Intrinsic neural timescales relate to the dynamics of infraslow neural waves.

The human brain is a highly dynamic organ that operates across a variety of timescales, the intrinsic neural timescales (INT). In addition to the INT, the neural waves featured by its phase-related processes including their cycles with peak/trough and rise/fall play a key role in shaping the brain's neural activity. However, the relationship between the brain's ongoing wave dynamics and INT remains yet unclear. In this study, we utilized functional magnetic resonance imaging (fMRI) rest and task data from the Human Connectome Project (HCP) to investigate the relationship of infraslow wave dynamics [as measured in terms of speed by changes in its peak frequency (PF)] with INT. Our findings reveal that: (i) the speed of phase dynamics (PF) is associated with distinct parts of the ongoing phase cycles, namely higher PF in peak/trough and lower PF in rise/fall; (ii) there exists a negative correlation between phase dynamics (PF) and INT such that slower PF relates to longer INT; (iii) exposure to a movie alters both PF and INT across the different phase cycles, yet their negative correlation remains intact. Collectively, our results demonstrate that INT relates to infraslow phase dynamics during both rest and task states.

Alpha and theta peak frequency track on- and off-thoughts.

Our thoughts are highly dynamic in their contents. At some points, our thoughts are related to external stimuli or tasks focusing on single content (on-single thoughts), While in other moments, they are drifting away with multiple simultaneous items as contents (off-multiple thoughts). Can such thought dynamics be tracked by corresponding neurodynamics? To address this question, here we track thought dynamics during post-stimulus periods by electroencephalogram (EEG) neurodynamics of alpha and theta peak frequency which, as based on the phase angle, must be distinguished from non-phase-based alpha and theta power. We show how, on the psychological level, on-off thoughts are highly predictive of single-multiple thought contents, respectively. Using EEG, on-single and off-multiple thoughts are mediated by opposite changes in the time courses of alpha (high in on-single but low in off-multiple thoughts) and theta (low in on-single but high in off-multiple thoughts) peak frequencies. In contrast, they cannot be distinguished by frequency power. Overall, these findings provide insight into how alpha and theta peak frequency with their phase-related processes track on- and off-thoughts dynamically. In short, neurodynamics track thought dynamics.

Porous structured cotton-based ACF for the adsorption of benzen.

Fibrous activated carbon has attracted emerging research interests due to its remarkable adsorption performance for volatile organic compounds (VOCs). Though this adsorption behavior for VOCs is closely related to the pore structure on the surface of activated carbon fiber (ACF), few researchers paid attentions to the influence of textural properties of this adsorption process. Especially, cotton-based activated carbon fiber (CACF) for adsorbing benzene pollutant is rarely reported. Herein, in order to develop a high-performance adsorbent for the removal of VOCs pollutants, this work studied the influence of textural properties of CACF on the adsorption of benzene. The results showed that the increase of carbonization temperature would lead to the reduction of mesopores but the increase of micropores for CACF; the embedment of phosphoric acid and its derivatives into the carbon layers contributed to the formation of pore structure for CACF; furthermore, specific surface area of CACF can also be enlarged by increasing the concentration of phosphoric acid. More importantly, it was found that the adsorption capacity of CACF for benzene was strongly dependent on the specific surface area and volume of micropores within CACF because micropores can provide more favorable binding sites. This adsorption process preferred to occur on the wall of micropores, then the accumulated benzene would slowly fill the pores. Interestingly, the decrease of pore size of micropores can unexpectedly improve the affinity of CACF to benzene on the contrary. This work provides a new strategy to develop porous structured ACF materials for the high-performance adsorption of VOCs.

Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT.

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed.

Domino Effect of Interleukin-15 and CD8 T-Cell-Mediated Neuronal Apoptosis in Experimental Traumatic Brain Injury.

The effects of local factors on activation of immune cells infiltrating the central nervous system (CNS) in a rat model of traumatic brain injury (TBI) remain elusive. The cytokine, interleukin (IL)-15, is crucial for development and activation of CD8 T lymphocytes, a prominent lymphocytic population present in TBI lesions. We investigated whether IL-15 originates from astrocytes and whether IL-15 can evoke the CD8 T-lymphocyte response in TBI. We observed that astrocytes were activated in a rat model of TBI and that IL-15 was overexpressed on the surface of astrocytes. Further, CD8 T lymphocytes infiltrating TBI lesions colocalized with IL-15-expressing astrocytes. Activated CD8 T lymphocytes released granzyme B (Gra-b), which, in turn, activated caspase-3-induced poly(ADP-ribose) polymerase cleavage and, ultimately, neuronal apoptosis. Conversely, inhibition of astrocyte activation by pre-treatment with the specific inhibitor, fluorocitrate (FC), that reduces carbon flux through the Krebs cycle in astrocytes resulted in improved neurological function and memory. FC pre-treatment was also associated with downregulated IL-15 expression and CD8 T-cell activation as well as decreased levels of neuronal apoptosis, suggesting that IL-15 initiated a domino effect toward apoptosis. In contrast, rats pre-treated with recombinant rat IL-15 showed upregulated CD8 T-cell numbers and Gra-b levels, in addition to induction of neuronal apoptosis. Together, our results indicated that IL-15 could induce neuronal apoptosis by enhancing CD8 T-cell function in a rat model of TBI.

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.