Nanometer Scale Dielectric Fluctuations at the Glass Transition

Using non-contact scanning probe microscopy (SPM) techniques, dielectric properties were studied on 50 nanometer length scales in poly-vinyl-acetate (PVAc) films in the vicinity of the glass transition. Low frequency (1/f) noise observed in the measurements, was shown to arise from thermal fluctuations of the electric polarization. Anomalous variations observed in the noise spectrum provide direct evidence for cooperative nano-regions with heterogeneous kinetics. The cooperative length scale was determined. Heterogeneity was long-lived only well below the glass transition for faster than average processes.Comment: 4 pages, 4 embedded PS figures, RevTeX - To appear in Phys. Rev. Let

Uncovering the Turnover Intention of Proactive Employees: The Mediating Role of Work Engagement and the Moderated Mediating Role of Job Autonomy

Retaining proactive employees with the potential to be high performers is recognized as an essential condition for an organization’s survival and prosperity. However, few studies have logically explained and empirically clarified the link between proactive personality, which represents a distal proactive tendency, and turnover intention to predict actual turnover behavior. With the research objective to address these research gaps, we expected that work engagement as a proximal motivational mechanism was likely to mediate the relationship between proactive personality and turnover intention, and that job autonomy as a critical job context was likely to moderate the relationship between proactive personality and work engagement. We developed a moderated mediation model incorporating these expectations. The results of the survey conducted on employees working for mid-sized manufacturing firms in Korea were consistent with our expectations. The findings of this study help uncover the intentions of turnover exhibited by proactive employees

Development of Harmaline-induced Tremor in a Swine Model

Background: In the field of translational neuroscience research, it is critical to utilize a large animal model to test the feasibility, safety, and functionality of novel therapies. Here, we describe a protocol for the development of a large animal model of tremor. Methods: In a pig model, tremor was induced with harmaline and measured with wireless accelerometers attached to the limbs. Three different doses of harmaline were tested and three repetitive injections were made at 72-hour intervals. To fully characterize the drug-induced tremor, onset time, tremor amplitude, maintained duration, and peak tremor frequency were analyzed. Results: Harmaline-induced tremor appeared immediately following intravenous injection of harmaline. Tremor was maintained over 2 hours. Its frequency was 10–16 Hz, which was independent of doses. Dose-dependent responses were observed in tremor amplitude, triggering time, and tremor-maintained duration. Repetitive injection of harmaline desensitized the harmaline effect. Discussion: We provide a detailed protocol for training, drug injection, device selection, and tremor recording optimized to create a swine model of tremor with harmaline. Our protocol provides reliable tremor in pigs and suggests pig as a valid translational large animal model of tremor

Deep brain stimulation induces BOLD activation in motor and non-motor networks: An fMRI comparison study of STN and EN/GPi DBS in large animals

AbstractThe combination of deep brain stimulation (DBS) and functional MRI (fMRI) is a powerful means of tracing brain circuitry and testing the modulatory effects of electrical stimulation on a neuronal network in vivo. The goal of this study was to trace DBS-induced global neuronal network activation in a large animal model by monitoring the blood oxygenation level-dependent (BOLD) response on fMRI. We conducted DBS in normal anesthetized pigs, targeting the subthalamic nucleus (STN) (n=7) and the entopeduncular nucleus (EN), the non-primate analog of the primate globus pallidus interna (n=4). Using a normalized functional activation map for group analysis and the application of general linear modeling across subjects, we found that both STN and EN/GPi DBS significantly increased BOLD activation in the ipsilateral sensorimotor network (FDR<0.001). In addition, we found differential, target-specific, non-motor network effects. In each group the activated brain areas showed a distinctive correlation pattern forming a group of network connections. Results suggest that the scope of DBS extends beyond an ablation-like effect and that it may have modulatory effects not only on circuits that facilitate motor function but also on those involved in higher cognitive and emotional processing. Taken together, our results show that the swine model for DBS fMRI, which conforms to human implanted DBS electrode configurations and human neuroanatomy, may be a useful platform for translational studies investigating the global neuromodulatory effects of DBS

Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p&lt;0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management