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Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency or the strength of excitation amplitude is the main consequence of the disorder. The automatic classification of healthy control and EVT is useful tool for the clinicians. A typical automatic EVT classification involves three steps. The first step is to compute the pitch contour from the speech. The second step is to compute the features from the pitch contour, and the final step is to use a classifier to classify the features into healthy or EVT. It is shown that a high-resolution pitch contour estimated from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC estimation can be very poor in the presence of noise. Hence, a probabilistic source filter model based noise robust GCI detection is used for HPRC estimation. The Empirical mode decomposition based feature extraction is used followed by a support vector machine classifier. The EVT classification performance is evaluated using recordings from 45 subjects. The proposed method is found to perform better than the baseline techniques in eight different additive noise conditions with six SNR levels.

Recently, neural speech synthesizers have achieved a high-quality synthesis for text-to-speech applications, but a real-time synthesis is possible only in the devices which have high memory and allow large computational complexity. In this work, we reduce the complexity of a speech synthesizer by reformulating the source-filter model of speech where the excitation signal is modeled as a sum of two signals. The first signal contains an impulse train that is computed from the pitch sequence. The second signal is modeled as white noise passed through a filter bank with frequency dependent gains. The parameters of the reformulated source-filter model are predicted using a neural network, referred to as SFNet. The network parameters are learnt by training the network using $l_1$ -error between the log Mel-spectrum of the predicted waveform and that of the ground-truth waveform. We demonstrate that there is a significant reduction in the memory and computational complexity compared to the state-of-the-art speaker independent neural speech synthesizer without any loss of the naturalness of the synthesized speech.

Objective: The objective of the study was to evaluate Ocimum gratissimum mucilage as a novel superdisintegrant in the formulation of fast-dissolving tablets (FDT) of Biopharmaceutical Classification System-II drug (Ibuprofen) employing a 23 factorial design. Methods: O. gratissimum mucilage was extracted by seeds and it was subjected to physical, chemical, and micrometric studies were evaluated. To establish FDT of ibuprofen with O. gratissimum mucilage as a superdisintegrants in different ratios using direct compression method employing 23 factorial design. All the formulation tablets were evaluated pre-compression and post-compression parameters like dissolution efficiency (DE%) percent of drug dissolved at 5 min. Results: The mucilage was to be found fine, free-flowing crystalline powder, and excellent swelling nature in all suitable solvents and buffers. The Fourier transform infrared and differential scanning calorimetry studies were indicated to no interactions between ibuprofen and O. gratissimum mucilage. All the FDT formulated employing novel mucilage shows good quality with regard drug content (98.05±0.31–99.39±0.54), hardness (3.6– 4 kg/sq. cm), and friability (0.12–0.15%). The optimized formulation batch shows less disintegrant time (30±0.06). In vitro wetting time was less (i.e., 90 s) in optimized formulation F2. The water absorption ratio of the formulated tablets was found to be in the range of 99±0.56. The cumulative drug dissolved in the optimized formulation F2 was found to be 99% in 10 min. Conclusion: O. gratissimum mucilage was found to be a novel superdisintegrant which enhanced the DE when combined with crospovidone and croscarmellose sodium; hence, it could be used in the formulation of FDT to provide immediate release of the contained drug within 5 min.

Source filter interaction (SFI) explains the drop in pitch caused due to the constriction in the vocal tract during voiced consonant production in a vowel-consonant-vowel (VCV) sequence. In this work, we examine how the drop in pitch alters when such a VCV sequence is spoken at three different speaking rates - slow, normal and fast. In the absence of electroglottograph (EGG) recording, a high resolution pitch contour is determined using a glottal closure instant (GCI) detector. For this, in this work, firstly, five different GCI detector and pitch estimation techniques are compared against EGG based pitch estimates on a small dataset where simultaneous EGG recordings are available. Yet Another GCI Algorithm (YAGA) is found to be the best choice among all. For examining the impact of speaking rate on SFI, VCV recordings from six subjects with five vowels (/a/, /e/, /i/, /o/, /u/) and five consonants (/b/, /d/, /g/, /v/, /z/) at three speaking rates are used. The study reveals a significant difference in the pitch drop values between slow and fast rates, with increasing pitch drop as speaking rate reduces. For slow speaking rate, vowel /o/ and /u/ tend to show higher pitch drop values compared to remaining vowels.

