Gauthiermurphy0369

-bulges are irregularities inside the -sheets. They represent more than 3% of the protein residues, i.e. they are as frequent as 3.10 helices. In terms of evolution, -bulges are not more conserved than any other local protein conformations within homologous protein structures. In a first of its kind study, we have investigated the dynamical behaviour of -bulges using the largest known set of protein molecular dynamics simulations. We observed that more than 50% of the existing -bulges in protein crystal structures remained stable during dynamics while more than1/6th were not stable at all and disappeared entirely. Surprisingly, 1.1% of -bulges that appeared remained stable. -bulges have been categorized in different subtypes. The most common -bulges types are the smallest insertion in -strands (namely AC and AG); they are found as stable as the whole -bulges dataset. Low occurring types (namely PC and AS), that have the largest insertions, are significantly more stable than expected. Thus, this pioneer study allowed to precisely quantify the stability of the -bulges, demonstrating their structural robustness, with few unexpected cases raising structural questions.In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and we investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and we compared the discriminant power with respect to standard power spectrum features. Tazemetostat At the group level, results showed that while spectral-coherence based network features were increasing in the sensorimotor areas, those based on imaginary-coherence were significantly decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we eventually assessed the potential of these network connectivity features in a simple off-line classification scenario. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.Invertible grayscale is a special kind of grayscale from which the original color can be recovered. Given an input color image, this seminal work tries to hide the color information into its grayscale counterpart while making it hard to recognize any anomalies. This powerful functionality is enabled by training a hiding sub-network and restoring sub-network in an end-to-end way. Despite its expressive results, two key limitations exist 1) The restored color image often suffers from some noticeable visual artifacts in the smooth regions. 2) It is very sensitive to JPEG compression, i.e., the original color information cannot be well recovered once the intermediate grayscale image is compressed by JPEG. To overcome these two limitations, this paper introduces adversarial training and JPEG simulator respectively. Specifically, two auxiliary adversarial networks are incorporated to make the intermediate grayscale images and final restored color images indistinguishable from normal grayscale and color images. And the JPEG simulator is utilized to simulate real JPEG compression during the online training so that the hiding and restoring sub-networks can automatically learn to be JPEG robust. Extensive experiments demonstrate that the proposed method is superior to the original invertible grayscale work both qualitatively and quantitatively while ensuring the JPEG robustness. We further show that the proposed framework can be applied under different types of grayscale constraints and achieve excellent results.Recent study has shown that the Total Generalized Variation (TGV) is highly effective in preserving sharp features as well as smooth transition variations for image processing tasks. However, currently there is no existing work that is suitable for applying TGV to 3D data, in particular, triangular meshes. In this paper, we develop a novel framework for discretizing second-order TGV on triangular meshes. Further, we propose a TGV-based variational method for the denoising of face normal fields on triangular meshes. The TGV regularizer in our method is composed of a first-order term and a second-order term, which are automatically balanced. The first-order term allows our TGV regularizer to locate and preserve sharp features, while the second-order term allows to recognize and recover smoothly curved regions. To solve the optimization problem, we introduce an efficient iterative algorithm based on variable-splitting and augmented Lagrangian method. Extensive results and comparisons on synthetic and real scanning data validate that the proposed method outperforms the state-of-the-art visually and numerically.Synthetic Aperture (SA) beamforming is a principal technology of modern medical ultrasound imaging. In it the use of focused transmission provides superior signal-to-noise ratio and is promising for cardiovascular diagnosis at the maximum imaging depth about 160 mm. But there is a pitfall in increasing the frame rate to more than 80 frames per second (FPS) without image degradation by the haze artifact produced when the transmit foci (SA virtual sources) placed within the imaging field. We hypothesize that the source of this artifact is a grating lobe caused by coarse (decimated) multiple transmission and manifesting in the low-brightness region in the accelerated-frame-rate images. We propose an inter-transmission coherence factor (ITCF) method suppressing haze artifacts caused by coarse-pitch multiple transmission. The method is expected to suppress the image blurring because the SA grating lobe signal is less coherent than the main lobe signals. We evaluated an ITCF algorithm for suppressing the grating artifact when the transmission pitch is up to 4 times larger than normal pitch (equivalent to 160 FPS).