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By clustering patients with the urologic chronic pelvic pain syndromes (UCPPS) into homogeneous subgroups and associating these subgroups with baseline covariates and other clinical outcomes, we provide opportunities to investigate different potential elements of pathogenesis, which may also guide us in selection of appropriate therapeutic targets. Motivated by the longitudinal urologic symptom data with extensive subject heterogeneity and differential variability of trajectories, we propose a functional clustering procedure where each subgroup is modeled by a functional mixed effects model, and the posterior probability is used to iteratively classify each subject into different subgroups. The classification takes into account both group-average trajectories and between-subject variabilities. We develop an equivalent state-space model for efficient computation. We also propose a cross-validation based Kullback–Leibler information criterion to choose the optimal number of subgroups. The performance of the proposed method is assessed through a simulation study. We apply our methods to longitudinal bi-weekly measures of a primary urological urinary symptoms score from a UCPPS longitudinal cohort study, and identify four subgroups ranging from moderate decline, mild decline, stable and mild increasing. The resulting clusters are also associated with the one-year changes in several clinically important outcomes, and are also related to several clinically relevant baseline predictors, such as sleep disturbance score, physical quality of life and painful urgency. for this article are available online. [ABSTRACT FROM AUTHOR]

In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time. This random time is used as a link to jointly model the longitudinal and survival data where the unknown time origins are integrated out in the joint likelihood function, which facilitates unbiased and consistent estimation. Since the disease progression pattern naturally predicts time-to-event, we further propose a new functional survival model using the registration function as a predictor of the time-to-event. The asymptotic consistency and semiparametric efficiency of the proposed models are proved. Simulation studies and two real data applications demonstrate the effectiveness of this new approach. for this article are available online. [ABSTRACT FROM AUTHOR]

In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. [ABSTRACT FROM AUTHOR]

In this article we propose a smoothing spline ANOVA model (SS-ANOVA) to estimate and to make inference on the time-varying log-spectrum of a locally stationary process. The time-varying spectrum is assumed to be smooth in both time and frequency. This assumption essentially turns a time-frequency spectral estimation problem into a 2-dimensional surface estimation problem. A smooth localized complex exponential (SLEX) basis is used to calculate the initial periodograms, and a SS-ANOVA is fitted to the log-periodograms. This approach allows the time and frequency domains to be modeled in a unified approach and jointly estimated. Inference procedures, such as confidence intervals, and hypothesis tests proposed for the SS-ANOVA can be adopted for the time-varying spectrum. Because of the smoothness assumption of the underlying spectrum, once we have the estimates on a time-frequency grid, we can calculate the estimate at any given time and frequency. This leads to a high computational efficiency, because for large datasets we need only estimate the initial raw periodograms at a much coarser grid. We study a penalized least squares estimator and a penalized Whittle likelihood estimator. The penalized Whittle likelihood estimator has smaller mean squared errors, whereas inference based on the penalized least squares method can adopt existing results. We present simulation results and apply our method to electroencephalogram data recorded during an epileptic seizure. [ABSTRACT FROM AUTHOR]

Hormones serve as regulating signals for many biological processes. In recent years, it was determined that many hormones are secreted in a pulsatile manner and that the pulsatile secretion pattern, in addition to the absolute concentration level, is important in regulating biological processes. Consequently, it is necessary to characterize the latent secretion patterns from measurements of concentration levels. The characterization is complicated by the presence of a biological circadian rhythm. When hormone concentrations are plotted over time, the resultant time series usually exhibits occasional short rises superimposed on a slowly changing baseline. This is a result of a mixture of pulsatile secretions and a circadian rhythm. In this article we present a signal extraction approach to model simultaneously a slowly changing component and a pulsatile component of a time series. A smoothing spline is used to model the baseline, and a multiprocess dynamic linear model is used to model the pulsatile component. An additive structure is assumed, and both components are estimated simultaneously using a multiprocess Kalman filter. The unknown parameters are estimated by approximate maximum likelihood. The locations and amplitudes of the pulses are also estimated as posterior means via the multiprocess Kalman filter. Bayesian confidence intervals can be constructed for the baseline. This approach is found to be robust in simulated data and effective in modeling hormone time series. [ABSTRACT FROM AUTHOR]

To evaluate the alloy Ti 0.3 V 0.4 Cr 0.3 as a candidate material for hydrogen isotope separation, the separation factor ( α H−D ), which is usually used to characterize the hydrogen isotope effects in metal-hydrogen systems, was investigated. We focused on the dependence of α H−D on pressure, deuterium concentration, and temperature. The results indicated that the dependence of α H−D on pressure was insensitive. However, α H−D decreased with deuterium concentration, and the dependence was characterized quantitatively. The dependence of α H−D on temperature was complicated. The experimental results agree well with the harmonic oscillator model (HOM) in 213 K < T < 773 K, but there were distinct differences between them in 77 K < T < 213 K. It is interesting that the value of α H−D was 2.31 at 213 K. Thus, the alloy Ti 0.3 V 0.4 Cr 0.3 may be a hopeful candidate, or a potential substitute of Pd, for hydrogen isotope separation. [ABSTRACT FROM AUTHOR]

Patients with obstructive sleep apnea (OSA) are at elevated risk of developing systemic vascular disease and cognitive dysfunction. Here, cerebral oxygen metabolism was assessed in patients with OSA by means of a magnetic resonance-based method involving simultaneous measurements of cerebral blood flow rate and venous oxygen saturation in the superior sagittal sinus for a period of 10 minutes at an effective temporal resolution of 1.3 seconds before, during, and after repeated 24-second breath-holds mimicking spontaneous apneas, yielding, along with pulse oximetry-derived arterial saturation, whole-brain CMRO2 via Fick's Principle. Enrolled subjects were classified based on their apnea-hypopnea indices into OSA (N = 31) and non-sleep apnea reference subjects (NSA = 21), and further compared with young healthy subjects (YH, N = 10). OSA and NSA subjects were matched for age and body mass index. CMRO2 was lower in OSA than in the YH group during normal breathing (105.6 ± 14.1 versus 123.7 ± 22.8 μmol O2/min/100g, P = 0.01). Further, the fractional change in CMRO2 in response to a breath-hold challenge was larger in OSA than in the YH group (15.2 ± 9.2 versus 8.5 ± 3.4%, P = 0.04). However, there was no significant difference in CMRO2 between OSA and NSA subjects. The data suggest altered brain oxygen metabolism in OSA and possibly in NSA as well. [ABSTRACT FROM AUTHOR]

Objectives. Violence remains a significant public health issue in the United States. To determine if urban vacant properties were associated with an increased risk of assaultive violence and if this association was modified by important neighborhood institutions (e.g., schools, parks/playgrounds, police stations, and alcohol outlets). Methods. Longitudinal ecologic study of all 1816 block groups in Philadelphia. Aggravated assault and vacant property data were compiled yearly from 2002 to 2006 and linked to block groups. A mixed effects negative binomial regression model examined the association of vacant properties and assaults between and within block groups. Results. Among all block groups, 84% experienced at least one vacant property, 89% at least one aggravated assault, and 64% at least one gun assault. Between block groups, the risk of aggravated assault increased 18% for every category shift of vacant properties (IRR 1.18, 95% CI: 1.12, 1.25, P < 0.001). Parks/playgrounds and alcohol outlets potentially modified the association between vacant properties and aggravated assaults but only at low levels of vacancy. Conclusions. Increasing levels of vacancy were associated with increased risk of assaultive violence in urban block groups. [ABSTRACT FROM AUTHOR]

