Notes

Multidrug Efflux Pumping systems Attenuate the result associated with MGMT Inhibitors.
se findings highlight the importance of subject-specific data-driven approaches for identifying discriminative spatio-temporal characteristics for an optimized BCI performance.Movement-based video games can provide engaging practice for repetitive therapeutic gestures towards improving manual ability in youth with cerebral palsy (CP). However, home-based gesture calibration and classification is needed to personalize therapy and ensure an optimal challenge point. Nineteen youth with CP controlled a video game during a 4-week home-based intervention using therapeutic hand gestures detected via electromyography and inertial sensors. The in-game calibration and classification procedure selects the most discriminating, person-specific features using random forest classification. Then, a support vector machine is trained with this feature subset for in-game interaction. The procedure uses features intended to be sensitive to signs of CP and leverages directional statistics to characterize muscle activity around the forearm. Home-based calibration showed good agreement with video verified ground truths (0.86 ± 0.11, 95%CI = 0.93-0.97). Across participants, classifier performance (F1-score) for the primary therapeutic gesture was 0.90 ± 0.05 (95%CI = 0.87-0.92) and, for the secondary gesture, 0.82 ± 0.09 (95%CI = 0.77-0.86). Features sensitive to signs of CP were significant contributors to classification and correlated to wrist extension improvement and increased practice time. This study contributes insights for classifying gestures in people with CP and demonstrates a new gesture controller to facilitate home-based therapy gaming.Change detection is an elementary task in computer vision and video processing applications. Recently, a number of supervised methods based on convolutional neural networks have reported high performance over the benchmark dataset. However, their success depends upon the availability of certain proportions of annotated frames from test video during training. Thus, their performance on completely unseen videos or scene independent setup is undocumented in the literature. In this work, we present a scene independent evaluation (SIE) framework to test the supervised methods in completely unseen videos to obtain generalized models for change detection. In addition, a scene dependent evaluation (SDE) is also performed to document the comparative analysis with the existing approaches. We propose a fast (speed-25 fps) and lightweight (0.13 million parameters, model size-1.16 MB) end-to-end 3D-CNN based change detection network (3DCD) with multiple spatiotemporal learning blocks. The proposed 3DCD consists of a gradual reductionist block for background estimation from past temporal history. It also enables motion saliency estimation, multi-schematic feature encoding-decoding, and finally foreground segmentation through several modular blocks. The proposed 3DCD outperforms the existing state-of-the-art approaches evaluated in both SIE and SDE setup over the benchmark CDnet 2014, LASIESTA and SBMI2015 datasets. To the best of our knowledge, this is a first attempt to present results in clearly defined SDE and SIE setups in three change detection datasets.Although it is well-known that the negative effects of VR sickness, and the desirable sense of presence are important determinants of a user's immersive VR experience, there remains a lack of definitive research outcomes to enable the creation of methods to predict and/or optimize the trade-offs between them. Most VR sickness assessment (VRSA) and VR presence assessment (VRPA) studies reported to date have utilized simple image patterns as probes, hence their results are difficult to apply to the highly diverse contents encountered in general, real-world VR environments. To help fill this void, we have constructed a large, dedicated VR sickness/presence (VR-SP) database, which contains 100 VR videos with associated human subjective ratings. Using this new resource, we developed a statistical model of spatio-temporal and rotational frame difference maps to predict VR sickness. We also designed an exceptional motion feature, which is expressed as the correlation between an instantaneous change feature and averaged temporal features. By adding additional features (visual activity, content features) to capture the sense of presence, we use the new data resource to explore the relationship between VRSA and VRPA. We also show the aggregate VR-SP model is able to predict VR sickness with an accuracy of 90% and VR presence with an accuracy of 75% using the new VR-SP dataset.In this paper, a recurrent neural network is designed for video saliency prediction considering spatial-temporal features. Honokiol research buy In our work, video frames are routed through the static network for spatial features and the dynamic network for temporal features. For the spatial-temporal feature integration, a novel select and re-weight fusion model is proposed which can learn and adjust the fusion weights based on the spatial and temporal features in different scenes automatically. Finally, an attention-aware convolutional long short term memory (ConvLSTM) network is developed to predict salient regions based on the features extracted from consecutive frames and generate the ultimate saliency map for each video frame. The proposed method is compared with state-of-the-art saliency models on five public video saliency benchmark datasets. The experimental results demonstrate that our model can achieve advanced performance on video saliency prediction.Temporal sentence grounding in videos aims to localize one target video segment, which semantically corresponds to a given sentence. Unlike previous methods mainly focusing on matching semantics between the sentence and different video segments, in this paper, we propose a novel semantic conditioned dynamic modulation (SCDM) mechanism, which leverages the sentence semantics to modulate the temporal convolution operations for better correlating and composing the sentence-relevant video contents over time. The proposed SCDM also performs dynamically with respect to the diverse video contents so as to establish a precise semantic alignment between sentence and video. By coupling the proposed SCDM with a hierarchical temporal convolutional architecture, video segments with various temporal scales are composed and localized. Besides, more fine-grained clip-level actionness scores are also predicted with the SCDM-coupled temporal convolution on the bottom layer of the overall architecture, which are further used to adjust the temporal boundaries of the localized segments and thereby lead to more accurate grounding results. Experimental results on benchmark datasets demonstrate that the proposed model can improve the temporal grounding accuracy consistently, and further investigation experiments also illustrate the advantages of SCDM on stabilizing the model training and associating relevant video contents for temporal sentence grounding.
Electrical impedance tomography (EIT) is an imaging modality in which voltage data arising from currents applied on the boundary are used to reconstruct the conductivity distribution in the interior. This paper provides a novel direct (noniterative) 3-D reconstruction algorithm for EIT in the cylindrical geometry.

