4,934 research outputs found
Stability boundaries of roll and square convection in binary fluid mixtures with positive separation ratio
Rayleigh-B\'{e}nard convection in horizontal layers of binary fluid mixtures
heated from below with realistic horizontal boundary conditions is studied
theoretically using multi-mode Galerkin expansions. For positive separation
ratios the main difference between the mixtures and pure fluids lies in the
existence of stable three dimensional patterns near onset in a wide range of
the parameter space. We evaluated the stationary solutions of roll, crossroll,
and square convection and we determined the location of the stability
boundaries for many parameter combinations thereby obtaining the Busse balloon
for roll and square patterns.Comment: 19 pages + 15 figures, accepted by Journal of Fluid Mechanic
Cug2 is essential for normal mitotic control and CNS development in zebrafish.
Background:
We recently identified a novel oncogene, Cancer-upregulated gene 2 (CUG2), which is essential for kinetochore formation and promotes tumorigenesis in mammalian cells. However, the in vivo function of CUG2 has not been studied in animal models.
Results:
To study the function of CUG2 in vivo, we isolated a zebrafish homologue that is expressed specifically in the proliferating cells of the central nervous system (CNS). Morpholino-mediated knockdown of cug2 resulted in apoptosis throughout the CNS and the development of neurodegenerative phenotypes. In addition, cug2-deficient embryos contained mitotically arrested cells displaying abnormal spindle formation and chromosome misalignment in the neural plate.
Conclusions:
Therefore, our findings suggest that Cug2 is required for normal mitosis during early neurogenesis and has functions in neuronal cell maintenance, thus demonstrating that the cug2 deficient embryos may provide a model system for human neurodegenerative disorders
Kinesthesia in a sustained-attention driving task
This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. © 2014 Elsevier Inc
Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%
FIH-1, a novel interactor of mindbomb, functions as an essential anti-angiogenic factor during zebrafish vascular development
Objective: It has been shown that Mindbomb (Mib), an E3 Ubiquitin ligase, is an essential modulator of Notch signaling during development. However, its effects on vascular development remain largely unknown
Dielectric catastrophe at the magnetic field induced insulator to metal transition in Pr1-xCaxMnO3 (x=0.30, 0.37) crystals
The dielectric permittivity and resistivity have been measured simultaneously
as a function of magnetic field in Pr1-xCaxMnO3 crystals with different doping.
A huge increase of dielectric permittivity was detected near percolation
threshold. The dielectric and conductive properties are found to be mutually
correlated throughout insulator to metal transition evidencing the dielectric
catastrophe phenomenon. Data are analyzed in a framework of Maxwell-Garnett
theory and the Mott-Hubbard theory attributed to the role of strong Coulomb
interactions.Comment: 5 pages, 5 figure
Multifractal characterization of stochastic resonance
We use a multifractal formalism to study the effect of stochastic resonance
in a noisy bistable system driven by various input signals. To characterize the
response of a stochastic bistable system we introduce a new measure based on
the calculation of a singularity spectrum for a return time sequence. We use
wavelet transform modulus maxima method for the singularity spectrum
computations. It is shown that the degree of multifractality defined as a width
of singularity spectrum can be successfully used as a measure of complexity
both in the case of periodic and aperiodic (stochastic or chaotic) input
signals. We show that in the case of periodic driving force singularity
spectrum can change its structure qualitatively becoming monofractal in the
regime of stochastic synchronization. This fact allows us to consider the
degree of multifractality as a new measure of stochastic synchronization also.
Moreover, our calculations have shown that the effect of stochastic resonance
can be catched by this measure even from a very short return time sequence. We
use also the proposed approach to characterize the noise-enhanced dynamics of a
coupled stochastic neurons model.Comment: 10 pages, 21 EPS-figures, RevTe
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