DNA topoisomerase I from mycobacteria - A potential drug target

DNA topoisomerases are ubiquitous group of enzymes altering the topology of DNA by concerted breakage and rejoining of the phosphodiester backbone of DNA. The enzymes are classified based on the pattern of DNA cleavage. Type IA enzymes found in all bacteria nick the DNA and attach themselves covalently to the 5' side of the nick during the first transesterification reaction. Most of the information on this group of enzymes comes from studies with E. coli topoisomerase I and III. Members of type IA group are single subunit Zn++ metalloenzymes recognizing single stranded DNA without high degree of sequence specificity during relaxation reaction of negatively super coiled DNA. So far no inhibitors are known for this group of enzymes inspite of their important role in maintaining homeostasis of DNA topology. Molecular characterization of DNA topoisomerase I from mycobacteria has revealed some of the important features of type IA enzymes hitherto unknown and provide scope for identifying novel inhibitors. The present review describes the recent developments in the area summarizing the distinctive features of mycobacterial topoisomerase I. The enzyme has several properties not shared by either type IA or 113 enzymes with respect to DNA binding, recognition, sequence specificity and interaction pattern. The physiological basis of the unusual features is discussed. The unique properties described would aid in developing the enzyme as a target molecule in pharmaceutical design. In addition, the findings lead to address some fundamental questions on the intracellular role of topoisomerase I in the biology of mycobacteria which are one of the most formidable group of pathogenic organisms.

Online Unsupervised Pattern Discovery in Speech Using Parallelization

Segmental dynamic time warping (DTW) has been demonstrated to be a useful technique for finding acoustic similarity scores between segments of two speech utterances. Due to its high computational requirements, it had to be computed in an offline manner, limiting the applications of the technique. In this paper, we present results of parallelization of this task by distributing the workload in either a static or dynamic way on an 8-processor cluster and discuss the trade-offs among different distribution schemes. We show that online unsupervised pattern discovery using segmental DTW is plausible with as low as 8 processors. This brings the task within reach of today's general purpose multi-core servers. We also show results on a 32-processor system, and discuss factors affecting scalability of our methods.

Biologically triggered exploding protein based microcapsules for drug delivery

Biologically triggered exploding microcapsules were synthesized by layer-by-layer assembly of biopolymers. The microcapsules showed controlled rupturing behaviour upon exposure to a pathologically relevant biomolecule, trypsin. These microcapsules offer significant potential for clinical applications.

Investigation of biodegradable and biocompatible castor oil poly(mannitol-citric-sebacate) polyester as a drug carrier

Castor oil-based poly(mannitol-citric sebacate) was synthesized by simple, catalyst-free melt condensation process using monomers having potential to be metabolized in vivo. The polymer was characterized using various techniques and the tensile and hydration properties of the polymers were also determined. The biocompatibility of the polymer was tested using human foreskin fibroblasts cells. The in vitro degradation studies show that the time for complete degradation of the polymer was more than 21 days. The usage of castor oil polyester as a drug carrier was analysed by doping the polymer with 5-fluorouracil model drug and the release rate was studied by varying the percentage loading of drugs and the pH of the PBS solution medium. The cumulative drug-release profiles exhibited a biphasic release with an initial burst release and cumulative 100% release within 42 h. To understand the role of the polymer as a drug carrier in the release behaviour, drug-release studies were conducted with another drug, isoniazid. The release behaviour of isoniazid drug from the same polymer matrix followed an nth order kinetic model and 100% cumulative release was achieved after 12 days. The variation in the release behaviour for two model drugs from the same polymer matrix suggests a strong interaction between the polymer and the drug molecule.

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.