Identification of a core promoter and a novel isoform of the human TSCI gene transcript and structural comparison with mouse homolog

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder with loci on chromosome 9q34.12 (TSC1) and chromosome 16p13.3 (TSC2). Genes for both loci have been isolated and characterized. The promoters of both genes have not been characterized so far and little is known about the regulation of these genes. This study reports the characterization of the human TSC1 promoter region for the first time. We have identified a novel alternative isoform in the 5' untranslated region (UTR) of the TSC1 gene transcript involving exon 1. Alternative isoforms in the 5' UTR of the mouse Tsc1 gene transcript involving exon I and exon 2 have also been identified. We have identified three upstream open reading frames (uORFs) in the 5' UTR of the TSC1/Tsc1 gene. A comparative study of the 5' UTR of TSC1/Tsc1 gene has revealed that there is a high degree of similarity not only in the sequence but also in the splicing pattern of both human and mouse TSC1 genes. We have used PCR methodology to isolate approximately 1.6 kb genomic DNA 5' to the TSC1 cDNA. This sequence has directed a high level of expression of luciferase activity in both HeLa and HepG2 cells. Successive 5' and 3' deletion analysis has suggested that a -587 bp region, from position +77 to -510 from the transcription start site (TSS), contains the promoter activity. Interestingly, this region contains no consensus TATA box or CAAT box. However, a 521-bp fragment surrounding the TSS exhibits the characteristics of a CpG island which overlaps with the promoter region. The identification of the TSC1 promoter region will help in designing a suitable strategy to identify mutations in this region in patients who do not show any mutations in the coding regions. It will also help to study the regulation of the TSC1 gene and its role in tumorigenesis. (C) 2003 Elsevier B.V. All rights reserved.

Tree structure for efficient data mining using rough sets

In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Identification and characterization of cysteine proteinases of Trypanosoma evansi

Trypanosoma evansi is a causative agent of `surra', a common haemoprotozoan disease of livestock in India causing high morbidity and mortality in disease endemic areas. The proteinases released by live and dead trypanosomes entail immunosuppression in the infected host, which immensely contribute in disease pathogenesis. Cysteine proteinases are identified in the infectious cycle of trypanosomes such as cruzain from Trypanosoma cruzi, rhodesain or brucipain from Trypanosoma brucei rhodesiense and congopain from Trypanosoma congelense. These enzymes localised in lysosome-like organelles, flagellar pocket and on cell surface, which play a critical role in the life cycle of protozoan parasites, viz. in host invasion, nutrition and alteration of the host immune response. The paper describes the identification of cysteine proteinases of T. evansi lysate, activity profile at different pH optima and inhibition pattern using a specific inhibitor, besides the polypeptide profile of an antigen. Eight proteinases of T. evansi were identified in the molecular weight (MW) ranges of 28-170 kDa using gelatin substrate-polyacrylamide gel electrophoresis (GS-PAGE), and of these proteinases, six were cysteine proteinases, as they were inhibited by L-3-carboxy-2,3-transepoxypropionyl-lecuylamido (4-guanidino)-butane (E-64), a specific inhibitor. These proteolytic enzymes were most reactive in acidic pH between 3.0 and 5.5 in the presence of dithiothreitol and completely inactive at alkaline pH 10.0. Similarly, the GS-PAGE profile of the serum samples of rats infected with T. evansi revealed strong proteolytic activity only at the 28-kDa zone at pH 5.5, while no proteolytic activity was observed in serum samples of uninfected rats. Further, the other zones of clearance, which were evident in T. evansi antigen zymogram, could not be observed in the serum samples of rats infected with T. evansi. The polypeptide pattern of the whole cell lysate antigen revealed 12-15 polypeptide bands ranging from 28 to 81 kDa along with five predominant polypeptides bands (MW of 81, 66, 62, 55 and 45 kDa), which were immunoreactive with hyperimmune serum (HIS) and serum of experimentally infected rabbits with T. evansi infection. The immunoblot recognised antibodies in experimentally infected rabbits and against HIS as well, corresponding to the zone of clearances at lower MW ranges (28-41 kDa), which may be attributed to the potential of these proteinases in the diagnosis of T. evansi infection. Since these thiol-dependent enzymes are most active in acidic pH and considering their inhibition characteristics, these data suggest that they resemble to the mammalian lysosomal cathepsin B and L.

