Notes

Cell-assisted lipotransfer from the specialized medical treatments for facial gentle muscle deformity.
iate counseling to the patients and their caregivers and provide future directions for PD preclinical research. Copyright © 2020 Avenali, Blandini and Cerri.There is currently a need for engaging, user-friendly, and repeatable tasks for assessment of cognitive and motor function in aging and neurodegenerative diseases. This study evaluated the feasibility of a maze-like Numberlink puzzle game in assessing differences in game-based measures of cognition and motor function due to age and neurodegenerative diseases. Fifty-five participants, including young (18-31 years, n = 18), older (64-79 years, n = 14), and oldest adults (86-98 years, n = 14), and patients with Parkinson's (59-76 years, n = 4) and Huntington's disease (HD; 35-66 years, n = 5) played different difficulty levels of the Numberlink puzzle game and completed usability questionnaires and tests for psychomotor, attentional, visuospatial, and constructional and executive function. Analyses of Numberlink game-based cognitive (solving time and errors) and motor [mean velocity and movement direction changes (MDC)] performance metrics revealed statistically significant differences between age groups and betof using Numberlink puzzles as a valid cognitive assessment tool. Copyright © 2020 Nef, Chesham, Schütz, Botros, Vanbellingen, Burgunder, Müllner, Müri and Urwyler.Epileptogenesis is the gradual process responsible for converting a healthy brain into an epileptic brain. This process can be triggered by a wide range of factors, including brain injury or tumors, infections, and status epilepticus. Epileptogenesis results in aberrant synaptic plasticity, neuroinflammation and seizure-induced cell death. As Matrix Metalloproteinases (MMPs) play a crucial role in cellular plasticity by remodeling the extracellular matrix (ECM), gelatinases (MMP-2 and MMP-9) were recently highlighted as key players in epileptogenesis. In this work, we engineered a biosensor to report in situ gelatinase activity in a model of epileptogenesis. This biosensor encompasses a gelatinase-sensitive activatable cell penetrating peptide (ACPP) coupled to a TAMRA fluorophore, allowing fluorescence uptake in cells displaying endogenous gelatinase activities. In a preclinical mouse model of temporal lobe epilepsy (TLE), the intrahippocampal kainate injection, ACPPs revealed a localized distribution of gelatinase activities, refining temporal cellular changes during epileptogenesis. The activity was found particularly but not only in the ipsilateral hippocampus, starting from the CA1 area and spreading to dentate gyrus from the early stages throughout chronic epilepsy, notably in neurons and microglial cells. Thus, our work shows that ACPPs are suitable molecular imaging probes for detecting the spatiotemporal pattern of gelatinase activity during epileptogenesis, suggesting their possible use as vectors to target cellular reactive changes with treatment for epileptogenesis. Copyright © 2020 Bouquier, Girard, Aparicio Arias, Fagni, Bertaso and Perroy.There has been substantial growth in research on the robot automation, which aims to make robots capable of directly interacting with the world or human. Robot learning for automation from human demonstration is central to such situation. However, the dependence of demonstration restricts robot to a fixed scenario, without the ability to explore in variant situations to accomplish the same task as in demonstration. Deep reinforcement learning methods may be a good method to make robot learning beyond human demonstration and fulfilling the task in unknown situations. The exploration is the core of such generalization to different environments. While the exploration in reinforcement learning may be ineffective and suffer from the problem of low sample efficiency. In this paper, we present Evolutionary Policy Gradient (EPG) to make robot learn from demonstration and perform goal oriented exploration efficiently. Through goal oriented exploration, our method can generalize robot learned skill to environments with different parameters. Our Evolutionary Policy Gradient combines parameter perturbation with policy gradient method in the framework of Evolutionary Algorithms (EAs) and can fuse the benefits of both, achieving effective and efficient exploration. With demonstration guiding the evolutionary process, robot can accelerate the goal oriented exploration to generalize its capability to variant scenarios. The experiments, carried out in robot control tasks in OpenAI Gym with dense and sparse rewards, show that our EPG is able to provide competitive performance over the original policy gradient methods and EAs. In the manipulator task, our robot can learn to open the door with vision in environments which are different from where the demonstrations are provided. Copyright © 2020 Cao, Liu, Liu and Yang.In perceptual psychology, estimations of visual depth and size under different spatial layouts have been extensively studied. However, research evidence in virtual environments (VE) is relatively lacking. The emergence of human-computer interaction (HCI) and virtual reality (VR) has raised the question of how human operators perform actions based on the estimation of visual properties in VR, especially when the sensory cues associated with the same object are