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Functionalization associated with Crosslinked Sea Alginate/Gelatin Wet-Spun Porous Fibres using Nisin Z . for the Inhibition of Staphylococcus aureus-Induced Infections.
Frontotemporal Spectrum Disorder (FTSD) and Amyotrophic Lateral Sclerosis (ALS) are neurodegenerative diseases often considered as a continuum from clinical, epidemiologic, and genetic perspectives. Selleck Isoproterenol sulfate We used localized brain volume alterations to evaluate common and specific features of FTSD, FTSD-ALS, and ALS patients to further understand this clinical continuum.

We used voxel-based morphometry on structural magnetic resonance images to localize volume alterations in group comparisons patients (20 FTSD, seven FTSD-ALS, and 18 ALS) versus healthy controls (39 CTR), and patient groups between themselves. We used mean whole-brain cortical thickness (




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) to assess whether its correlations with local brain volume could propose mechanistic explanations of the heterogeneous clinical presentations. We also assessed whether volume reduction can explain cognitive impairment, measured with frontal assessment battery, verbal fluency, and semantt cerebral and cerebellar involvement.
We identified common elements that explain the FTSD-ALS clinical continuum, while also identifying specificities of each group, partially explained by different cerebral and cerebellar involvement.Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional single reward function under the same random initialization of a Q-matrix. We trained this control algorithm to adapt to several walking speed datasets under one control policy and subsequently compared its performance with that of other control algorithms. The results showed that our proposed reward function performed better than the conventional single reward function in terms of the normalized root mean squared error and also showed a faster convergence trend. Furthermore, our control strategy converged within our desired performance index and could adapt to several walking speeds. Our proposed control structure has also an overall better performance compared to user-adaptive control, while some of its walking speeds performed better than the neural network predictive control from existing studies.Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.Carotid plaque neovascularization is one of the major factors for the classification of vulnerable plaque, but the axial force effects of the pulsatile blood flow on the plaque with neovessel and intraplaque hemorrhage was unclear. Together with the severity of stenosis, the fibrous cap thickness, large lipid core, and the neovascularization followed by intraplaque hemorrhage (IPH) have been regarded as high-risk features of plaque rupture. In this work, the effects of these factors were evaluated on the progression and rupture of the carotid atherosclerotic plaques. Five geometries of carotid artery plaque were developed based on contrast-enhanced ultrasound (CEUS) images, which contain two types of neovessel and IPH, and geometry without neovessel and IPH. A one-way fluid-structure interaction model was applied to compute the maximum principal stress and strain in the plaque. For that hyper-elastic and non-linear material, Yeoh 3rd Order strain energy density function was used for components of the plaque. The simulation results indicated that the maximum principal stress of plaque in the carotid artery was higher when the degree of the luminal stenosis increased and the thickness of the fibrous cap decreased.
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