Compression and R-wave detection of ECG/VCG data

Application of information theory to eliminate redundant part of electrocardiogram or vectorcardiogram is described. Operation of medical equipment to obtain three dimensional study of patient is discussed. Use of fast Fourier transform to accomplish data compression is explained

The Sequential Normal Scores Transformation

The sequential analysis of series often requires nonparametric procedures, where the most powerful ones frequently use rank transformations. Re-ranking the data sequence after each new observation can become too intensive computationally. This led to the idea of sequential ranks, where only the most recent observation is ranked. However, difficulties finding, or approximating, the null distribution of the statistics may have contributed to the lack of popularity of these methods. In this paper, we propose transforming the sequential ranks into sequential normal scores which are independent, and asymptotically standard normal random variables. Thus original methods based on the normality assumption may be used. A novel approach permits the inclusion of a priori information in the form of quantiles. It is developed as a strategy to increase the sensitivity of the scoring statistic. The result is a powerful convenient method to analyze non-normal data sequences. Also, four variations of sequential normal scores are presented using examples from the literature. Researchers and practitioners might find this approach useful to develop nonparametric procedures to address new problems extending the use of parametric procedures when distributional assumptions are not met. These methods are especially useful with large data streams where efficient computational methods are required.Comment: 39 pages, 8 figure

Some Locally Most Powerful Rank Tests For Correlation

Four examples are given to illustrate the ease and practicality of the procedure for finding locally most powerful rank tests for correlation. The first two examples deal with bivariate exponential models. The third example uses the bivariate normal distribution, and the fourth example analyzes the Morgenstem’s general correlation model

A Group-Based Yule Model for Bipartite Author-Paper Networks

This paper presents a novel model for author-paper networks, which is based on the assumption that authors are organized into groups and that, for each research topic, the number of papers published by a group is based on a success-breeds-success model. Collaboration between groups is modeled as random invitations from a group to an outside member. To analyze the model, a number of different metrics that can be obtained in author-paper networks were extracted. A simulation example shows that this model can effectively mimic the behavior of a real-world author-paper network, extracted from a collection of 900 journal papers in the field of complex networks.Comment: 13 pages (preprint format), 7 figure

Portraits of Complex Networks

We propose a method for characterizing large complex networks by introducing a new matrix structure, unique for a given network, which encodes structural information; provides useful visualization, even for very large networks; and allows for rigorous statistical comparison between networks. Dynamic processes such as percolation can be visualized using animations. Applications to graph theory are discussed, as are generalizations to weighted networks, real-world network similarity testing, and applicability to the graph isomorphism problem.Comment: 6 pages, 9 figure

Geometric and dynamic perspectives on phase-coherent and noncoherent chaos

Statistically distinguishing between phase-coherent and noncoherent chaotic dynamics from time series is a contemporary problem in nonlinear sciences. In this work, we propose different measures based on recurrence properties of recorded trajectories, which characterize the underlying systems from both geometric and dynamic viewpoints. The potentials of the individual measures for discriminating phase-coherent and noncoherent chaotic oscillations are discussed. A detailed numerical analysis is performed for the chaotic R\"ossler system, which displays both types of chaos as one control parameter is varied, and the Mackey-Glass system as an example of a time-delay system with noncoherent chaos. Our results demonstrate that especially geometric measures from recurrence network analysis are well suited for tracing transitions between spiral- and screw-type chaos, a common route from phase-coherent to noncoherent chaos also found in other nonlinear oscillators. A detailed explanation of the observed behavior in terms of attractor geometry is given.Comment: 12 pages, 13 figure

Structure-based discovery of glycomimetic FmlH ligands as inhibitors of bacterial adhesion during urinary tract infection

Significance The emergence of multidrug-resistant bacteria, including uropathogenic Escherichia coli (UPEC), makes the development of targeted antivirulence therapeutics a critical focus of research. During urinary tract infections (UTIs), UPEC uses chaperone–usher pathway pili tipped with an array of adhesins that recognize distinct receptors with sterochemical specificity to facilitate persistence in various tissues and habitats. We used an interdisciplinary approach driven by structural biology and synthetic glycoside chemistry to design and optimize glycomimetic inhibitors of the UPEC adhesin FmlH. These inhibitors competitively blocked FmlH in vitro, in in vivo mouse UTI models, and in ex vivo healthy human kidney tissue. This work demonstrates the utility of structure-driven drug design in the effort to develop antivirulence therapeutic compounds. </jats:p

Inference with interference between units in an fMRI experiment of motor inhibition

An experimental unit is an opportunity to randomly apply or withhold a treatment. There is interference between units if the application of the treatment to one unit may also affect other units. In cognitive neuroscience, a common form of experiment presents a sequence of stimuli or requests for cognitive activity at random to each experimental subject and measures biological aspects of brain activity that follow these requests. Each subject is then many experimental units, and interference between units within an experimental subject is likely, in part because the stimuli follow one another quickly and in part because human subjects learn or become experienced or primed or bored as the experiment proceeds. We use a recent fMRI experiment concerned with the inhibition of motor activity to illustrate and further develop recently proposed methodology for inference in the presence of interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package cin (Causal Inference for Neuroscience) implementing the proposed method is freely available on CRAN at https://CRAN.R-project.org/package=ci

Spin measurements for 147Sm+n resonances: Further evidence for non-statistical effects

We have determined the spins J of resonances in the 147Sm(n,gamma) reaction by measuring multiplicities of gamma-ray cascades following neutron capture. Using this technique, we were able to determine J values for all but 14 of the 140 known resonances below En = 1 keV, including 41 firm J assignments for resonances whose spins previously were either unknown or tentative. These new spin assignments, together with previously determined resonance parameters, allowed us to extract separate level spacings and neutron strength functions for J = 3 and 4 resonances. Furthermore, several statistical test of the data indicate that very few resonances of either spin have been missed below En = 700eV. Because a non-statistical effect recently was reported near En = 350 eV from an analysis of 147Sm(n,alpha) data, we divided the data into two regions; 0 < En < 350 eV and 350 < En < 700 eV. Using neutron widths from a previous measurement and published techniques for correcting for missed resonances and for testing whether data are consistent with a Porter-Thomas distribution, we found that the reduced-neutron-width distribution for resonances below 350 eV is consistent with the expected Porter-Thomas distribution. On the other hand, we found that reduced-neutron-width data in the 350 < En < 700 eV region are inconsistent with a Porter-Thomas distribution, but in good agreement with a chi-squared distribution having two or more degrees of freedom. We discuss possible explanations for these observed non-statistical effects and their possible relation to similar effects previously observed in other nuclides.Comment: 40 pages, 13 figures, accepted by Phys. Rev.