Objective: A validated liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed for canagliflozin in human plasma along with stability studies. Methods: The chromatographic separation of canagliflozin was performed on Zorbax XDB phenyl (75 × 4.6 mm, 3.5 mm) using methanol:acetate buffer (80:20 v/v) at a flow rate of 1.0 ml/min. The LC–MS/MS system consists of API 4000 triple quadrupole mass spectrometer equipped with turbospray ionization and an AS8020 automatic sample injector. Results: The retention time of canagliflozin was 1.15 min and total runtime was 2 min. The multiple reaction monitoring was 462.5/267.1 (m/z) for canagliflozin and 466.4/267.2 (m/z) for internal standard (canagliflozin D4), respectively. The method was linear over the range of 10–7505 ng/ml. The calculated slope ranged from 0.0451 to 0.0502 and intercepts from 0.0102 to 0.0456 with coefficients of the determination of 0.9970. The overall mean recovery of internal standard and canagliflozin was 76.66 and 79.77, respectively. Conclusion: The method was successfully validated and it was found to be within the limits for accuracy, precision, and linearity and it is stable under analytical conditions used.

Detection and tracking of the P- and T-waves are important issues in the analysis and interpretation of the ECG signals. This paper addresses the problem by using two mixture Gaussian function and the Dynamic programming. A key feature of the proposed algorithm is that it allows to incorporate the prior knowledge about the P/T wave location variations and robustness to errors in QRS detection. The proposed algorithm is evaluated on the annotated QT-database and compared against the algorithms based on differential evolution optimization strategy (DEOS) and generating blocks of interest (GBI). The experiments show that the proposed method determines the P- and T-peak locations with a root mean square error of 0.085 s and 0.091 s respectively. Both these values are better than the corresponding values from DEOS and GBI. Similarly, the proposed algorithm achieves a sensitivity of 96.13% and predictivity of 97.70%. While the predictivity is higher than both DEOS and GBI, the sensitivity is on par with GBOI and higher than that of DEOS.

With the availability of large data, ASRs perform well on native English but poorly for non-native English data. Training nonnative ASRs or adapting a native English ASR is often limited by the availability of data, particularly for low resource scenarios. A typical HMM-DNN based ASR decoding requires pseudo-likelihood of states given an acoustic observation, which changes significantly from native to non-native speech due to accent variation. In order to improve the performance of a native English ASR on non-native English data, we, in this work, propose a DNN-based pseudo-likelihood correction (PLC) technique, in which a non-native pseudo-likelihood vector is mapped to match its native counterpart. Instead of correcting all elements of a non-native pseudo-likelihood vector, a loss function is proposed to correct only top few of them. Experiments with one native and multiple Indian English corpora show an improvement of WER by ~12% and over ~5% using the proposed PLC technique unadapted and adapted native English ASR respectively, when recognition is performed on an Indian English corpus different from that used for both PLC and adaptation. Experiments with upto 2 hours of parallel native and non-native English data reveal that, PLC performs better than adaptation for all unseen cases considered.

Objective: To assess the success rate of DOTS for tuberculosis patients and the secondary objective was to identify the factors associated with unsuccessful treatment outcome. Methods: A retrospective study was conducted to review the medical records of patients (n = 1113) registered at the Directly Observed Treatment Short-Course (DOTS) clinic of Government Infectious Disease (Govt. ID) Hospital, Guntur, India. Multivariate logistic regression model was used to determine the factors associated with the treatment success rate. Results: The overall mean success rate of TB patients was found to be 82.8%. Treatment success rate (TSR) was steadily increased across the years from 73.9% in 2015 to 84.3% in 2016 and 88.9% in 2017 while the death rate was steadily decreased from 11.2% in 2015 to 6.25% in 2016 and 4.33% in 2017. Risk factors significantly associated with unsuccessful treatment outcome were found to be HIV positive (P Key words: Directly observed treatment short-course, Retrospective study, Risk factors, Treatment success rate, Tuberculosis.

A novel prodrug approach was undertaken to develop the safe and therapeutically efficacious dexibuprofen to avoid oral NSAIDs induced ulceration. Dexibuprofen was esterified with dextran, using N,N-carbonyldiimidazole in one pot reaction. Synthesized dexibuprofen prodrug was characterized and evaluated by FT-IR and NMR spectroscopy, molecular weight, lipophilicity, partition coefficient, protein binding, degree of substitution, hydrolysis in simulated GI fluids, in-silico ADME properties and pharmacological potentials. Structural profile of dexibuprofen prodrug was elucidated by an ester linkage, glucosidic ring anomeric proton, dextran monomer protons and ester carbonyl carbon signals. Prodrug possessed physicochemical features as molecular weight of 83,368.11 g/mol, log P of 5.4 with optimal protein binding of 66% and degree of substitution of 25.3%. It was significantly hydrolyzed in SIF (99.53%) by following first-order kinetics with 85.9 min half-life. In-silico ADME properties of prodrug satisfied the Lipinski' rule of five and Jorgensen's rule of three without any CNS activity and cardiac toxicity, thus prodrug was suitable for oral administration. Prodrug has exhibited superior analgesic, anti-inflammatory, antipyretic activities devoid of antigenecity and ulceration in experimental animals. Data of the study were thus evinced that dexibuprofen prodrug is a safer therapeutic moiety in effective management of acute inflammation, pain and fever.

