Formant Estimation from DCTC\u27s Using a Feedforward Neural Network

Formants are the natural frequencies of the human vocal tract. Existing methods for estimating formants from speech signals are computationally complex and subject to errors for certain type of speech sounds. This thesis describes a method for estimating vowel formant frequencies from Discrete Cosine Transform Coefficients (DCTC\u27s), a form of cepstral coefficients, using a feedforward neural network with back-propagation training. Experimental results are based on a large multispeaker data base. The results are obtained for both a linear transformation and a feedforward neural network with a nonlinear hidden layer. In general, the neural network transformation is superior to the linear transformation for formant estimation. Thus our experiments indicate the nonlinear nature of the relationship between DCTC\u27s and formants. However, since the results are always much better for training data as compared to test data, a large data base is necessary for adequate neural network training. Vowel classification experiments show that estimated formants can discriminate vowels nearly as well as 14 DCTC\u27s

Asymmetry to symmetry transition of Fano line-shape: Analytical derivation

An analytical derivation of Fano line-shape asymmetry ratio has been presented here for a general case. It is shown that Fano line-shape becomes less asymmetric as \q is increased and finally becomes completely symmetric in the limiting condition of q equal to infinity. Asymmetry ratios of Fano line-shapes have been calculated and are found to be in good consonance with the reported expressions for asymmetry ratio as a function of Fano parameter. Application of this derivation is also mentioned for explanation of asymmetry to symmetry transition of Fano line-shape in quantum confined silicon nanostructures.Comment: 3 figures, Latex files, Theoretica

X-ray Diffraction and Molecular Dynamics Study of Medium-range Order in Ambient and Hot Water

We have developed x-ray diffraction measurements with high energy-resolution and accuracy to study water structure at three different temperatures (7, 25 and 66 C) under normal pressure. Using a spherically curved Ge crystal an energy resolution better than 15 eV has been achieved which eliminates influence from Compton scattering. The high quality of the data allows a precise oxygen-oxygen pair correlation function (PCF) to be directly derived from the Fourier transform of the experimental data resolving shell structure out to ~12 {\AA}, i.e. 5 hydration shells. Large-scale molecular dynamics (MD) simulations using the TIP4P/2005 force-field reproduce excellently the experimental shell-structure in the range 4-12 {\AA} although less agreement is seen for the first peak in the PCF. The Local Structure Index [J. Chem. Phys. 104, 7671 (1996)] identifies a tetrahedral minority giving the intermediate-range oscillations in the PCF and a disordered majority providing a more featureless background in this range. The current study supports the proposal that the structure of liquid water, even at high temperatures, can be described in terms of a two-state fluctuation model involving local structures related to the high-density and low-density forms of liquid water postulated in the liquid-liquid phase transition hypothesis.Comment: Submitted to Phys. Chem. Chem. Phy

Remote frequency measurement of the 1S0-3P1 transition in laser cooled Mg-24

We perform Ramsey-Bord\'e spectroscopy on laser-cooled magnesium atoms in free fall to measure the 1S0 \rightarrow 3P1 intercombination transition frequency. The measured value of 655 659 923 839 730 (48) Hz is consistent with our former atomic beam measurement (Friebe et al 2008 Phys. Rev. A 78 033830). We improve upon the fractional accuracy of the previous measurement by more than an order of magnitude to 7e-14. The magnesium frequency standard was referenced to a fountain clock of the Physikalisch-Technische Bundesanstalt (PTB) via a phase-stabilized telecom fiber link and its stability was characterized for interrogation times up to 8000 s. The high temperature of the atomic ensemble leads to a systematic shift due to the motion of atoms across the spectroscopy beams. In our regime, this leads to a counterintuitive reduction of residual Doppler shift with increasing resolution. Our theoretical model of the atom-light interaction is in agreement with the observed effect and allows us to quantify its contribution in the uncertainty budget.Comment: 16 pages, 8 figures. Accepted in New Journal of Physic

Near Real-Time Identification of Recent Human Immunodeficiency Virus Transmissions, Transmitted Drug Resistance Mutations, and Transmission Networks by Multiplexed Primer ID–Next-Generation Sequencing in North Carolina

Background: The identification of recent human immunodeficiency virus (HIV) 1 infections among people with new HIV diagnoses is important to both tailoring and assessing the impact of HIV-1 prevention strategies. Methods: We developed a multiplexed Primer ID-next-generation sequencing approach to identify recent infections by measuring the intrahost viral diversity over multiple regions of the HIV-1 genome, in addition to detecting drug resistance mutations (DRMs) and phylogenetically linked clusters. We summarize the field implementation of this all-in-one platform among persons with newly diagnosed HIV-1 by the North Carolina State Laboratory of Public Health in 2018. Results: Overall, recent infection was identified in 94 (35%) of 268 patients with new HIV diagnoses. People <30 years old, and people who inject drugs were more likely to have diagnoses of recent infection. The reverse-transcriptase region K103N was the most commonly detected DRM (prevalence, approximately 15%). We found a total of 28 clusters, and persons with recent infection were more likely to be cluster members than were those with chronic infections (P =. 03). Conclusions: We demonstrate the rapid identification of recent infection and pretreatment DRMs coupled with cluster analysis that will allow prioritization of linkage to care, treatment, and prevention interventions to those at highest risk of onward transmission

Evolution of complexity in the zebrafish synapse proteome

The proteome of human brain synapses is highly complex and mutated in over 130 diseases. This complexity arose from two whole genome duplications early in the vertebrate lineage. Zebrafish are used in modelling human diseases, however its synapse proteome is uncharacterised and whether the teleost-specific genome duplication (TSGD) influenced complexity is unknown. We report the characterisation of the proteomes and ultrastructure of central synapses in zebrafish and analyse the importance of the TSGD. While the TSGD increases overall synapse proteome complexity, the Post Synaptic Density (PSD) proteome of zebrafish has lower complexity than mammals. A highly conserved set of ~1000 proteins is shared across vertebrates. PSD ultrastructural features are also conserved. Lineage-specific proteome differences indicate vertebrate species evolved distinct synapse types and functions. The datasets are a resource for a wide range of studies and have important implications for the use of zebrafish in modelling human synaptic diseases

Tools and techniques for solvent selection: green solvent selection guides

Driven by legislation and evolving attitudes towards environmental issues, establishing green solvents for extractions, separations, formulations and reaction chemistry has become an increasingly important area of research. Several general purpose solvent selection guides have now been published with the aim to reduce use of the most hazardous solvents. This review serves the purpose of explaining the role of these guides, highlighting their similarities and differences. How they can be used most effectively to enhance the greenness of chemical processes, particularly in laboratory organic synthesis and the pharmaceutical industry, is addressed in detail

Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates