MULTILEVEL SPARSE FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS

The basic observational unit in this paper is a function. Data are assumed to have a natural hierarchy of basic units. A simple example is when functions are recorded at multiple visits for the same subject. Di et al. (2009) proposed Multilevel Functional Principal Component Analysis (MFPCA) for this type of data structure when functions are densely sampled. Here we consider the case when functions are sparsely sampled and may contain as few as 2 or 3 observations per function. As with MFPCA, we exploit the multilevel structure of covariance operators and data reduction induced by the use of principal component bases. However, we address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional scores and predict the underlying curves. We show that in the sparse context 1) is harder and propose an algorithm to circumvent the problem. Surprisingly, we show that 2) is easier via new BLUP calculations. Using simulations and real data analysis we show that the ability of our method to reconstruct underlying curves with few observations is stunning. This approach is illustrated by an application to the Sleep Heart Health Study, which contains two electroencephalographic (EEG) series at two visits for each subject

Multilevel functional principal component analysis

The Sleep Heart Health Study (SHHS) is a comprehensive landmark study of sleep and its impacts on health outcomes. A primary metric of the SHHS is the in-home polysomnogram, which includes two electroencephalographic (EEG) channels for each subject, at two visits. The volume and importance of this data presents enormous challenges for analysis. To address these challenges, we introduce multilevel functional principal component analysis (MFPCA), a novel statistical methodology designed to extract core intra- and inter-subject geometric components of multilevel functional data. Though motivated by the SHHS, the proposed methodology is generally applicable, with potential relevance to many modern scientific studies of hierarchical or longitudinal functional outcomes. Notably, using MFPCA, we identify and quantify associations between EEG activity during sleep and adverse cardiovascular outcomes.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS206 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

High prevalence of plasmid-mediated 16S rRNA methylase gene rmtB among Escherichia coli clinical isolates from a Chinese teaching hospital

<p>Abstract</p> <p>Background</p> <p>Recently, production of 16S rRNA methylases by Gram-negative bacilli has emerged as a novel mechanism for high-level resistance to aminoglycosides by these organisms in a variety of geographic locations. Therefore, the spread of high-level aminoglycoside resistance determinants has become a great concern.</p> <p>Methods</p> <p>Between January 2006 and July 2008, 680 distinct <it>Escherichia coli </it>clinical isolates were collected from a teaching hospital in Wenzhou, China. PCR and DNA sequencing were used to identify 16S rRNA methylase and extended-spectrum β-lactamase (ESBL) genes, including <it>armA </it>and <it>rmtB</it>, and in situ hybridization was performed to determine the location of 16S rRNA methylase genes. Conjugation experiments were subsequently performed to determine whether aminoglycoside resistance was transferable from the <it>E. coli </it>isolates via 16S rRNA methylase-bearing plasmids. Homology of the isolates harboring 16S rRNA methylase genes was determined using pulse-field gel electrophoresis (PFGE).</p> <p>Results</p> <p>Among the 680 <it>E. coli </it>isolates, 357 (52.5%), 346 (50.9%) and 44 (6.5%) isolates were resistant to gentamicin, tobramycin and amikacin, respectively. Thirty-seven of 44 amikacin-resistant isolates harbored 16S rRNA methylase genes, with 36 of 37 harboring the <it>rmtB </it>gene and only one harboring <it>armA</it>. The positive rates of 16S rRNA methylase genes among all isolates and amikacin-resistant isolates were 5.4% (37/680) and 84.1% (37/44), respectively. Thirty-one isolates harboring 16S rRNA methylase genes also produced ESBLs. In addition, high-level aminoglycoside resistance could be transferred by conjugation from four <it>rmtB</it>-positive donors. The plasmids of incompatibility groups IncF, IncK and IncN were detected in 34, 3 and 3 isolates, respectively. Upstream regions of the <it>armA </it>gene contained <it>IS</it>CR1 and <it>tnpU</it>, the latter a putative transposase gene,. Another putative transposase gene, <it>tnpD</it>, was located within a region downstream of <it>armA</it>. Moreover, a transposon, Tn<it>3</it>, was located upstream of the <it>rmtB</it>. Nineteen clonal patterns were obtained by PFGE, with type H representing the prevailing pattern.</p> <p>Conclusion</p> <p>A high prevalence of plasmid-mediated <it>rmtB </it>gene was found among clinical <it>E. coli </it>isolates from a Chinese teaching hospital. Both horizontal gene transfer and clonal spread were responsible for the dissemination of the <it>rmtB </it>gene.</p

Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS

Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin

The rapid development of ultrafast ultraintense laser technology continues to create opportunities for studying strong-field physics under extreme conditions. However, accurate determination of the spatial and temporal characteristics of a laser pulse is still a great challenge, especially when laser powers higher than hundreds of terawatts are involved. In this paper, by utilizing the radiative spin-flip effect, we find that the spin depolarization of an electron beam can be employed to diagnose characteristics of ultrafast ultraintense lasers with peak intensities around 1020–1022 W/cm2. With three shots, our machine-learning-assisted model can predict, simultaneously, the pulse duration, peak intensity, and focal radius of a focused Gaussian ultrafast ultraintense laser (in principle, the profile can be arbitrary) with relative errors of 0.1%–10%. The underlying physics and an alternative diagnosis method (without the assistance of machine learning) are revealed by the asymptotic approximation of the final spin degree of polarization. Our proposed scheme exhibits robustness and detection accuracy with respect to fluctuations in the electron beam parameters. Accurate measurements of ultrafast ultraintense laser parameters will lead to much higher precision in, for example, laser nuclear physics investigations and laboratory astrophysics studies. Robust machine learning techniques may also find applications in more general strong-field physics scenarios

6,8-di-<i>C</i>-glycosyl flavones with <i>β</i>-furanoarabinose from <i>Scutellaria baicalensis</i> and their anti-inflammatory activities

<p>Two flavone di-<i>C</i>-glycosides, a pair of isomers, were isolated from <i>Scutellaria baicalensis</i>. The structures of compounds <b>1</b> and <b>2</b> were elucidated by means of physical data, including 1D and 2D NMR and HR-ESI-MS. Supporting theoretical calculations of the compound conformational landscape has also been conducted for geometry optimization. This is the first report of the natural occurrence of <i>β</i>-furanoarabinoside. In addition, the effects of compounds <b>1</b> and <b>2</b> on NO, pro-inflammatory cytokines, PGE2 and COX-2 levels were measured in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. The pair of isomers exhibited significant inhibitory effects on inflammation.</p