Biphenyl ethers conjugated CdSe/ZnS core/shell quantum dots and interpretation of the mechanism of amyloid fibril disruption

The biphenyl ethers (BPEs) are the potent inhibitors of TTR fibril formation and are efficient fibril disrupter. However, the mechanism by which the fibril disruption occurs is yet to be fully elucidated. To gain insight into the mechanism, we synthesized and used a new QD labeled BPE to track the process of fibril disruption. Our studies showed that the new BPE-QDs bind to the fiber uniformly and has affinity and specificity for TTR fiber and disrupted the pre-formed fiber at a relatively slow rate. Based on these studies we put forth the probable mechanism of fiber disruption by BPEs. Also, we show here that the BPE-QDs interact with high affinity to the amyloids of A beta(42), lysozyme and insulin. The potential of BPE-QDs in the detection of senile plaque in the brain of transgenic Alzheimer's mice has also been explored. (C) 2010 Elsevier Ltd. All rights reserved.

Demixing of severely overlapped H-1 NMR resonances and interpretation of anomalous intensity pattern of dipolar coupled A(3) spins in a weakly aligning medium

We report a single C-13 spin edited selective proton-proton correlation experiment to decipher overcrowded 13C coupled proton NMR spectra of weakly dipolar coupled spin systems. The experiment unravels the masked C-13 satellites in proton spectrum and permits the measurement of one bond carbon-proton residual dipolar couplings in I3S and for each diastereotopic proton in I2S groups. It also provides all the possible homonuclear proton-proton residual couplings which are otherwise difficult to extract from the broad and featureless one dimensional H-1 spectrum, in addition to enantiodifferentiation in a chiral molecule. Employment of heteronuclear (C-13) decoupling in the evolution period results in complete demixing of overlapped signals from enantiomers. The observed anomalous intensity pattern in strongly dipolar coupled methyl protons in methyl selective correlation experiment has been interpreted using polarization operator formalism. (C) 2010 Elsevier Inc. All rights reserved.

Challenges in the quantification and interpretation of spike-LFP relationships

Brain signals often show fluctuations in particular frequency bands, which are highly conserved across species and are associated with specific behavioural states. Such rhythmic patterns can be captured in the local field potential (LFP), which is obtained by low-pass filtering the extracellular signal recorded from microelectrodes. However, LFP also captures other neural processes that are associated with spikes, such as synaptic events preceding a spike, low-frequency component of the action potential (spike bleed-through'') and spike afterhyperpolarization, which pose difficulties in the estimation of the amplitude and phase of the rhythm with respect to spikes. Here we discuss these issues and different techniques that have been used to dissociate the rhythm from other neural events in the LFP.

Asymptotic behavior and interpretation of virtual states: The effects of confinement and of basis sets

A real-space high order finite difference method is used to analyze the effect of spherical domain size on the Hartree-Fock (and density functional theory) virtual eigenstates. We show the domain size dependence of both positive and negative virtual eigenvalues of the Hartree-Fock equations for small molecules. We demonstrate that positive states behave like a particle in spherical well and show how they approach zero. For the negative eigenstates, we show that large domains are needed to get the correct eigenvalues. We compare our results to those of Gaussian basis sets and draw some conclusions for real-space, basis-sets, and plane-waves calculations. (C) 2016 AIP Publishing LLC.

Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.