Pruned Universal Symbol Sequences for LZW based Language identification

We present a improved language modeling technique for Lempel-Ziv-Welch (LZW) based LID scheme. The previous approach to LID using LZW algorithm prepares the language pattern table using LZW algorithm. Because of the sequential nature of the LZW algorithm, several language specific patterns of the language were missing in the pattern table. To overcome this, we build a universal pattern table, which contains all patterns of different length. For each language it's corresponding language specific pattern table is constructed by retaining the patterns of the universal table whose frequency of appearance in the training data is above the threshold.This approach reduces the classification score (Compression Ratio [LZW-CR] or the weighted discriminant score[LZW-WDS]) for non native languages and increases the LID performance considerably.

Lamb wave based identification and parameter estimation of corrosion in metallic plate structure using a circular PWAS array

A circular array of Piezoelectric Wafer Active Sensor (PWAS) has been employed to detect surface damages like corrosion using lamb waves. The array consists of a number of small PWASs of 10 mm diameter and 1 mm thickness. The advantage of a circular array is its compact arrangement and large area of coverage for monitoring with small area of physical access. Growth of corrosion is monitored in a laboratory-scale set-up using the PWAS array and the nature of reflected and transmitted Lamb wave patterns due to corrosion is investigated. The wavelet time-frequency maps of the sensor signals are employed and a damage index is plotted against the damage parameters and varying frequency of the actuation signal (a windowed sine signal). The variation of wavelet coefficient for different growth of corrosion is studied. Wavelet coefficient as function of time gives an insight into the effect of corrosion in time-frequency scale. We present here a method to eliminate the time scale effect which helps in identifying easily the signature of damage in the measured signals. The proposed method becomes useful in determining the approximate location of the corrosion with respect to the location of three neighboring sensors in the circular array. A cumulative damage index is computed for varying damage sizes and the results appear promising.

Identification and purification of tRNAs containing N6-(delta 2-isopentenyl) adenosine using antibodies specific for N6-(delta-isopentenyl) adenosine

Antibodies specific for the modified nucleoside N6-(delta 2-isopentenyl) adenosine (i6A) were employed to identify the tRNAs containing i6A from an unfractionated tRNA mixture by a nitrocellulose filter binding assay. When radioactive aminoacyl-tRNAs were incubated with i6A-specific antibodies and filtered through nitrocellulose membrane filters, the tRNAs possessing i6A (tRNAtyr and tRNAser) remained on the filters. tRNAarg and tRNAlys which do not contain i6A showed no binding. This finding will be useful as a very simple and rapid assay of such RNAs under a variety of conditions. Purification of i6A containing tRNAs from an unfractionated tRNA mixture was achieved by affinity chromatography of the tRNAs on an i6A antibody-Sepharose column. Nonspecific binding of tRNAs to the column was avoided by the use of purified antibodies.

An O(mn) Gomory-Hu Tree Construction Algorithm for Unweighted Graphs

We present a fast algorithm for computing a Gomory-Hu tree or cut tree for an unweighted undirected graph G = (V,E). The expected running time of our algorithm is Õ(mc) where |E| = m and c is the maximum u-vedge connectivity, where u,v ∈ V. When the input graph is also simple (i.e., it has no parallel edges), then the u-v edge connectivity for each pair of vertices u and v is at most n-1; so the expected running time of our algorithm for simple unweighted graphs is Õ(mn).All the algorithms currently known for constructing a Gomory-Hu tree [8,9] use n-1 minimum s-t cut (i.e., max flow) subroutines. This in conjunction with the current fastest Õ(n20/9) max flow algorithm due to Karger and Levine [11] yields the current best running time of Õ(n20/9n) for Gomory-Hu tree construction on simpleunweighted graphs with m edges and n vertices. Thus we present the first Õ(mn) algorithm for constructing a Gomory-Hu tree for simple unweighted graphs.We do not use a max flow subroutine here; we present an efficient tree packing algorithm for computing Steiner edge connectivity and use this algorithm as our main subroutine. The advantage in using a tree packing algorithm for constructing a Gomory-Hu tree is that the work done in computing a minimum Steiner cut for a Steiner set S ⊆ V can be reused for computing a minimum Steiner cut for certain Steiner sets S' ⊆ S.