Gene regulatory network discovery using pairwise Granger causality

Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time‐series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full‐model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell‐cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.

Generating reference models for structurally complex data. Application to the stabilometry medical domain

OBJECTIVES

We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of single-valued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc.

METHODS

We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series.

RESULTS

The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basketball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%).

CONCLUSIONS

We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.

Reconstructing shifts in vital rates driven by long-term environmental change: a new demographic method based on readily available data

Frequently, vital rates are driven by directional, long-term environmental changes. Many of these are of great importance, such as land degradation, climate change, and succession. Traditional demographic methods assume a constant or stationary environment, and thus are inappropriate to analyze populations subject to these changes. They also require repeat surveys of the individuals as change unfolds. Methods for reconstructing such lengthy processes are needed. We present a model that, based on a time series of population size structures and densities, reconstructs the impact of directional environmental changes on vital rates. The model uses integral projection models and maximum likelihood to identify the rates that best reconstructs the time series. The procedure was validated with artificial and real data. The former involved simulated species with widely different demographic behaviors. The latter used a chronosequence of populations of an endangered cactus subject to increasing anthropogenic disturbance. In our simulations, the vital rates and their change were always reconstructed accurately. Nevertheless, the model frequently produced alternative results. The use of coarse knowledge of the species' biology (whether vital rates increase or decrease with size or their plausible values) allowed the correct rates to be identified with a 90% success rate. With real data, the model correctly reconstructed the effects of disturbance on vital rates. These effects were previously known from two populations for which demographic data were available. Our procedure seems robust, as the data violated several of the model's assumptions. Thus, time series of size structures and densities contain the necessary information to reconstruct changing vital rates. However, additional biological knowledge may be required to provide reliable results. Because time series of size structures and densities are available for many species or can be rapidly generated, our model can contribute to understand populations that face highly pressing environmental problems.

Associations between fine and coarse particles and mortality in Mediterranean cities: results from the MED-PARTICLES project

BACKGROUND

Few studies have investigated the independent health effects of different size fractions of particulate matter (PM) in multiple locations, especially in Europe.

OBJECTIVES

We estimated the short-term effects of PM with aerodynamic diameter ≤ 10 μm (PM10), ≤ 2.5 μm (PM2.5), and between 2.5 and 10 μm (PM2.5-10) on all-cause, cardiovascular, and respiratory mortality in 10 European Mediterranean metropolitan areas within the MED-PARTICLES project.

METHODS

We analyzed data from each city using Poisson regression models, and combined city-specific estimates to derive overall effect estimates. We evaluated the sensitivity of our estimates to co-pollutant exposures and city-specific model choice, and investigated effect modification by age, sex, and season. We applied distributed lag and threshold models to investigate temporal patterns of associations.

RESULTS

A 10-μg/m3 increase in PM2.5 was associated with a 0.55% (95% CI: 0.27, 0.84%) increase in all-cause mortality (0-1 day cumulative lag), and a 1.91% increase (95% CI: 0.71, 3.12%) in respiratory mortality (0-5 day lag). In general, associations were stronger for cardiovascular and respiratory mortality than all-cause mortality, during warm versus cold months, and among those ≥ 75 versus < 75 years of age. Associations with PM2.5-10 were positive but not statistically significant in most analyses, whereas associations with PM10 seemed to be driven by PM2.5.

CONCLUSIONS

We found evidence of adverse effects of PM2.5 on mortality outcomes in the European Mediterranean region. Associations with PM2.5-10 were positive but smaller in magnitude. Associations were stronger for respiratory mortality when cumulative exposures were lagged over 0-5 days, and were modified by season and age.

Probabilistic measures of persistence and extinction in measles (meta)populations

Persistence and extinction are fundamental processes in ecological systems that are difficult to accurately measure due to stochasticity and incomplete observation. Moreover, these processes operate on multiple scales, from individual populations to metapopulations. Here, we examine an extensive new data set of measles case reports and associated demographics in pre-vaccine era US cities, alongside a classic England & Wales data set. We first infer the per-population quasi-continuous distribution of log incidence. We then use stochastic, spatially implicit metapopulation models to explore the frequency of rescue events and apparent extinctions. We show that, unlike critical community size, the inferred distributions account for observational processes, allowing direct comparisons between metapopulations. The inferred distributions scale with population size. We use these scalings to estimate extinction boundary probabilities. We compare these predictions with measurements in individual populations and random aggregates of populations, highlighting the importance of medium-sized populations in metapopulation persistence.

Therapeutic Assessment of Complex Trauma: A Single-Case Time-Series Study

The cumulative effect of repeated traumatic experiences in early childhood incrementally increases the risk of adjustment problems later in life. Surviving traumatic environments can lead to the development of an interrelated constellation of emotional and interpersonal symptoms termed complex posttraumatic stress disorder (CPTSD). Effective treatment of trauma begins with a multimethod psychological assessment and requires the use of several evidence-based therapeutic processes, including establishing a safe therapeutic environment, reprocessing the trauma, constructing a new narrative, and managing emotional dysregulation. Therapeutic Assessment (TA) is a semistructured, brief intervention that uses psychological testing to promote positive change. The case study of Kelly, a middle-aged woman with a history of repeated interpersonal trauma, illustrates delivery of the TA model for CPTSD. Results of this single-case time-series experiment indicate statistically significant symptom improvement as a result of participating in TA. We discuss the implications of these findings for assessing and treating trauma-related concerns, such as CPTSD.

Seasonal variation in the acute effect of particulate air pollution on mortality in the China Air Pollution and Health Effects Study (CAPES)

Epidemiological findings concerning the seasonal variation in the acute effect of particulate matter (PM) are inconsistent. We investigated the seasonality in the association between PM with an aerodynamic diameter of less than 10 μm (PM10) and daily mortality in 17 Chinese cities. We fitted the "main" time-series model after adjustment for time-varying confounders using smooth functions with natural splines. We established a "seasonal" model to obtain the season-specific effect estimates of PM10, and a "harmonic" model to show the seasonal pattern that allows PM10 effects to vary smoothly with the day in a year. At the national level, a 10 μg/m(3) increase in the two-day moving average concentrations (lag 01) of PM10 was associated with 0.45% [95% posterior interval (PI), 0.15% to 0.76%], 0.17% (95% PI, -0.09% to 0.43%), 0.55% (95% PI, 0.15% to 0.96%) and 0.25% (95%PI, -0.05% to 0.56%) increases in total mortality for winter, spring, summer and fall, respectively. For the smoothly-varying plots of seasonality, we identified a two-peak pattern in winter and summer. The observed seasonal pattern was generally insensitive to model specifications. Our analyses suggest that the acute effect of particulate air pollution could vary by seasons with the largest effect in winter and summer in China. To our knowledge, this is the first multicity study in developing countries to analyze the seasonal variations of PM-related health effects.

Using Google Dengue Trends to Estimate Climate Effects in Mexico

Objective

To evaluate the association between Dengue Fever (DF) and climate in Mexico with real-time data from Google Dengue Trends (GDT) and climate data from NASA Earth observing systems.

