Speaking-rate adaptation for task-based spoken dialogue systems

Spoken dialog systems are used in diverse applications and they help users accomplish various tasks. Contemporary spoken dialogue systems use prerecorded or synthesized prompts which are invariant in speed throughout the application. Such unvarying speaking-rates may not be suitable to different users pursuing varied tasks. This thesis examines the possibility of predicting the operator speaking-rate at the utterance level in a corpus of billing-support dialogs. It presents some key factors which affect speaking-rate, including factors based on user-state, dialog-state, user-utterance and operator-utterance characteristics. Based on this analysis it presents a predictive model to govern the operator-side speaking-rate to use in a system for automatically handling these types of dialogs. Factors that were found to be most useful for predicting the operator-side speaking-rate were the type of the current task, the semantic type of operator\u27s utterance and the duration of the operator\u27s utterance

A Novel Method for Feature Identification of Proteins

With a rapidly growing database of protein structures, one needs fast algorithms for comparison of two protein structures, based on an efficient representation of a protein. As such, the problem has exponential time complexity, which is prohibitive if one has to perform the comparison at the residue level. One needs efficient representation methods to compare 3-D patterns of residues. In this paper, we propose the use of nearest-neighbour clustering as an efficient means to find out higher-level features in a large database of proteins. We also propose a method to estimate the optimal size of a biologically significant feature

Evaluation of risedronate as an antibiofilm agent

Escherichia col cra null mutants have been reported in the literature to be impaired in biofilm formation. To develop E. coli biofilm-inhibiting agents for prevention and control of adherent behaviour, analogues of a natural Cra ligand, fructose-1,6-bisphosphate, were identified based on two-dimensional similarity to the natural ligand. Of the analogues identified, those belonging to the bisphosphonate class of drug molecules were selected for study, as these are approved for clinical use in humans and their safety has been established. Computational and in vitro studies with purified Cra protein showed that risedronate sodium interacted with residues in the fructose-1,6-bisphosphate-binding site. Using a quantitative biofilm assay, risedronate sodium, at a concentration of 300-400 mu M, was found to decrease E. coli and Salmonella pullorum biofilm formation by >60 %. Risedronate drastically reduced the adherence of E. coli cells to a rubber Foley urinary catheter, demonstrating its utility in preventing the formation of biofilm communities on medical implant surfaces. The use of risedronate, either alone or in combination with other agents, to prevent the formation of biofilms on surfaces is a novel finding that can easily be translated into practical applications

Distinctive microRNA signature of medulloblastomas associated with the WNT signaling pathway

Aim: Medulloblastoma is a malignant brain tumor that occurs predominantly in children. Current risk stratification based on clinical parameters is inadequate for accurate prognostication. MicroRNA expression is known to be deregulated in various cancers and has been found to be useful in predicting tumor behavior. In order to get a better understanding of medulloblastoma biology, miRNA profiling of medulloblastomas was carried out in parallel with expression profiling of protein-coding genes. Materials and Methods: miRNA profiling of medulloblastomas was carried out using Taqman Low Density Array v 1.0 having 365 human microRNAs. In parallel, genome-wide expression profiling of protein-coding genes was carried out using Affymetrix gene 1.0 ST arrays. Results: Both the profiling studies identified four molecular subtypes of medulloblastomas. Expression levels of select protein-coding genes and miRNAs could classify an independent set of medulloblastomas. Twelve of 31 medulloblastomas were found to overexpress genes belonging to the canonical WNT signaling pathway and carry a mutation in CTNNB1 gene. A number of miRNAs like miR-193a, miR-224/miR-452 cluster, miR-182/miR-183/miR-96 cluster, and miR-148a having potential tumor/metastasis suppressive activity were found to be overexpressed in the WNT signaling associated medulloblastomas. Exogenous expression of miR-193a and miR-224, two miRNAs that have the highest WNT pathway specific upregulation, was found to inhibit proliferation, increase radiation sensitivity and reduce anchorage-independent growth of medulloblastoma cells. Conclusion: Expression level of tumor/metastasis suppressive miRNAs in the WNT signaling associated medulloblastomas is likely to determine their response to treatment, and thus, these miRNAs would be important biomarkers for risk stratification within the WNT signaling associated medulloblastomas

