Wavelength-selective fluorescence as a novel tool to study organization and dynamics in complex biological systems

The dynamics exhibited by a given component of a large macromolecule such as a folded globular protein or an organized supramolecular assembly like the biological membrane is a function of its precise localization within the larger system. A set of approaches based on the red edge effect in fluorescence spectroscopy, which can be used to monitordirectly the environment and dynamics around a fluorophore in a complex biological system, is reviewed in this article. A shift in the wavelength of maximum fluorescence emission toward higher wavelengths, caused by a shift in the excitation wavelength toward the red edge of the absorption band, is termed the red edge excitation shift (REES). This effect is mostly observed with polar fluorophores in motionally restricted media such as very viscous solutions or condensed phases. This phenomenon arises from the slow rates of solvent relaxation around an excited-state fluorophore, which is a function of the motional restriction imposed on the solvent molecules in the immediate vicinity of the fluorophore. Utilizing this approach, it becomes possible to probe the mobility parameters of the environment itself (which is represented by the relaxing solvent molecules) using the fluorophore merely as a reporter group. Further, since the ubiquitous solvent for biological systems is water, the information obtained in such cases will come from the otherwise optically silent water molecules. This makes REES and related techniques extremely useful in biology since hydration plays a crucial modulatory role in a large number of important cellular events.     

Improved distinguishers for HC-128

HC-128 is an eSTREAM final portfolio stream cipher. Several authors have investigated its security and, in particular, distinguishing attacks have been considered. Still, no one has been able to provide a distinguisher stronger than the one presented by Wu in the original HC-128 paper. In this paper we first argue that the keystream requirement in Wu’s original attack is underestimated by a factor of almost 2⁸. Our revised analysis shows that the keystream complexity of Wu’s original attack is 2^160.471 32-bit keystream blocks. We then go on to investigate two new types of distinguishers on HC-128. One of them, a distinguisher counting the number of zeros in created blocks of bits, gives a biased distribution that requires 2^143.537 such constructed block samples (2^152.537 32-bit keystream blocks). For fairness, the same metric is used to compare our attack to Wu’s, and our improvement is significant compared to Wu’s original result. Furthermore, the vector-based methodology used is general and can be applied to any cryptographic primitive that reveals a suitable probability distribution.     

Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data

Among the large amount of genes presented in microarray gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. In this regard, a new feature selection algorithm is presented based on rough set theory. It selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes. A theoretical analysis is presented to justify the use of both relevance and significance criteria for selecting a reduced gene set with high predictive accuracy. The importance of rough set theory for computing both relevance and significance of the genes is also established. The performance of the proposed algorithm, along with a comparison with other related methods, is studied using the predictive accuracy of K-nearest neighbor rule and support vector machine on five cancer and two arthritis microarray data sets. Among seven data sets, the proposed algorithm attains 100% predictive accuracy for three cancer and two arthritis data sets, while the rough set based two existing algorithms attain this accuracy only for one cancer data set.