BackgroundAnterior lumbar interbody fusion (ALIF) followed by pedicle screw fixation (PSF) is used to restore the height of the intervertebral disc and provide stability. Recently, stand-alone interbody cage with anterior fixation has been introduced, which eliminates the need for posterior surgery. We compared the biomechanics of the stand-alone interbody cage to that of the interbody cage with additional PSF in ALIF.MethodsA three-dimensional, non-linear finite element model (FEM) of the L2-5 segment was modified to simulate ALIF in L3-4. The models were tested under the following conditions: (1) intact spine, (2) destabilized spine, (3) with the interbody cage alone (type 1), (4) with the stand-alone cage with anterior fixation (SynFix-LR®; type 2), and (5) with type 1 in addition to PSF (type 3). Range of motion (ROM) and the stiffness of the operated level, ROM of the adjacent segments, load sharing distribution, facet load, and vertebral body stress were quantified with external loading.ResultsThe implanted models had decreased ROM and increased stiffness compared to those of the destabilized spine. The type 2 had differences in ROM limitation of 8%, 10%, 4%, and 6% in flexion, extension, axial rotation, and lateral bending, respectively, compared to those of type 3. Type 2 had decreased ROM of the upper and lower adjacent segments by 3-11% and 3-6%, respectively, compared to those of type 3. The greatest reduction in facet load at the operated level was observed in type 3 (71%), followed by type 2 (31%) and type 1 (23%). An increase in facet load at the adjacent level was highest in type 3, followed by type 2 and type 1. The distribution of load sharing in type 2 (anterior:posterior, 95:5) was similar to that of the intact spine (89:11), while type 3 migrated posterior (75:25) to the normal. Type 2 reduced about 15% of the stress on the lower vertebral endplate compared to that in type 1. The stress of type 2 increased two-fold compared to the stress of type 3, especially in extension.ConclusionsThe stand-alone interbody cage can provide sufficient stability, reduce stress in adjacent levels, and share the loading distribution in a manner similar to an intact spine.

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a stateof-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

People with disabilities (PWD) face a number of challenges such as obstacle avoidance or taking a minimum path to reach a destination while travelling or taking public transport, especially in airports or bus stations. In some cases, PWD, and specifically visually impaired people, have to wait longer to overcome these situations. In order to solve these problems, the computer-vision community has applied a number of techniques that are nonetheless insufficient to handle these situations. In this paper, we propose a visual simultaneous localization and mapping for moving-person tracking (VSLAMMPT) method that can assist PWD in smooth movement by knowing a position in an unknown environment. We applied expected error reduction with active-semisupervised-learning (EER–ASSL)-based person detection to eliminate noisy samples in dynamic environments. After that, we applied VSLAMMPT for effective smoothing, obstacle avoidance, and uniform navigation in an indoor environment. We analyze the joint approach symmetrically and applied the proposed method to benchmark datasets and obtained impressive performance.

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In this study, we investigated the effect of enhanced collagen synthesis by Mychonastes sp. 249 (a type of freshwater microalgae) extract (MSE) on skin regeneration. The effect of MSE on human dermal fibroblast (HDF) migration was studied to confirm its skin regenerative effect, and at 1.0 mg/mL MSE increased HDF cell migration by 21.2%. To study the mechanism responsible, the mRNA levels of MMP-1 (matrix metalloproteinase-1), COL1A1 (collagen type I alpha 1), and SMAD-3 (suppressor of mothers against decapentaplegic-3) were evaluated by RT-PCR. The results showed that in the concentration range 0.0 to 1.0 mg/mL, MSE dose-dependently affected mRNA expressions in HDF cells (P<0.05). LC-MS/MS analysis identified eriodictyol (a flavonoid) as the main component of MSE. These results suggest that MSE could be used as a skin regeneration agent that inhibits collagen decomposition in HDF cells.