In many experiments, time series data can be collected from multiple units and multiple time series segments can be collected from the same unit. This article introduces a mixed effects Cramér spectral representation which can be used to model the effects of design covariates on the second-order power spectrum while accounting for potential correlations among the time series segments collected from the same unit. The transfer function is composed of a deterministic component to account for the population-average effects and a random component to account for the unit-specific deviations. The resulting log-spectrum has a functional mixed effects representation where both the fixed effects and random effects are functions in the frequency domain. It is shown that, when the replicate-specific spectra are smooth, the log-periodograms converge to a functional mixed effects model. A data-driven iterative estimation procedure is offered for the periodic smoothing spline estimation of the fixed effects, penalized estimation of the functional covariance of the random effects, and unit-specific random effects prediction via the best linear unbiased predictor. [ABSTRACT FROM PUBLISHER]

Objective: To validate recently characterized curves of hCG rise and fall that are seen in viable and nonviable pregnancies in a population of women who have pregnancies of unknown location. Design: Historical cohort study. Setting: University hospital. Patient(s): One thousand two hundred forty-nine women with symptomatic early pregnancies. Intervention(s): None. Main Outcome Measure(s): Comparison of observed hCG values to predicted hCG values; time to diagnosis of ectopic pregnancy; accuracy of diagnosis. Result(s): Of the 1,249 patients included in this study, 196 had ectopic pregnancy (EP); 261, intrauterine pregnancy (IUP); and 792, spontaneous abortion (SAB). By determining the rate of change in hCG values from two consecutive visits and comparing them with a minimal rise of 35% in 2 days (the bound that is defined by the 99.9% confidence interval [CI] for the rise of hCG in an IUP) or a minimal fall of 21%–35% (the bound that is defined by the 90% CI for the fall of hCG in an SAB), we were able to make the diagnosis of EP an average of 2.5 days sooner than by standard clinical practice. Only 12% of patients had an EP go undiagnosed by using these rules, because the curve of rise or fall of their hCG mimicked that of a non-EP gestation. Conclusion(s): Recently redefined curves of rise and fall in hCG for IUP and SAB are valid for clinical use on the basis of our application to this large cohort of patients. Using them can shorten the time needed to make the diagnosis of EP. Use of a more conservative cutoff for minimal rise in hCG, one as slow as 35% over 2 days, to characterize a potentially viable gestation would minimize potential interruption of a desired pregnancy. [Copyright &y& Elsevier]

We develop a procedure for analyzing multivariate nonstationary time series using the SLEX library (smooth localized complex exponentials), which is a collection of bases, each basis consisting of waveforms that are orthogonal and time-localized versions of the Fourier complex exponentials. Under the SLEX framework, we build a family of multivariate models that can explicitly characterize the time- varying spectral and coherence properties. Every model has a spectral representation in terms of a unique SLEX basis. Before selecting a model, we first decompose the multivariate time series into nonstationary components with uncorrelated (nonredundant) spectral information. The best SLEX model is selected using the penalized log energy criterion, which we derive in this article to be the Kullback-Leibler distance between a model and the SLEX principal components of the multivariate time series. The model selection criterion takes into account all of the pairwise cross-correlation simultaneously in the multivariate time series. The proposed SLEX analysis gives results that are easy to interpret, because it is an automatic time-dependent generalization of the classical Fourier analysis of stationary time series. Moreover, the SLEX method uses computationally efficient algorithms and hence is able to provide a systematic framework for extracting spectral features from a massive dataset. We illustrate the SLEX analysis with an application to a multichannel brain wave dataset recorded during an epileptic seizure. [ABSTRACT FROM AUTHOR]

Neurologic injury is a leading cause of morbidity and mortality following pediatric cardiac arrest. In this study, we assess the feasibility of quantitative, non-invasive, frequency-domain diffuse optical spectroscopy (FD-DOS) neuromonitoring during cardiopulmonary resuscitation (CPR), and its predictive utility for return of spontaneous circulation (ROSC) in an established pediatric swine model of cardiac arrest. Cerebral tissue optical properties, oxy- and deoxy-hemoglobin concentration ([HbO2], [Hb]), oxygen saturation (StO2) and total hemoglobin concentration (THC) were measured by a FD-DOS probe placed on the forehead in 1-month-old swine (8–11 kg; n = 52) during seven minutes of asphyxiation followed by twenty minutes of CPR. ROSC prediction and time-dependent performance of prediction throughout early CPR (< 10 min), were assessed by the weighted Youden index (Jw, w = 0.1) with tenfold cross-validation. FD-DOS CPR data was successfully acquired in 48/52 animals; 37/48 achieved ROSC. Changes in scattering coefficient (785 nm), [HbO2], StO2 and THC from baseline were significantly different in ROSC versus No-ROSC subjects (p < 0.01) after 10 min of CPR. Change in [HbO2] of + 1.3 µmol/L from 1-min of CPR achieved the highest weighted Youden index (0.96) for ROSC prediction. We demonstrate feasibility of quantitative, non-invasive FD-DOS neuromonitoring, and stable, specific, early ROSC prediction from the third minute of CPR. [ABSTRACT FROM AUTHOR]

Photodynamic therapy (PDT) is a well-established treatment modality for cancer and other malignant diseases; however, quantities such as light fluence and PDT dose do not fully account for all of the dynamic interactions between the key components involved. In particular, fluence rate (ϕ) effects, which impact the photochemical oxygen consumption rate, are not accounted for. In this preclinical study, reacted reactive oxygen species ([ROS]rx) was investigated as a dosimetric quantity for PDT outcome. The ability of [ROS]rx to predict the cure index (CI) of tumor growth, CI = 1 − k / kctr, where k and kctr are the growth rate of tumor under PDT study and the control tumor without PDT, respectively, for benzoporphyrin derivative (BPD)-mediated PDT, was examined. Mice bearing radiation-induced fibrosarcoma (RIF) tumors were treated with different in-air fluences (Φ = 22.5 to 166.7 J / cm2) and in-air fluence rates (ϕair = 75 to 250 mW / cm2) with a BPD dose of 1 mg / kg and a drug-light interval (DLI) of 15 min. Treatment was delivered with a collimated laser beam of 1-cm-diameter at 690 nm. Explicit measurements of in-air light fluence rate, tissue oxygen concentration, and BPD concentration were used to calculate for [ROS]rx. Light fluence rate at 3-mm depth (ϕ3 mm), determined based on Monte-Carlo simulations, was used in the calculation of [ROS]rx at the base of tumor. CI was used as an endpoint for three dose metrics: light fluence, PDT dose, and [ROS]rx. PDT dose was defined as the product of the time-integral of photosensitizer concentration and ϕ3 mm. Preliminary studies show that [ROS]rx best correlates with CI and is an effective dosimetric quantity that can predict treatment outcome. The threshold dose for [ROS]rx for vascular BPD-mediated PDT using DLI of 15 min is determined to be 0.26 mM and is about 3.8 times smaller than the corresponding value for conventional BPD-mediated PDT using DLI of 3 h. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is characterized by intermittent obstruction of the airways during sleep. Cerebrovascular reactivity (CVR) is an index of cerebral vessels' ability to respond to a vasoactive stimulus, such as increased CO2. We hypothesized that OSA alters CVR, expressed as a breath-hold index (BHI) defined as the rate of change in CBF or BOLD signal during a controlled breath-hold stimulus mimicking spontaneous apneas by being both hypercapnic and hypoxic. In 37 OSA and 23 matched non sleep apnea (NSA) subjects, we obtained high temporal resolution CBF and BOLD MRI data before, during, and between five consecutive BH stimuli of 24 s, each averaged to yield a single BHI value. Greater BHI was observed in OSA relative to NSA as derived from whole-brain CBF (78.6 ± 29.6 vs. 60.0 ± 20.0 mL/min2/100 g, P = 0.010) as well as from flow velocity in the superior sagittal sinus (0.48 ± 0.18 vs. 0.36 ± 0.10 cm/s2, P = 0.014). Similarly, BOLD-based BHI was greater in OSA in whole brain (0.19 ± 0.08 vs. 0.15 ± 0.03%/s, P = 0.009), gray matter (0.22 ± 0.09 vs. 0.17 ± 0.03%/s, P = 0.011), and white matter (0.14 ± 0.06 vs. 0.10 ± 0.02%/s, P = 0.010). The greater CVR is not currently understood but may represent a compensatory mechanism of the brain to maintain oxygen supply during intermittent apneas. [ABSTRACT FROM AUTHOR]

Strengths-based strategies to reduce youth violence in low-resource urban communities are urgently needed. Supportive adolescent-adult relationships may confer protection, but studies have been limited by self-reported composite outcomes. We conducted a population-based case-control study among 10- to 24-year-old males in low-resource neighborhoods to examine associations between supportive adult connection and severe assault injury. Cases were victims of gunshot assault injury (n = 143) and non-gun assault injury (n = 206) from two level I trauma centers. Age- and race-matched controls (n = 283) were recruited using random digit dial from the same catchment. Adolescent-adult connections were defined by: (1) brief survey questions and (2) detailed family genograms. Analysis used conditional logistic regression. There were no significant associations between positive adult connection, as defined by brief survey questions, and either gunshot or non-gun assault injury among adolescents with high prior violence involvement (GSW OR = 2.46, 95% CI 0.81-7.49; non-gun OR = 1.59, 95% CI 0.54-4.67) or low prior violence involvement (GSW OR = 0.92, 95% CI 0.34-2.44; non-gun OR = 1.96, 95% CI 0.73-5.28). In contrast, among adolescents with high levels of prior violence involvement, reporting at least one supportive adult family member in the family genogram was associated with higher odds of gunshot assault injury (OR = 4.01, 95% CI 1.36-11.80) and non-gun assault injury (OR = 4.22, 95% CI 1.48-12.04). We were thus unable to demonstrate that positive adult connections protected adolescent males from severe assault injury in this highly under-resourced environment. However, at the time of injury, assault-injured adolescents, particularly those with high prior violence involvement, reported high levels of family support. The post-injury period may provide opportunities to intervene to enhance and leverage family connections to explore how to better safeguard adolescents. [ABSTRACT FROM AUTHOR]

Purpose/objectives: The clinical effects of radiation dose-intensification in locally advanced non-small cell lung (NSCLCa) and other cancers are challenging to predict and are ideally studied in randomized trials. The purpose of this study was to assess the use of dose-escalated radiation for locally advanced NSCLCa in the U.S., 2004-2013, a period in which there were no published level 1 studies on dose-escalation.Materials/methods: We performed analyses on two cancer registry databases with complementary strengths and weaknesses: the National Oncology Data Alliance (NODA) 2004-2013 and the National Cancer Database (NCDB) 2004-2012. We classified locally advanced patients according to the use of dose-escalation (>70 Gy). We used adjusted logistic regression to assess the association of year of treatment with dose-escalated radiation use in two periods representing time before and after the closure of a cooperative group trial (RTOG 0617) on dose-escalation: 2004-2010 and 2010-2013. To determine the year in which a significant change in dose could have been detected had dose been prospectively monitored within the NODA network, we compared the average annual radiation dose per year with the forecasted dose (average of the prior 3 years) adjusted for patient age and comorbidities.Results: Within both the NODA and NCDB, use of dose-escalation increased from 2004 to 2010 (p < 0.0001) and decreased from 2010 to 2013 (p = 0.0018), even after controlling for potential confounders. Had the NODA network been monitoring radiation dose in this cohort, significant changes in average annual dose would have been detected at the end of 2008 and 2012.Conclusions: Patterns of radiation dosing in locally advanced NSCLCa changed in the U.S. in the absence of level 1 evidence. Monitoring radiation dose is feasible using an existing national cancer registry data collection infrastructure. [ABSTRACT FROM AUTHOR]

