eed for new clinical trials using vulnerable plaque imaging to select high-risk patients despite maximal medical therapy who may benefit from procedural intervention. We found heterogeneity in current practices of carotid stenosis imaging and management in this worldwide survey with many respondents including vulnerable plaque imaging into their decision analysis despite the lack of proven benefit from clinical trials. This study highlights the need for new clinical trials using vulnerable plaque imaging to select high-risk patients despite maximal medical therapy who may benefit from procedural intervention.Objective.Understanding and differentiating brain states is an important task in the field of cognitive neuroscience with applications in health diagnostics, such as detecting neurotypical development vs. autism spectrum or coma/vegetative state vs. locked-in state. Electroencephalography (EEG) analysis is a particularly useful tool for this task as EEG data can detect millisecond-level changes in brain activity across a range of frequencies in a non-invasive and relatively inexpensive fashion. The goal of this study is to apply machine learning methods to EEG data in order to classify visual language comprehension across multiple participants.Approach.26-channel EEG was recorded for 24 Deaf participants while they watched videos of sign language sentences played in time-direct and time-reverse formats to simulate interpretable vs. uninterpretable sign language, respectively. Sparse optimal scoring (SOS) was applied to EEG data in order to classify which type of video a participant was watching, time-direct or time-reversed. The use of SOS also served to reduce the dimensionality of the features to improve model interpretability.Main results.The analysis of frequency-domain EEG data resulted in an average out-of-sample classification accuracy of 98.89%, which was far superior to the time-domain analysis. This high classification accuracy suggests this model can accurately identify common neural responses to visual linguistic stimuli.Significance.The significance of this work is in determining necessary and sufficient neural features for classifying the high-level neural process of visual language comprehension across multiple participants.The repair and treatment of articular cartilage injury is a huge challenge of orthopedics. Currently, most of the clinical methods applied in treating cartilage injuries are mainly to relieve pains rather than to cure them, while the strategy of tissue engineering is highly expected to achieve the successful repair of osteochondral defects. Clear understandings of the physiological structures and mechanical properties of cartilage, bone and osteochondral tissues have been established, but the understanding of their physiological heterogeneity still needs further investigation. Apart from the gradients in the micromorphology and composition of cartilage-to-bone extracellular matrixes, an oxygen gradient also exists in natural osteochondral tissue. The response of hypoxia-inducible factor (HIF)-mediated cells to oxygen would affect the differentiation of stem cells and the maturation of osteochondral tissue. This article reviews the roles of oxygen level and HIF signaling pathway in the development of articular cartilage tissue, and their prospective applications in bone and cartilage tissue engineering. The strategies for regulating HIF signaling pathway and how these strategies finding their potential applications in the regeneration of integrated osteochondral tissue are also discussed.For tissue engineering (TE), decellularized matrices gained huge potential as they consist of natural biomolecules which help in cell attachment and proliferation. Among various animal tissues, goat tissue has gained least attention in spite of the fact that goat tissue is less susceptible to disease transmission as compared to cadaveric porcine and bovine tissue. In this study, goat small intestine submucosa (G-SIS) was isolated from goat small intestine (G-SI), a waste from goat-slaughterhouse, and decellularized to obtain decellularized G-SIS (DG-SIS) biomatrix in the form of powder, gel and sponge form, so that it can be used for healing various types of wounds. Further, nanoceria (NC), owing to its free radical scavenging, anti-inflammatory, antibacterial and angiogenic properties, was incorporated in the DG-SIS in to fabricate DG-SIS/NC nanobiocomposite scaffold, which may exhibit synergistic effects to accelerate tissue regeneration. The scaffolds were found to be hydrophilic, biodegradable, haemocompatible, biocompatible, antibacterial and showed free radical scavenging capability. https://www.selleckchem.com/products/arv-825.html The scaffold containing NC concentration (500 µg ml-1) depicted highest cell (fibroblast cells) adhesion, MTT activity and free radical scavenging as compared to the DG-SIS and other nanobiocomposite scaffolds. Thus, DG-SIS/NC3 (NC with concentration 500 µg ml-1) scaffold could be a potential scaffold biomaterial for skin TE application.Objective.We address the problem of hemodynamic response (HR) estimation when task-evoked extra-cerebral components are present in functional near-infrared spectroscopy (fNIRS) signals. These components might bias the HR estimation; therefore, careful and accurate denoising of data is needed.Approach.We propose a dictionary-based algorithm to process each single event-related segment of the acquired signal for both long separation (LS) and short separation (SS) channels. Stimulus-evoked components and physiological noise are modeled by means of two distinct waveform dictionaries. For each segment, after removal of the physiological noise component in each channel, a template is employed to estimate stimulus-evoked responses in both channels. Then, the estimate from the SS channel is employed to correct the evoked superficial response and refine the HR estimate from the LS channel.Main results.Analysis of simulated, semi-simulated and real data shows that, by averaging single-segment estimates over multiple trials in an experiment, reliable results and improved accuracy compared to other methods can be obtained. The average estimation error of the proposed method for the semi-simulated data set is 34% for oxy-hemoglobin (HbO) and 78% for deoxy-hemoglobin (HbR), considering 40 trials. The proposed method outperforms the results of the methods proposed in the literature. While still far from the possibility of single-trial HR estimation, a significant reduction in the number of averaged trials can also be obtained.Significance.This work proves that dedicated dictionaries can be successfully employed to model all different components of fNIRS signals. We demonstrate the effectiveness of a specifically designed algorithm structure in dealing with a complex denoising problem, enhancing the possibilities of fNIRS-based HR analysis.