Dynamic cerebral autoregulation in postpartum individuals with and without preeclampsia.

BACKGROUND: Changes in dynamic cerebral autoregulation (DCA) may contribute to postpartum maternal cerebrovascular complications after preeclampsia. We hypothesized that DCA is impaired in the first week postpartum after diagnosis of preeclampsia with severe features (PSF), compared with normotensive postpartum individuals and healthy non-pregnant female volunteers. METHODS: We measured DCA within seven days after delivery in individuals with and without PSF, using transcranial Doppler and continuous arterial blood pressure monitoring with finger plethysmography. Historical data from 28 healthy female non-pregnant volunteers, collected using the same methods, were used for comparison. We used generalized harmonic wavelets to estimate autoregulation parameters (phase shift and gain) in very low frequency and low frequency bands, with lower phase shift and higher gain indicating impaired DCA function. We compared DCA parameters between the three groups using the Kruskal Wallis test. RESULTS: A total of 69 postpartum participants contributed data, of whom 49 had preeclampsia with severe features. Median phase shifts in both postpartum groups were higher compared with historical controls across all frequency ranges (p = 0.001), indicating faster autoregulatory response. Gain was higher in both postpartum groups than in historical controls across all frequency ranges (p = 0.04), indicating impaired dampening effect. CONCLUSION: We found that postpartum individuals, regardless of preeclampsia diagnosis, had higher phase shifts and higher gain than healthy non-pregnant/postpartum female volunteers. Our results suggest hyperdynamic DCA with impaired dampening effect in the first week postpartum, regardless of preeclampsia diagnosis.

Addressing the curse of dimensionality in stochastic dynamics: a Wiener path integral variational formulation with free boundaries.

A Wiener path integral variational formulation with free boundaries is developed for determining the stochastic response of high-dimensional nonlinear dynamical systems in a computationally efficient manner. Specifically, a Wiener path integral representation of a marginal or lower-dimensional joint response probability density function is derived. Due to this a priori marginalization, the associated computational cost of the technique becomes independent of the degrees of freedom (d.f.) or stochastic dimensions of the system, and thus, the 'curse of dimensionality' in stochastic dynamics is circumvented. Two indicative numerical examples are considered for highlighting the capabilities of the technique. The first relates to marine engineering and pertains to a structure exposed to nonlinear flow-induced forces and subjected to non-white stochastic excitation. The second relates to nano-engineering and pertains to a 100-d.f. stochastically excited nonlinear dynamical system modelling the behaviour of large arrays of coupled nano-mechanical oscillators. Comparisons with pertinent Monte Carlo simulation data demonstrate the computational efficiency and accuracy of the developed technique.

Exploring Explanations of Subglacial Bedform Sizes Using Statistical Models.

Sediments beneath modern ice sheets exert a key control on their flow, but are largely inaccessible except through geophysics or boreholes. In contrast, palaeo-ice sheet beds are accessible, and typically characterised by numerous bedforms. However, the interaction between bedforms and ice flow is poorly constrained and it is not clear how bedform sizes might reflect ice flow conditions. To better understand this link we present a first exploration of a variety of statistical models to explain the size distribution of some common subglacial bedforms (i.e., drumlins, ribbed moraine, MSGL). By considering a range of models, constructed to reflect key aspects of the physical processes, it is possible to infer that the size distributions are most effectively explained when the dynamics of ice-water-sediment interaction associated with bedform growth is fundamentally random. A 'stochastic instability' (SI) model, which integrates random bedform growth and shrinking through time with exponential growth, is preferred and is consistent with other observations of palaeo-bedforms and geophysical surveys of active ice sheets. Furthermore, we give a proof-of-concept demonstration that our statistical approach can bridge the gap between geomorphological observations and physical models, directly linking measurable size-frequency parameters to properties of ice sheet flow (e.g., ice velocity). Moreover, statistically developing existing models as proposed allows quantitative predictions to be made about sizes, making the models testable; a first illustration of this is given for a hypothesised repeat geophysical survey of bedforms under active ice. Thus, we further demonstrate the potential of size-frequency distributions of subglacial bedforms to assist the elucidation of subglacial processes and better constrain ice sheet models.