Cytotoxic T lymphocytes raised against Japanese encephalitis virus: effector cell phenotype, target specificity and in vitro virus clearance.

Several H-2 defined cell lines were examined for their ability to support infection and replication of Japanese encephalitis virus (JEV) before their use in in vitro and in vivo stimulation protocols for generating cytotoxic T lymphocytes (CTLs) against JEV. Among II different cell lines tested, two H-2(d) macrophage tumour lines (P388D1, RAW 264.7), an H-2(d) hybridoma (Sp2/0), an H-2K(k)D(d) neuroblastoma (Neuro 2a), and H-2(k) fibroblast cell line (L929) were found to support JEV infection and replication. These cell lines were used to generate anti-JEV CTLs by using in vivo immunization followed by in vitro stimulation of BALB/c mice. We observed that not only syngeneic and allogeneic infected cells but also JEV-infected xenogeneic cells could prime BALB/c mice for the generation of JEV-specific CTLs upon subsequent in vitro stimulation of splenocytes with JEV-infected syngeneic cells. Although infected xenogeneic cells were used for immunization, the anti-JEV effecters that were generated lysed infected syngeneic targets but not JEV-infected xenogeneic or allogeneic target cells in a 5h Cr-51 release assay. These anti-JEV effecters recognized syngeneic target cells infected with West Nile virus to a lesser extent and were shown to be Lyt-2.2(+) T cells. The results of unlabelled cold target competition studies suggested alterations in the cell surface expression of viral antigenic determinants recognized by these CTLs. We further demonstrate that the JEV-specific CTLs generated could virtually block the release of infectious virus particles from infected P388D1 and Neuro 2a cells in vitro.

Protection of adult but not newborn mice against lethal intracerebral challenge with Japanese encephalitis virus by adoptively transferred virus-specific cytotoxic T lymphocytes: Requirement for L3T4(+) T cells

The protective ability of cytotoxic T cells (CTL) raised in vitro against Japanese encephalitis virus (JEV) was examined by adoptive transfer experiments. Adoptive transfer of anti-JEV effecters by intracerebral (i.c.) but not by intraperitoneal (i.p.) or intravenous (i.v.) routes protected adult BALB/c mice against lethal i.c. JEV challenge. In contrast to adult mice, adoptive transfer of anti-JEV effecters into newborn (4-day-old) and suckling (8-14-day-old) mice did not confer protection. However, virus-induced death was delayed in suckling mice compared to newborn mice upon adoptive transfer. The specific reasons for lack of protection in newborn mice are not clear but virus load was found to be higher in newborn mice brains compared to those of adults and virus clearance was observed only in adult mice brains but not in newborn mice brains upon adoptive transfer. Specific depletion of Lyt 2.2(+), L3T4(+) or Thy-1(+) T cell populations before adoptive transfer abrogated the protective ability of transferred effecters. However, when Lyt 2.2(+) cell-depleted and L3T4(+) cell-depleted effecters were mixed and transferred into adult mice the protective activity was retained, demonstrating that both Lyt 2.2(+) and L3T4(+) T cells are necessary to confer protection. Although the presence of L3T4(+) T cells in adoptively transferred effector populations enhanced virus-specific serum neutralizing antibodies, the presence of neutralizing antibodies alone without Lyt 2.2(+) cells was not sufficient to confer protection.

Expression of the Japanese encephalitis virus NS3 and NS2b proteins as glutathione S-transferase fusions

Flaviviruses generate their structural and nonstructural proteins by proteolytic processing of a single large polyprotein precursor. These proteolytic events are brought about both by host cell signalase and a virally encoded protease. The virally encoded proteolytic activity has been shown to reside within the nonstructural protein 3 (NS3) and requires the product of the nonstructural 2b (NS2b) gene. In order to obtain sufficient quantities of pure NS2b and NS3 proteins for kinetic analysis, we have expressed both these proteins in recombinant systems as fusions to glutathione S-transferase (GST). The fusion constructs were driven by the strong bacteriophage T7 promoter. Transfection of these constructs into the African green monkey kidney cell line CV-1 previously infected with a recombinant vaccinia virus expressing the T7 RNA polymerase resulted in synthesis of the fusion proteins. Both the fusion proteins could be purified to homogeneity in a single step using a glutathione agarose affinity matrix.

Japanese encephalitis virus infection of mouse cell lines: ability to prime mice for generation of virus specific cytotoxic T lymphocytes and differences in CTL recognisable viral determinants

Ten different mouse cell lines were examined for Japanese encephalitis virus (JEV) infection in vitro and then tested for their ability to generate virus specific cytotoxic T lymphocytes (CTL). Among all cell lines examined, Neuro La (a neuroblastoma) was readily infected with JEV as examined by immunofluorescence and viral replication. Among other cells, P388D1, RAW 264.7 (Macrophage origin), Sp2/0 (B-cell Hybridoma), YAC-1 (T-cell lymphoma), and L929 (Fibroblast) were semipermissive to JEV infection. The cytopathic effects caused by progressive JEV infection varied from cell line to cell line. In the case of YAC-1 cells long-term viral antigen expression was observed without significant alterations in cell viability. Intermediate degrees of cytopathicity are seen in RAW 264.7 and L929 cells while infection of PS, Neuro 2a, P388D1 and Sp2/0 caused major viability losses. All infected cell lines were able to prime adult BALB/c (H-2(d)) mice for the generation of secondary JEV specific CTL. In contrast to YAC-1, the permissive neuroblastoma cell line Neuro 2a (H-2K(k)D(d)) was found to be least efficient in its ability to stimulate anti-viral CTL generation. Cold target competition studies demonstrated that both Neuro 2a and YAC-1 (H-2K(k)D(d)) cells expressed similar viral determinants that are recognised by CTL, suggesting that the reason for the lower ability of Neuro 2a to stimulate anti-viral CTL was not due to lack of viral CTL determinants. These findings demonstrate that a variety of mouse cell lines can be infected with Japanese encephalitis virus, and that these infected cells could be utilised to generate virus specific CTL in BALB/c mice.

In vivo clearance of Japanese encephalitis virus by adoptively transferred virus specific cytotoxic T lymphocytes

Japanese encephalitis virus (JEV) is a positive stranded RNA virus that belongs to the flavivirus group, JEV infection damages the central nervous system (CNS) and is one of the main causative agents of acute encephalitis, H-2 restricted virus-specific cytotoxic T lymphocytes (CTL) have been generated specifically against JEV in our laboratory and these CTL have been shown to protect mice against lethal challenge with JEV, Virus replication was found to be inhibited in the brains of animals that mere adoptively transferred with JEV specific CTL as revealed by immunohistological staining as,veil as viral plaque assays. We further show that virus specific CTL could be recovered from such protected mice as long as 45 days after adoptive transfer.

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.

LZW Based Distance Measures for Spoken Language Identification

We present a new approach to spoken language modeling for language identification (LID) using the Lempel-Ziv-Welch (LZW) algorithm. The LZW technique is applicable to any kind of tokenization of the speech signal. Because of the efficiency of LZW algorithm to obtain variable length symbol strings in the training data, the LZW codebook captures the essentials of a language effectively. We develop two new deterministic measures for LID based on the LZW algorithm namely: (i) Compression ratio score (LZW-CR) and (ii) weighted discriminant score (LZW-WDS). To assess these measures, we consider error-free tokenization of speech as well as artificially induced noise in the tokenization. It is shown that for a 6 language LID task of OGI-TS database with clean tokenization, the new model (LZW-WDS) performs slightly better than the conventional bigram model. For noisy tokenization, which is the more realistic case, LZW-WDS significantly outperforms the bigram technique

Computational Biology and Language

Current scientific research is characterized by increasing specialization, accumulating knowledge at a high speed due to parallel advances in a multitude of sub-disciplines. Recent estimates suggest that human knowledge doubles every two to three years – and with the advances in information and communication technologies, this wide body of scientific knowledge is available to anyone, anywhere, anytime. This may also be referred to as ambient intelligence – an environment characterized by plentiful and available knowledge. The bottleneck in utilizing this knowledge for specific applications is not accessing but assimilating the information and transforming it to suit the needs for a specific application. The increasingly specialized areas of scientific research often have the common goal of converting data into insight allowing the identification of solutions to scientific problems. Due to this common goal, there are strong parallels between different areas of applications that can be exploited and used to cross-fertilize different disciplines. For example, the same fundamental statistical methods are used extensively in speech and language processing, in materials science applications, in visual processing and in biomedicine. Each sub-discipline has found its own specialized methodologies making these statistical methods successful to the given application. The unification of specialized areas is possible because many different problems can share strong analogies, making the theories developed for one problem applicable to other areas of research. It is the goal of this paper to demonstrate the utility of merging two disparate areas of applications to advance scientific research. The merging process requires cross-disciplinary collaboration to allow maximal exploitation of advances in one sub-discipline for that of another. We will demonstrate this general concept with the specific example of merging language technologies and computational biology.

Language identification using parallel sub-word recognition

Parallel sub-word recognition (PSWR) is a new model that has been proposed for language identification (LID) which does not need elaborate phonetic labeling of the speech data in a foreign language. The new approach performs a front-end tokenization in terms of sub-word units which are designed by automatic segmentation, segment clustering and segment HMM modeling. We develop PSWR based LID in a framework similar to the parallel phone recognition (PPR) approach in the literature. This includes a front-end tokenizer and a back-end language model, for each language to be identified. Considering various combinations of the statistical evaluation scores, it is found that PSWR can perform as well as PPR, even with broad acoustic sub-word tokenization, thus making it an efficient alternative to the PPR system.

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

Language models for online handwritten Tamil word recognition

N-gram language models and lexicon-based word-recognition are popular methods in the literature to improve recognition accuracies of online and offline handwritten data. However, there are very few works that deal with application of these techniques on online Tamil handwritten data. In this paper, we explore methods of developing symbol-level language models and a lexicon from a large Tamil text corpus and their application to improving symbol and word recognition accuracies. On a test database of around 2000 words, we find that bigram language models improve symbol (3%) and word recognition (8%) accuracies and while lexicon methods offer much greater improvements (30%) in terms of word recognition, there is a large dependency on choosing the right lexicon. For comparison to lexicon and language model based methods, we have also explored re-evaluation techniques which involve the use of expert classifiers to improve symbol and word recognition accuracies.