Exploring the potential of adjunct therapy in tuberculosis

A critical unmet need for treatment of drug-resistant tuberculosis (TB) is to find novel therapies that are efficacious, safe, and shorten the duration of treatment. Drug discovery approaches for TB primarily target essential genes of the pathogen Mycobacterium tuberculosis (Mtb) but novel strategies such as host-directed therapies and nonmicrobicidal targets are necessary to bring about a paradigm shift in treatment. Drugs targeting the host pathways and nonmicrobicidal proteins can be used only in conjunction with existing drugs as adjunct therapies. Significantly, host-directed adjunct therapies have the potential to decrease duration of treatment, as they are less prone to drug resistance, target the immune responses, and act via novel mechanism of action. Recent advances in targeting host-pathogen interactions have implicated pathways such as eicosanoid regulation and angiogenesis. Furthermore, several approved drugs such as metformin and verapamil have been identified that appear suitable for repurposing for the treatment of TB. These findings and the challenges in the area of host- and/or pathogen-directed adjunct therapies and their implications for TB therapy are discussed.

Demodulation of Narrowband Speech Spectrograms Using the Riesz Transform

We propose a two-dimensional (2-D) multicomponent amplitude-modulation, frequency-modulation (AM-FM) model for a spectrogram patch corresponding to voiced speech, and develop a new demodulation algorithm to effectively separate the AM, which is related to the vocal tract response, and the carrier, which is related to the excitation. The demodulation algorithm is based on the Riesz transform and is developed along the lines of Hilbert-transform-based demodulation for 1-D AM-FM signals. We compare the performance of the Riesz transform technique with that of the sinusoidal demodulation technique on real speech data. Experimental results show that the Riesz-transform-based demodulation technique represents spectrogram patches accurately. The spectrograms reconstructed from the demodulated AM and carrier are inverted and the corresponding speech signal is synthesized. The signal-to-noise ratio (SNR) of the reconstructed speech signal, with respect to clean speech, was found to be 2 to 4 dB higher in case of the Riesz transform technique than the sinusoidal demodulation technique.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

Chronic lung infection by Pseudomonas aeruginosa biofilm is cured by L-Methionine in combination with antibiotic therapy

Bacterial biofilms are associated with 80-90% of infections. Within the biofilm, bacteria are refractile to antibiotics, requiring concentrations >1,000 times the minimum inhibitory concentration. Proteins, carbohydrates and DNA are the major components of biofilm matrix. Pseudomonas aeruginosa (PA) biofilms, which are majorly associated with chronic lung infection, contain extracellular DNA (eDNA) as a major component. Herein, we report for the first time that L-Methionine (L-Met) at 0.5 mu M inhibits Pseudomonas aeruginosa (PA) biofilm formation and disassembles established PA biofilm by inducing DNase expression. Four DNase genes (sbcB, endA, eddB and recJ) were highly up-regulated upon L-Met treatment along with increased DNase activity in the culture supernatant. Since eDNA plays a major role in establishing and maintaining the PA biofilm, DNase activity is effective in disrupting the biofilm. Upon treatment with L-Met, the otherwise recalcitrant PA biofilm now shows susceptibility to ciprofloxacin. This was reflected in vivo, in the murine chronic PA lung infection model. Mice treated with L-Met responded better to antibiotic treatment, leading to enhanced survival as compared to mice treated with ciprofloxacin alone. These results clearly demonstrate that L-Met can be used along with antibiotic as an effective therapeutic against chronic PA biofilm infection.

A fast algorithm for speech polarity detection using long-term linear prediction

Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

The Holy Grail of Polymer Therapeutics for Cancer Therapy: An Overview on the Pharmacokinetics and Bio Distribution

In recent years, multifaceted clinical benefits of polymeric therapeutics have been reported. Over the past decades, cancer has been one of the leading causes of mortality in the world. Many clinically approved chemotherapeutics encounter potential challenges against deadly cancer. Moreover, safety and efficacy of anticancer agents have been limited by undesirable pharmacokinetics and biodistribution. To address these limitations, various polymer drug conjugates are being studied and developed to improve the antitumor efficacy. Among other therapeutics, polymer therapeutics are well established platforms that circumvent anticancer therapeutics from enzymatic metabolism via direct conjugation to therapeutic molecules. Interestingly, polymer therapeutics meets an unmet need of small molecules. Further clinical study showed that polymer-drug conjugation can achieve desired pharmacokinetics and biodistribution properties of several anticancer drugs. The present retrospective review mainly enlightens the most recent preclinical and clinical studies include safety, stability, pharmacokinetic behavior and distribution of polymer therapeutics.

Successful treatment of biofilm infections using shock waves combined with antibiotic therapy

Many bacteria secrete a highly hydrated framework of extracellular polymer matrix on suitable substrates and embed within the matrix to form a biofilm. Bacterial biofilms are observed on many medical devices, endocarditis, periodontitis and lung infections in cystic fibrosis patients. Bacteria in biofilm are protected from antibiotics and >1,000 times of the minimum inhibitory concentration may be required to treat biofilm infections. Here, we demonstrated that shock waves could be used to remove Salmonella, Pseudomonas and Staphylococcus biofilms in urinary catheters. The studies were extended to a Pseudomonas chronic pneumonia lung infection and Staphylococcus skin suture infection model in mice. The biofilm infections in mice, treated with shock waves became susceptible to antibiotics, unlike untreated biofilms. Mice exposed to shock waves responded to ciprofloxacin treatment, while ciprofloxacin alone was ineffective in treating the infection. These results demonstrate for the first time that, shock waves, combined with antibiotic treatment can be used to treat biofilm infection on medical devices as well as in situ infections.

Speaker verification based on the fusion of speech acoustics and inverted articulatory signals

We propose apractical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks. (C) 2015 Elsevier Ltd. All rights reserved.

Dictionary-Learning-Based Post-Filter for HMM-Based Speech Synthesis

Oversmoothing of speech parameter trajectories is one of the causes for quality degradation of HMM-based speech synthesis. Various methods have been proposed to overcome this effect, the most recent ones being global variance (GV) and modulation-spectrum-based post-filter (MSPF). However, there is still a significant quality gap between natural and synthesized speech. In this paper, we propose a two-fold post-filtering technique to alleviate to a certain extent the oversmoothing of spectral and excitation parameter trajectories of HMM-based speech synthesis. For the spectral parameters, we propose a sparse coding-based post-filter to match the trajectories of synthetic speech to that of natural speech, and for the excitation trajectory, we introduce a perceptually motivated post-filter. Experimental evaluations show quality improvement compared with existing methods.

SPEECH ENHANCEMENT USING DISCRIMINATIVE RANDOM FIELDS

Speech enhancement in stationary noise is addressed using the ideal channel selection framework. In order to estimate the binary mask, we propose to classify each time-frequency (T-F) bin of the noisy signal as speech or noise using Discriminative Random Fields (DRF). The DRF function contains two terms - an enhancement function and a smoothing term. On each T-F bin, we propose to use an enhancement function based on likelihood ratio test for speech presence, while Ising model is used as smoothing function for spectro-temporal continuity in the estimated binary mask. The effect of the smoothing function over successive iterations is found to reduce musical noise as opposed to using only enhancement function. The binary mask is inferred from the noisy signal using Iterated Conditional Modes (ICM) algorithm. Sentences from NOIZEUS corpus are evaluated from 0 dB to 15 dB Signal to Noise Ratio (SNR) in 4 kinds of additive noise settings: additive white Gaussian noise, car noise, street noise and pink noise. The reconstructed speech using the proposed technique is evaluated in terms of average segmental SNR, Perceptual Evaluation of Speech Quality (PESQ) and Mean opinion Score (MOS).

Trans-dichlorooxovandium (IV) complex as a novel photoinducible DNA interstrand crosslinker for cancer therapy

Although DNA interstrand crosslinking (ICL) agents such as mitomycin C, cisplatin and psoralen serve as potent anticancer drugs, these agents are known to have dose-limiting toxic effects on normal cells. Moreover, tumor resistance to these agents has been reported. Here, we show that trans-dichlorooxovanadium (IV) complex of pyrenyl terpyridine (VDC) is a novel photoinducible DNA crosslinking agent. By a combination of in vitro and ex vivo experiments including plasmid-based assays, we find that VDC forms monoadducts on the DNA and can be activated by UV-A and visible light to generate DNA interstrand crosslinks. VDC efficiently activates Fanconi anemia (FA) pathway of DNA interstrand crosslink repair. Strikingly, photoinduction of VDC induces prolonged activation of cell cycle checkpoint and a high degree of cell death in homologous recombination (HR)/ICL repair defective cells. Moreover, VDC specifically targets cells that express pathological RAD51C mutants. These data imply that VDC can be potentially used for cancer therapy and suggest that tumors arising in patients with gene mutations in FA and HR repair pathway can be specifically targeted by a photoactivatable VDC.

A mode-shape classification technique for robust speech rate estimation and syllable nuclei detection

Acoustic feature based speech (syllable) rate estimation and syllable nuclei detection are important problems in automatic speech recognition (ASR), computer assisted language learning (CALL) and fluency analysis. A typical solution for both the problems consists of two stages. The first stage involves computing a short-time feature contour such that most of the peaks of the contour correspond to the syllabic nuclei. In the second stage, the peaks corresponding to the syllable nuclei are detected. In this work, instead of the peak detection, we perform a mode-shape classification, which is formulated as a supervised binary classification problem - mode-shapes representing the syllabic nuclei as one class and remaining as the other. We use the temporal correlation and selected sub-band correlation (TCSSBC) feature contour and the mode-shapes in the TCSSBC feature contour are converted into a set of feature vectors using an interpolation technique. A support vector machine classifier is used for the classification. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora in a five-fold cross validation setup. The average correlation coefficients for the syllable rate estimation turn out to be 0.6761, 0.6928 and 0.3604 for three corpora respectively, which outperform those obtained by the best of the existing peak detection techniques. Similarly, the average F-scores (syllable level) for the syllable nuclei detection are 0.8917, 0.8200 and 0.7637 for three corpora respectively. (C) 2016 Elsevier B.V. All rights reserved.