Wendy Meiring

Biological communities in San Francisco Bay track large-scale climate forcing over the North Pacific.

Long-term observations show that fish and plankton populations in the ocean fluctuate in synchrony with large-scale climate patterns, but similar evidence is lacking for estuaries because of shorter observational records. Marine fish and invertebrates have been sampled in San Francisco Bay since 1980 and exhibit large, unexplained population changes including record-high abundances of common species after 1999. Our analysis shows that populations of demersal fish, crabs and shrimp covary with the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO), both of which reversed signs in 1999. A time series model forced by the atmospheric driver of NPGO accounts for two-thirds of the variability in the first principal component of species abundances, and generalized linear models forced by PDO and NPGO account for most of the annual variability of individual species. We infer that synchronous shifts in climate patterns and community variability in San Francisco Bay are related to changes in oceanic wind forcing that modify coastal currents, upwelling intensity, surface temperature, and their influence on recruitment of marine species that utilize estuaries as nursery habitat. Ecological forecasts of estuarine responses to climate change must therefore consider how altered patterns of atmospheric forcing across ocean basins influence coastal oceanography as well as watershed hydrology.

Space-time estimation of grid-cell hourly ozone levels for assessment of a deterministic model

We present an approach to estimate hourly grid-cell surface ozone concentrations based on observations from point monitoring sites in space, for comparison with grid-based results from the SARMAP photochemical air-quality model for a region of northern California. Statistical estimation is carried out on a transformed (square root) scale, followed by back-transforming to the original scale of ozone in parts per billion, adjusting for bias and variance. We estimate a spatially-varying diurnal mean structure and a non-separable space-time correlation structure on the transformed scale. Temporal pre-whitening is followed by modelling of a spatially non-stationary, diurnally-varying spatial correlation structure using a spatial deformation approach. Comparisons of SARMAP model results with the estimated grid-cell ozone levels are presented.

Biases of April 1 snow water equivalent records in the Sierra Nevada and their associations with large‐scale climate indices

Do the April 1 snow course measurements underestimate the snow accumulation peak in the Sierra Nevada, thus possibly introducing biases in studies of trends and interannual variability that use the April 1 data? Although their period of record is many decades shorter, measurements of snow water equivalent (SWE) from automated snow pillows provide daily records throughout each year, enabling us to examine the timing and magnitude of the maximum SWE accumulation. Using generalized additive mixed models, on average, we find that the peak occurs earlier than April 1 at the lowest elevations and later than April 1 at the highest elevations and that the degree to which April 1 SWE under-measures maximum SWE is greatest at the lowest and highest elevations. In addition, our results show that the biases are associated with cold and warm phase interactions of certain climate indices.

Oscillations and time trends in stratospheric ozone levels : A functional data analysis approach

A functional data analysis approach is presented to study altitude-dependent patterns of ozone variation in data from a sequence of ozonesonde flights. Ozonesondes are balloon-based instruments that measure ozone as the balloon ascends through the troposphere and lower stratosphere. This article concentrates on variation in the altitude range of 15.5–30.5 km, in January–July in 1967–1998. Ozonesonde flights originating at a mid-latitude site, Hohenpeissenberg in Germany, are studied. Estimates are obtained of altitude-dependent nonlinear time trends in ozone partial pressures, together with ozone variation associated with the quasi-biennial oscillation (QBO), an atmospheric process thought to influence global ozone transport. Both methodological and scientific contributions are made. The data analysis approach combines dimension-reduction basis function approximations with low-dimensional spline-based models on the basis function coefficients. This provides an efficient and flexible approach for studying complex time/altitude variation in the ozone partial pressure profiles, including nonlinear time trends. In contrast, the standard approach uses multiple linear regression models to estimate linear or piecewise-linear ozone time trends separately for each altitude level. Scientific results include identifying clear bimodalities (in altitude) in the estimated QBO components of variation in certain months. These may relate to planetary wave transport of ozone from the tropics to mid-latitudes. Additional empirical evidence of an 11-year cycle in ozone levels also is provided, possibly linked with a solar cycle. However, ozone peaks at Hohenpeissenberg do not always coincide with the timing of the maxima of the 11-year solar cycle. This may indicate a transport-related lag in ozone maxima at some altitudes. The estimated QBO features are robust to the presence or absence of the data quality “correction factors” commonly used in ozonesonde studies. However, the nonlinear time trend components show greater sensitivity to these.

Nonparametric Regression with Basis Selection from Multiple Libraries

New nonparametric regression procedures called BSML (Basis Selection from Multiple Libraries) are proposed in this article for estimating a complex function by a linear combination of basis functions adaptively selected from multiple libraries. Different classes of basis functions are chosen to model various features of the function, for example, truncated constants can model change points in the function, while polynomial spline representers may be used to model smooth components. The generalized cross-validation (GCV) and covariance inflation criteria are used to balance goodness of fit and model complexity where the model complexity is estimated adaptively by either the generalized degrees of freedom or covariance penalty. The cross-validation (CV) method is also considered for model selection. Spatially adaptive regression and model selection in multivariate nonparametric regression will be used to illustrate the flexibility and efficiency of the BSML procedures. Extensive simulations show that the BSML procedures are more adaptive than some well-known existing nonparametric regression methods. Analyses of real datasets are used to illustrate the BSML procedures. This article has supplementary materials online.

On the relative efficiency of a monotone parameter curve estimator in a functional nonlinear model

Functional regression models that relate functional covariates to a scalar response are becoming more common due to the availability of functional data and computational advances. We introduce a functional nonlinear model with a scalar response where the true parameter curve is monotone. Using the Newton-Raphson method within a backfitting procedure, we discuss a penalized least squares criterion for fitting the functional nonlinear model with the smoothing parameter selected using generalized cross validation. Connections between a nonlinear mixed effects model and our functional nonlinear model are discussed, thereby providing an additional model fitting procedure using restricted maximum likelihood for smoothing parameter selection. Simulated relative efficiency gains provided by a monotone parameter curve estimator relative to an unconstrained parameter curve estimator are presented. In addition, we provide an application of our model with data from ozonesonde measurements of stratospheric ozone in which the measurements are biased as a function of altitude.

A candidate neuromechanical biomarker and dosimeter for monitoring cumulative head impact trauma

A candidate neuromechanical biomarker is demonstrated for monitoring cumulative head impact trauma. This biomarker demonstrates a pronounced threshold behavior, corresponding to the onset of physiological changes observed using high resolution brain imaging, and may enable a universally deployable wearable dosimeter.

Quantifying rapid changes in cardiovascular state with a moving ensemble average

MEAP, the moving ensemble analysis pipeline, is a new open-source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed-window ensemble averaging. We describe MEAPs interface and features for signal processing, artifact correction, and cardiovascular-based fMRI analysis. We demonstrate the accuracy of MEAPs novel B point detection algorithm on a large collection of hand-labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast-acting event-related changes in cardiovascular state.

Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI

ABSTRACT White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion‐weighted MRI (dMRI). These long‐range projections are typically characterized in a two‐step process: dMRI signal is used to estimate the orientation of axon segments within each voxel, then these local orientations are linked together to estimate the spatial extent of putative white matter bundles. Tractography, the process of tracing bundles across voxels, either requires computationally expensive (probabilistic) simulations to model uncertainty in fiber orientation or ignores it completely (deterministic). Furthermore, simulation necessarily generates a finite number of trajectories, introducing “simulation error” to trajectory estimates. Here we introduce a method to analytically (via a closed‐form solution) take an orientation distribution function (ODF) from each voxel and calculate the probabilities that a trajectory projects from a voxel into each directly adjacent voxels. We validate our method by demonstrating experimentally that probabilistic simulations converge to our analytically computed transition probabilities at the voxel level as the number of simulated seeds increases. We then show that our method accurately calculates the ground‐truth transition probabilities from a publicly available phantom dataset. As a demonstration, we incorporate our analytic method for voxel transition probabilities into the Voxel Graph framework, creating a quantitative framework for assessing white matter structure, which we call “analytic tractography”. The long‐range connectivity problem is reduced to finding paths in a graph whose adjacency structure reflects voxel‐to‐voxel analytic transition probabilities. We demonstrate that this approach performs comparably to the current most widely‐used probabilistic and deterministic approaches at a fraction of the computational cost. We also demonstrate that analytic tractography works on multiple diffusion sampling schemes, reconstruction method or parameters used to define paths. Open source software compatible with popular dMRI reconstruction software is provided. HighlightsIntroduces a method for calculating the probability that a WM structure spans adjacent voxels based on geometry and ODFs.These calculations are analytic: requiring no probabilistic simulation and are free of simulation‐related error.Transition probabilities estimated via probabilistic tractography converge to analytic transition probabilities.Shortest path queries in voxel graphs produce similar results to current probabilistic tractography methods.Open source software is provided for calculating analytic transition probabilities and creating/querying Voxel Graphs.

An F-type test for detecting departure from monotonicity in a functional linear model

When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study.