Introduction

The incidence of dengue fever (DF) has increased 30 fold between 1960 and 2010 [1]. The literature suggests that temperature plays a major role in the life cycle of the mosquito vector and in turn, the timing of DF outbreaks [2]. We use real-time data from GDT and real-time temperature estimates from NASA Earth observing systems to examine the relationship between dengue and climate in 17 Mexican states from 2003–2011. For the majority of states, we predict that a warming climate will increase the number of days the minimum temperature is within the risk range for dengue.

Methods

The GDT estimates are derived from internet search queries and use similar methods as those developed for Google Flu Trends [3]. To validate GDT data, we ran a correlation between GDT and dengue data from the Mexican Secretariat of Health (2003–2010). To analyze the relationship between GDT and varying lags of temperature, we constructed a time series meta-analysis. The mean, max and min of temperature were tested at lags 0 –12 weeks using data from the Modern Era Retrospective-Analysis for Research and Applications. Finally, we built a binomial model to identify the minimum 5° C temperature range associated with a 50% or higher Dengue activity threshold as predicted by GDT.

Results

The time series plot of GDT data and the Mexican Secretariat of Health data (2003– 2010) (Figure 1) produced a correlation coefficient of 0.87. The time series meta-analysis results for 17 states showed an increase in minimum temperature at lag week 8 had the greatest odds of dengue incidence, 1.12 Odds Ratio (1.09–1.16, 95% Confidence Interval). The comparison of dengue activity above 50% in each state to the minimum temperature at lag week 8 showed 14/17 states had an association with warmest 5 degrees of the minimum temperature range. The state of Sonora was the only state to show an association between dengue and the coldest 5 degrees of the minimum temperature range.

Conclusions

Overall, the incidence data from the Mexican Secretariat of Health showed a close correlation with the GDT data. The meta-analysis indicates that an increase in the minimum temperature at lag week 8 is associated with an increased dengue risk. This is consistent with the Colon-Gonzales et al. Mexico study which also found a strong association with the 8 week lag of increasing minimum temperature [4]. The results from this binomial regression show, for the majority of states, the warmest 5 degree range for the minimum temperature had the greatest association with dengue activity 8 weeks later. Inevitably, several other factors contribute to dengue risk which we are unable to include in this model [5]. IPCC climate change predictions suggest a 4° C increase in Mexico. Under such scenario, we predict an increase in the number of days the minimum temperature falls within the range associated with DF risk.

Comparison of time series models for predicting campylobacteriosis risk in New Zealand

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic.

Yearly variations of demographic indices and mortality data in Italy from 1901 to 2008 as related to the caloric intake

The aim of the present study was to evaluate, by Join Point regression method, the yearly variations in demographic indices and mortality data in Italy from 1901 to 2008, as related to the caloric intake. The relationships between mortality and caloric intake were studied by time series. The results showed that, from 1901 to 2008, the Italian population grew from 32.5 to 59.6 millions; the live births rates decreased from 31.8 to 10.1‰ (males) and from 33.3 to 9.0‰ (females); the infant mortality rates fell from 184.1 to 3.7‰ (males) and from 149.4 to 3.2‰ (females); males and females gained 35.7 and 40.6 years in life expectancy at birth, respectively. In 1901 the 61% of deaths occurred in the youngest, whereas in 2008 the elderly accounted for the 80%. In 1901, in terms of age-adjusted data, other and undefined causes overcame the specific causes of death, whose rank was: respiratory, digestive, infectious, cardiovascular, cerebrovascular, cancers, accidents, endocrine, and nervous system diseases. In 2008, undefined causes ranked 3rd (males) and 4th (females), while cancers became the leading cause of death, followed by cardiovascular, cerebrovascular, accidental, respiratory, endocrine, digestive, nervous system, and infectious diseases. The caloric intake showed a negative correlation with all-cause mortality, infant mortality, and mortality for a number of specific causes. These patterns reflect the progress in average nutritional status, lifestyle quality, socioeconomic level, and hygienic conditions.

CANONICAL CORRELATION ANALYSIS BETWEEN TIME SERIES AND STATIC OUTCOMES, WITH APPLICATION TO THE SPECTRAL ANALYSIS OF HEART RATE VARIABILITY

Although many studies collect biomedical time series signals from multiple subjects, there is a dearth of models and methods for assessing the association between frequency domain properties of time series and other study outcomes. This article introduces the random Cramér representation as a joint model for collections of time series and static outcomes where power spectra are random functions that are correlated with the outcomes. A canonical correlation analysis between cepstral coefficients and static outcomes is developed to provide a flexible yet interpretable measure of association. Estimates of the canonical correlations and weight functions are obtained from a canonical correlation analysis between the static outcomes and maximum Whittle likelihood estimates of truncated cepstral coefficients. The proposed methodology is used to analyze the association between the spectrum of heart rate variability and measures of sleep duration and fragmentation in a study of older adults who serve as the primary caregiver for their ill spouse.

Spatio-temporal dynamics of pneumonia in bighorn sheep

1. Bighorn sheep mortality related to pneumonia is a primary factor limiting population recovery across western North America, but management has been constrained by an incomplete understanding of the disease. We analysed patterns of pneumonia-caused mortality over 14 years in 16 interconnected bighorn sheep populations to gain insights into underlying disease processes. 2. We observed four age-structured classes of annual pneumonia mortality patterns: all-age, lamb-only, secondary all-age and adult-only. Although there was considerable variability within classes, overall they differed in persistence within and impact on populations. Years with pneumonia-induced mortality occurring simultaneously across age classes (i.e. all-age) appeared to be a consequence of pathogen invasion into a naïve population and resulted in immediate population declines. Subsequently, low recruitment due to frequent high mortality outbreaks in lambs, probably due to association with chronically infected ewes, posed a significant obstacle to population recovery. Secondary all-age events occurred in previously exposed populations when outbreaks in lambs were followed by lower rates of pneumonia-induced mortality in adults. Infrequent pneumonia events restricted to adults were usually of short duration with low mortality. 3. Acute pneumonia-induced mortality in adults was concentrated in fall and early winter around the breeding season when rams are more mobile and the sexes commingle. In contrast, mortality restricted to lambs peaked in summer when ewes and lambs were concentrated in nursery groups. 4. We detected weak synchrony in adult pneumonia between adjacent populations, but found no evidence for landscape-scale extrinsic variables as drivers of disease. 5. We demonstrate that there was a >60% probability of a disease event each year following pneumonia invasion into bighorn sheep populations. Healthy years also occurred periodically, and understanding the factors driving these apparent fade-out events may be the key to managing this disease. Our data and modelling indicate that pneumonia can have greater impacts on bighorn sheep populations than previously reported, and we present hypotheses about processes involved for testing in future investigations and management.

Influenza as a proportion of pneumonia mortality: United States, 1959-2009

As causes of death, influenza and pneumonia are typically analyzed together. We quantify influenza's contribution to the combined pneumonia and influenza mortality time series for the United States, 1959-2009. A key statistic is I/(P + I), the proportion of pneumonia and influenza mortality accounted for by influenza. Year-to-year, I/(P + I) is highly variable and shows long-term decline. Extreme values of I/(P + I) are associated with extreme P + I death rates and vice versa, but I/(P + I) is a weak predictor of P + I mortality overall. Prominence of influenza in the medical news is associated with high I/(P + I). Influenza and pneumonia should be analyzed as a combined cause.

Fundamentals of microbial community resistance and resilience

Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.

Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures.

Modeling the Nonlinear Time Dynamics of Multidimensional Hormonal Systems

In most hormonal systems (as well as many physiological systems more generally), the chemical signals from the brain, which drive much of the dynamics, can not be observed in humans. By the time the molecules reach peripheral blood, they have been so diluted so as to not be assayable. It is not possible to invasively (surgically) measure these agents in the brain. This creates a difficult situation in terms of assessing whether or not the dynamics may have changed due to disease or aging. Moreover, most biological feedforward and feedback interactions occur after time delays, and the time delays need to be properly estimated. We address the following two questions: (1) Is it possible to devise a combination of clinical experiments by which, via exogenous inputs, the hormonal system can be perturbed to new steady-states in such a way that information about the unobserved components can be ascertained; and, (2) Can one devise methods to estimate (possibly, time-varying) time delays between components of a multidimensional nonlinear time series, which are more robust than traditional methods? We present methods for both questions, using the Stress (ACTH-cortisol) hormonal system as a prototype, but the approach is more broadly applicable.

Fine-scale temporal variation in marine extracellular enzymes of coastal southern california

Extracellular enzymes are functional components of marine microbial communities that contribute to nutrient remineralization by catalyzing the degradation of organic substrates. Particularly in coastal environments, the magnitude of variation in enzyme activities across timescales is not well characterized. Therefore, we established the MICRO time series at Newport Pier, California, to assess enzyme activities and other ocean parameters at high temporal resolution in a coastal environment. We hypothesized that enzyme activities would vary most on daily to weekly timescales, but would also show repeatable seasonal patterns. In addition, we expected that activities would correlate with nutrient and chlorophyll concentrations, and that most enzyme activity would be bound to particles. We found that 34-48% of the variation in enzyme activity occurred at timescales <30 days. About 28-56% of the variance in seawater nutrient concentrations, chlorophyll concentrations, and ocean currents also occurred on this timescale. Only the enzyme β-glucosidase showed evidence of a repeatable seasonal pattern, with elevated activities in the spring months that correlated with spring phytoplankton blooms in the Southern California Bight. Most enzyme activities were weakly but positively correlated with nutrient concentrations (r = 0.24-0.31) and upwelling (r = 0.29-0.35). For the enzymes β-glucosidase and leucine aminopeptidase, most activity was bound to particles. However, 81.2% of alkaline phosphatase and 42.8% of N-acetyl-glucosaminidase activity was freely dissolved. These results suggest that enzyme-producing bacterial communities and nutrient dynamics in coastal environments vary substantially on short timescales (<30 days). Furthermore, the enzymes that degrade carbohydrates and proteins likely depend on microbial communities attached to particles, whereas phosphorus release may occur throughout the water column.

Variogram estimation in the presence of trend

Estimation of covariance function parameters of the error process in the presence of an unknown smooth trend is an important problem because solving it allows one to estimate the trend nonparametrically using a smoother corrected for dependence in the errors. Our work is motivated by spatial statistics but is applicable to other contexts where the dimension of the index set can exceed one. We obtain an estimator of the covariance function parameters by regressing squared differences of the response on their expectations, which equal the variogram plus an offset term induced by the trend. Existing estimators that ignore the trend produce bias in the estimates of the variogram parameters, which our procedure corrects for. Our estimator can be justified asymptotically under the increasing domain framework. Simulation studies suggest that our estimator compares favorably with those in the current literature while making less restrictive assumptions. We use our method to estimate the variogram parameters of the short-range spatial process in a U.S. precipitation data set.

Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic

BACKGROUND

Spatial outbreak detection algorithms using routinely collected healthcare data have been developed since the late 90s to identify and locate disease outbreaks. However, current well-received spatial algorithms assume only one outbreak cluster present at the same point of time which may not be valid during a pandemic when several clusters of geographic areas concurrently occur. Based on a retrospective evaluation on time-series and spatial algorithms, this paper suggests that time series analysis in detection of pandemics is still a desirable process, which may achieve more sensitive performance with better timeliness.

METHODS

In this paper, we first prove in theory that two existing spatial models, the likelihood ratio and the Bayesian spatial scan statistics, are not useful if multiple clusters occur at the same point of time in different geographic regions. Then we conduct a comparison between a spatial algorithm, the Bayesian Spatial Scan Statistic (BSS), and a time series algorithm, the wavelet anomaly detector (WAD), on the performance of detecting the increase of the over-the-counter (OTC) medicine sales during 2009 H1N1 pandemic.

RESULTS

The experiments demonstrated that the Bayesian spatial algorithm responded to the increase of thermometer sales about 3 days later than the time series algorithm.

CONCLUSION

Time-series algorithms demonstrated an advantage for early outbreak detection, especially when multiple clusters occur at the same time in different geographic regions. Given spatial-temporal algorithms for outbreak detection are widely used, this paper suggests that epidemiologists or public health officials would benefit by applying time series algorithms as a complement to spatial algorithms for public health surveillance.

Estimated acute effects of ambient ozone and nitrogen dioxide on mortality in the Pearl River Delta of southern China

BACKGROUND AND OBJECTIVES

Epidemiologic studies have attributed adverse health effects to air pollution; however, controversy remains regarding the relationship between ambient oxidants [ozone (O₃) and nitrogen dioxide (NO₂)] and mortality, especially in Asia. We conducted a four-city time-series study to investigate acute effects of O₃ and NO₂ in the Pearl River Delta (PRD) of southern China, using data from 2006 through 2008.

METHODS

We used generalized linear models with Poisson regression incorporating natural spline functions to analyze acute mortality in association with O₃ and NO₂, with PM₁₀ (particulate matter ≤ 10 μm in diameter) included as a major confounder. Effect estimates were determined for individual cities and for the four cities as a whole. We stratified the analysis according to high- and low- exposure periods for O₃.

RESULTS

We found consistent positive associations between ambient oxidants and daily mortality across the PRD cities. Overall, 10-μg/m³ increases in average O₃ and NO₂ concentrations over the previous 2 days were associated with 0.81% [95% confidence interval (CI): 0.63%, 1.00%] and 1.95% (95% CI: 1.62%, 2.29%) increases in total mortality, respectively, with stronger estimated effects for cardiovascular and respiratory mortality. After adjusting for PM₁₀, estimated effects of O₃ on total and cardiovascular mortality were stronger for exposure during high-exposure months (September through November), whereas respiratory mortality was associated with O₃ exposure during nonpeak exposure months only.

CONCLUSIONS

Our findings suggest significant acute mortality effects of O₃ and NO₂ in the PRD and strengthen the rationale for further limiting the ambient pollution levels in the area.

Regime shifts and heterogeneous trends in malaria time series from Western Kenya Highlands

Large malaria epidemics in the East African highlands during the mid and late 1990s kindled a stream of research on the role that global warming might have on malaria transmission. Most of the inferences using temporal information have been derived from a malaria incidence time series from Kericho. Here, we report a detailed analysis of 5 monthly time series, between 15 and 41 years long, from West Kenya encompassing an altitudinal gradient along Lake Victoria basin. We found decreasing, but heterogeneous, malaria trends since the late 1980s at low altitudes (<1600 m), and the early 2000s at high altitudes (>1600 m). Regime shifts were present in 3 of the series and were synchronous in the 2 time series from high altitudes. At low altitude, regime shifts were associated with a shift from increasing to decreasing malaria transmission, as well as a decrease in variability. At higher altitudes, regime shifts reflected an increase in malaria transmission variability. The heterogeneity in malaria trends probably reflects the multitude of factors that can drive malaria transmission and highlights the need for both spatially and temporally fine-grained data to make sound inferences about the impacts of climate change and control/elimination interventions on malaria transmission.

Time-frequency analyses of tide-gauge sensor data

The real world phenomena being observed by sensors are generally non-stationary in nature. The classical linear techniques for analysis and modeling natural time-series observations are inefficient and should be replaced by non-linear techniques of whose theoretical aspects and performances are varied. In this manner adopting the most appropriate technique and strategy is essential in evaluating sensors' data. In this study, two different time-series analysis approaches, namely least squares spectral analysis (LSSA) and wavelet analysis (continuous wavelet transform, cross wavelet transform and wavelet coherence algorithms as extensions of wavelet analysis), are applied to sea-level observations recorded by tide-gauge sensors, and the advantages and drawbacks of these methods are reviewed. The analyses were carried out using sea-level observations recorded at the Antalya-II and Erdek tide-gauge stations of the Turkish National Sea-Level Monitoring System. In the analyses, the useful information hidden in the noisy signals was detected, and the common features between the two sea-level time series were clarified. The tide-gauge records have data gaps in time because of issues such as instrumental shortcomings and power outages. Concerning the difficulties of the time-frequency analysis of data with voids, the sea-level observations were preprocessed, and the missing parts were predicted using the neural network method prior to the analysis. In conclusion the merits and limitations of the techniques in evaluating non-stationary observations by means of tide-gauge sensors records were documented and an analysis strategy for the sequential sensors observations was presented.

Non-random assembly of bacterioplankton communities in the subtropical north pacific ocean

The exploration of bacterial diversity in the global ocean has revealed new taxa and previously unrecognized metabolic potential; however, our understanding of what regulates this diversity is limited. Using terminal restriction fragment length polymorphism (T-RFLP) data from bacterial small-subunit ribosomal RNA genes we show that, independent of depth and time, a large fraction of bacterioplankton co-occurrence patterns are non-random in the oligotrophic North Pacific subtropical gyre (NPSG). Pair-wise correlations of all identified operational taxonomic units (OTUs) revealed a high degree of significance, with 6.6% of the pair-wise co-occurrences being negatively correlated and 20.7% of them being positive. The most abundant OTUs, putatively identified as Prochlorococcus, SAR11, and SAR116 bacteria, were among the most correlated OTUs. As expected, bacterial community composition lacked statistically significant patterns of seasonality in the mostly stratified water column except in a few depth horizons of the sunlit surface waters, with higher frequency variations in community structure apparently related to populations associated with the deep chlorophyll maximum. Communities were structured vertically into epipelagic, mesopelagic, and bathypelagic populations. Permutation-based statistical analyses of T-RFLP data and their corresponding metadata revealed a broad range of putative environmental drivers controlling bacterioplankton community composition in the NPSG, including concentrations of inorganic nutrients and phytoplankton pigments. Together, our results suggest that deterministic forces such as environmental filtering and interactions among taxa determine bacterioplankton community patterns, and consequently affect ecosystem functions in the NPSG.

Distributed Lag Linear and Non-Linear Models in R: The Package dlnm

Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.

Estimating the global public health implications of electricity and coal consumption

BACKGROUND

The growing health risks associated with greenhouse gas emissions highlight the need for new energy policies that emphasize efficiency and low-carbon energy intensity.

OBJECTIVES

We assessed the relationships among electricity use, coal consumption, and health outcomes.

METHODS

Using time-series data sets from 41 countries with varying development trajectories between 1965 and 2005, we developed an autoregressive model of life expectancy (LE) and infant mortality (IM) based on electricity consumption, coal consumption, and previous year's LE or IM. Prediction of health impacts from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) integrated air pollution emissions health impact model for coal-fired power plants was compared with the time-series model results.

RESULTS

The time-series model predicted that increased electricity consumption was associated with reduced IM for countries that started with relatively high IM (> 100/1,000 live births) and low LE (< 57 years) in 1965, whereas LE was not significantly associated with electricity consumption regardless of IM and LE in 1965. Increasing coal consumption was associated with increased IM and reduced LE after accounting for electricity consumption. These results are consistent with results based on the GAINS model and previously published estimates of disease burdens attributable to energy-related environmental factors, including indoor and outdoor air pollution and water and sanitation.

CONCLUSIONS

Increased electricity consumption in countries with IM < 100/1,000 live births does not lead to greater health benefits, whereas coal consumption has significant detrimental health impacts.

Effects of weather and climate on the dynamics of animal population time series

Weather is one of the most basic factors impacting animal populations, but the typical strength of such impacts on population dynamics is unknown. We incorporate weather and climate index data into analysis of 492 time series of mammals, birds and insects from the global population dynamics database. A conundrum is that a multitude of weather data may a priori be considered potentially important and hence present a risk of statistical over-fitting. We find that model selection or averaging alone could spuriously indicate that weather provides strong improvements to short-term population prediction accuracy. However, a block randomization test reveals that most improvements result from over-fitting. Weather and climate variables do, in general, improve predictions, but improvements were barely detectable despite the large number of datasets considered. Climate indices such as North Atlantic Oscillation are not better predictors of population change than local weather variables. Insect time series are typically less predictable than bird or mammal time series, although all taxonomic classes display low predictability. Our results are in line with the view that population dynamics is often too complex to allow resolving mechanisms from time series, but we argue that time series analysis can still be useful for estimating net environmental effects.

Distributed lag analyses of daily hospital admissions and source-apportioned fine particle air pollution

BACKGROUND

Past time-series studies of the health effects of fine particulate matter [aerodynamic diameter ≤ 2.5 µm (PM2.5)] have used chemically nonspecific PM2.5 mass. However, PM2.5 is known to vary in chemical composition with source, and health impacts may vary accordingly.

OBJECTIVE

We tested the association between source-specific daily PM2.5 mass and hospital admissions in a time-series investigation that considered both single-lag and distributed-lag models.

METHODS

Daily PM2.5 speciation measurements collected in midtown Manhattan were analyzed via positive matrix factorization source apportionment. Daily and distributed-lag generalized linear models of Medicare respiratory and cardiovascular hospital admissions during 2001-2002 considered PM2.5 mass and PM2.5 from five sources: transported sulfate, residual oil, traffic, steel metal works, and soil.

RESULTS

Source-related PM2.5 (specifically steel and traffic) was significantly associated with hospital admissions but not with total PM2.5 mass. Steel metal works-related PM2.5 was associated with respiratory admissions for multiple-lag days, especially during the cleanup efforts at the World Trade Center. Traffic-related PM2.5 was consistently associated with same-day cardiovascular admissions across disease-specific subcategories. PM2.5 constituents associated with each source (e.g., elemental carbon with traffic) were likewise associated with admissions in a consistent manner. Mean effects of distributed-lag models were significantly greater than were maximum single-day effect models for both steel- and traffic-related PM2.5.

CONCLUSIONS

Past analyses that have considered only PM2.5 mass or only maximum single-day lag effects have likely underestimated PM2.5 health effects by not considering source-specific and distributed-lag effects. Differing lag structures and disease specificity observed for steel-related versus traffic-related PM2.5 raise the possibility of distinct mechanistic pathways of health effects for particles of differing chemical composition.

Interactions between rate processes with different timescales explain counterintuitive foraging patterns of arctic wintering eiders

To maximize fitness, animals must respond to a variety of processes that operate at different rates or timescales. Appropriate decisions could therefore involve complex interactions among these processes. For example, eiders wintering in the arctic sea ice must consider locomotion and physiology of diving for benthic invertebrates, digestive processing rate and a nonlinear decrease in profitability of diving as currents increase over the tidal cycle. Using a multi-scale dynamic modelling approach and continuous field observations of individuals, we demonstrate that the strategy that maximizes long-term energy gain involves resting during the most profitable foraging period (slack currents). These counterintuitive foraging patterns are an adaptive trade-off between multiple overlapping rate processes and cannot be explained by classical rate-maximizing optimization theory, which only considers a single timescale and predicts a constant rate of foraging. By reducing foraging and instead digesting during slack currents, eiders structure their activity in order to maximize long-term energetic gain over an entire tide cycle. This study reveals how counterintuitive patterns and a complex functional response can result from a simple trade-off among several overlapping rate processes, emphasizing the necessity of a multi-scale approach for understanding adaptive routines in the wild and evaluating mechanisms in ecological time series.

Daily visibility and mortality: assessment of health benefits from improved visibility in Hong Kong

Visibility in Hong Kong has deteriorated significantly over 40 years with visibility below 8km in the absence of fog, mist, or precipitation, increasing from 6.6 days in 1968 to 54.1 days in 2007. We assessed the short-term mortality effects of daily loss of visibility. During 1996-2006, we obtained mortality data for non-accidental and cardiorespiratory causes, visibility recorded as visual range in kilometers, temperature, and relative humidity from an urban observatory, and concentrations of four criteria pollutants. A generalized additive Poisson regression model with penalized cubic regression splines was fitted to control for time variant covariates. For non-accidental mortality, an interquartile range (IQR) of 6.5km decrease in visibility at lag0-1 days was associated with an excess risk (ER%) [95% CI] of 1.13 [0.49, 1.76] for all ages and 1.37 [0.65, 2.09] for ages 65 years and over; for cardiovascular mortality of 1.31 [0.13, 2.49] for all ages, and 1.72 [0.44, 3.00] for ages 65 years and over; and for respiratory mortality of 1.92 [0.49, 3.35] for all ages and 1.76 [0.28, 3.25] for ages 65 years and over. The estimated ER% for daily mortality derived from both visibility and air pollutant data were comparable in terms of magnitude, lag pattern, and exposure-response relationships especially when using particulate matter with aerodynamic diameter < or = 10 microm to predict the mortality associated with visibility. Visibility provides a useful proxy for the assessment of environmental health risks from ambient air pollutants and a valid approach for the assessment of the public health impacts of air pollution and the benefits of air quality improvement measures in developing countries where pollutant monitoring data are scarce.

Nonparametric Transfer Function Models

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between 'input' and 'output' time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modeling the correlation in the noise, the transfer function can be estimated more efficiently. The parsimonious ARMA structure improves the estimation efficiency in finite samples. The asymptotic properties of the estimators are investigated. The finite-sample properties are illustrated through simulations and one empirical example.

Modeling lateral control in driving studies

In driving studies based on simulators and instrumented vehicles, specific models are needed to capture key aspects of driving data such as lateral control. We propose a model that uses weighted polynomial projections to predict each data point from the previous three time points, and accommodates the attempts of the drivers to re-center the vehicle before crossing the borders of the traffic lane. Our model also allows the possibility that average position within the lane may vary from driver to driver. We demonstrate how to fit the model using standard statistical procedures available in software packages such as SAS. We used a fixed-base driving simulator to obtain data from 67 drivers with Alzheimer's disease and 128 elderly drivers without dementia. Using these data, we estimated the subject-specific parameters of our model, and we compared the two groups with respect to these parameters. We found that the parameters based on our model were able to distinguish between the groups in an interpretable manner. Hence, this model may be a useful tool to define outcome measures for observational and interventional driving studies.

Computational methods for analyzing dynamic regulatory networks

Regulatory and other networks in the cell change in a highly dynamic way over time and in response to internal and external stimuli. While several different types of high-throughput experimental procedures are available to study systems in the cell, most only measure static properties of such networks. Information derived from sequence data is inherently static, and most interaction data sets are measured in a static way as well. In this chapter we discuss one of the few abundant sources for temporal information, time series expression data. We provide an overview of the methods suggested for clustering this type of data to identify functionally related genes. We also discuss methods for inferring causality and interactions using lagged correlations and regression analysis. Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks. We point to software tools implementing the methods discussed in this chapter. As more temporal measurements become available, the importance of analyzing such data and of combining it with other types of data will greatly increase.

Farmworker children's residential non-dietary exposure estimates from micro-level activity time series

Farmworkers' children may have increased pesticide exposure through dermal absorption and non-dietary ingestion, routes that are difficult to measure and model. The Cumulative Aggregate Simulation of Exposure (CASE) model, integrates the complexity of human behavior and variability of exposure processes by combining micro-level activity time series (MLATS) and mechanistic exposure equations. CASE was used to estimate residential non-dietary organophosphate pesticide exposure (i.e., inhalation, dermal, and non-dietary ingestion) to California farmworker children and evaluate the micro-activity approach. MLATS collected from children and distributions developed from pesticide measurements in farmworkers' residences served as inputs. While estimated diazinon exposure was greater for inhalation, chlorpyrifos exposure was greater for the other routes. Greater variability existed between children (sigma(B)(2)=0.22-0.39) than within each child's simulations (sigma(W)(2)=0.01-0.02) for dermal and non-dietary ingestion. Dermal exposure simulations were not significantly different than measured values from dosimeters worn by the children. Non-dietary ingestion exposure estimates were comparable to duplicate diet measurements, indicating this route may contribute substantially to aggregate exposure. The results suggest the importance of the micro-activity approach for estimating non-dietary exposure. Other methods may underestimate exposure via these routes. Model simulations can be used to identify at-risk children and target intervention strategies.

Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change

The United States produces 41% of the world's corn and 38% of the world's soybeans. These crops comprise two of the four largest sources of caloric energy produced and are thus critical for world food supply. We pair a panel of county-level yields for these two crops, plus cotton (a warmer-weather crop), with a new fine-scale weather dataset that incorporates the whole distribution of temperatures within each day and across all days in the growing season. We find that yields increase with temperature up to 29 degrees C for corn, 30 degrees C for soybeans, and 32 degrees C for cotton but that temperatures above these thresholds are very harmful. The slope of the decline above the optimum is significantly steeper than the incline below it. The same nonlinear and asymmetric relationship is found when we isolate either time-series or cross-sectional variations in temperatures and yields. This suggests limited historical adaptation of seed varieties or management practices to warmer temperatures because the cross-section includes farmers' adaptations to warmer climates and the time-series does not. Holding current growing regions fixed, area-weighted average yields are predicted to decrease by 30-46% before the end of the century under the slowest (B1) warming scenario and decrease by 63-82% under the most rapid warming scenario (A1FI) under the Hadley III model.

The effect of fine and coarse particulate air pollution on mortality: a national analysis

BACKGROUND

Although many studies have examined the effects of air pollution on mortality, data limitations have resulted in fewer studies of both particulate matter with an aerodynamic diameter of 2.5 and < 10 microm; PM coarse). We conducted a national, multicity time-series study of the acute effect of PM(2.5) and PM coarse on the increased risk of death for all causes, cardiovascular disease (CVD), myocardial infarction (MI), stroke, and respiratory mortality for the years 1999-2005.

METHOD

We applied a city- and season-specific Poisson regression in 112 U.S. cities to examine the association of mean (day of death and previous day) PM(2.5) and PM coarse with daily deaths. We combined the city-specific estimates using a random effects approach, in total, by season and by region.

RESULTS

We found a 0.98% increase [95% confidence interval (CI), 0.75-1.22] in total mortality, a 0.85% increase (95% CI, 0.46-1.24) in CVD, a 1.18% increase (95% CI, 0.48-1.89) in MI, a 1.78% increase (95% CI, 0.96-2.62) in stroke, and a 1.68% increase (95% CI, 1.04-2.33) in respiratory deaths for a 10-microg/m(3) increase in 2-day averaged PM(2.5). The effects were higher in spring. For PM coarse, we found significant but smaller increases for all causes analyzed.

CONCLUSIONS

We conclude that our analysis showed an increased risk of mortality for all and specific causes associated with PM(2.5), and the risks are higher than what was previously observed for PM(10). In addition, coarse particles are also associated with more deaths.

ADAPTIVE DATA ANALYSIS OF COMPLEX FLUCTUATIONS IN PHYSIOLOGIC TIME SERIES

We introduce a generic framework of dynamical complexity to understand and quantify fluctuations of physiologic time series. In particular, we discuss the importance of applying adaptive data analysis techniques, such as the empirical mode decomposition algorithm, to address the challenges of nonlinearity and nonstationarity that are typically exhibited in biological fluctuations.

Analysis, modeling, and simulation of the accuracy of continuous glucose sensors

BACKGROUND

Continuous glucose monitors (CGMs) collect a detailed time series of consecutive observations of the underlying process of glucose fluctuations. To some extent, however, the high temporal resolution of the data is accompanied by increased probability of error in any single data point. Due to both physiological and technical reasons, the structure of these errors is complex and their analysis is not straightforward. In this article, we describe some of the methods needed to obtain a description of the sensor error that is detailed enough for simulation.

METHODS

Data were provided by Abbott Diabetes Care and included two data sets collected by the FreeStyle Navigator(™) CGM: The first set consisted of 1032 time series of glucose readings from 136 patients with type 1 diabetes and parallel time series of reference blood glucose (BG) collected via self-monitoring at irregular intervals. The average duration of a time series was 5 days; the total number of sensor-reference data pairs was approximately 20,600. The second data set consisted of 56 time series of glucose readings from 28 patients with type 1 diabetes and a parallel time series of reference BG measured via the YSI 2300 Stat Plus(™) analyzer every 15 minutes. The average duration of a time series was 2 days; the total number of sensor-reference data pairs was approximately 7000.

RESULTS

THREE SETS OF RESULTS ARE DISCUSSED: analysis of sensor errors with respect to the BG rate of change, mathematical modeling of sensor error patterns and distribution, and computer simulation of sensor errors: SENSOR ERRORS DEPEND NONLINEARLY ON THE BG RATE OF CHANGE: Errors tend to be positive (high readings) when the BG rate of change is negative and negative (low readings) when the BG rate of change is positive, which is indicative of an underlying time delay. In addition, the sensor noise is non-white (non-Gaussian) and the consecutive sensor errors are highly interdependent.Thus, the modeling of sensor errors is based on a diffusion model of blood-to-interstitial glucose transport, which accounts for the time delay, and a time-series approach, which includes autoregressive moving average (ARMA) noise to account for the interdependence of consecutive sensor errors.Based on modeling, we have developed a computer simulator of sensor errors that includes both generic and sensor-specific error components. A χ(2) test showed that no significant difference exists between the observed and the simulated distribution of sensor errors and the distribution of errors of the FreeStyle Navigator (p > .46).

CONCLUSIONS

CGM accuracy was modeled via diffusion and additive ARMA noise, which allowed for designing a computer simulator of sensor errors. The simulator, a component of a larger simulation platform approved by the Food and Drug Administration in January 2008 for pre-clinical testing of closed-loop strategies, has been successfully applied to in silico testing of closed-loop control algorithms, resulting in an investigational device exemption for closed-loop trials at the University of Virginia.

The Public Health and Air Pollution in Asia (PAPA) Project: estimating the mortality effects of particulate matter in Bangkok, Thailand

BACKGROUND

Air pollution data in Bangkok, Thailand, indicate that levels of particulate matter with aerodynamic diameter < or = 10 microm (PM(10)) are significantly higher than in most cities in North America and Western Europe, where the health effects of PM(10) are well documented. However, the pollution mix, seasonality, and demographics are different from those in developed Western countries. It is important, therefore, to determine whether the large metropolitan area of Bangkok is subject to similar effects of PM(10).

OBJECTIVES

This study was designed to investigate the mortality risk from air pollution in Bangkok, Thailand.

METHODS

The study period extended from 1999 to 2003, for which the Ministry of Public Health provided the mortality data. Measures of air pollution were derived from air monitoring stations, and information on temperature and relative humidity was obtained from the weather station in central Bangkok. The statistical analysis followed the common protocol for the multicity PAPA (Public Health and Air Pollution Project in Asia) project in using a natural cubic spline model with smooths of time and weather.

RESULTS

The excess risk for non-accidental mortality was 1.3% [95% confidence interval (CI), 0.8-1.7] per 10 microg/m(3) of PM(10), with higher excess risks for cardiovascular and above age 65 mortality of 1.9% (95% CI, 0.8-3.0) and 1.5% (95% CI, 0.9-2.1), respectively. In addition, the effects from PM(10) appear to be consistent in multipollutant models.

CONCLUSIONS

The results suggest strong associations between several different mortality outcomes and PM(10). In many cases, the effect estimates were higher than those typically reported in Western industrialized nations.

Sixty years of environmental change in the world's largest freshwater lake – Lake Baikal, Siberia

High-resolution data collected over the past 60 years by a single family of Siberian scientists on Lake Baikal reveal significant warming of surface waters and long-term changes in the basal food web of the world's largest, most ancient lake. Attaining depths over 1.6 km, Lake Baikal is the deepest and most voluminous of the world's great lakes. Increases in average water temperature (1.21 °C since 1946), chlorophyll a (300% since 1979), and an influential group of zooplankton grazers (335% increase in cladocerans since 1946) may have important implications for nutrient cycling and food web dynamics. Results from multivariate autoregressive (MAR) modeling suggest that cladocerans increased strongly in response to temperature but not to algal biomass, and cladocerans depressed some algal resources without observable fertilization effects. Changes in Lake Baikal are particularly significant as an integrated signal of long-term regional warming, because this lake is expected to be among those most resistant to climate change due to its tremendous volume. These findings highlight the importance of accessible, long-term monitoring data for understanding ecosystem response to large-scale stressors such as climate change.

Temporal variability of groundwater chemistry in shallow and deep aquifers of Araihazar, Bangladesh

Samples were collected every 2-4 weeks from a set of 37 monitoring wells over a period of 2-3 years in Araihazar, Bangladesh, to evaluate the temporal variability of groundwater composition for As and other constituents. The monitoring wells are grouped in 6 nests and span the 5-91 m depth range. Concentrations of As, Ca, Fe, K, Mg, Mn, Na, P, and S were measured by high-resolution ICPMS with a precision of 5% or better; concentrations of Cl were measured by ion chromatography. In shallow wells <30 m deep, As and P concentrations generally varied by <30%, whereas concentrations of the major ions (Na, K, Mg, Ca and Cl) and the redox-sensitive elements (Fe, Mn, and S) varied over time by up to +/-90%. In wells tapping the deeper aquifers >30 m often below clay layers concentrations of groundwater As were much lower and varied by <10%. The concentrations of major cations also varied by <10% in these deep aquifers. In contrast, the concentration of redox-sensitive constituents Fe, S, and Mn in deep aquifers varied by up to 97% over time. Thus, strong decoupling between variations in As and Fe concentrations is evident in groundwaters from shallow and deep aquifers. Comparison of the time series data with groundwater ages determined by (3)H/(3)He and (14)C dating shows that large seasonal or inter-annual variations in major cation and chloride concentrations are restricted to shallow aquifers and groundwater recharged <5 years ago. There is no corresponding change in As concentrations despite having significant variations of redox sensitive constituents in these very young waters. This is attributed to chemical buffering due to rapid equilibrium between solute and solid As. At two sites where the As content of groundwater in existing shallow wells averages 102 microg/L (range: <5 to 648 microg/L; n=118) and 272 microg/L (range: 10 to 485 microg/L; n=65), respectively, a systematic long-term decline in As concentrations lends support to the notion that flushing may slowly deplete an aquifer of As. Shallow aquifer water with >5 years (3)H/(3)He age show a constant As:P molar ratio of 9.6 over time, suggesting common mechanisms of mobilization.

The impact of secondary particles on the association between ambient ozone and mortality

BACKGROUND

Although several previous studies have found a positive association between ambient ozone and mortality, the observed effect may be confounded by other secondary pollutants that are produced concurrently with ozone.

OBJECTIVES

We addressed the question of whether the ozone-mortality relationship is entirely a reflection of the adverse effect of ozone, or whether it is, at least in part, an effect of other secondary pollutants.

METHODS

Separate time-series models were fit to 3-6 years of data between 2000 and 2005 from 18 U.S. communities. The association between nonaccidental mortality was examined with ozone alone and with ozone after adjustment for fine particle mass, sulfate, organic carbon, or nitrate concentrations. The effect estimates from each of these models were pooled using a random-effects meta-analysis to obtain an across-community average.

RESULTS

We found a 0.89% [95% confidence interval (CI), 0.45-1.33%] increase in nonaccidental mortality with a 10-ppb increase in same-day 24-hr summertime ozone across the 18 communities. After adjustment for PM(2.5) (particulate matter with aerodynamic diameter <or= 2.5 microm) mass or nitrate, this estimate decreased slightly; but when adjusted for particle sulfate, the estimate was substantially reduced to 0.58% (95% CI, -0.33 to 1.49%).

CONCLUSIONS

Our results demonstrate that the association between ozone and mortality is confounded by particle sulfate, suggesting that some secondary particle pollutants could be responsible for part of the observed ozone effect.

Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology

Therapeutic advances in type 1 diabetes (T1DM) are currently focused on developing a closed-loop control system using a continuous glucose monitor (CGM), subcutaneous insulin delivery, and a control algorithm. Because a CGM assesses blood glucose indirectly (and therefore often inaccurately), it limits the effectiveness of the controller. In order to improve the quality of CGM data, a series of analyses are suggested. These analyses evaluate and compensate for CGM errors, assess risks associated with glucose variability, predict glucose fluctuation, and forecast hypo- and hyperglycemia. These analyses are illustrated with data collected using the MiniMed CGMS® (Medtronic, Northridge, CA) and Freestyle Navigator(™) (Abbott Diabetes Care, Alameda, CA). It is important to remember that traditional statistics do not work with CGM data because consecutive CGM readings are highly interdependent.

Tobacco, alcohol, and marijuana use among first-year U.S. college students: a time series analysis

The present study sought to evaluate the day-to-day patterns of tobacco, alcohol, and marijuana use among first-year college students in the United States. Using 210 days of weekly time-line follow-back diary data collected in 2002 to 2003, the authors examined within-person patterns of use. The sample was 48% female and 90% Caucasian. Sixty-eight percent of the participants were permanent residents of Indiana. Univariate time series analysis was employed to evaluate behavioral trends for each substance across the academic year and to determine the predictive value of day-to-day substance use. Some of the most common trends included higher levels of substance use at the beginning or end of the academic year. Use on any given day could be predicted best from the amount of corresponding substance use 1 day prior.

CONCLUSIONS

Although universal intervention might best be focused in the earliest weeks on campus and at the end of the year when substance use is at its highest, the diversity of substance use trajectories suggests the need for more targeted approaches to intervention. Study limitations are noted.

Scaling analysis of paces of fetal breathing, gross-body and extremity movements

Using detrended fluctuation analysis (DFA), we studied the scaling properties of the time instances (occurrence) of the fetal breathing, gross-body, and extremity movements scored on a second by second basis from the recorded ultrasound measurements of 49 fetuses. The DFA exponent α of all the three movements of the fetuses varied between 0.63 and 1.1. We found an increase in α obtained for the movement due to breathing as a function of the gestational age while this trend was not observed for gross-body and extremity movements. This trend was argued as the indication of the maturation of lung and functional development of respiratory aspect of the fetal central nervous system. This result may be useful in discriminating normal fetuses from high-risk fetuses.

CHRONOMICS AND GENETICS

The mapping of time structures, chronomes, constitutes an endeavor spawned by chronobiology: chronomics. This cartography in time shows signatures on the surface of the earth, cycles, also accumulating in life on the earth's surface. We append a glossary of these and other cycles, the names being coined in the light of approximate cycle length. These findings are transdisciplinary, in view of their broad representation and critical importance in the biosphere. Suggestions of mechanisms are derived from an analytical statistical documentation of characteristics with superposed epochs and superposed cycles and other "remove-and-replace" approaches. These approaches use the spontaneously changing presence or absence of an environmental, cyclic or other factor for the study of any corresponding changes in the biosphere. We illustrate the indispensability of the mapping of rhythm characteristics in broader structures, chronomes, along several or all available different time scales. We present results from a cooperative cartography of about 10, about 20, and about 50-year rhythms in the context of a broad endeavor concerned with the Biosphere and the Cosmos, the BIOCOS project. The participants in this project are our co-authors worldwide, beyond Brno and Minneapolis; the studies of human blood pressure and heart rate around the clock and along the week may provide the evidence for those influences that Mendel sought in meteorology and climatology.

Time-dependent spectral analysis of epidemiological time-series with wavelets

In the current context of global infectious disease risks, a better understanding of the dynamics of major epidemics is urgently needed. Time-series analysis has appeared as an interesting approach to explore the dynamics of numerous diseases. Classical time-series methods can only be used for stationary time-series (in which the statistical properties do not vary with time). However, epidemiological time-series are typically noisy, complex and strongly non-stationary. Given this specific nature, wavelet analysis appears particularly attractive because it is well suited to the analysis of non-stationary signals. Here, we review the basic properties of the wavelet approach as an appropriate and elegant method for time-series analysis in epidemiological studies. The wavelet decomposition offers several advantages that are discussed in this paper based on epidemiological examples. In particular, the wavelet approach permits analysis of transient relationships between two signals and is especially suitable for gradual change in force by exogenous variables.

Using supervised principal components analysis to assess multiple pollutant effects

BACKGROUND

Many investigations of the adverse health effects of multiple air pollutants analyze the time series involved by simultaneously entering the multiple pollutants into a Poisson log-linear model. This method can yield unstable parameter estimates when the pollutants involved suffer high intercorrelation; therefore, traditional approaches to dealing with multicollinearity, such as principal component analysis (PCA), have been promoted in this context.

OBJECTIVES

A characteristic of PCA is that its construction does not consider the relationship between the covariates and the adverse health outcomes. A refined version of PCA, supervised principal components analysis (SPCA), is proposed that specifically addresses this issue.

METHODS

Models controlling for longterm trends and weather effects were used in conjunction with each SPCA and PCA to estimate the association between multiple air pollutants and mortality for U.S. cities. The methods were compared further via a simulation study.

RESULTS

Simulation studies demonstrated that SPCA, unlike PCA, was successful in identifying the correct subset of multiple pollutants associated with mortality. Because of this property, SPCA and PCA returned different estimates for the relationship between air pollution and mortality.

CONCLUSIONS

Although a number of methods for assessing the effects of multiple pollutants have been proposed, such methods can falter in the presence of high correlation among pollutants. Both PCA and SPCA address this issue. By allowing the exclusion of pollutants that are not associated with the adverse health outcomes from the mixture of pollutants selected, SPCA offers a critical improvement over PCA.

The impact of sugar cane-burning emissions on the respiratory system of children and the elderly

We analyzed the influence of emissions from burning sugar cane on the respiratory system during almost 1 year in the city of Piracicaba in southeast Brazil. From April 1997 through March 1998, samples of inhalable particles were collected, separated into fine and coarse particulate mode, and analyzed for black carbon and tracer elements. At the same time, we examined daily records of children (<13 years of age) and elderly people (>64 years of age) admitted to the hospital because of respiratory diseases. Generalized linear models were adopted with natural cubic splines to control for season and linear terms to control for weather. Analyses were carried out for the entire period, as well as for burning and nonburning periods. Additional models were built using three factors obtained from factor analysis instead of particles or tracer elements. Increases of 10.2 microg/m3 in particles<or=2.5 microm/m3 aerodynamic diameter (PM2.5) and 42.9 microg/m3 in PM10 were associated with increases of 21.4% [95% confidence interval (CI), 4.3-38.5] and 31.03% (95% CI, 1.25-60.21) in child and elderly respiratory hospital admissions, respectively. When we compared periods, the effects during the burning period were much higher than the effects during nonburning period. Elements generated from sugar cane burning (factor 1) were those most associated with both child and elderly respiratory admissions. Our results show the adverse impact of sugar cane burning emissions on the health of the population, reinforcing the need for public efforts to reduce and eventually eliminate this source of air pollution.

Temperature and summer mortality: geographical and temporal variations in four Italian cities

STUDY OBJECTIVE

To investigate geographical and temporal variations in the temperature-mortality relation.

DESIGN

The relation between mortality and maximum apparent temperature (Tappmax) in 2003, 2004, and a previous reference period was explored by using segmented regression and generalised additive models.

SETTING

Four Italian cities (Bologna, Milano, Roma, and Torino), included in a national network of prevention programmes and heat health watch warning systems (HHWWS) were considered.

PARTICIPANTS

Daily mortality counts of the resident population dying in each city during summer (June to September).

MAIN RESULTS

The impact of Tappmax on mortality differed between cities and varied in the three periods analysed. The geographical heterogeneity of the J shaped relation was seen in the reference period with Tappmax thresholds ranging from 28 degrees C in Torino to 32 degrees C in Milano and Roma. In all cities, the percentage variation in mortality was greatest in 2003. In Torino and Roma a significant increase was seen also at lower Tappmax values that are usually not associated to an increase in mortality (26-28 degrees C). In summer 2004 the exposure levels were similar to the reference period; only in Torino the effect of Tappmax on mortality remained relevant even if reduced compared with 2003, while in Bologna no statistically significant effect was seen for any temperature range.

CONCLUSIONS

The observed heterogeneous reduction in the impact of temperature on mortality from 2003 to 2004 may be partly explained by the lower levels of exposure. Changes in the ability of individuals and communities to adjust to high temperatures as a consequence of the implementation of public health interventions, based on HHWWS, characterised by a diverse effectiveness, may also have played an important part.

Applying a moving total mortality count to the cities in the NMMAPS database to estimate the mortality effects of particulate matter air pollution

OBJECTIVES

To apply a new method for estimating the association between daily ambient particulate matter air pollution (PM) and daily mortality to data from over 100 United States cities contained in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) database and to see whether the results from the 90 cities NMMAPS analysis are robust to this different modelling approach. This new method has recently been shown to provide improved estimates for the association between PM and daily mortality when every-day PM data are unavailable. It avoids the need for selecting a lag of PM at which the mortality effects of PM are to be investigated.

METHODS

With the aid of analytical methods and databases developed for NMMAPS, Poisson log linear models controlling for long term trends and weather effects were used to estimate the association between PM and mortality for cities in the NMMAPS database using the new method. A two stage Bayesian hierarchical model was then used to combine city specific estimates to form a national average PM mortality effect estimate.

RESULTS

A 10 microg/m3 increase in PM was associated with a 0.12% increment in total mortality and a 0.17% increment in cardiovascular and respiratory mortality. These results are consistent with those found in the NMMAPS analysis.

CONCLUSIONS

There is a statistically significant association between short term changes in PM and mortality on average for the cities contained in the NMMAPS database. These findings are further evidence that this widespread pollutant adversely affects public health.