Notes

Evaluation of the Braden size throughout predicting operative results in more mature people undergoing significant head and neck surgical procedure.
Motor evoked potential (MEP), which was elicited by transcranial magnetic stimulation (TMS), has been widely used to detect corticospinal projection from TMS cortical site to trunk muscles. It can help to find the stimulation hotspot in the scalp. However, it fails to precisely describe coordinated activities of trunk muscle groups with only single-channel myoelectric signal. In this study, we aimed to use high-density surface electromyography (sEMG) to explore the effect of cortical TMS on lumbar paravertebral muscles in healthy subjects. The cortical site at 1 cm anterior and 4 cm lateral to vertex was chosen to simulate using a single-pulse TMS with different intensities and forward-bending angles. A high-density electrode array (45 channels) was placed on the surface of lumbar paravertebral muscles to record sEMG signals during a TMS experiment. MEP signals elicited by TMS were extracted from 45-channel recordings and one topographic map of the MEP amplitudes with six spatial features was constructed at each sampling point. The results showed TMS could successfully evoke an oval area with high intensity in the MEP topographic map, while this area mainly located in ipsilateral side of the TMS site. Intensity features related to the high intensity area rose significantly with TMS intensity and forward-bending angle increasing, but location features showed no change. The optimal stimulation parameters were 80% of maximum stimulator output (MSO) for TMS intensity and 30/60 degree for forward-bending angle. This study provided a potentially effective mapping tool to explore the hotspot for transcranial stimulation on trunk muscles.The state-of-the-art optical see-through head-mounted displays (OST-HMD) for augmented reality applications lack the ability to render correct light interaction behavior between digital and physical objects, known as mutual occlusion capability. This paper presents a novel optical architecture for enabling a compact, high performance, occlusion-capable optical see-through head-mounted display (OCOST-HMD) with correct, pupil-matched viewing perspective. The proposed design utilizes a single-layer, double-pass architecture, offering a compact OCOST-HMD solution that is capable of rendering per-pixel mutual occlusion, a correctly pupil-matched viewing between virtual and real views, and a very wide see-through field of view (FOV). Based on this architecture, we demonstrate a design embodiment and a compact prototype implementation. The prototype offers a virtual display with an FOV of 34 by 22, an angular resolution of 1.06 arc minutes per pixel, and an average image contrast greater than 40% at the Nyquist frequency of 53 cycles/mm. Further, the prototype system affords a wide see-though FOV of 90 by 50, within which about 40 diagonally is occlusion-enabled, along with an angular resolution of 1.0 arc minutes, comparable to a 20/20 vision and a dynamic range greater than 1001. Lastly, we composed a quantitative color study that compares the effects of occlusion between a conventional HMD system and our OCOST-HMD system and the resulting response exhibited in different studies.Single Image Super-Resolution (SISR) is one of the low-level computer vision problems that has received increased attention in the last few years. (R)Propranolol Current approaches are primarily based on harnessing the power of deep learning models and optimization techniques to reverse the degradation model. Owing to its hardness, isotropic blurring or Gaussians with small anisotropic deformations have been mainly considered. Here, we widen this scenario by including large non-Gaussian blurs that arise in real camera movements. Our approach leverages the degradation model and proposes a new formulation of the Convolutional Neural Network (CNN) cascade model, where each network sub-module is constrained to solve a specific degradation deblurring or upsampling. A new densely connected CNN-architecture is proposed where the output of each sub-module is restricted using some external knowledge to focus it on its specific task. As far we know, this use of domain-knowledge to module-level is a novelty in SISR. To fit the finest model, a final sub-module takes care of the residual errors propagated by the previous sub-modules. We check our model with three state-of-the-art (SOTA) datasets in SISR and compare the results with the SOTA models. The results show that our model is the only one able to manage our wider set of deformations. Furthermore, our model overcomes all current SOTA methods for a standard set of deformations. In terms of computational load, our model also improves on the two closest competitors in terms of efficiency. Although the approach is non-blind and requires an estimation of the blur kernel, it shows robustness to blur kernel estimation errors, making it a good alternative to blind models.The automatic detection and identification of fish from underwater videos is of great significance for fishery resource assessment and ecological environment monitoring. However, due to the poor quality of underwater images and unconstrained fish movement, traditional hand-designed feature extraction methods or convolutional neural network (CNN)-based object detection algorithms cannot meet the detection requirements in real underwater scenes. Therefore, to realize fish recognition and localization in a complex underwater environment, this paper proposes a novel composite fish detection framework based on a composite backbone and an enhanced path aggregation network called Composited FishNet. By improving the residual network (ResNet), a new composite backbone network (CBresnet) is designed to learn the scene change information (source domain style), which is caused by the differences in the image brightness, fish orientation, seabed structure, aquatic plant movement, fish species shape and texture differences. Thus, the interference of underwater environmental information on the object characteristics is reduced, and the output of the main network to the object information is strengthened. In addition, to better integrate the high and low feature information output from CBresnet, the enhanced path aggregation network (EPANet) is also designed to solve the insufficient utilization of semantic information caused by linear upsampling. The experimental results show that the average precision (AP)0.50.95, AP50 and average recall (AR)max=10 of the proposed Composited FishNet are 75.2%, 92.8% and 81.1%, respectively. The composite backbone network enhances the characteristic information output of the detected object and improves the utilization of characteristic information. This method can be used for fish detection and identification in complex underwater environments such as oceans and aquaculture.
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