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Foregut Dysmotility in the Respiratory Implant Individual.
Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.Connectionist and dynamic field models consist of a set of coupled first-order differential equations describing the evolution in time of different units. We compare three numerical methods for the integration of these equations the Euler method, and two methods we have developed and present here a modified version of the fourth-order Runge Kutta method, and one semi-analytical method. We apply them to solve a well-known nonlinear connectionist model of retrieval in single-digit multiplication, and show that, in many regimes, the semi-analytical and modified Runge Kutta methods outperform the Euler method, in some regimes by more than three orders of magnitude. Given the outstanding difference in execution time of the methods, and that the EM is widely used, we conclude that the researchers in the field can greatly benefit from our analysis and developed methods.Upper-limb prostheses are subject to high rates of abandonment. Prosthesis abandonment is related to a reduced sense of embodiment, the sense of self-location, agency, and ownership that humans feel in relation to their bodies and body parts. If a prosthesis does not evoke a sense of embodiment, users are less likely to view them as useful and integrated with their bodies. Currently, visual feedback is the only option for most prosthesis users to account for their augmented activities. However, for activities of daily living, such as grasping actions, haptic feedback is critically important and may improve sense of embodiment. Therefore, we are investigating how converting natural haptic feedback from the prosthetic fingertips into vibrotactile feedback administered to another location on the body may allow participants to experience haptic feedback and if and how this experience affects embodiment. While we found no differences between our experimental manipulations of feedback type, we found evidence that embodiment was not negatively impacted when switching from natural feedback to proximal vibrotactile feedback. Proximal vibrotactile feedback should be further studied and considered when designing prostheses.Lower-limb exoskeletons often use torque control to manipulate energy flow and ensure human safety. The accuracy of the applied torque greatly affects how well the motion is assisted and therefore improving it is always of interest. Feed-forward iterative learning, which is similar to predictive stride-wise integral control, has proven an effective compensation to feedback control for torque tracking in exoskeletons with complicated dynamics during human walking. Although the intention of iterative learning was initially to benefit average tracking performance over multiple strides, we found that, after proper gain tuning, it can also help improve real-time torque tracking. We used theoretical analysis to predict an optimal iterative-learning gain as the inverse of the passive actuator stiffness. Walking experiments resulted in an optimum gain equal to 0.99 ± 0.38 times the predicted value, confirming our hypothesis. see more The results of this study provide guidance for the design of torque controllers in robotic legged locomotion systems and will help improve the performance of robots that assist gait.Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. link2 However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based ose. CoMB-Deep maintains two classification categories binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.[This corrects the article DOI 10.3389/fncom.2020.00029.].Background Individuals' information processing includes automatic and effortful processes and the latter require sustained concentration or attention and larger amounts of cognitive "capacity." Event-related potentials (ERPs) reflect all neural activities that are related to a certain stimulus. Investigating ERP characteristics of effortful cognitive processing in people with schizophrenia would be helpful in further understanding the neural mechanism of schizophrenia. Methods Both schizophrenia patients (SCZ, n = 33) and health controls (HC, n = 33) completed ERP measurements during the performance of the basic facial emotion identification test (BFEIT) and the face-vignette task (FVT). Data of ERP components (N100, P200, and N250), BFEIT and FVT performances were analyzed. Results Schizophrenia patients' accuracies of face emotion detection in the BFEIT and vignette emotion detection in the FVT were both significantly worse than the performance of the HC group. Repeated-measures ANOVAs performed on mean amplitudes and latencies revealed that the interaction effect for group × experiment × site (prefrontal, frontal, central, parietal, and occipital site) was significant for N250 amplitude. In FVT experiment, N250 amplitudes at prefrontal and frontal sites in schizophrenia group were larger than those of HC group; the maximum N250 amplitude was present at the prefrontal site in both the groups. For N250 latency, the interaction effect for group × experiment was significant; N250 latencies in the schizophrenia group were longer than those of the HC group. Conclusion Schizophrenia patients present effortful cognitive processing dysfunctions which reflect in abnormal ERP components, especially N250 at prefrontal cortex and frontal cortex sites. These findings have important implications for further clarifying the neural mechanism of effortful cognitive processing deficits in schizophrenia.Several studies have demonstrated that individuals' ability to perceive a speech sound contrast is related to the production of that contrast in their native language. The theoretical account for this relationship is that speech perception and production have a shared multimodal representation in relevant sensory spaces (e.g., auditory and somatosensory domains). This gives rise to a prediction that individuals with more narrowly defined targets will produce greater separation between contrasting sounds, as well as lower variability in the production of each sound. However, empirical studies that tested this hypothesis, particularly with regard to variability, have reported mixed outcomes. The current study investigates the relationship between perceptual ability and production ability, focusing on the auditory domain. We examined whether individuals' categorical labeling consistency for the American English /ε/-/æ/ contrast, measured using a perceptual identification task, is related to distance between the ct to further investigation.Patients with advanced Alzheimer's disease (AD) experience cognitive impairment and physical disabilities in daily life. Currently, there are no treatments available to slow down the course of the disease, and limited treatments exist only to treat symptoms. However, deep brain stimulation of the nucleus basalis of Meynert (NBM-DBS) has been reported to improve cognitive function in individuals with AD. Here, we report the effects of NBM-DBS on cognitive function in a subject with severe AD. An 80-year-old male with severe AD (Clinical Dementia Rating scale 3.0 points) underwent surgery for bilateral NBM-DBS electrode placement. After 10 weeks of stimulation, Mini-Mental State Examination (MMSE) assessment improved from a score of 5 to 9 points, and assessment using the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog) showed a marked reduction in total score from 43 to 33 points, suggesting cognitive benefits from NBM-DBS. The patient's postoperative course was complicated by a subdural effusion that occurred several days after surgery, with complete recovery. Interestingly, the subject also displayed abnormal thermoregulation with stimulation initiation and stimulation parameter modifications. NBM-DBS may serve as a potential therapy for severe AD patients. link3 Clinical Trial Registration ChiCTR1900022324.Transcranial alternating-current stimulation (tACS) in the frequency range of 1-100 Hz has come to be used routinely in electroencephalogram (EEG) studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data.Chronic Social Isolation (CSI) is a model of prolonged stress employed in a variety of studies to induce depression and anxious behavior in rats. The present study aims to evaluate the effect of CSI on male Wistar rats in terms of "anhedonic-type" behavior in the Sucrose Preference Test (SPT) and anxiogenic profile in the elevated-plus-maze (EPM) test, as well as evaluating the effect of resocialization upon sucrose consumption. A total of 24 adolescent male Wistar rats were evaluated. The animals were housed either together (communally) or socially isolated for 21 days, and then exposed for four consecutive days to the SPT test [water vs. a 32% sucrose solution (SS)]. Four days later, they were again subjected to the SPT test (32% vs. 0.7% SS), and then tested on the EPM apparatus 3 days later. Following the completion of the anxiogenic profile of the model, the animals were resocialized for 72 h and then re-tested once again using the SPT (32% vs. 0.7% SS). Twenty-four hours after this final consumption, the animals were euthanized to record the weight of their adrenal glands (AG).
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