A simple statistical speech recognition of mandarin monosyllables

Abstract Each mandarin syllable is represented by a sequence of vectors of linear predict coding cepstra (LPCC). Since all syllables have a simple phonetic structure, in our speech recognition, we partition the sequence of LPCC vectors of all syllables into equal segments and average the LPCC vectors in each segment. The mean vector of LPCC is used as the feature of a syllable. Our simple feature does not need any time consuming and complicated nonlinear contraction and expansion as adopted by the dynamic time-warping. We propose several probability distributions for the feature values. A simplified Bayes decision rule is used for classification of mandarin syllables. For the speaker-independent mandarin digits, the recognition rate is 98.6% if a normal distribution is used for feature values and the rate is 98.1% if an exponential distribution is used for the absolute values of the features. The feature proposed in this paper to represent a syllable is the simplest one, much easier to be extracted than any other known features. The computation for feature extraction and classification is much faster and more accurate than using the HMM method or any other known techniques

Jerantinine A induces tumor-specific cell death through modulation of splicing factor 3b subunit 1 (SF3B1)

Precursor mRNA (pre-mRNA) splicing is catalyzed by a large ribonucleoprotein complex known as the spliceosome. Numerous studies have indicated that aberrant splicing patterns or mutations in spliceosome components, including the splicing factor 3b subunit 1 (SF3B1), are associated with hallmark cancer phenotypes. This has led to the identification and development of small molecules with spliceosome-modulating activity as potential anticancer agents. Jerantinine A (JA) is a novel indole alkaloid which displays potent anti-proliferative activities against human cancer cell lines by inhibiting tubulin polymerization and inducing G2/M cell cycle arrest. Using a combined pooled-genome wide shRNA library screen and global proteomic profiling, we showed that JA targets the spliceosome by up-regulating SF3B1 and SF3B3 protein in breast cancer cells. Notably, JA induced significant tumor-specific cell death and a significant increase in unspliced pre-mRNAs. In contrast, depletion of endogenous SF3B1 abrogated the apoptotic effects, but not the G2/M cell cycle arrest induced by JA. Further analyses showed that JA stabilizes endogenous SF3B1 protein in breast cancer cells and induced dissociation of the protein from the nucleosome complex. Together, these results demonstrate that JA exerts its antitumor activity by targeting SF3B1 and SF3B3 in addition to its reported targeting of tubulin polymerization

HIV-1 Vpr Triggers Mitochondrial Destruction by Impairing Mfn2-Mediated ER-Mitochondria Interaction

Human immunodeficiency virus 1 (HIV-1) viral protein R (Vpr) has been shown to induce host cell death by increasing the permeability of mitochondrial outer membrane (MOM). The mechanism underlying the damage to the mitochondria by Vpr, however, is not clearly illustrated. In this study, Vpr that is introduced, via transient transfection or lentivirus infection, into the human embryonic kidney cell line HEK293, human CD4+ T lymphoblast cell line SupT1, or human primary CD4+ T cells serves as the model system to study the molecular mechanism of Vpr-mediated HIV-1 pathogenesis. The results show that Vpr injures MOM and causes a loss in membrane potential (MMP) by posttranscriptionally reducing the expression of mitofusin 2 (Mfn2) via VprBP-DDB1-CUL4A ubiquitin ligase complex, gradually weakening MOM, and increasing mitochondrial deformation. Vpr also markedly decreases cytoplasmic levels of dynamin-related protein 1 (DRP1) and increases bulging in mitochondria-associated membranes (MAM), the specific regions of endoplasmic reticulum (ER) which form physical contacts with the mitochondria. Overexpression of Mfn2 and DRP1 significantly decreased the loss of MMP and apoptotic cell death caused by Vpr. Furthermore, by employing time-lapse confocal fluorescence microscopy, we identify the transport of Vpr protein from the ER, via MAM to the mitochondria. Taken together, our results suggest that Vpr-mediated cellular damage may occur on an alternative protein transport pathway from the ER, via MAM to the mitochondria, which are modulated by Mfn2 and DRP1

A family of minimax estimators of a multivariate normal mean

Let X be a p-dimensional normal random vector with unknown mean vector [theta] and covariance [sigma]2I. Let S/[sigma]2, independent of X, be chi-square with n degrees of freedom. Relative to the squared error loss, James and Stein (1961) have obtained an estimator which dominates the usual estimator X. Baranchik (1970) has extended James and Stein's results. We obtain a theorem which can provide a different family of minimax estimators containing James-Stein's estimator. Two interesting minimax estimators are presented in this paper.Admissible Bayes minimax

A family of dominating minimax estimators of a multivariate normal mean

Let X have a p-variate normal distribution with mean vector [theta] and identity covariance matrix I. In the squared error estimation of [theta], Baranchik (1970) gives a wide family G of minimax estimators. In this paper, a subfamily C of dominating estimators in G is found such that for each estimator [delta]1 in G not in C, there exists an estimator [delta]2 in C which which dominates [delta]1.admissible Bayes minimax

Bayes empirical Bayes estimation of a poisson mean

In the empirical Bayes (EB) decision problem consisting of squared error estimation of a Poisson mean, a prior distribution [lambda] is placed on the gamma family of prior distributions to produce Bayes EB estimators which are admissible. A subclass of such estimators is shown to be asymptotically optimal (a.o.). The results of a Monte Carlo study are presented to demonstrate the favorable a.o. property of the Bayes EB estimators in comparison with other competitors.admissible asymptotic optimality empirical Bayes

Bayes minimax estimators of a multivariate normal mean

Let X have a p-dimensional normal distribution with mean vector [theta] and identity covariance matrix I. In a compound decision problem consisting of squared error estimation of [theta] based on X, a prior distribution [Lambda] is placed on a normal class of priors to produce a family of Bayes estimators t. Let g(w) be the density of the prior distribution [Lambda]. If wg'(w)/g(w) does not change sign and is bounded, t is minimax. This condition is different from the condition obtained by Faith (1978), where wg'(w)/g(w) is nonincreasing. Based on this condition, we obtain several new families of minimax Bayes estimators.Admissible Bayes estimation compound decision problem minimax