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Introduction: Smooth muscle is integral to multiple autonomic systems, including cerebrovascular dynamics through vascular smooth muscle cells and in ocular muscle dynamics, by regulating pupil size. In the brain, smooth muscle function plays a role in cerebrovascular reactivity (CVR) that describes changes in blood vessel calibre in response to vasoactive stimuli. Similarly, pupil size regulation can be measured using the pupillary light response (PLR), the pupil's reaction to changes in light levels. The primary aim of this study was to explore the interplay between cerebral blood flow and pupil dynamics, evaluated using CVR and PLR, respectively. Methods: A total of 20 healthy adults took part in a CVR gas stimulus protocol and a light and dark flash PLR protocol. CVR was calculated as the blood flow velocity change in the middle cerebral artery, measured using transcranial Doppler ultrasound in response to a 5% increase in CO2. Multiple PLR metrics were evaluated with a clinical pupillometer. Results: CVR and PLRmetrics were all within the expected physiological ranges for healthy adults. Nine different PLR metrics, assessed through the light and dark flash protocols, were compared against CVR. A significant negative relationship was observed between the latency of the PLR in the dark flash protocol and CVR. No statistically significant relationshipswere found betweenCVR and other PLRmetrics. Conclusion: This is the first study to investigate the relationship between cerebral blood flow and pupil dynamics. A significant relationship between dark flash latency and CVR was observed. Future work includes evaluating these relationships using more robust CVR and PLR measurement techniques in a larger, more diverse cohort. Notably, more research is warranted into the PLR using a dark flash protocol and its connection to cerebrovascular function. [ABSTRACT FROM AUTHOR]

External stresses alter the magnetic properties of ferromagnetic materials such as iron and steel, a fact that has been the basis of substantial study in nondestructive testing. Existing theories and models have so far not proven reliable or accurate enough to develop a practical means of using the developed theory relating stress and magnetization to measure biaxial strains without prior knowledge of the strain or magnetic history of the sample. A deterministic model of ferromagnetic hysteresis and the effects of external stresses in materials such as iron and steel is introduced by this study. Changes in hysteresis loops due to stress are explained via changes in the magnetocrystalline anisotropy at the crystal-unit level, and are extended to the macroscopic effects that are seen in experiments. An original equation is presented which accurately describes experimentally acquired major hysteresis loops and directly relates two parameters to the two perpendicular principal strain axes thereby providing a technique able to determine the absolute stress/strain experienced by the sample. This model will potentially enable quantitative, nondestructive stress measuring devices to be developed. Index Terms--Hysteresis loops, pursuit curve, strain, stress.

Background: Under normal physiological conditions, the spin–lattice relaxation rate (R1) in blood is influenced by many factors, including hematocrit, field strength, and the paramagnetic effects of deoxyhemoglobin and dissolved oxygen. In addition, techniques such as oxygen‐enhanced magnetic resonance imaging (MRI) require high fractions of inspired oxygen to induce hyperoxia, which complicates the R1 signal further. A quantitative model relating total blood oxygen content to R1 could help explain these effects. Purpose: To propose and assess a general model to estimate the R1 of blood, accounting for hematocrit, SO2, PO2, and B0 under both normal physiological and hyperoxic conditions. Study Type: Mathematical modeling. Population: One hundred and twenty‐six published values of R1 from phantoms and animal models. Field Strength/Sequence: 5–8.45 T. Assessment: We propose a two‐compartment nonlinear model to calculate R1 as a function of hematocrit, PO2, and B0. The Akaike Information Criterion (AIC) was used to select the best‐performing model with the fewest parameters. A previous model of R1 as a function of hematocrit, SO2, and B0 has been proposed by Hales et al, and our work builds upon this work to make the model applicable under hyperoxic conditions (SO2 > 0.99). Models were assessed using the AIC, mean squared error (MSE), coefficient of determination (R2), and Bland–Altman analysis. The effect of volume fraction constants WRBC and Wplasma was assessed by the SD of resulting R1. The range of the model was determined by the maximum and minimum B0, hematocrit, SO2, and PO2 of the literature data points. Statistical Tests: Bland–Altman, AIC, MSE, coefficient of determination (R2), SD. Results: The model estimates agreed well with the literature values of R1 of blood (R2 = 0.93, MSE = 0.0013 s−2), and its performance was consistent across the range of parameters: B0 = 1.5–8.45 T, SO2 = 0.40–1, PO2 = 30–700 mmHg. Data Conclusion: Using the results from this model, we have quantified and explained the contradictory decrease in R1 reported in oxygen‐enhanced MRI and oxygen‐delivery experiments. Level of Evidence: 3 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]