Zero-phase inverse filtering for extraction of source characteristics

The instants at which significant excitation of vocal tract take place during voicing are referred to as epochs. Epochs and strengths of excitation pulses at epochs are useful in characterizing voice source. Epoch filtering technique proposed by the authors determine epochs from speech waveform. In this paper we propose zero-phase inverse filtering to obtain strengths of excitation pulses at epochs. Zero-phase inverse filter compensates the gross spectral envelope of short-time spectrum of speech without affecting phase characteristics. Linear prediction analysis is used to realize the zero-phase inverse filter. Source characteristics that can be derived from speech using this technique are illustrated with examples.

Mini Forest - An experiment to evaluate the adaptability of Western Ghats species for afforestation

Saplings of forty nine species of trees from Western Ghats forests were planted on a 1.5 hectare tract of Deccan plateau (in the campus of Indian Institute of Science, Bangalore) and their performance monitored for 23 years. The objective was to evaluate their adaptability to a habitat and conditions apparently alien to these species. The study was also meant to understand the linkages of these trees with the surrounding environment. Contrary to the belief that tree species are very sensitive to change of location and conditions, the introduced trees have grown as good as they would do in their native habitat and maintained their phenology. Further, they have grown in perfect harmony with trees native to the location. The results show that the introduced species are opportunistic and readily acclimatized and grew well overcoming the need for the edaphic and other factors that are believed to be responsible for their endemicity. Besides ex situ conservation, the creation of miniforest has other accrued ecosystem benefits. For instance, the ground water level has risen and the ambient temperature has come down by two degrees.

Site-specific N-Linked Glycosylation of Receptor Guanylyl Cyclase C Regulates Ligand Binding, Ligand-mediated Activation and Interaction with Vesicular Integral Membrane Protein 36, VIP36

Guanylyl cyclase C (GC-C) is a multidomain, membrane-associated receptor guanylyl cyclase. GC-C is primarily expressed in the gastrointestinal tract, where it mediates fluid-ion homeostasis, intestinal inflammation, and cell proliferation in a cGMP-dependent manner, following activation by its ligands guanylin, uroguanylin, or the heat-stable enterotoxin peptide (ST). GC-C is also expressed in neurons, where it plays a role in satiation and attention deficiency/hyperactive behavior. GC-C is glycosylated in the extracellular domain, and differentially glycosylated forms that are resident in the endoplasmic reticulum (130 kDa) and the plasma membrane (145 kDa) bind the ST peptide with equal affinity. When glycosylation of human GC-C was prevented, either by pharmacological intervention or by mutation of all of the 10 predicted glycosylation sites, ST binding and surface localization was abolished. Systematic mutagenesis of each of the 10 sites of glycosylation in GC-C, either singly or in combination, identified two sites that were critical for ligand binding and two that regulated ST-mediated activation. We also show that GC-C is the first identified receptor client of the lectin chaperone vesicular integral membrane protein, VIP36. Interaction with VIP36 is dependent on glycosylation at the same sites that allow GC-C to fold and bind ligand. Because glycosylation of proteins is altered in many diseases and in a tissue-dependent manner, the activity and/or glycan-mediated interactions of GC-C may have a crucial role to play in its functions in different cell types.

A finite-rate-of-innovation signal sampling perspective of the source-filter model of speech production

We consider the speech production mechanism and the asso- ciated linear source-filter model. For voiced speech sounds in particular, the source/glottal excitation is modeled as a stream of impulses and the filter as a cascade of second-order resonators. We show that the process of sampling speech signals can be modeled as filtering a stream of Dirac impulses (a model for the excitation) with a kernel function (the vocal tract response),and then sampling uniformly. We show that the problem of esti- mating the excitation is equivalent to the problem of recovering a stream of Dirac impulses from samples of a filtered version. We present associated algorithms based on the annihilating filter and also make a comparison with the classical linear prediction technique, which is well known in speech analysis. Results on synthesized as well as natural speech data are presented.

Effect of a natural mutation in the 5 ` untranslated region on the translational control of p53 mRNA

Tumor-suppressor protein p53, the `guardian of the genome', is critical in maintaining cellular homeostasis and genomic stability. Earlier, we have reported the discovery of internal ribosome entry sites (IRESs) within the p53 mRNA that regulate the translation of the full length and its N-terminal-truncated isoform, Delta N-p53. Polypyrimidine tract-binding protein (PTB) is an IRES trans-acting factor that positively regulates the IRES activities of both p53 isoforms by relocating from nucleus to the cytoplasm during stress conditions. Here we have demonstrated the putative contact points of PTB on the p53 IRES RNA. Studies on mutations that occur naturally in the 5' untranslated region (5' UTR) in p53 mRNA were lacking. We have investigated a naturally occurring C-to-T single-nucleotide polymorphism (SNP) first reported in human melanoma tumors. This SNP is at position 119 in the 5' UTR of p53 mRNA and we demonstrate that it has consequences on the translational control of p53. Introduction of this SNP has led to decrease in cap-independent translation from p53 5' UTR in bicistronic reporter assay. Further, the effects of this SNP on cap-independent translation have been studied in the context of p53 cDNA as well. Interestingly, the 5' UTR with this SNP has shown reduced binding to PTB that can be corroborated to its weaker IRES activity. Previously, it has been shown that G2-M checkpoint, DNA-damaging stress and oncogenic insult favor IRES-mediated translation. Under similar conditions, we demonstrate that this SNP interferes with the enhancement of the IRES activity of the 5' UTR. Taken together, the results demonstrate for the first time that SNP in the 5' UTR of the p53 mRNA might have a role in translational control of this critical tumor-suppressor gene.

Reversible induction of translational isoforms of p53 in glucose deprivation

Tumor suppressor protein p53 is a master transcription regulator, indispensable for controlling several cellular pathways. Earlier work in our laboratory led to the identification of dual internal ribosome entry site (IRES) structure of p53 mRNA that regulates translation of full-length p53 and Delta 40p53. IRES-mediated translation of both isoforms is enhanced under different stress conditions that induce DNA damage, ionizing radiation and endoplasmic reticulum stress, oncogene-induced senescence and cancer. In this study, we addressed nutrient-mediated translational regulation of p53 mRNA using glucose depletion. In cell lines, this nutrient-depletion stress relatively induced p53 IRES activities from bicistronic reporter constructs with concomitant increase in levels of p53 isoforms. Surprisingly, we found scaffold/matrix attachment region-binding protein 1 (SMAR1), a predominantly nuclear protein is abundant in the cytoplasm under glucose deprivation. Importantly under these conditions polypyrimidine-tract-binding protein, an established p53 ITAF did not show nuclear-cytoplasmic relocalization highlighting the novelty of SMAR1-mediated control in stress. In vivo studies in mice revealed starvation-induced increase in SMAR1, p53 and Delta 40p53 levels that was reversible on dietary replenishment. SMAR1 associated with p53 IRES sequences ex vivo, with an increase in interaction on glucose starvation. RNAi-mediated-transient SMAR1 knockdown decreased p53 IRES activities in normal conditions and under glucose deprivation, this being reflected in changes in mRNAs in the p53 and Delta 40p53 target genes involved in cell-cycle arrest, metabolism and apoptosis such as p21, TIGAR and Bax. This study provides a new physiological insight into the regulation of this critical tumor suppressor in nutrient starvation, also suggesting important functions of the p53 isoforms in these conditions as evident from the downstream transcriptional target activation.

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.