conflicting. We report on an experiment in which participants compared the size of a visual sphere to a haptic sphere, belonging to the same object in a VE. The sizes from the visual and haptic modalities were either identical or conflicting (with visual size being larger than haptic size, or vice versa). We used three standard haptic references (small, medium, and large sizes) and asked participants to compare the visual sizes with the given reference, by method of constant stimuli. Results show a dominant functional priority of the visual size perception. Moreover, observers demonstrated a central tendency effect over-estimation for smaller haptic sizes but under-estimation for larger haptic sizes. The results are in-line with previous studies in real environments (RE). We discuss the current findings in the framework of adaptation level theory for haptic size reference. This work provides important implications for the optimal design of human-computer interactions when integrating 3D visual-haptic information in a VE. Copyright © 2020 Katzakis, Chen and Steinicke.Traditionally, radiologists have crudely quantified tumor extent by measuring the longest and shortest dimension by dragging a cursor between opposite boundary points across a single image rather than full segmentation of the volumetric extent. For algorithmic-based volumetric segmentation, the degree of radiologist experiential involvement varies from confirming a fully automated segmentation, to making a single drag on an image to initiate semi-automated segmentation, to making multiple drags and clicks on multiple images during interactive segmentation. An experiment was designed to test an algorithm that allows various levels of interaction. Given the ground-truth of the BraTS training data, which delimits the brain tumors of 285 patients on multi-spectral MR, a computer simulation mimicked the process that a radiologist would follow to perform segmentation with real-time interaction. Clicks and drags were placed only where needed in response to the deviation between real-time segmentation results and assumed radiologist's goal, as provided by the ground-truth. Results of accuracy for various levels of interaction are presented along with estimated elapsed time, in order to measure efficiency. Average total elapsed time, including loading the study through confirming 3D contours, was 46 s. Copyright © 2020 Gering, Kotrotsou, Young-Moxon, Miller, Avery, Kohli, Knapp, Hoffman, Chylla, Peitzman and Mackie.Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored. Copyright © 2020 Lindsay.The majority of neurons in the neuronal system of the brain have a complex morphological structure, which diversifies the dynamics of neurons. In the granular layer of the cerebellum, there exists a unique cell type, the unipolar brush cell (UBC), that serves as an important relay cell for transferring information from outside mossy fibers to downstream granule cells. The distinguishing feature of the UBC is that it has a simple morphology, with only one short dendritic brush connected to its soma. Based on experimental evidence showing that UBCs exhibit a variety of dynamic behaviors, here we develop two simple models, one with a few detailed ion channels for simulation and the other one as a two-variable dynamical system for theoretical analysis, to characterize the intrinsic dynamics of UBCs. The reasonable values of the key channel parameters of the models can be determined by analysis of the stability of the resting membrane potential and the rebound firing properties of UBCs. Considered together with a large variety of synaptic dynamics installed on UBCs, we show that the simple-structured UBCs, as relay cells, can extend the range of dynamics and information from input mossy fibers to granule cells with low-frequency resonance and transfer stereotyped inputs to diverse amplitudes and phases of the output for downstream granule cells. SU11274 purchase These results suggest that neuronal computation, embedded within intrinsic ion channels and the diverse synaptic properties of single neurons without sophisticated morphology, can shape a large variety of dynamic behaviors to enhance the computational ability of local neuronal circuits. Copyright © 2020 An, Tang, Wang, Jia, Pei, Wang, Yu and Liu.Adolescence is an essential developmental period characterized by reward-related processes. The current study investigated the development of monetary and social reward processes in adolescents compared with that in children and adults; furthermore, it assessed whether adolescents had different levels of sensitivity to various types of rewards. Two adapted incentive delay tasks were employed for each participant, and event-related potentials (ERPs) were recorded. The behavioral results showed that both monetary and social rewards could motivate response speed, and participants were more accurate under the monetary reward condition than under the social reward condition. The behavioral performances of individuals increased with age. For the ERP data, the cue-P3, target-P2, target-P3 and feedback-related negativity (FRN) components were investigated to identify reward motivation, emotional arousal, attention allocation and feedback processing. Children and adolescents showed higher motivation (larger cue-P3) to rewards than adults.
Website: https://www.selleckchem.com/products/SU11274.html