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BACKGROUND AND PURPOSE There is no consensus on endovascular treatment for terminal ICA. The purpose of this study was to evaluate the comparative safety and efficacy of preferred aspiration thrombectomy and stent retriever thrombectomy for revascularization in patients with isolated terminal ICA occlusion. MATERIALS AND METHODS We conducted a retrospective analysis of patients with terminal ICA occlusion treated with aspiration thrombectomy or stent retriever thrombectomy in our center, from September 2013 to November 2018. To minimize the case bias, propensity score matching was performed. The primary outcomes were successful reperfusion defined by expanded TICI grades 2b-3 at the end of all endovascular procedures and puncture-to-reperfusion time. RESULTS A total of 109 consecutive patients with terminal ICA occlusion were divided into the aspiration thrombectomy group (40 patients) and the stent retriever thrombectomy group (69 patients), and 30 patients were included in each group after propensity score matching. The proportion of complete reperfusion was significantly higher in the aspiration thrombectomy group (OR 4.75 [95% CI, 1.10-1.38]; P = .002). The median puncture-to-reperfusion time in the aspiration thrombectomy group was shorter than that in the stent retriever thrombectomy group (38 versus 69 minutes; P = .001). Fewer intracerebral hemorrhage events were recorded in the aspiration thrombectomy group (OR 0.29 [95% CI, 0.09-0.90]; P = .028). No significant differences were observed for good outcomes (OR 1.92 [95% CI, 0.86-4.25]) and mortality (OR 0.84 [95% CI, 0.29-2.44]) at 90 days. CONCLUSIONS For the treatment of terminal ICA occlusion, aspiration thrombectomy was technically superior to stent retriever thrombectomy in the absence of a balloon guide catheter in achieving successful reperfusion with shorter puncture-to-reperfusion time and procedure-related adverse events. © 2020 by American Journal of Neuroradiology.Stroke is the leading cause of long term disability in developed countries and one of the top causes of mortality worldwide. The past decade has seen substantial advances in the diagnostic and treatment options available to minimize the impact of acute ischemic stroke. The key first step in stroke care is early identification of patients with stroke and triage to centers capable of delivering the appropriate treatment, as fast as possible. Here, we review the data supporting pre-hospital and emergency stroke care, including use of emergency medical services protocols for identification of patients with stroke, intravenous thrombolysis in acute ischemic stroke including updates to recommended patient eligibility criteria and treatment time windows, and advanced imaging techniques with automated interpretation to identify patients with large areas of brain at risk but without large completed infarcts who are likely to benefit from endovascular thrombectomy in extended time windows from symptom onset. We also review protocols for management of patient physiologic parameters to minimize infarct volumes and recent updates in secondary prevention recommendations including short term use of dual antiplatelet therapy to prevent recurrent stroke in the high risk period immediately after stroke. Finally, we discuss emerging therapies and questions for future research. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http//group.bmj.com/group/rights-licensing/permissions.This article investigates the finite-time output multiformation tracking (OMFT) problem of networked heterogeneous robotic systems (NHRSs), where each robot model involves external disturbances, parametric uncertainties, and possible kinematic redundancy. Besides, the interactions among robotic systems are described as a directed graph with an acyclic partition. Then, several novel practical finite-time hierarchical control (FTHC) algorithms are designed. The convergence analysis of the closed-loop dynamics is extremely difficult due to the lack of effective analysis methods. Based on the mathematics induction and reductio ad absurdum, a new nonsmooth Lyapunov function is proposed to derive the sufficient conditions and settling time functions. Finally, numerical simulations are performed on the NHRS to verify the main results.Body language is an important aspect of human communication, which an effective human-robot interaction interface should mimic well. Human beings exchange information and convey their thoughts and feelings through gaze, facial expressions, body language, and tone of voice along with spoken words, and infer 65% of the meaning of the communicated messages from these nonverbal cues. Modern robotic platforms are, however, limited in their ability to automatically generate behaviors that align with their speech. In this article, we develop a neural-network-based system that takes audio from a user as an input and generates upper-body gestures, including head, hand, and torso movements of the user on a humanoid robot, namely, Softbank Robotics' Pepper. Our system was evaluated quantitatively as well as qualitatively using Web surveys when driven by natural speech and synthetic speech. We compare the impact of generic and person-specific neural-network models on the quality of synthesized movements. We further investigate the relationships between quantitative and qualitative evaluations and examine how the speaker's personality traits affect