Paracrine action of sFLT-1 secreted by stably-transfected Ehrlich ascites tumor cells and therapy using sFLT-1 inhibits ascites tumor growth in vivo

Background Vascular endothelial growth factor (VEGF) is known to play a major role in angiogenesis. A soluble form of Flt-1, a VEGF receptor, is potentially useful as an antagonist of VEGF, and accumulating evidence suggests the applicability of sFlt-1 in tumor suppression. In the present study, we have developed and tested strategies targeted specifically to VEGF for the treatment of ascites formation.Methods As an initial strategy, we produced recombinant sFLT-1 in the baculovirus expression system and used it as a trap to sequester VEGF in the murine ascites carcinoma model. The effect of the treatment on the weight of the animal, cell number, ascites volume and proliferating endothelial cells was studied. The second strategy involved, producing Ehrlich ascites tumor (EAT) cells stably transfected with vectors carrying cDNA encoding truncated form of Flt-1 and using these cells to inhibit ascites tumors in a nude mouse model. Results The sFLT-1 produced by the baculovirus system showed potent antiangiogenic activity as assessed by rat cornea and tube formation assay. sFLT-1 treatment resulted in reduced peritoneal angiogenesis with a concomitant decrease in tumor cell number, volume of ascites, amount of free VEGF and the number of invasive tumor cells as assayed by CD31 staining. EAT cells stably transfected with truncated form of Flt-1 also effectively reduced the tumor burden in nude mice transplanted with these cells, and demonstrated a reduction in ascites formation and peritoneal angiogenesis. Conclusions The inhibition of peritoneal angiogenesis and tumor growth by sequestering VEGF with either sFlt-1 gene expression by recombinant EAT cells or by direct sFLT-1 protein therapy is shown to comprise a potential therapy. Copyright (C) 2009 John Wiley & Sons, Ltd.

Multi Pattern Dynamic Time Warping For Automatic Speech Recognition

We are addressing the problem of jointly using multiple noisy speech patterns for automatic speech recognition (ASR), given that they come from the same class. If the user utters a word K times, the ASR system should try to use the information content in all the K patterns of the word simultaneously and improve its speech recognition accuracy compared to that of the single pattern based speech recognition. T address this problem, recently we proposed a Multi Pattern Dynamic Time Warping (MPDTW) algorithm to align the K patterns by finding the least distortion path between them. A Constrained Multi Pattern Viterbi algorithm was used on this aligned path for isolated word recognition (IWR). In this paper, we explore the possibility of using only the MPDTW algorithm for IWR. We also study the properties of the MPDTW algorithm. We show that using only 2 noisy test patterns (10 percent burst noise at -5 dB SNR) reduces the noisy speech recognition error rate by 37.66 percent when compared to the single pattern recognition using the Dynamic Time Warping algorithm.

Compressive Sensing For Sparsely Excited Speech Signals

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived from the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

Robust and High-resolution Voiced/Unvoiced Classification in Noisy Speech Using A Signal Smoothness Criterion

We propose a novel technique for robust voiced/unvoiced segment detection in noisy speech, based on local polynomial regression. The local polynomial model is well-suited for voiced segments in speech. The unvoiced segments are noise-like and do not exhibit any smooth structure. This property of smoothness is used for devising a new metric called the variance ratio metric, which, after thresholding, indicates the voiced/unvoiced boundaries with 75% accuracy for 0dB global signal-to-noise ratio (SNR). A novelty of our algorithm is that it processes the signal continuously, sample-by-sample rather than frame-by-frame. Simulation results on TIMIT speech database (downsampled to 8kHz) for various SNRs are presented to illustrate the performance of the new algorithm. Results indicate that the algorithm is robust even in high noise levels.

Two Stage Transform Vector Quantization of LSFs for Wideband Speech Coding

We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.

