Too Much Flexibility in a Dynamical Model of Repetitive Negative Thinking?

Iftach and Bernstein propose a dynamical system model of task-unrelated thought that is designed to explain how repetitive negative thinking (RNT) and maladaptive internally-directed cognition more generally arises from attentional biases, working memory, and negative affect. They show that specifically during a period of low task demands, it is easier for negative affect to grab resources and take over with RNT. They also postulate that for individuals with high cognitive reactivity, this tendency for RNT to take over is increased. We argue this paper is an important move forward toward understanding in what circumstances RNT takes over, but also that the model is not yet sufficiently “formalized.” Specifically, we notice excessive levels of flexibility and redundancy that could undermine the explainability of the model. Moreover, the likelihood of negative thinking, as implemented in the proposed model, relies heavily on working memory capacity. In response to this observation, we give suggestions for how the parametrization of this model could be done in a more principled manner. We think such an analysis paves the way for more principled computational modeling of RNT which can be applied to describing empirical data and eventually, to inform decision-making in clinical settings.</p

Post Herpetic Anti-NMDA- Receptor Encephalitis in an 18-month-old Infant

&nbsp; Herpes simplex encephalitis (HSE), caused by herpes simplex virus type 1 (HSV-1), is the most common cause of severe sporadic encephalitis worldwide.HSE is occasionally accompanied by the recurrence of clinical symptoms that usually occur a few weeks following the initial infection. According to recent studies, the recurrence can be due to a secondary autoimmune mechanism rather than the virus invasion.One of the most common etiologies for autoimmunity is Anti-NMethyl D-Aspartate receptor encephalitis. This disorder is a treatable autoimmune encephalitis manifesting as movement disorder or neuropsychological involvemen

Optimizing parameters and algorithms of multivariate pattern classification for hypothesis testing in high-density EEG

Multivariate pattern analysis (MVPA) has come into widespread use for analysis of neuroimaging data in recent years and is gaining further momentum. Given the task of detecting a generalizable pattern in neural activity, MVPA allows to detect fine multidimensional spatiotemporal contrasts between two or more conditions and is thus able to take the full advantage of multivariate information encoded in the data. In particular, MVPA based approaches lend themselves very well to the analysis of electroencephalogram (EEG) data because, unlike the widely-used averaging methods, they consider the signal in its entirety and are thus less susceptible to the confounding effects of single points with abnormal amplitudes. However, using MVPA for hypothesis testing purposes in high-density EEG data has remained a challenging issue. Although MVPA is getting more and more mainstream to detect information in neural activity, its behavior is not well understood, yet. EEG data are high dimensional, yet sample size is usually low in comparison. Moreover, due to the low signal-to-noise ratio, the effect size is small and differences between classes are hard to detect. In such cases, MVPA behaves unexpectedly which makes the overall accuracy of the classifier difficult to interpret. In addition, because MVPA is sensitive to any kind of structure in the data, confounding factors or additional variance within data can bias accuracy. Such complexities warrant extra caution when interpreting classification results, thereby requiring further investigation and guidelines. On the other hand, MVPA literature is mainly dominated by methods suited for fMRI data and most of the dedicated EEG methodology is developed for brain computer interfaces (BCI) or single trial analysis of event-related potentials. Specifically, decoding continuous EEG increasingly suffers from the curse of dimensionality because of the lack of clear prior knowledge on which frequency bands and time points carry relevant information, or an onset where the effect of stimulation can be expected. In this thesis, we addressed the aforementioned challenges involved in using MVPA for decoding EEG data. Chapter 2 describes the statistical properties of MVPA in realistic neuroimaging data and provides important guidelines to interpret classification results. We show that the probability distribution of classification accuracies does not follow any known parametric distribution and can be strongly biased and skewed. We describe unexpected properties of the distribution of classification rates which forbid their use as estimates of the size of experimental effects. Importantly, we scrutinize the finding of below chance level classification rates, which often occur in low sample size, low effect size data and their implications on the shape of classification rates distribution. Next, in chapter 3, we investigate neuroimaging data that, next to a main effect of class, additionally contains a nested subclass structure. We show that in these data sets, correct classification ratios are systematically biased from chance even in absence of class effect. We propose a nonparametric permutation algorithm which can detect the subclass bias and account for its effect by adjusting permutation tests to consider the subclass structure of the data, using subclass-level randomization. Finally, in chapter 4, we used MVPA to decode continuous high-density EEG across subjects. We developed a classification framework along with a specific preprocessing procedure that is optimized for three purposes: 1) to increase signal-to-noise ratio, 2) to reduce the dimensionality of the data, and 3) to adapt the signal better to between-subject classification. Our algorithm uses a two-step classification procedure based on ensemble of linear support vector machines (SVM) which learns the spatial and temporal components of neural activity separately and then aggregates the two components of information to build a classification hyperplane using another linear SVM. We then use this method to see whether human sleep EEG contains any information about what has been learned before sleep

