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

Why Is Less Cationic Lipid Required To Prepare Lipoplexes from Plasmid DNA than Linear DNA in Gene Therapy?

The most important objective of the present study was to explain why cationic lipid (CL)-mediated delivery of plasmid DNA (pDNA) is better than that of linear DNA in gene therapy, a question that, until now, has remained unanswered. Herein for the first time we experimentally show that for different types of CLs, pDNA, in contrast to linear DNA, is compacted with a large amount of its counterions, yielding a lower effective negative charge. This feature has been confirmed through a number of physicochemical and biochemical investigations. This is significant for both in vitro and in vivo transfection studies. For an effective DNA transfection, the lower the amount of the CL, the lower is the cytotoxicity. The study also points out that it is absolutely necessary to consider both effective charge ratios between CL and pDNA and effective pDNA charges, which can be determined from physicochemical experiments.

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.

Curcumin-Arteether Combination Therapy of Plasmodium berghei-Infected Mice Prevents Recrudescence Through Immunomodulation

Earlier studies in this laboratory have shown the potential of artemisinin-curcumin combination therapy in experimental malaria. In a parasite recrudescence model in mice infected with Plasmodium berghei (ANKA), a single dose of alpha, beta-arteether (ART) with three oral doses of curcumin prevented recrudescence, providing almost 95% protection. The parasites were completely cleared in blood with ART-alone (AE) or ART+curcumin (AC) treatments in the short-term, although the clearance was faster in the latter case involving increased ROS generation. But, parasites in liver and spleen were not cleared in AE or AC treatments, perhaps, serving as a reservoir for recrudescence. Parasitemia in blood reached up to 60% in AE-treated mice during the recrudescence phase, leading to death of animals. A transient increase of up to 2-3% parasitemia was observed in AC-treatment, leading to protection and reversal of splenomegaly. A striking increase in spleen mRNA levels for TLR2, IL-10 and IgG-subclass antibodies but a decrease in those for INF gamma and IL-12 was observed in AC-treatment. There was a striking increase in IL-10 and IgG subclass antibody levels but a decrease in INF gamma levels in sera leading to protection against recrudescence. AC-treatment failed to protect against recrudescence in TLR2(-/-) and IL-10(-/-) animals. IL-10 injection to AE-treated wild type mice and AC-treated TLR22/2 mice was able to prolong survival. Blood from the recrudescence phase in AE-treatment, but not from AC-treatment, was able to reinfect and kill naive animals. Sera from the recrudescence phase of AC-treated animals reacted with several parasite proteins compared to that from AE-treated animals. It is proposed that activation of TLR2-mediated innate immune response leading to enhanced IL-10 production and generation of anti-parasite antibodies contribute to protective immunity in AC-treated mice. These results indicate a potential for curcumin-based combination therapy to be tested for prevention of recrudescence in falciparum and relapse in vivax malaria.

Artemisinin-based combination with curcumin adds a new dimension to malaria therapy

Malaria afflicts 300 million people worldwide, with over a million deaths every year. With no immediate prospect of a vaccine against the disease, drugs are the only choice to treat it. Unfortunately, the parasite has become resistant to most antimalarials, restricting the option to use artemisinins (ARTs) for effective cure. With the use of ARTs as the front-line antimalarials, reports are already available on the possible resistance development to these drugs as well. Therefore, it has become necessary to use ART-based combination therapies to delay emergence of resistance. It is also necessary to discover new pharmacophores to eventually replace ART. Studies in our laboratory have shown that curcumin not only synergizes with ART as an antimalarial to kill the parasite, but is also uniquely able to prime the immune system to protect against parasite recrudescence in the animal model. The results indicate a potential for the use of ART curcumin combination against recrudescence/relapse in falciparum and vivax malaria. In addition, studies have also suggested the use of curcumin as an adjunct therapy against cerebral malaria. In this review we have attempted to highlight these aspects as well as the studies directed to discover new pharmacophores as potential replacements for ART.

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.

How Does the Spacer Length of Cationic Gemini Lipids Influence the Lipoplex Formation with Plasmid DNA? Physicochemical and Biochemical Characterizations and their Relevance in Gene Therapy

Lipoplexes formed by the pEGFP-C3 plasmid DNA (pDNA) and lipid mixtures containing cationic gemini surfactant of the 1,2-bis(hexadecyl dimethyl ammonium) Acmes family referred to as C16CnC16, where n = 2 3, 5, or 12, and the zwitterionic helper lipid, 1,2-dioleoyl-sn-glycero-3-phosphatidylethanolamine (DOPE) have been studied from a wide variety of physical, chemical, and biological standpoints. The study has been carried out using several experimental methods, such as zeta potential, gel electrophoresis, small-angle X-ray scattering (SAXS), cryo-TEM, gene transfection, cell viability/cytotoxicity, and confocal fluorescence microscopy. As reported recently in a communication (J. Am. Chem. Soc. 2011, 133, 18014), the detailed physicochemical and biological studies confirm that, in the presence of the studied series lipid mixtures, plasmid DNA is compacted with a large number of its associated Na+ counterions. This in turn yields a much lower effective negative charge, q(pDNA)(-), a value that has been experimentally obtained for each mixed lipid mixture. Consequently, the cationic lipid (CL) complexes prepared with pDNA and CL/DOPE mixtures to be used in gene transfection require significantly less amount of CL than the one estimated assuming a value of q(DNA)(-) = -2. This drives to a considerably lower cytotoxicity of the gene vector. Depending on the CL molar composition, alpha, of the lipid mixture, and the effective charge ratio of the lipoplex, rho(eff), the reported SAXS data indicate the presence of two or three structures in the same lipoplex, one in the DOPE-rich region, other in the CL-rich region, and another one present at any CL composition. Cryo-TEM and SAXS studies with C16CnC16/DOPE-pDNA lipoplexes indicate that pDNA is localized between the mixed lipid bilayers of lamellar structures within a monolayer of similar to 2 nm. This is consistent with a highly compacted supercoiled pDNA conformation compared with that of linear DNA. Transfection studies were carried out with HEK293T, HeLa, CHO, U343, and H460 cells. The alpha and rho(eff) values for each lipid mixture were optimized on HEK293T cells for transfection, and using these values, the remaining cells were also transfected in absence (-FBS-FBS) and presence (-FBS+FBS) of serum. The transfection efficiency was higher with the CLs of shorter gemini spacers (n = 2 or 3). Each formulation expressed GFP on pDNA transfection and confocal fluorescence microscopy corroborated the results. C16C2C16/DOPE mixtures were the most efficient toward transfection among all the lipid mixtures and, in presence of serum, even better than the Lipofectamine2000, a commercial transfecting agent Each lipid combination was safe and did not show any significant levels of toxicity. Probably, the presence of two coexisting lamellar structures in lipoplexes synergizes the transfection efficiency of the lipid mixtures which are plentiful in the lipoplexes formed by CLs with short spacer (n = 2, 3) than those with the long spacer (n = 5, 12).

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.