is used as an embolic aneurysm filler. The performance of this treatment method was compared to that of bare metal coils in a head-to-head in vivo study utilizing a porcine vein pouch aneurysm model. After 90 and 180 days post-treatment, gross and histological observations were used to assess aneurysm healing. At 90 days, the foam-treated aneurysms were at an advanced stage of healing compared to the coil-treated aneurysms and showed no signs of chronic inflammation. At 180 days, the foam-treated aneurysms exhibited an 89-93% reduction in cross-sectional area; whereas coiled aneurysms displayed an 18-34% area reduction. The superior healing in the foam-treated aneurysms at earlier stages suggests that SMP foam may be a viable alternative to current treatment methods. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1892-1905, 2017.

In vitro and in vivo evaluation of a shape memory polymer foam-over-wire embolization device delivered in saccular aneurysm models.

Current endovascular therapies for intracranial saccular aneurysms result in high recurrence rates due to poor tissue healing, coil compaction, and aneurysm growth. We propose treatment of saccular aneurysms using shape memory polymer (SMP) foam to improve clinical outcomes. SMP foam-over-wire (FOW) embolization devices were delivered to in vitro and in vivo porcine saccular aneurysm models to evaluate device efficacy, aneurysm occlusion, and acute clotting. FOW devices demonstrated effective delivery and stable implantation in vitro. In vivo porcine aneurysms were successfully occluded using FOW devices with theoretical volume occlusion values greater than 72% and rapid, stable thrombus formation. © 2015 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 104B: 1407-1415, 2016.

Markov Network-Based Unified Classifier for Face Recognition.

In this paper, we propose a novel unifying framework using a Markov network to learn the relationships among multiple classifiers. In face recognition, we assume that we have several complementary classifiers available, and assign observation nodes to the features of a query image and hidden nodes to those of gallery images. Under the Markov assumption, we connect each hidden node to its corresponding observation node and the hidden nodes of neighboring classifiers. For each observation-hidden node pair, we collect the set of gallery candidates most similar to the observation instance, and capture the relationship between the hidden nodes in terms of a similarity matrix among the retrieved gallery images. Posterior probabilities in the hidden nodes are computed using the belief propagation algorithm, and we use marginal probability as the new similarity value of the classifier. The novelty of our proposed framework lies in the method that considers classifier dependence using the results of each neighboring classifier. We present the extensive evaluation results for two different protocols, known and unknown image variation tests, using four publicly available databases: 1) the Face Recognition Grand Challenge ver. 2.0; 2) XM2VTS; 3) BANCA; and 4) Multi-PIE. The result shows that our framework consistently yields improved recognition rates in various situations.

Design and biocompatibility of endovascular aneurysm filling devices.

The rupture of an intracranial aneurysm, which can result in severe mental disabilities or death, affects approximately 30,000 people in the United States annually. The traditional surgical method of treating these arterial malformations involves a full craniotomy procedure, wherein a clip is placed around the aneurysm neck. In recent decades, research and device development have focused on new endovascular treatment methods to occlude the aneurysm void space. These methods, some of which are currently in clinical use, utilize metal, polymeric, or hybrid devices delivered via catheter to the aneurysm site. In this review, we present several such devices, including those that have been approved for clinical use, and some that are currently in development. We present several design requirements for a successful aneurysm filling device and discuss the success or failure of current and past technologies. We also present novel polymeric-based aneurysm filling methods that are currently being tested in animal models that could result in superior healing.

Estimation of aneurysm wall stresses created by treatment with a shape memory polymer foam device.

In this study, compliant latex thin-walled aneurysm models are fabricated to investigate the effects of expansion of shape memory polymer foam. A simplified cylindrical model is selected for the in-vitro aneurysm, which is a simplification of a real, saccular aneurysm. The studies are performed by crimping shape memory polymer foams, originally 6 and 8 mm in diameter, and monitoring the resulting deformation when deployed into 4-mm-diameter thin-walled latex tubes. The deformations of the latex tubes are used as inputs to physical, analytical, and computational models to estimate the circumferential stresses. Using the results of the stress analysis in the latex aneurysm model, a computational model of the human aneurysm is developed by changing the geometry and material properties. The model is then used to predict the stresses that would develop in a human aneurysm. The experimental, simulation, and analytical results suggest that shape memory polymer foams have potential of being a safe treatment for intracranial saccular aneurysms. In particular, this work suggests oversized shape memory foams may be used to better fill the entire aneurysm cavity while generating stresses below the aneurysm wall breaking stresses.

Face recognition system using multiple face model of hybrid Fourier feature under uncontrolled illumination variation.

The authors present a robust face recognition system for large-scale data sets taken under uncontrolled illumination variations. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid Fourier-based facial feature extraction, and a score fusion scheme. First, in the preprocessing stage, a face image is transformed into an illumination-insensitive image, called an "integral normalized gradient image," by normalizing and integrating the smoothed gradients of a facial image. Then, for feature extraction of complementary classifiers, multiple face models based upon hybrid Fourier features are applied. The hybrid Fourier features are extracted from different Fourier domains in different frequency bandwidths, and then each feature is individually classified by linear discriminant analysis. In addition, multiple face models are generated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system using the face recognition grand challenge (FRGC) experimental protocols is evaluated; FRGC is a large available data set. Experimental results on the FRGC version 2.0 data sets have shown that the proposed method shows an average of 81.49% verification rate on 2-D face images under various environmental variations such as illumination changes, expression changes, and time elapses.