Specific Small-Molecule Activator of Aurora Kinase A Induces Autophosphorylation in a Cell-Free System

Aurora kinases are essential for chromosomal segregation and cell division and thereby important for maintaining the proper genomic integrity. There are three classes of aurora kinases in humans: A, B, and C. Aurora kinase A is frequently overexpressed in various cancers. The link of the overexpression and tumorigenesis is yet to be understood. By employing virtual screening, we have found that anacardic acid, a pentadecane aliphatic chain containing hydroxylcarboxylic acid, from cashew nut shell liquid could be docked in Aurora kinases A and B. Remarkably, we found that anacardic acid could potently activate the Aurora kinase A mediated phosphorylation of histone H3, but at a similar concentration the activity of aurora kinase B remained unaffected in vitro. Mechanistically, anacardic acid induces the structural changes and also the autophosphorylation of the aurora kinase A to enhance the enzyme activity. This data thus indicate anacardic acid as the first small-molecule activator of Aurora kinase, which could be highly useful for probing the function of hyperactive (overexpressed) Aurora kinase A.

Geomagnetic Storms Over India: A Close Look at Historic Space Weather at Mumbai

The swirling colors of aurorae, familiar to many in polar communities, can occasionally be seen at middle latitudes in locations such as southern Canada and central Europe. But in rare instances, aurorae can even be seen in the tropics. On 6 February 1872, news of the sighting of one such aurora was carried by the Times of India newspaper. The aurora occurred on 4 February 1872 and, as noted, was also observed over the Middle East.

Comparison of AM-FM Based Features For Robust Speech Recognition

Effective feature extraction for robust speech recognition is a widely addressed topic and currently there is much effort to invoke non-stationary signal models instead of quasi-stationary signal models leading to standard features such as LPC or MFCC. Joint amplitude modulation and frequency modulation (AM-FM) is a classical non-parametric approach to non-stationary signal modeling and recently new feature sets for automatic speech recognition (ASR) have been derived based on a multi-band AM-FM representation of the signal. We consider several of these representations and compare their performances for robust speech recognition in noise, using the AURORA-2 database. We show that FEPSTRUM representation proposed is more effective than others. We also propose an improvement to FEPSTRUM based on the Teager energy operator (TEO) and show that it can selectively outperform even FEPSTRUM

Gammatone Wavelet Cepstral Coefficients for Robust Speech Recognition

We develop noise robust features using Gammatone wavelets derived from the popular Gammatone functions. These wavelets incorporate the characteristics of human peripheral auditory systems, in particular the spatially-varying frequency response of the basilar membrane. We refer to the new features as Gammatone Wavelet Cepstral Coefficients (GWCC). The procedure involved in extracting GWCC from a speech signal is similar to that of the conventional Mel-Frequency Cepstral Coefficients (MFCC) technique, with the difference being in the type of filterbank used. We replace the conventional mel filterbank in MFCC with a Gammatone wavelet filterbank, which we construct using Gammatone wavelets. We also explore the effect of Gammatone filterbank based features (Gammatone Cepstral Coefficients (GCC)) for robust speech recognition. On AURORA 2 database, a comparison of GWCCs and GCCs with MFCCs shows that Gammatone based features yield a better recognition performance at low SNRs.