Gene therapy: Principles, practice, problems and prospects

The remarkable advances made in recombinant DNA technology over the last two decades have paved way for the use of gene transfer to treat human diseases. Several protocols have been developed for the introduction and expression of genes in humans, but the clinical efficacy has not been conclusively demonstrated in any of them. The eventual success of gene therapy for genetic and acquired disorders depends on the development of better gene transfer vectors for sustained, long term expression of foreign genes as well as a better understanding of the pathophysiology of human diseases, it is heartening to note that some of the gene therapy protocols have found other applications such as the genetic immunization or DNA vaccines, which is being heralded as the third vaccine revolution, Gene therapy is yet to become a dream come true, but the light is seen at the end of the tunnel.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

HDAC inhibitor valproic acid enhances tumor cell kill in adenovirus-HSVtk mediated suicide gene therapy in HNSCC xenograft mouse model

Safety, efficacy and enhanced transgene expression are the primary concerns while using any vector for gene therapy. One of the widely used vectors in clinical. trials is adenovirus which provides a safe way to deliver the therapeutic gene. However, adenovirus has poor transduction efficiency in vivo since most tumor cells express low coxsackie and adenovirus receptors. Similarly transgene expression remains low, possibly because of the chromatization of adenoviral genome upon infection in eukaryotic cells, an effect mediated by histone deacetylases (HDACs). Using a recombinant adenovirus (Ad-HSVtk) carrying the herpes simplex thymidine kinase (HSVtk) and GFP genes we demonstrate that HDAC inhibitor valproic acid can bring about an increase in CAR expression on host cells and thereby enhanced Ad-HSVtk infectivity. It also resulted in an increase in transgene (HSVtk and GFP) expression. This, in turn, resulted in increased cell kill of HNSCC cells, following ganciclovir treatment in vitro as well as in vivo in a xenograft nude mouse model.

Joint decoding of multiple speech patterns for robust speech recognition

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

Ternary Iron(III) complex showing photocleavage of DNA in the photodynamic therapy window

A new ternary iron(III) complex [FeL(dpq)] containing dipyridoquinoxaline (dpq) and 2,2-bis(3,5-di-tert-butyl-2-hydroxybenzyl)aminoacetic acid (H3L) is prepared and structurally characterized by X-ray crystallography. The high-spin complex with a FeN3O3 core shows a quasi-reversible iron(III)/iron(II) redox couple at -0.62 V (vs SCE) in DMF/0.1 M TBAP and a broad visible band at 470 nm in DMF/Tris buffer. Laser photoexcitation of this phenolate (L)-to-iron(III) charge-transfer band at visible wavelengths including red light of >= 630 nm leads to cleavage of supercoiled pUC19 DNA to its nicked circular form via a photoredox pathway forming hydroxyl radicals.

A speech-music discriminator using HILN model based features

We propose a simple speech music discriminator that uses features based on HILN(Harmonics, Individual Lines and Noise) model. We have been able to test the strength of the feature set on a standard database of 66 files and get an accuracy of around 97%. We also have tested on sung queries and polyphonic music and have got very good results. The current algorithm is being used to discriminate between sung queries and played (using an instrument like flute) queries for a Query by Humming(QBH) system currently under development in the lab.

Dynamic programming based optimum non-uniform samples for speech reconstruction and coding

Non-uniform sampling of a signal is formulated as an optimization problem which minimizes the reconstruction signal error. Dynamic programming (DP) has been used to solve this problem efficiently for a finite duration signal. Further, the optimum samples are quantized to realize a speech coder. The quantizer and the DP based optimum search for non-uniform samples (DP-NUS) can be combined in a closed-loop manner, which provides distinct advantage over the open-loop formulation. The DP-NUS formulation provides a useful control over the trade-off between bitrate and performance (reconstruction error). It is shown that 5-10 dB SNR improvement is possible using DP-NUS compared to extrema sampling approach. In addition, the close-loop DP-NUS gives a 4-5 dB improvement in reconstruction error.

Comparative study of filter-bank mean-energy distance for automated segmentation of speech signals

This paper describes a method of automated segmentation of speech assuming the signal is continuously time varying rather than the traditional short time stationary model. It has been shown that this representation gives comparable if not marginally better results than the other techniques for automated segmentation. A formulation of the 'Bach' (music semitonal) frequency scale filter-bank is proposed. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks considering this model. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. 'Bach' filters are seen to marginally outperform the other filters.

Language independent automated segmentation of speech using Bach scale filter-banks

This correspondence describes a method for automated segmentation of speech. The method proposed in this paper uses a specially designed filter-bank called Bach filter-bank which makes use of 'music' related perception criteria. The speech signal is treated as continuously time varying signal as against a short time stationary model. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. The Bach filters are seen to marginally outperform the other filters.