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Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

Loss of retinal ganglion cells (RGCs) in optic neuropathies results in permanent partial or complete blindness. Myocyte enhancer factor 2 (MEF2) transcription factors have been shown to play a pivotal role in neuronal systems, and in particular MEF2A knockout was shown to enhance RGC survival after optic nerve crush injury. Here we expanded these prior data to study bi-allelic, tri-allelic and heterozygous allele deletion. We observed that deletion of all MEF2A, MEF2C, and MEF2D alleles had no effect on RGC survival during development. Our extended experiments suggest that the majority of the neuroprotective effect was conferred by complete deletion of MEF2A but that MEF2D knockout, although not sufficient to increase RGC survival on its own, increased the positive effect of MEF2A knockout. Conversely, MEF2A over-expression in wildtype mice worsened RGC survival after optic nerve crush. Interestingly, MEF2 transcription factors are regulated by post-translational modification, including by calcineurin-catalyzed dephosphorylation of MEF2A Ser-408 known to increase MEF2A-dependent transactivation in neurons. However, neither phospho-mimetic nor phospho-ablative mutation of MEF2A Ser-408 affected the ability of MEF2A to promote RGC deathin vivoafter optic nerve injury. Together these findings demonstrate that MEF2 gene expression opposes RGC survival following axon injury in a complex hierarchy, and further support the hypothesis that loss of or interference with MEF2A expression might be beneficial for RGC neuroprotection in diseases such as glaucoma and other optic neuropathies.

Melanopsin is a visual pigment expressed in a small subset of ganglion cells in the mammalian retina known as intrinsically photosensitive retinal ganglion cells (ipRGCs) and is implicated in regulating non-image forming functions such as circadian photoentrainment and pupil constriction and contrast sensitivity in image formation. Mouse melanopsin's Carboxy-terminus (C-terminus) possesses 38 serine and threonine residues, which can potentially serve as phosphorylation sites for a G-protein Receptor Kinase (GRK) and be involved in the deactivation of signal transduction. Previous studies suggest that S388, T389, S391, S392, S394, S395 on the proximal region of the C-terminus of mouse melanopsin are necessary for melanopsin deactivation. We expressed a series of mouse melanopsin C-terminal mutants in HEK293 cells and using calcium imaging, and we found that the necessary cluster of six serine and threonine residues, while being critical, are insufficient for proper melanopsin deactivation. Interestingly, the additional six serine and threonine residues adjacent to the required six sites, in either proximal or distal direction, are capable of restoring wild-type deactivation of melanopsin. These findings suggest an element of plasticity in the molecular basis of melanopsin phosphorylation and deactivation. In addition, C-terminal chimeric mutants and molecular modeling studies support the idea that the initial steps of deactivation and β-arrestin binding are centered around these critical phosphorylation sites (S388-S395). The degree of functional versatility described in this study, along with ipRGC biophysical heterogeneity and the possible use of multiple signal transduction cascades, might contribute to the diverse ipRGC light responses for use in non-image and image forming behaviors, even though all six sub types of ipRGCs express the same melanopsin gene OPN4.

Acute exposure to light within the blue wavelengths has been shown to enhance alertness and vigilance, and lead to improved speed on reaction time tasks, possibly due to activation of the noradrenergic system. It remains unclear, however, whether the effects of blue light extend beyond simple alertness processes to also enhance other aspects of cognition, such as memory performance. The aim of this study was to investigate the effects of a thirty minute pulse of blue light versus placebo (amber light) exposure in healthy normally rested individuals in the morning during verbal memory consolidation (i.e., 1.5 hours after memory acquisition) using an abbreviated version of the California Verbal Learning Test (CVLT-II). At delayed recall, individuals who received blue light (n = 12) during the consolidation period showed significantly better long-delay verbal recall than individuals who received amber light exposure (n = 18), while controlling for the effects of general intelligence, depressive symptoms and habitual wake time. These findings extend previous work demonstrating the effect of blue light on brain activation and alertness to further demonstrate its effectiveness at facilitating better memory consolidation and subsequent retention of verbal material. Although preliminary, these findings point to a potential application of blue wavelength light to optimize memory performance in healthy populations. It remains to be determined whether blue light exposure may also enhance performance in clinical populations with memory deficits.

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity.