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In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which we can borrow the information from other units in forecasting the targeted unit. We first build a hierarchical state space model for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit‐specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. By running the Kalman filtering based on the conditional state space model on the targeted unit, we incorporate both the information from the other units and the history of the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state‐level new COVID‐19 cases in United States is used for illustration. [ABSTRACT FROM AUTHOR]

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]

This paper uses data from a population-based case control study of daily activities and assault injury to examine residence-based versus actual path-based approaches to measuring environmental exposures that pose risks for violence among adolescents. Defining environmental exposures based on participant home address resulted in significant misclassification compared to gold standard daily travel path measures. Dividing participant daily travel paths into origin-destination segments, we explore a method for defining spatial counterfactuals by comparing actual trip path exposures to shortest potential trip path exposures. Spatial methods explored herein can be utilized in future research to more accurately quantify environmental exposures and associations with health outcomes. [ABSTRACT FROM AUTHOR]

Objectives Dysfunctions in stress biology are hypothesized to contribute to anxiety disorders, and to be ameliorated during successful treatment, but limited clinical data exist to support this hypothesis. We evaluated whether increases in morning cortisol and the diurnal cortisol slope, markers of stress biology, are associated with clinical response to chamomile therapy among subjects with generalized anxiety disorder (GAD). Methods Among 45 subjects with DSM-IV diagnosed GAD in an open-label clinical trial of chamomile, salivary cortisol was assessed for three days each pre- and post-treatment, at 8am, 12pm, 4pm, and 8pm. Mixed model analyses assessed whether GAD symptom change predicted the degree to which cortisol levels changed during treatment. Results Symptom improvement during treatment was significantly associated with pre-to-post treatment changes in cortisol. Subjects who experienced more symptomatic improvement experienced significant increases in their morning salivary cortisol ( β = 0.48, p < 0.001), and a greater decrease in cortisol from morning to the rest of the day ( β = 0.55, p < 0.001). In addition, at baseline a lower cortisol level ( β = −0.24, p = 0.023) and a lesser decrease in cortisol after morning ( β = 0.30, p = 0.003) were associated with greater symptomatic improvement. Conclusion Increases in morning salivary cortisol and the diurnal cortisol slope are associated with symptom improvement in chamomile treatment of GAD. Response to treatment for GAD could partially stem from normalization of stress biology dysfunction, but further work involving establishing abnormalities within-sample, ruling out of confounds (e.g., sleep), and a placebo control is necessary to conclude an amelioration effect. Registration code NCT01072344 . URL https://clinicaltrials.gov/ct2/show/NCT01072344 . [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]

Objective: To assess whether variation in serum human chorionic gonadotropin (hCG) measures, used to assess early gestation viability, are associated with differences in clinical presentation and patient factors.Method: This retrospective cohort study included 285 women with first-trimester pain and bleeding and a pregnancy of unknown location for whom a normal intrauterine pregnancy was ultimately confirmed. Serial samples were collected at three U.S. sites and hCG changes were analyzed for differences by race, ethnicity, and clinical factors. A nonlinear, mixed-effects regression model was used assuming a random subject shift in the time axis.Results: The hCG rise in symptomatic women with ongoing intrauterine pregnancy differs by patient factors and level at presentation. The 2-day minimum (first percentile) rise in hCG was faster when presenting hCG values were low and slower when presenting hCG value was high. African American women had a faster hCG rise (P<.001) compared with non-African American women. Variation in hCG curves was associated with prior miscarriage (P=.014), presentation of bleeding (P<.001), and pain (P=.002). For initial hCG values of less than 1,500, 1,500-3,000 and greater than 3,000 milli-international units/mL, the predicted 2-day minimal (first percentile) rise was 49%, 40%, and 33%, respectively.Conclusion: The rise of hCG levels in women with viable intrauterine pregnancies and symptoms of potential pregnancy failure varies significantly by initial value. Changes in hCG rise related to race should not affect clinical care. To limit interruption of a potential desired intrauterine pregnancy, a more conservative "cutoff" (slower rise) is needed when hCG values are high.Clinical Trial Registration: ClinicalTrials.gov, https://clinicaltrials.gov, NCT00194168. [ABSTRACT FROM AUTHOR]

Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls. [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]

Purpose To describe quality of life (QoL) in the year following minor injury and to test the hypothesis that individuals with depression in the postinjury year experience lower QoL than do individuals with no depression. Design Prospective, longitudinal, cohort design. A total of 275 adults were randomly selected from injured patients presenting to an urban emergency department. Methods All participants underwent structured psychiatric diagnostic interviews immediately after injury and at 3, 6, and 12 months. The primary outcome, QoL, was measured using the Quality of Life Index. Covariates included demographics, injury status, preinjury functional status, preinjury social support, and anticipation of problems postdischarge. The General Estimating Equation was used to compare changes in QoL between participants with and without depression over 3, 6, and 12 months, adjusting for covariates. Results An 18.1% proportion (95% confidence interval [CI] 13.3, 22.9%) of the sample met criteria for a mood disorder in the postinjury year. The depressed group reported a QoL that was 4.2 points (95% CI 2.8-5.6) lower in the year postinjury compared with that of the nondepressed group. Conclusions Depression after minor injury negatively affects QoL even a full year postinjury. Clinical Relevance The findings of this study show that patients who have injuries that are treated and discharged from an emergency department can have significantly lower QoL in the year after that injury that is attributed, in part, to postinjury depression. Nurses should provide anticipatory guidance to patients that they may experience feelings of sadness or being 'blue,' and that if they do, they should seek care. [ABSTRACT FROM AUTHOR]

Functional mixed effects model (FMM) is a mixed effects modeling framework that both the fixed effects and the random effects are modeled by nonparametric curves. The combination of mixed effects model and nonparametric smoothing enables FMMs to handle outcomes with complex profiles and at the same time to incorporate complex experimental designs and include covariates. Estimation and inference can be performed either using techniques from linear mixed effects models or using fully Bayesian approaches. As in functional data analysis, inference in FMMs is preliminary and needs to be further investigated. Several software packages have been developed to implement FMMs, although computational challenges do exist no matter which smoothing method is used. WIREs Comput Stat 2012, 4:527-534. doi: 10.1002/wics.1226 For further resources related to this article, please visit the WIREs website [ABSTRACT FROM AUTHOR]

Abstract: Background: The aim of the study was to create a mathematical model that reproduces the prevalence of shunt-treated hydrocephalus in the United States over the years and predicts trends in the near future. Methods: A structured search was performed of the English language literature for case series reporting rates of shunt insertion and revision, shunt removal, and patient mortality. A meta-analytic model was constructed to pool data from multiple studies and to calculate these rates at various time intervals. Separate Markov models were used to predict numbers of shunts at any one time for children (<17 years old) and adults. The models analyze the number of shunts inserted every year since 1955 and follow the likely fates of those patients as time passes. Results: Prevalence rates predicted by the model agree closely with those reported in the literature. However, the model''s structure creates considerable variability around point estimates. Conclusions: The model gives a comprehensive view of the prevalence of shunt-treated hydrocephalus in both children and adults from 1955 to the present. This model may prove useful in predicting resource use and needs for patients with hydrocephalus. [Copyright &y& Elsevier]

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]

Abstract. In many cases, multiple time series can be viewed as realizations of the same underlying process and such data usually accumulate in time. The historic time-series data provide important information for our current prediction. In this paper, we extend the traditional state-space model to a general dynamic scheme, in which estimation and prediction across time series and within a time series are handled by a unified O(N) sequential procedure. Under this framework, the information from historic data serves as the prior for the current time series and the estimation and prediction of a time series can incorporate the information from other time series as well as its own history. The solution is to construct sequentially a new state-space model for the next time series conditional on the past time series. Because we achieve the general dynamic estimation and prediction through constructing new conditional state-space models, existing estimation procedures for state-space models can be adapted into this framework with minimal modifications. An application to infant growth curves is used as illustration. [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]

While adolescent–adult connections have been shown to be protective against violence perpetration and victimization, mechanisms through which these connections confer protection from violence are poorly understood. We assessed whether adolescent–adult connections protected youth in lower resource urban neighborhoods from exposure to environmental risk factors for violence during daily activities. We overlaid on the city landscape minute-by-minute activity paths from 274 randomly sampled predominantly African American male youth, ages 10 to 24, enrolled in a population-based study of daily activities in Philadelphia, PA, to calculate environmental exposures and to compare exposures along actual versus shortest potential travel routes. Adolescent–adult connections were defined using brief survey questions and detailed family genograms. Analyses demonstrated that youth's selected travel routes resulted in significantly lower exposure to several types of crime, including vandalism, narcotics arrests, and disorderly conduct, than would have occurred on shortest potential routes. On average, youth with adolescent–adult connections spent less time outdoors than youth without connections, although these differences did not reach statistical significance (p =.06). There were no significant differences in environmental risk factors encountered by youth with versus without adolescent–adult connections. Future mixed-methods research combining qualitative and geographic information systems (GIS) approaches should investigate which factors shape travel decisions during daily activities to guide multimodal violence prevention interventions. [ABSTRACT FROM AUTHOR]

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time‐varying and time‐invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data. [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]

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor. [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]

We extend a random pattern mixture joint model for longitudinal ordinal outcomes and informative dropouts. The patients are generalized to 'pattern' groups based on known covariates that are potentially surrogated for the severity of the underlying condition. The random pattern effects are defined as the latent effects linking the dropout process and the ordinal longitudinal outcome. Conditional on the random pattern effects, the longitudinal outcome and the dropout times are assumed independent. Estimates are obtained via the Expectation-maximization algorithm. We applied the model to the end-stage renal disease data. Anemia was found to be significantly affected by the baseline iron treatment when the dropout information was adjusted via the study model; as opposed to an independent or shared parameter model. Simulations were performed to evaluate the performance of the random pattern mixture model under various assumptions. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Branching curves are a technique for modeling curves that change trajectory at a change (branching) point. Currently, the estimation framework is limited to independent data, and smoothing splines are used for estimation. This article aims to extend the branching curve framework to the longitudinal data setting where the branching point varies by subject. If the branching point is modeled as a random effect, then the longitudinal branching curve framework is a semiparametric nonlinear mixed effects model. Given existing issues with using random effects within a smoothing spline, we express the model as a B-spline based semiparametric nonlinear mixed effects model. Simple, clever smoothness constraints are enforced on the B-splines at the change point. The method is applied to Women's Health data where we model the shape of the labor curve (cervical dilation measured longitudinally) before and after treatment with oxytocin (a labor stimulant). Copyright © 2014 John Wiley & Sons, Ltd. [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]

Background: Dehydroepiandrosterone sulfate (DHEAS) has been proposed as an antiaging hormone, but its importance is unclear. Assessment of an individual’s ability to maintain a DHEAS set point, through examination of multiple DHEAS levels over time, may provide insight into biologic aging. [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]

Background: Diffuse correlation spectroscopy (DCS) noninvasively permits continuous, quantitative, bedside measurements of cerebral blood flow (CBF). To test whether optical monitoring (OM) can detect decrements in CBF producing cerebral hypoxia, we applied the OM technique continuously to probe brain-injured patients who also had invasive brain tissue oxygen (PbO2) monitors.Methods: Comatose patients with a Glasgow Coma Score (GCS) < 8) were enrolled in an IRB-approved protocol after obtaining informed consent from the legally authorized representative. Patients underwent 6-8 h of daily monitoring. Brain PbO2 was measured with a Clark electrode. Absolute CBF was monitored with DCS, calibrated by perfusion measurements based on intravenous indocyanine green bolus administration. Variation of optical CBF and mean arterial pressure (MAP) from baseline was measured during periods of brain hypoxia (defined as a drop in PbO2 below 19 mmHg for more than 6 min from baseline (PbO2 > 21 mmHg). In a secondary analysis, we compared optical CBF and MAP during randomly selected 12-min periods of "normal" (> 21 mmHg) and "low" (< 19 mmHg) PbO2. Receiver operator characteristic (ROC) and logistic regression analysis were employed to assess the utility of optical CBF, MAP, and the two-variable combination, for discrimination of brain hypoxia from normal brain oxygen tension.Results: Seven patients were enrolled and monitored for a total of 17 days. Baseline-normalized MAP and CBF significantly decreased during brain hypoxia events (p < 0.05). Through use of randomly selected, temporally sparse windows of low and high PbO2, we observed that both MAP and optical CBF discriminated between periods of brain hypoxia and normal brain oxygen tension (ROC AUC 0.761, 0.762, respectively). Further, combining these variables using logistic regression analysis markedly improved the ability to distinguish low- and high-PbO2 epochs (AUC 0.876).Conclusions: The data suggest optical techniques may be able to provide continuous individualized CBF measurement to indicate occurrence of brain hypoxia and guide brain-directed therapy. [ABSTRACT FROM AUTHOR]

Background: Disruption of insulin-like growth factor-I (IGF-I) increases health and life span in animal models, though this is unconfirmed in humans. If IGF-I stability indicates homeostasis, the absolute level of IGF-I may be less clinically relevant than maintaining an IGF-I setpoint.Methods: Participants were 945 U.S. community-dwelling individuals aged ≥65 years enrolled in the Cardiovascular Health Study with IGF-I levels at 3-6 timepoints. We examined the association of baseline IGF-I level, trajectory slope, and variability around the trajectory with mortality.Results: There were 633 deaths over median 11.3 years of follow-up. Lower IGF-I levels, declining or increasing slope, and increasing variability were each individually associated with higher mortality (all p < .001). In an adjusted model including all three trajectory parameters, baseline IGF-I levels <70 ng/mL (hazard ratio [HR] 1.58, 95% CI 1.28-1.96 relative to IGF-I levels of 170 ng/mL), steep declines and steep increases in trajectory slope (HR 2.22, 1.30-3.80 for a 15% decline; HR 1.40, 1.07-1.84 for a 10% decline; HR 1.80, 1.12-2.89 for a 15% increase; HR 1.31, 1.00-1.72 for a 10% increase, each vs no change), and variability ≥10% (HR 1.59, 1.09-2.32 for ≥ 30%; HR 1.36, 1.06-1.75 for 20%; and HR 1.17, 1.03-1.32 for 10% variability, each vs 0%) in IGF-I levels were independently associated with mortality.Conclusions: In contrast to data from animal models, low IGF-I levels are associated with higher mortality in older humans. Irrespective of the actual IGF-I level, older individuals with stability of IGF-I levels have lower mortality than those whose IGF-I levels fluctuate over time. [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]

Background: Enoxaparin 30 mg twice daily and dalteparin 5,000 units once daily are two common low-molecular-weight heparin (LMWH) thromboprophylaxis regimens used in the trauma population. Pharmacodynamic studies suggest that enoxaparin provides more potent anticoagulation than does dalteparin.Methods: In 2009, our institution switched its formulary LMWH from enoxaparin to dalteparin followed by a switch back to enoxaparin in 2013. Using a difference in differences design, we contrasted the change in the VTE rate accompanying the LMWH switch with the change in a control group of trauma patients given unfractionated heparin (UFH) during the same period.Results: The study included 5,880 patients: enoxaparin period (enoxaparin, n = 2,371; UFH, n = 1,539) vs the dalteparin period (dalteparin, n = 1,046; UFH, n = 924). The VTE rate was unchanged in the LMWH group: 3.3/1000 days in the enoxaparin period vs 3.8/1000 days in the dalteparin period: rate ratio (RR), 1.16; 95% CI 0.74-1.81. The rate was also unchanged in the UFH control subjects: 5.7/1,000 days in the enoxaparin period vs 5.2/1,000 days in the dalteparin period: RR, 0.92; 95% CI, 0.61-1.38. After confounding adjustment, the ratio of the change in VTE rate between the LMWH and UFH groups was similar: RR, 1.06; 95% CI 0.71-2.00. A secondary analysis excluding patients with delayed or interrupted prophylaxis (or both) altered this estimate nonsignificantly in favor of enoxaparin: RR, 2.39; 95% CI, 0.80-7.09.Conclusions: Our results suggest that dalteparin has an effectiveness similar to that of enoxaparin in real-world trauma patients. Future research should investigate how the timing and consistency of prophylaxis affects LMWH effectiveness. [ABSTRACT FROM AUTHOR]

This preclinical study examines light fluence, photodynamic therapy ( PDT) dose and 'apparent reacted singlet oxygen,' [1O2]rx, to predict local control rate ( LCR) for Photofrin-mediated PDT of radiation-induced fibrosarcoma ( RIF) tumors. Mice bearing RIF tumors were treated with in-air fluences (50-250 J cm−2) and in-air fluence rates (50-150 mW cm−2) at Photofrin dosages of 5 and 15 mg kg−1 and a drug-light interval of 24 h using a 630-nm, 1-cm-diameter collimated laser. A macroscopic model was used to calculate [1O2]rx and PDT dose based on in vivo explicit dosimetry of the drug concentration, light fluence and tissue optical properties. PDT dose and [1O2]rx were defined as a temporal integral of drug concentration and fluence rate, and singlet oxygen concentration consumed divided by the singlet oxygen lifetime, respectively. LCR was stratified for different dose metrics for 74 mice (66 + 8 control). Complete tumor control at 14 days was observed for [1O2]rx ≥ 1.1 m m or PDT dose ≥1200 μ m J cm−2 but cannot be predicted with fluence alone. LCR increases with increasing [1O2]rx and PDT dose but is not well correlated with fluence. Comparing dosimetric quantities, [1O2]rx outperformed both PDT dose and fluence in predicting tumor response and correlating with LCR. [ABSTRACT FROM AUTHOR]

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks. [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]

To improve characteristics of hydrophobic catalysts for hydrogen isotope separation between hydrogen and water which is a key reaction for water detritiation and heavy water production, monodispersed Pt 3 M bimetal hydrophobic catalysts were fabricated. The average diameters of Pt 3 M nanoparticles ranged from 2.2 nm to 2.6 nm. The lattice parameters of Pt 3 M were 3.87 Å, 3.83 Å, 3.78 Å and 3.90 Å, respectively. All compositions were well-controlled to the similar value. The addition of the metallic Fe, Ni, Cr, and Co hindered the oxidation of the metallic Pt during the preparation of Pt 3 M/C catalysts. The catalytic activities for H/D isotope separation increased in the following order: Pt/C < Pt 3 Ni/C ≈ Pt 3 CoC < Pt 3 Fe/C < Pt 3 Cr/C. Catalytic performance tests demonstrated that selection of Pt 3 M bimetal is a very effective approach to improve hydrophobic catalytic. Finally, the enhanced activities of Pt 3 M bimetals over Pt were discussed by different mechanisms. [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]

Abstract: The hydrogen isotope exchange reaction between hydrogen and water is a key process in water detritiation and heavy water production. To improve the characteristics of hydrophobic catalysts for this type of reaction, foamed and cellular structures of hydrophobic catalysts were successfully prepared by adding a blowing agent. The mean particle size of Pt nanoparticles was 2.3 nm. The proportions of elemental Pt and Pt oxides were 62% and 38%, respectively. The addition of polymethyl methacrylate powder decreased internal diffusion of water vapor and hydrogen inside the catalyst and improved active site utilization. As a result, the catalytic activities of the hydrophobic catalysts are significantly higher than those of conventional hydrophobic catalysts for hydrogen isotope exchange. All catalysts exhibited excellent hydrophobic performances after 30 days of testing. It was reasonable to mix Pt/C catalysts, polytetrafluoroethene and polymethyl methacrylate in a ratio of 1:2:1 before hydrophobic treatment. Finally, the mechanisms of hydrogen–water diffusion and exchange were investigated. [Copyright &y& Elsevier]

Abstract: This paper is motivated by the pioneering work of Emanuel Parzen wherein he advanced the estimation of (spectral) densities via kernel smoothing and established the role of reproducing kernel Hilbert spaces (RKHS) in field of time series analysis. Here, we consider analysis of power (ANOPOW) for replicated time series collected in an experimental design where the main goals are to estimate, and to detect differences among, group spectra. To accomplish these goals, we obtain smooth estimators of the group spectra by assuming that each spectral density is in some RKHS; we then apply penalized least squares in a smoothing spline ANOPOW. For inference, we obtain simultaneous confidence intervals for the estimated group spectra via bootstrapping. [ABSTRACT FROM AUTHOR]

Study Design: Meta-analytic costeffectiveness analysis.Objective: Our goal was to compare the results of different management strategies for trauma patients in whom the cervical spine was not clinically evaluable due to impaired consciousness, endotracheal intubation, or painful distracting injuries.Summary Of Background Data: We performed a structured literature review related to cervical spine trauma, radiographic clearance techniques (plain radiography, flexion/extension, CT, and MRI), and complications associated with semirigid collar use.Methods: Meta-analytic techniques were used to pool data from multiple sources to calculate pooled mean estimates of sensitivities and specificities of imaging techniques for cervical spinal clearance, rates of complications from various clearance strategies and from empirical use of semirigid collars. A decision analysis model was used to compare outcomes and costs among these strategies.Results: Slightly more than 7.5% of patients who are clinically unevaluable have cervical spine injuries, and 42% of these injuries are associated with spinal instability. Sensitivity of plain radiography or fluoroscopy for spinal clearance was 57% (95% CI: 57%-60%). Sensitivities for CT and MRI alone were 83% (82%-84%) and 87% (84%-89%), respectively. Complications associated with collar use ranged from 1.3% (2 days) to 7.1% (10 days) but were usually minor and short-lived. Quadriplegia resulting from spinal instability missed by a clearance test had enormous impacts on longevity, quality of life, and costs. These impacts overshadowed the effects of prolonged collar application, even when the incidence of quadriplegia was extremely low.Conclusion: As currently used, neuroimaging studies for cervical spinal clearance in clinically unevaluable patients are not cost-effective compared with empirical immobilization in a semirigid collar. [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]

Regulated proteolysis by the ubiquitin pathway has been implicated in control of the cell cycle, transcriptional activation, cell fate and growth, and synaptogenesis. The ubiquitin-proteasome system is involved in synaptic plasticity and is proposed to be part of a molecular switch that converts short-term synaptic potentiation to long-term changes in synaptic strength. In Aplysia, a component of the ubiquitin system termed ubiquitin C-terminal hydrolase ( Ap-Uch) has been shown to be essential for long-term facilitation. To examine whether Uch plays a role in learning, memory, and synaptic plasticity in mammals, we have analyzed mice homozygous for a targeted mutation in ubiquitin C-terminal hydrolase L3 ( Uchl3), an orthologue of Ap-Uch. Mice homozygous for the mutation in Uchl3 are viable, with no obvious developmental, histological, or fertility abnormalities. We demonstrate that Uchl3−/− mice have a significant learning deficit relative to wild type littermates in the spatial version of the Morris water maze and the 8-arm radial maze. Further, the impaired performance in the 8-arm radial maze of Uchl3−/− mice is due to significantly increased working memory errors. Examination of hippocampal long-term potentiation (LTP), a form of synaptic plasticity thought to underlie memory storage, revealed no significant differences in LTP in hippocampal slices from Uchl3−/− mice. Our results suggest a novel role for ubiquitin C-terminal hydrolase enzymes in mammals in spatial learning and working memory. © 2005 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

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.