Distinctive microRNA signature of medulloblastomas associated with the WNT signaling pathway

Aim: Medulloblastoma is a malignant brain tumor that occurs predominantly in children. Current risk stratification based on clinical parameters is inadequate for accurate prognostication. MicroRNA expression is known to be deregulated in various cancers and has been found to be useful in predicting tumor behavior. In order to get a better understanding of medulloblastoma biology, miRNA profiling of medulloblastomas was carried out in parallel with expression profiling of protein-coding genes. Materials and Methods: miRNA profiling of medulloblastomas was carried out using Taqman Low Density Array v 1.0 having 365 human microRNAs. In parallel, genome-wide expression profiling of protein-coding genes was carried out using Affymetrix gene 1.0 ST arrays. Results: Both the profiling studies identified four molecular subtypes of medulloblastomas. Expression levels of select protein-coding genes and miRNAs could classify an independent set of medulloblastomas. Twelve of 31 medulloblastomas were found to overexpress genes belonging to the canonical WNT signaling pathway and carry a mutation in CTNNB1 gene. A number of miRNAs like miR-193a, miR-224/miR-452 cluster, miR-182/miR-183/miR-96 cluster, and miR-148a having potential tumor/metastasis suppressive activity were found to be overexpressed in the WNT signaling associated medulloblastomas. Exogenous expression of miR-193a and miR-224, two miRNAs that have the highest WNT pathway specific upregulation, was found to inhibit proliferation, increase radiation sensitivity and reduce anchorage-independent growth of medulloblastoma cells. Conclusion: Expression level of tumor/metastasis suppressive miRNAs in the WNT signaling associated medulloblastomas is likely to determine their response to treatment, and thus, these miRNAs would be important biomarkers for risk stratification within the WNT signaling associated medulloblastomas

Distinctive microRNA signature of medulloblastomas associated with the WNT signaling pathway

Aim: Medulloblastoma is a malignant brain tumor that occurs predominantly in children. Current risk stratification based on clinical parameters is inadequate for accurate prognostication. MicroRNA expression is known to be deregulated in various cancers and has been found to be useful in predicting tumor behavior. In order to get a better understanding of medulloblastoma biology, miRNA profiling of medulloblastomas was carried out in parallel with expression profiling of protein-coding genes. Materials and Methods: miRNA profiling of medulloblastomas was carried out using Taqman Low Density Array v 1.0 having 365 human microRNAs. In parallel, genome-wide expression profiling of protein-coding genes was carried out using Affymetrix gene 1.0 ST arrays. Results: Both the profiling studies identified four molecular subtypes of medulloblastomas. Expression levels of select protein-coding genes and miRNAs could classify an independent set of medulloblastomas. Twelve of 31 medulloblastomas were found to overexpress genes belonging to the canonical WNT signaling pathway and carry a mutation in CTNNB1 gene. A number of miRNAs like miR-193a, miR-224/miR-452 cluster, miR-182/miR-183/miR-96 cluster, and miR-148a having potential tumor/metastasis suppressive activity were found to be overexpressed in the WNT signaling associated medulloblastomas. Exogenous expression of miR-193a and miR-224, two miRNAs that have the highest WNT pathway specific upregulation, was found to inhibit proliferation, increase radiation sensitivity and reduce anchorage-independent growth of medulloblastoma cells. Conclusion: Expression level of tumor/metastasis suppressive miRNAs in the WNT signaling associated medulloblastomas is likely to determine their response to treatment, and thus, these miRNAs would be important biomarkers for risk stratification within the WNT signaling associated medulloblastomas