Traditional Chinese herbal medications (TCHMs) are composed of a multitude of compounds and the identification of their active composition is an important area of research. Chromatography provides a visual representation of a TCHM sample’s composition by outputting a curve characterized by spikes corresponding to compounds in the sample. Across different experimental conditions, the location of the spikes can be shifted, preventing direct comparison of curves and forcing compound identification to be possible only within each experiment. In this article, we propose a sparse semiparametric nonlinear modeling framework for the establishment of a standardized chromatographic fingerprint. Data-driven basis expansion is used to model the common shape of the curves, while a parametric time warping function registers across individual curves. Penalized weighted least-squares with the adaptive lasso penalty provides a unified criterion for registration, model selection, and estimation. Furthermore, the adaptive lasso estimators possess attractive sampling properties. A back-fitting algorithm is proposed for estimation. Performance is assessed through simulation and we apply the model to chromatographic data of rhubarb collected from different experimental conditions and establish a standardized fingerprint as a first step in TCHM research. [ABSTRACT FROM AUTHOR]

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this article, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

In this article we develop a nonparametric estimation procedure for the varying coefficient model when the within-subject covariance is unknown. Extending the idea of iterative reweighted least squares to the functional setting, we iterate between estimating the coefficients conditional on the covariance and estimating the functional covariance conditional on the coefficients. Smoothing splines for correlated errors are used to estimate the functional coefficients with smoothing parameters selected via the generalized maximum likelihood. The covariance is nonparametrically estimated using a penalized estimator with smoothing parameters chosen via a Kullback–Leibler criterion. Empirical properties of the proposed method are demonstrated in simulations and the method is applied to the data collected from an ovarian tumor study in mice to analyze the effects of different chemotherapy treatments on the volumes of two classes of tumors. [ABSTRACT FROM AUTHOR]

Because of its low toxicity, low-dose (LD) chemotherapy is ideally suited for combination with antiangiogenic drugs. We investigated the impact of tumor vascular endothelial growth factor A (VEGF-A) expression on the efficacy of LD paclitaxel chemotherapy and its interactions with the tyrosine kinase inhibitor SU5416 in the ID8 and ID8-Vegf models of ovarian cancer. Functional linear models using weighted penalized least squares were utilized to identify interactions between Vegf, LD paclitaxel and antiangiogenic therapy. LD paclitaxel yielded additive effects with antiangiogenic therapy against tumors with low Vegf expression, while it exhibited antagonism to antiangiogenic therapy in tumors with high Vegf expression. This is the first preclinical study that models interactions of LD paclitaxel chemotherapy with antiangiogenic therapy and tumor VEGF expression and offers important lessons for the rational design of clinical trials. [ABSTRACT FROM AUTHOR]

Abstract: The aging behaviors of uranium tritide, such as the release of helium-3, changes of equilibrium desorption isotherms, and tritium heels, have been investigated. It is suggested that part of 3He from decay should be retained in the aged uranium tritide. The plateau of P–C–T curve is still wide and flat, and the change of the equilibrium plateau pressure is inconspicuous. Significant tritium heel is not observed in present works, so it can be neglected. The reduction of the reversible storage capacity is very little. In conclusion, the tritium aging effects in uranium tritide are slight, and uranium can be used as an effective material for long-term storage of tritium. [Copyright &y& Elsevier]

Presents a time-frequency spectral estimation of multichannel electroencephalography (EEG) using the auto-smooth localized complex exponentials (SLEX) method. Segmentation on a dyadic and nondyadic boundaries; Stationarity of preseizure EEG; Computation of the periodogram using SLEX transform.

The article discusses the need to refine and improve a proposed method for the study of traditional Chinese herbal medications (TCHMs) and extend it to other statistical and applied problems. The role of the mean chromatographic fingerprint technique in the simplification of the covariance structure is mentioned. The importance of combining the preprocessing and estimation procedures in biomedical applications is also noted.