Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int

Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int

Scaling for turbulent viscosity of buoyant plumes in stratified fluids : PIV measurement with implications for submarine hydrothermal plume turbulence

© The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part I: Oceanographic Research Papers 129 (2017): 89-98, doi:10.1016/j.dsr.2017.10.006.Time-resolved particle image velocimetry (PIV) has been used to measure instantaneous twodimensional velocity vector fields of laboratory-generated turbulent buoyant plumes in linearly stratified saltwater over extended periods of time. From PIV-measured time-series flow data, characteristics of plume mean flow and turbulence have been quantified. To be specific, maximum plume penetration scaling and entrainment coefficient determined from the mean flow agree well with the theory based on the entrainment hypothesis for buoyant plumes in stratified fluids. Besides the well-known persistent entrainment along the plume stem (i.e., the ‘plumestem’ entrainment), the mean plume velocity field shows persistent entrainment along the outer edge of the plume cap (i.e., the ‘plume-cap’ entrainment), thereby confirming predictions from previous numerical simulation studies. To our knowledge, the present PIV investigation provides the first measured flow field data in the plume cap region. As to measured plume turbulence, both the turbulent kinetic energy field and the turbulence dissipation rate field attain their maximum close to the source, while the turbulent viscosity field reaches its maximum within the plume cap region; the results also show that maximum turbulent viscosity scales as νt,max = 0.030 (B/N)1/2, where B is source buoyancy flux and N is ambient buoyancy frequency. These PIV data combined with previously published numerical simulation results have implications for understanding the roles of hydrothermal plume turbulence, i.e. plume turbulence within the cap region causes the ‘plume-cap’ entrainment that plays an equally important role as the ‘plume-stem’ entrainment in supplying the final volume flux at the plume spreading level.Part of this work was financially supported by the National Natural Science Foundation of China and Natural Science Foundation of Zhejiang Province under respective Project no. 11672267 and LR16E090001 to ZH. HJ was supported by a National Science Foundation Grant NSF OCE-1038055 through the RIDGE2000 program and an internal funding from WHOI

Unipolar Resistance Switching in Amorphous High-k dielectrics Based on Correlated Barrier Hopping Theory

We have proposed a kind of nonvolatile resistive switching memory based on amorphous LaLuO3, which has already been established as a promising candidate of high-k gate dielectric employed in transistors. Well-developed unipolar switching behaviors in amorphous LaLuO3 make it suited for not only logic but memory applications using the conventional semiconductor or the emerging nano/CMOS architectures. The conduction transition between high- and low- resistance states is attributed to the change in the separation between oxygen vacancy sites in the light of the correlated barrier hopping theory. The mean migration distances of vacancies responsible for the resistive switching are demonstrated in nanoscale, which could account for the ultrafast programming speed of 6 ns. The origin of the distributions in switching parameters in oxides can be well understood according to the switching principle. Furthermore, an approach has also been developed to make the operation voltages predictable for the practical applications of resistive memories.Comment: 18 pages, 6 figure

Numerical simulation of two coalescing turbulent forced plumes in linearly stratified fluids.

Author Posting. © AIP Publishing, 2019. This article is posted here by permission of [publisher] for personal use, not for redistribution. The definitive version was published in Lou, Y., He, Z., Jiang, H., & Han, X. Numerical simulation of two coalescing turbulent forced plumes in linearly stratified fluids. Physics of Fluids, 31(3), (2019):037111, doi:10.1063/1.5087534.A computational fluid dynamic model that can solve the Reynolds-averaged Navier-Stokes equations and the species transport equation is developed to simulate two coalescing turbulent forced plumes, which are released with initial momentum and buoyancy flux into a linearly stable stratified environment. The velocity fields, turbulence structures, and entrainment of two plumes with different source separations and source buoyancy fluxes are analyzed quantitatively, in comparison with a series of physical experiments. An empirical parameterization is proposed to predict the amplification of the maximum rise height of two coalescing forced plumes caused by superposition and mutual entrainment. The maximum values of both turbulent kinetic energy and turbulence dissipation rate decrease monotonically with the increase in source separation of the two turbulent plumes. However, the trajectory of the maximum turbulent viscosity attained in the plume cap region presents two notable enhancements. This variation may be attributed to the turbulence transported from the touching region and the strong mixing around the neutrally buoyant layer between two plumes, while the mixing is caused by the lateral convection and the rebound after overshooting. The plume entrainment coefficient in near vent stems has a positive relationship with the source Richardson number. A transition of flow regimes to plume-like flows would occur when the contribution of initial momentum is important. The entrainment coefficient will decrease in the touching region of two plumes due to mutual entrainment, while the superposition of plumes can lead to distortion of the boundary of plume sectors.This work was financially supported by the National Natural Science Foundation of China (Grant No. 11672267) and Fundamental Research Funds for the Central Universities (Grant No. 2017XZZX001-02A). This work was supported by HPC Center of ZJU (Zhoushan campus). Yingzhong Lou would like to thank Liang Zhao at Zhejiang University for fruitful discussions. The authors gratefully acknowledge the constructive suggestions offered by the anonymous referees.2020-03-2