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.

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.

Access path query language for relational database systems

Database management systems offer a very reliable and attractive data organization for fast and economical information storage and processing for diverse applications. It is much more important that the information should be easily accessible to users with varied backgrounds, professional as well as casual, through a suitable data sublanguage. The language adopted here (APPLE) is one such language for relational database systems and is completely nonprocedural and well suited to users with minimum or no programming background. This is supported by an access path model which permits the user to formulate completely nonprocedural queries expressed solely in terms of attribute names. The data description language (DDL) and data manipulation language (DML) features of APPLE are also discussed. The underlying relational database has been implemented with the help of the DATATRIEVE-11 utility for record and domain definition which is available on the PDP-11/35. The package is coded in Pascal and MACRO-11. Further, most of the limitations of the DATATRIEVE-11 utility have been eliminated in the interface package.

An interactive graphics system for 2-D drawing and design

This paper is about a software system, GRASS-Graphic Software System for 2-D drawing and design—which has been implemented on a PDP-11/35 system with RSX-11M operating system. It is a low cost interactive graphics system for the design of two dimensional drawings and uses a minimum of hardware. It provides comprehensive facilities for creating, editing, storing and retrieving pictures. It has been implemented in the language Pascal and has the potential to be used as a powerful data-imputting tool for a design-automation system. The important features of the system are its low cost, software character generation and a user-trainable character recognizer, which has been included.

Detection and characterisation of circulating cysticercal antigens in human cerebrospinal fluid

With biotin labelled and unlabelled immunoglobulin fraction of anticysticercal antibodies raised in rabbits, tandem-enzyme linked immunosorbent assay (T-ELISA), capture-dot immunobinding assay (C-DIA) and reverse passive haemagglutination (RPHA) tests were developed for the detection of cysticercal antigens. The sensitivity levels were respectively, 9 ng ml−1, 2 ng ml−1 and 45 ng ml−1. All three methods were of equal specificity as none of the antigens of Mycobacterium tuberculosis, Japanese encephalitis virus and Echinococcus granulosus reacted with anticysticercal IgG. Cysticercal antigens were detected in the cerebrospinal fluid (CSF) of confirmed neurocysticercosis at sensitivity levels of 91·6% by T-ELISA, 83·33% by C-DIA and 75% by RPHA and specificity levels of >93%. Western analysis of these antigens in CSF showed mainly antigens of 64–68 kDa and 24–28 kDA. By crossed immunoelectrophoresis (CIE) with an intermediate gel technique, five circulating antigens were found to be released from scolex and fluid.

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.

Translation Induction on Indian Language Corpora Using Translingual Themes from Other Languages

Identifying translations from comparable corpora is a well-known problem with several applications, e.g. dictionary creation in resource-scarce languages. Scarcity of high quality corpora, especially in Indian languages, makes this problem hard, e.g. state-of-the-art techniques achieve a mean reciprocal rank (MRR) of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. There exist comparable corpora in many Indian languages with other ``auxiliary'' languages. We observe that translations have many topically related words in common in the auxiliary language. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for translation induction. Extensive experiments on 35 comparable corpora using English and French as auxiliary languages show that this approach can yield dramatic improvements in performance (e.g. MRR improves by 124% to 0.419 for Telugu-Kannada). A user study on WikiTSu, a system for cross-lingual Wikipedia title suggestion that uses our approach, shows a 20% improvement in the quality of titles suggested.