Estimating arterial cyclic strain from the spacing of endothelial nuclei

Background: The non-uniform distribution of atherosclerosis within the arterial system is widely attributed to variation in haemodynamic wall shear stress. It may also depend on variation in pressure-induced stresses and strains within the arterial wall; these have been less widely investigated, at least in part because of a lack of suitable techniques. Objectives: Here we show that local arterial strain can be determined from impressions left by endothelial cells on the surface of vascular corrosion casts made at different pressures, even though only one pressure can be examined in each vessel. The pattern of pits in the cast caused by protruding endothelial nuclei was subject to “retro-deformation” to identify the pattern that would have occurred in the absence of applied stresses. Methods: Retaining the nearest-neighbour pairs found under this condition, changes in nearest-neighbour vectors were calculated for the pattern seen in the cast, and the ratio of mean changes at different pressures determined. This approach removes errors in simple nearest-neighbour analyses caused by the nearest neighbour changing as deformation occurs. Results: The accuracy, precision and robustness of the approach were validated using simulations. The method was implemented using confocal microscopy of casts of the rabbit aorta made at systolic and diastolic pressures; results agreed well with the ratio of the macroscopic dimensions of the casts. Conclusions: Applying the new technique to areas around arterial branches could support or refute the hypothesis that the development of atherosclerosis is influenced by mural strain, and the method may be applicable to other tissues

Change of direction in the biomechanics of atherosclerosis

The non-uniform distribution of atherosclerosis within the arterial system has been attributed to pro-atherogenic influences of low, oscillatory haemodynamic wall shear stress (WSS) on endothelial cells (EC). This theory is challenged by the changes in lesion location that occur with age in human and rabbit aortas. Furthermore, a number of point-wise comparisons of lesion prevalence and WSS have failed to support it. Here we investigate the hypothesis that multidirectional flow-characterized as the average magnitude of WSS components acting transversely to the mean vector (transWSS)-plays a key role. Maps of lesion prevalence around aortic branch ostia in immature and mature rabbits were compared with equivalent maps of time average WSS, the OSI (an index characterizing oscillatory flow) and transWSS, obtained from computational simulations; Spearman's rank correlation coefficients were calculated for aggregated data and 95% confidence intervals were obtained by bootstrapping methods. Lesion prevalence correlated positively, strongly and significantly with transWSS at both ages. Correlations of lesion prevalence with the other shear metrics were not significant or were significantly lower than those obtained for transWSS. No correlation supported the low, oscillatory WSS theory. The data are consistent with the view that multidirectional near-wall flow is highly pro-atherogenic. Effects of multidirectional flow on EC, and methods for investigating them, are reviewed. The finding that oscillatory flow has pro-inflammatory effects when acting perpendicularly to the long axis of EC but anti-inflammatory effects when acting parallel to it may explain the stronger correlation of lesion prevalence with transWSS than with the OSI

Stakeholder perspectives on implementing the National Cancer Institute’s patient-reported outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

The National Cancer Institute (NCI) is developing a patient-reported version of its Common Terminology Criteria for Adverse Events, called the "PRO-CTCAE." The PRO-CTCAE consists of a library of patient-reported items which can be administered in clinical trials to directly capture the patient experience of adverse events during cancer treatment, as well as a software platform for administering these items via computer or telephone. In order to better understand the impressions of stakeholders involved in cancer clinical research about the potential value of the PRO-CTCAE approach to capturing adverse event information in clinical research, as well as their perspectives about barriers and strategies for implementing the PRO-CTCAE in NCI-sponsored cancer trials, a survey was conducted. A survey including structured and open-ended questions was developed to elicit perceptions about the use of patient-reported outcomes (PROs) for adverse event reporting, and to explore logistical considerations for implementing the PRO-CTCAE in cancer trials. The survey was distributed electronically and by paper to a convenience sample of leadership and committee members in the NCI's cooperative group network, including principal investigators, clinical investigators, research nurses, data managers, patient advocates, and representatives of the NCI and Food and Drug Administration. Between October, 2008 through February, 2009, 727 surveys were collected. Most respondents (93%) agreed that patient reporting of adverse symptoms would be useful for improving understanding of the patient experience with treatment in cancer trials, and 88%, 80%, and 76%, respectively, endorsed that administration of PRO-CTCAE items in clinical trials would improve the completeness, accuracy, and efficiency of symptom data collection. More than three fourths believed that patient reports would be useful for informing treatment dose modifications and towards FDA regulatory evaluation of drugs. Eighty-eight percent felt that patients in clinical trials would be willing to self-report adverse symptoms at clinic visits via computer, and 68% felt patients would self-report weekly from home via the internet or an automated telephone system. Lack of computers and limited space and personnel were seen as potential barriers to in-clinic self-reporting, but these were judged to be surmountable with adequate funding. The PRO-CTCAE items and software are viewed by a majority of survey respondents as a means to improve adverse event data quality and comprehensiveness, enhance clinical decision-making, and foster patient-clinician communication. Research is ongoing to assess the measurement properties and feasibility of implementing this measure in cancer clinical trials

Elevated Uptake of Plasma Macromolecules by Regions of Arterial Wall Predisposed to Plaque Instability in a Mouse Model

Atherosclerosis may be triggered by an elevated net transport of lipid-carrying macromolecules from plasma into the arterial wall. We hypothesised that whether lesions are of the thin-cap fibroatheroma (TCFA) type or are less fatty and more fibrous depends on the degree of elevation of transport, with greater uptake leading to the former. We further hypothesised that the degree of elevation can depend on haemodynamic wall shear stress characteristics and nitric oxide synthesis. Placing a tapered cuff around the carotid artery of apolipoprotein E -/- mice modifies patterns of shear stress and eNOS expression, and triggers lesion development at the upstream and downstream cuff margins; upstream but not downstream lesions resemble the TCFA. We measured wall uptake of a macromolecular tracer in the carotid artery of C57bl/6 mice after cuff placement. Uptake was elevated in the regions that develop lesions in hyperlipidaemic mice and was significantly more elevated where plaques of the TCFA type develop. Computational simulations and effects of reversing the cuff orientation indicated a role for solid as well as fluid mechanical stresses. Inhibiting NO synthesis abolished the difference in uptake between the upstream and downstream sites. The data support the hypothesis that excessively elevated wall uptake of plasma macromolecules initiates the development of the TCFA, suggest that such uptake can result from solid and fluid mechanical stresses, and are consistent with a role for NO synthesis. Modification of wall transport properties might form the basis of novel methods for reducing plaque rupture

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the “neural noise” hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance

Mapping local and global variability in plant trait distributions

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration - specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways - without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Comparison of statistical methods for assessing spatial correlations between maps of fifferent arterial properties

Assessing the anatomical correlation of atherosclerosis with biomechanical localizing factors is hindered by spatial autocorrelation (SA), wherein neighboring arterial regions tend to have similar properties rather than being independent, and by the use of aggregated data, which artificially inflates correlation coefficients. Resampling data at lower resolution or reducing degrees-of-freedom in significance tests negated effects of SA but only in artificial situations where it occurred at a single length scale. Using Fourier or wavelet transforms to generate autocorrelation-preserving surrogate datasets, and thus to compute the null distribution, avoided this problem. Bootstrap methods additionally circumvented the errors caused by aggregating data. The bootstrap technique showed that wall shear stress (WSS) was significantly correlated with atherosclerotic lesion frequency and endothelial nuclear elongation, but not with the permeability of the arterial wall to albumin, in immature rabbits