			Home About us Contact

Functional Observations (functional + observation)

Distribution by Scientific Domains

Mathematics and Statistics	60%

Selected Abstracts

Enhanced rat sciatic nerve regeneration through silicon tubes filled with pyrroloquinoline quinone

MICROSURGERY, Issue 4 2005
Shiqing Liu M.D.
Pyrroloquinoline quinone (PQQ) is an antioxidant that also stimulates nerve growth factor (NGF) synthesis and secretion. In an earlier pilot study in our laboratory, Schwann cell growth was accelerated, and NGF mRNA expression and NGF secretion were promoted. The present study was designed to explore the possible nerve-inducing effect of PQQ on a nerve tube model over a 1-cm segmental deficit. An 8-mm sciatic nerve deficit was created in a rat model and bridged by a 1-cm silicone tube. Then,10 ,l of 0.03 mmol/l PQQ were perfused into the silicone chamber in the PQQ group. The same volume of normal saline was delivered in the control group. Each animal underwent functional observation (SFI) at 2-week intervals and electrophysiological studies at 4-week intervals for 12 weeks. Histological and morphometrical analyses were performed at the end of the experiment, 12 weeks after tube implantation. Using a digital image-analysis system, thickness of the myelin sheath was measured, and total numbers of regenerated axons were counted. There was a significant difference in SFI, electrophysiological index (motor-nerve conduct velocity and amplitude of activity potential), and morphometrical results (regenerated axon number and thickness of myelin sheath) in nerve regeneration between the PQQ group and controls (P < 0.05). More mature, high-density, newly regenerated nerve was observed in the PQQ group. We conclude that PQQ is a potent enhancer for the regeneration of peripheral nerves. © 2005 Wiley-Liss, Inc. Microsurgery 25:329,337, 2005. [source]

Functional Regression Analysis of Fluorescence Curves

BIOMETRICS, Issue 2 2009
Christian Ritz
Summary Fluorescence curves are useful for monitoring changes in photosynthesis activity. Various summary measures have been used to quantify differences among fluorescence curves corresponding to different treatments, but these approaches may forfeit valuable information. As each individual fluorescence curve is a functional observation, it is natural to consider a functional regression model. The proposed model consists of a nonparametric component capturing the general form of the curves and a semiparametric component describing the differences among treatments and allowing comparisons of treatments. Several graphical model-checking approaches are introduced. Both approximate, asymptotic confidence intervals as well as simulation-based confidence intervals are available. Analysis of data from a crop experiment using the proposed model shows that the salient features in the fluorescence curves are captured adequately. The proposed functional regression model is useful for analysis of high throughput fluorescence curve data from regular monitoring or screening of plant growth. [source]

Detecting changes in the mean of functional observations

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 5 2009
István Berkes
Summary., Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as Xi(t)=,(t)+,1,l<,,i, l+ vl(t), where , is the common mean, vl are the eigenfunctions of the covariance operator and the ,i, l are the scores. Inferential procedures assume that the mean function ,(t) is the same for all values of i. If, in fact, the observations do not come from one population, but rather their mean changes at some point(s), the results of principal component analysis are confounded by the change(s). It is therefore important to develop a methodology to test the assumption of a common functional mean. We develop such a test using quantities which can be readily computed in the R package fda. The null distribution of the test statistic is asymptotically pivotal with a well-known asymptotic distribution. The asymptotic test has excellent finite sample performance. Its application is illustrated on temperature data from England. [source]

Structure of relaxed-state human hemoglobin: insight into ligand uptake, transport and release

ACTA CRYSTALLOGRAPHICA SECTION D, Issue 1 2009
Joy D. Jenkins
Hemoglobin was one of the first protein structures to be determined by X-ray crystallography and served as a basis for the two-state MWC model for the mechanism of allosteric proteins. Since then, there has been an ongoing debate about whether Hb allostery involves the unliganded tense T state and the liganded relaxed R state or whether it involves the T state and an ensemble of liganded relaxed states. In fact, the former model is inconsistent with many functional observations, as well as the recent discoveries of several relaxed-state Hb structures such as RR2, R3 and R2. One school of thought has suggested the R2 state to be the physiologically relevant relaxed end state, with the R state mediating the T,R2 transition. X-ray studies have been performed on human carbonmonoxy Hb at a resolution of 2.8,Å. The ensuing liganded quaternary structure is different from previously reported liganded Hb structures. The distal ,-heme pocket is the largest when compared with other liganded Hb structures, partly owing to rotation of ,His63(E7) out of the distal pocket, creating a ligand channel to the solvent. The structure also shows unusually smaller ,- and ,-clefts. Results from this study taken in conjunction with previous findings suggest that multiple liganded Hb states with different quaternary structures may be involved in ligand uptake, stabilization, transport and release. [source]

A Bayesian Hierarchical Model for Classification with Selection of Functional Predictors

BIOMETRICS, Issue 2 2010
Hongxiao Zhu
Summary In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis,Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification. [source]