The algorithm is based on Calderon's method [Calderon, 1980], and is implemented for data collected on two or four rows of electrodes on the boundary of a cylinder.

The effectiveness of the method to localize inhomogeneities in the plane of the electrodes and in the z-direction is demonstrated on simulated and experimental data.

The results from simulated and experimental data show that the method is effective for distinguishing inplane and nearby out-of-plane inhomogeneities with good spatial resolution in the vertical z direction with computational efficiency.
The results from simulated and experimental data show that the method is effective for distinguishing inplane and nearby out-of-plane inhomogeneities with good spatial resolution in the vertical z direction with computational efficiency.Several studies have reported that stroke survivors displayed improved voluntary planar movements when forces supporting the upper limb were increased, and when impeding forces were decreased. Earlier haptic devices interacting with the human upper limb were potentially impacted by undesired residual friction force and device inertia. To explore natural, undisturbed voluntary motor control in stroke survivors, we describe the development of a Decoupled-Operational space Robot for wide Impedance Switching (DORIS) with minimized mechanical impedances. This design is based on a novel decoupling mechanism separating the end effector from a manipulator. While the user manipulates the end effector freely inside the workspace of the decoupling mechanism, to which a manipulator of the robot is attached, the robot detects such change in position using a lightweight linkage system. The manipulator of the robot then follows such movements of the end effector swiftly. Consequently, the user can explore the extended workspace, which can be as large as the manipulator's workspace. Since the end effector is mechanically decoupled from the manipulators and actuators, the user can remain unaffected by mechanical impedances of the manipulator. Mechanical impedances perceived by the user and bandwidth of the control system were estimated. The developed robot was capable of detecting larger maximum acceleration and larger jerk of the reaching movement in chronic stroke survivors with hemiparesis. We propose that this device can be utilized for evaluating voluntary motor control of the upper limb while minimizing the impact of robot inertia and friction on limb behavior.Gamma oscillations are a prominent activity pattern in the cerebral cortex. While gamma rhythms have been extensively studied in the adult prefrontal cortex in the context of cognitive (dys)functions, little is known about their development. We addressed this issue by using extracellular recordings and optogenetic stimulations in mice across postnatal development. We show that fast rhythmic activity in the prefrontal cortex becomes prominent during the second postnatal week. While initially at about 15 Hz, fast oscillatory activity progressively accelerates with age and stabilizes within gamma frequency range (30-80 Hz) during the fourth postnatal week. Activation of layer 2/3 pyramidal neurons drives fast oscillations throughout development, yet the acceleration of their frequency follows similar temporal dynamics as the maturation of fast-spiking interneurons. These findings uncover the development of prefrontal gamma activity and provide a framework to examine the origin of abnormal gamma activity in neurodevelopmental disorders.The nucleus of the solitary tract (NTS) is critical for the central integration of signals from visceral organs and contains preproglucagon (PPG) neurons, which express leptin receptors in the mouse and send direct projections to the paraventricular nucleus of the hypothalamus (PVH). Here, we visualized projections of PPG neurons in leptin-deficient Lepob/ob mice and found that projections from PPG neurons are elevated compared with controls, and PPG projections were normalized by targeted rescue of leptin receptors in LepRbTB/TB mice, which lack functional neuronal leptin receptors. Moreover, Lepob/ob and LepRbTB/TB mice displayed increased levels of neuronal activation in the PVH following vagal stimulation, and whole-cell patch recordings of GLP-1 receptor-expressing PVH neurons revealed enhanced excitatory neurotransmission, suggesting that leptin acts cell autonomously to suppress representation of excitatory afferents from PPG neurons, thereby diminishing the impact of visceral sensory information on GLP-1 receptor-expressing neurons in the PVH.
Website: https://www.selleckchem.com/products/Honokiol.html