Sustainable Robots 4D Printing

Nature frequently serves as an inspiration for modern robotics innovations that emphasize secure human–machine interaction. However, the advantages of increased automation and digital technology integration conflict with the global environmental objectives. Accordingly, biodegradable soft robots have been proposed for a range of intelligent applications. Biodegradability provides soft robotics with an extraordinary functional advantage for operations involving intelligent shape transformation in response to external stimuli such as heat, pH, and light. Soft robot fabrication using conventional manufacturing techniques is inflexible, time-consuming, and labor-intensive. Recent advances in 3D and 4D printing of soft materials and multi-materials have become the key to enabling the direct manufacture of soft robotics with complex designs and functions. This review comprises a detailed survey of 3D and 4D printing advances in biodegradable soft sensors and actuators (BSSA), which serve as the most prominent parts of each robotic system. In addition, a concise overview of biodegradable materials for the fabrication of 3D-printed flexible devices with medical along with industrial applications is provided. A complete summary of current additive manufacturing techniques for BSSA is discussed in depth. Moreover, the concept of biodegradable 4D-printed soft actuators and sensors and biohybrid soft robots is reviewed

Electro-Magnetic Ship Propulsion Stability under Gusts

The purpose of this study is to analytically investigate the effect of Stuart number as well as magnetic and electrical angular frequency on the velocity distribution in a magneto-hydro-dynamic pump. Results show that as Stuart number approaches zero the velocity profile becomes similar to that of fully developed flow in a pipe. Furthermore, for high Stuart number there is a frequency limit for stability of fluid flow in certain direction of flow. This stability frequency is depending on geometric parameters of channel. Furthermore stability frequency of electro-magnetic field is independent of gusts frequency and fluid thermo-physical properties

AAHES: A hybrid expert system realization of Adaptive Autonomy for smart grid

Abstract--Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment

Cyber security for smart grid: a human-automation interaction framework

Abstract-- Power grid cyber security is turning into a vital concern, while we are moving from the traditional power grid toward modern Smart Grid (SG). To achieve the smart grid objectives, development of Information Technology (IT) infrastructure and computer based automation is necessary. This development makes the smart grid more prone to the cyber attacks. This paper presents a cyber security strategy for the smart grid based on Human Automation Interaction (HAI) theory and especially Adaptive Autonomy (AA) concept. We scheme an adaptive Level of Automation (LOA) for Supervisory Control and Data Acquisition (SCADA) systems. This level of automation will be adapted to some environmental conditions which are presented in this paper. The paper presents a brief background, methodology (methodology design), implementation and discussions. Index Terms—smart grid, human automation interaction, adaptive autonomy, cyber security, performance shaping facto

EEG revealed improved vigilance regulation after stress exposure under Nx4: A randomized, placebo-controlled, double-blind, cross-over trial

ObjectivesVigilance is characterized by alertness and sustained attention. The hyper-vigilance states are indicators of stress experience in the resting brain. Neurexan (Nx4) has been shown to modulate the neuroendocrine stress response. Here, we hypothesized that the intake of Nx4 would alter brain vigilance states at rest.MethodIn this post-hoc analysis of the NEURIM study, EEG recordings of three, 12 min resting-state conditions in 39 healthy male volunteers were examined in a randomized, placebo-controlled, double-blind, cross-over clinical trial. EEG was recorded at three resting-state sessions: at baseline (RS0), after single-dose treatment with Nx4 or placebo (RS1), and subsequently after a psychosocial stress task (RS2). During each resting-state session, each 2-s segment of the consecutive EEG epochs was classified into one of seven different brain states along a wake-sleep continuum using the VIGALL 2.1 algorithm.ResultsIn the post-stress resting-state, subjects exhibited a hyper-stable vigilance regulation characterized by an increase in the mean vigilance level and by more rigidity in the higher vigilance states for a longer period of time. Importantly, Nx4-treated participants exhibited significantly lower mean vigilance level compared to placebo-treated ones. Also, Nx4- compared to placebo-treated participants spent comparably less time in higher vigilance states and more time in lower vigilance states in the post-stress resting-state.ConclusionStudy participants showed a significantly lower mean vigilance level in the post-stress resting-state condition and tended to stay longer in lower vigilance states after treatment with Nx4. These findings support the known stress attenuation effect of Nx4

Decoding material-specific memory reprocessing during sleep in humans

Neuronal learning activity is reactivated during sleep but the dynamics of this reactivation in humans are still poorly understood. Here we use multivariate pattern classification to decode electrical brain activity during sleep and determine what type of images participants had viewed in a preceding learning session. We find significant patterns of learning-related processing during rapid eye movement (REM) and non-REM (NREM) sleep, which are generalizable across subjects. This processing occurs in a cyclic fashion during time windows congruous to critical periods of synaptic plasticity. Its spatial distribution over the scalp and relevant frequencies differ between NREM and REM sleep. Moreover, only the strength of reprocessing in slow-wave sleep influenced later memory performance, speaking for at least two distinct underlying mechanisms between these states. We thus show that memory reprocessing occurs in both NREM and REM sleep in humans and that it pertains to different aspects of the consolidation process