Biomedical subjects

Rui Zou

Publications and source records attributed to Rui Zou.

6 recordsLinked to original sources

PubMed2006

[Effects of osteoporosis on experimental tooth movement in aged rats].

OBJECTIVE: To examine the experimental tooth movement and recruitment of new osteoclasts in aged variotomized (OVX) rats. METHODS: Eighty-two 3-month female virgin health rats were divided into two groups. Each group was divided into seven sub-groups of tooth movement: 0,1,3,5,7,10 and 14 days. The tooth movement and the population of osteoclasts were measured and comparatively analyzed. RESULTS: The osteoporosis rats showed larger and faster tooth movement (P<0.05). In the osteoporosis group, the instantaneous tooth movement lasted longer time and traversed larger distance, the delay period was shorter, the population of osteoclasts was larger and displayed a faster recruitment. CONCLUSION: These findings indicate that osteoporosis accelerates tooth movement in aged OVX rats.

Aging↗

PubMed2005

Estimation of time-varying coherence function using time-varying transfer functions.

We introduce a new method to estimate reliable time-varying coherence functions (TVCF) for causal systems. The technique is based on our previously developed method to estimate time-varying transfer functions (TVTF), known as the time-varying optimal parameter search algorithm [Zou, R., H. Wang, and K. H. Chon. A robust time-varying identification algorithm using basis functions. Ann. Biomed. Eng. 31: 840-853, 2003]. The TVCF is estimated by the multiplication of two TVTFs. The two TVTFs are obtained using signal x as the input and signal y as the output to produce the first TVTF, and signal y as the input and signal x as the output to produce the second TVTF. Demonstration of the feasibility and efficacy of the proposed approach is provided with both simulation examples and application to renal blood flow and pressure data. The proposed approach provides higher time-frequency resolution TVCF than afforded by the short time Fourier transform based TVCF.

Animals↗

PubMed2005

Multiple time-varying dynamic analysis using multiple sets of basis functions.

We extend a recently developed algorithm that expands the time-varying parameters onto a single set of basis functions, to multiple sets of basis functions. This feature allows the capability to capture many different dynamics that may be inherent in the system. A single set of basis functions that has its own unique characteristics can best capture dynamics of the system that have similar features. Therefore, for systems that have multiple dynamics, the use of a single set of basis functions may not be adequate. Computer simulation examples do indeed show the benefit of using multiple sets of basis functions over the single set of basis functions for cases with many switching dynamics. Moreover, the proposed method remains accurate even under significant noise contamination. Application of the proposed approach to blood pressure data likewise indicate better tracking capability of the two sets of basis function than the recursive least squares or a single set of basis functions.

Algorithms↗

PubMed2004

Robust algorithm for estimation of time-varying transfer functions.

We introduce a new method to estimate reliable time-varying (TV) transfer functions (TFs) and TV impulse response functions. The method is based on TV autoregressive moving average models in which the TV parameters are accurately obtained using the optimal parameter search method which we have previously developed. The new method is more accurate than the recursive least-squares (RLS), and remains robust even in the case of significant noise contamination. Furthermore, the new method is able to track dynamics that change abruptly, which is certainly a deficiency of the RLS. Application of the new method to renal blood pressure and flow revealed that hypertensive rats undergo more complex and TV autoregulation in maintaining stable blood flow than do normotensive rats. This observation has not been previously revealed using time-invariant TF analyses. The newly developed approach may promote the broader use of TV system identification in studies of physiological systems and makes linear and nonlinear TV modeling possible in certain cases previously thought intractable.

Algorithms↗

PubMed2002

Time-varying properties of renal autoregulatory mechanisms.

In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between normotensive and hypertensive blood flow recordings at the whole kidney level for both forced (p < 0.037) and spontaneous arterial pressure fluctuations (p < 0.033), and at the single-nephron level (p < 0.008). Especially at the single-nephron level, the mean Renyi entropy is significantly larger for hypertensive than normotensive rats, suggesting more complex dynamics in the hypertensive condition. To further evaluate whether or not the separation of dynamics between normotensive and hypertensive rats is found in the prescribed frequency ranges of the myogenic and TGF mechanisms, we employed multiresolution wavelet transform. Our analysis revealed that exclusively over scale ranges corresponding to the frequency intervals of the myogenic and TGF mechanisms, the widths of the blood flow wavelet coefficients fall into disjoint sets for normotensive and hypertensive rats. The separation of the scales at the myogenic and TGF frequency ranges is distinct and obtained with 100% accuracy. However, this observation remains valid only for the whole kidney blood pressure/flow data. The results suggest that understanding of the time-varying properties of the two mechanisms is required for a complete description of renal autoregulation.

Algorithms↗

PubMedDate not supplied

A robust time-varying identification algorithm using basis functions.

We extend a recently developed time invariant (TIV) model order search criterion named the optimal parameter search algorithm (OPS) for identification of time varying (TV) autoregressive (AR) and autoregressive moving average (ARMA) models. Using the TV algorithm is facilitated by the fact that expanding each TV coefficient onto a finite set of basis sequences permits TV parameters to become TIV. Taking advantage of this TIV feature of expansion parameters exploits the features of the OPS, which has been shown to provide accurate model order selection as well as extraction of only the significant model terms. Another advantage of the new algorithm is its ability to discriminate insignificant basis sequences thereby reducing the number of expansion parameters to be estimated. Due to these features, the resulting algorithm can accurately estimate TV AR or ARMA models and determine their orders. Indeed, comparison via computer simulations of AR models between the proposed method and one of the well-known iterative methods, recursive least squares, shows the greater capability of the new method to track TV parameters. Furthermore, application of the new method to experimentally obtained renal blood flow signals shows that the new method provides higher-resolution time-varying spectral capability than does the short-time Fourier transform (STFT), concomitant with fewer spurious frequency peaks than obtained with the STFT spectrogram.

Algorithms↗