the synthesized movements.This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.Extranodal natural killer/T cell lymphoma (ENKL), nasal type is a kind of rare disease with a low survival rate that primarily affects Asian and South American populations. Segmentation of ENKL lesions is crucial for clinical decision support and treatment planning. This paper is the first study on computer-aided diagnosis systems for the ENKL segmentation problem. We propose an automatic, coarse-to-fine approach for ENKL segmentation using adversarial networks. In the coarse stage, we extract the region of interest bounding the lesions utilizing a segmentation neural network. In the fine stage, we use an adversarial segmentation network and further introduce a multi-scale L1 loss function to drive the network to learn both global and local features. The generator and discriminator are alternately trained by backpropagation in an adversarial fashion in a min-max game. Furthermore, we present the first exploration of zone-based uncertainty estimates based on Monte Carlo dropout technique in the context of deep networks for medical image segmentation. Specifically, we propose the uncertainty criteria based on the lesion and the background, and then linearly normalize them to a specific interval. This is not only the crucial criterion for evaluating the superiority of the algorithm, but also permits subsequent optimization by engineers and revision by clinicians after quantitatively understanding the main source of uncertainty from the background or the lesion zone. Panobinostat mw Experimental results demonstrate that the proposed method is more effective and lesion-zone stable than state-of-the-art deep-learning based segmentation model.Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for Inter-ictal spike detection of brain signals is via visual scanning of the recordings, which is a subjective and a very time-consuming task. Motivated by that, this paper focuses on using machine learning for epileptic spikes classification in magnetoencephalography (MEG) signals. First, we used the Position Weight Matrix (PWM) method combined with a uniform quantizer to generate useful features from time domain and frequency domain through a Fast Fourier Transform (FFT) of the framed raw MEG signals. Second, the extracted features are fed to standard classifiers for inter-ictel spikes classification. The proposed technique shows great potential in spike classification and reducing the feature vector size. Specifically, the proposed technique achieved average sensitivity up to 87% and specificity up to 97% using 5-folds cross-validation applied to a balanced dataset. These samples are extracted from nine epileptic subjects using a sliding frame of size 95 sample points with a step-size of 8 sample-points.Medicine has entered the digital era, driven by data from new modalities, especially genomics and imaging, as well as new sources such as wearables and Internet of Things. As we gain a deeper understanding of the disease biology and how diseases affect an individual, we are developing targeted therapies to personalize treatments. There is a need for technologies like Artificial Intelligence (AI) to be able to support predictions for personalized treatments. In order to mainstream AI in healthcare we will need to address issues such as explainability, liability and privacy. Developing explainable algorithms and including AI training in medical education are many of the solutions that can help alleviate these concerns.In this article, we present an intermittent framework for safe reinforcement learning (RL) algorithms. First, we develop a barrier function-based system transformation to impose state constraints while converting the original problem to an unconstrained optimization problem. Second, based on optimal derived policies, two types of intermittent feedback RL algorithms are presented, namely, a static and a dynamic one. We finally leverage an actor/critic structure to solve the problem online while guaranteeing optimality, stability, and safety. Simulation results show the efficacy of the proposed approach.The tensor-on-tensor regression can predict a tensor from a tensor, which generalizes most previous multilinear regression approaches, including methods to predict a scalar from a tensor, and a tensor from a scalar. However, the coefficient array could be much higher dimensional due to both high-order predictors and responses in this generalized way. Compared with the current low CANDECOMP/PARAFAC (CP) rank approximation-based method, the low tensor train (TT) approximation can further improve the stability and efficiency of the high or even ultrahigh-dimensional coefficient array estimation. In the proposed low TT rank coefficient array estimation for tensor-on-tensor regression, we adopt a TT rounding procedure to obtain adaptive ranks, instead of selecting ranks by experience. Besides, an ℓ₂ constraint is imposed to avoid overfitting. The hierarchical alternating least square is used to solve the optimization problem. Numerical experiments on a synthetic data set and two real-life data sets demonstrate that the proposed method outperforms the state-of-the-art methods in terms of prediction accuracy with comparable computational complexity, and the proposed method is more computationally efficient when the data are high dimensional with small size in each mode.
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