Glottal inverse filtering is a noninvasive method for getting the glottal flow estimate from the speech. In this paper, we propose a method for glottal inverse filtering based on probabilistic weighted linear prediction (PWLP) in which the speech is assumed to be the output of an all-pole filter with glottal flow as an excitation. First, we introduce a probabilistic interpretation of the WLP, and we propose a probabilistic temporal weighting as convolution of a binary vector and a fixed window. We construct the posterior distribution based on the PWLP likelihood and a Gaussian prior on the filter coefficients. The parameters are estimated using the Gibbs sampling. The experiments are performed using the Lijencrants- Fant (LF) model based synthetic data, a physical model based synthetic data of different vowels and real speech data. Results demonstrate that the proposed method outperforms the best of the existing state-of-the-art methods in terms of the normalized amplitude quotient by 0.035 and 0.12 for the LF model and physical model based synthetic data, respectively. The results based on real speech data show that the glottal flow estimated by the proposed method in the closed phase is flatter and has less formant ripple compared to existing state-of-the-art methods. We also show two key features of the proposed method: first, the proposed method does not need prior detection of glottal closure or opening instants. The temporal weights are learnt in a data-driven manner, which is often found to be high near the closed phase of the glottal cycle, second, the Gaussian prior helps in estimating the filter coefficients when the closed phase duration is small.

We consider the problem of automatic identification of native language (L1) of non-native English (L2) speakers from eleven L1 backgrounds. Analyzing the influence of each L1 pronunciation variabilities on L2 pronunciation, different sets of linguistic units are chosen to compute supra-segmental features by considering the acoustic and prosodic variations within and across these sets. Using these features, we implement a multi-class classifier comprising 55 binary (one versus another) support vector machine (SVM) classifiers. We select optimal set of features for each binary classifier using two feature selection strategies (FSSs) based on Fisher discriminant ratio (FDR). The first strategy considers the features that maximizes the each binary classifier performance. However, the second strategy selects the features by maximizing a multi-class classifier performance for which an algorithm is proposed. Experiments are performed on the ETS corpus of non-native spoken English, comprising 4099 files. When the proposed features along with FSSs are used, the unweighted average recall (UAR) on the test set for each selection strategy is found to be 1.3% and 2.1% (absolute) higher compared to using all features; as well as 3.0% and 3.8% higher than the baseline technique respectively.

Accurate estimation of glottal closure instant (GCI) enables several pitch synchronous speech analysis, such as prosody modifications, glottal inverse filtering, and study of pathological speech. We propose a probabilistic source-filter model (PSFM) for voiced speech, where the source is modeled using the Bernoulli Gaussian distribution, which models the GCI locations and the all-pole filter coefficients are modeled using Gaussian distribution. The probability of GCIs at each speech sample is estimated using the Gibbs sampling. We propose a cost to estimate the exact GCI locations using the N-best dynamic programming. A key feature of the proposed PSFM is that it allows us to include the second-order statistics of the noise for estimating the GCI locations, thereby resulting in a noise robust GCI detection technique, although it has high computational complexity. Evaluation on archivable priority list actual-word database (APLAWD) database shows the proposed algorithm performs at par with the state-of-the-art GCI detection method on clean speech. However, when evaluated in noisy conditions using five types of noises at six different signal-to-noise ratio (SNR) levels, we observe that the proposed method performs better than the best of the existing GCI detection scheme, particularly at low SNR condition indicating the noise robustness of the proposed method.

Over recent years, accumulated evidence suggests that autophagy induction is protective in animal models of a number of neurodegenerative diseases. Intense research in the field has elucidated different pathways through which autophagy can be upregulated and it is important to establish how modulation of these pathways impacts upon disease progression in vivo and therefore which, if any, may have further therapeutic relevance. In addition, it is important to understand how alterations in these target pathways may affect normal physiology when constitutively modulated over a long time period, as would be required for treatment of neurodegenerative diseases. Here we evaluate the potential protective effect of downregulation of calpains. We demonstrate, in Drosophila, that calpain knockdown protects against the aggregation and toxicity of proteins, like mutant huntingtin, in an autophagy-dependent fashion. Furthermore, we demonstrate that, overexpression of the calpain inhibitor, calpastatin, increases autophagosome levels and is protective in a mouse model of Huntington's disease, improving motor signs and delaying the onset of tremors. Importantly, long-term inhibition of calpains did not result in any overt deleterious phenotypes in mice. Thus, calpain inhibition, or activation of autophagy pathways downstream of calpains, may be suitable therapeutic targets for diseases like Huntington's disease.