Multiaxial pulsatile dynamics of the thoracic aorta and impact of thoracic endovascular repair

Purpose: The thoracic aorta is a highly mobile organ whose dynamics are altered by thoracic endovascular aorta repair (TEVAR). The aim of this study was to quantify cardiac pulsatility-induced multi-axial deformation of the thoracic aorta before and after descending aortic TEVAR. Methods: Eleven TEVAR patients (8 males and 3 females, age 57–89) underwent retrospective cardiac-gated CT angiography before and after TEVAR. 3D geometric models of the thoracic aorta were constructed, and lumen centerlines, inner and outer surface curves, and cross-sections were extracted to measure aortic arclength, centerline, inner surface, and outer surface longitudinal curvatures, as well as cross-sectional effective diameter and eccentricity for the ascending and stented aortic portions. Results: From pre- to post-TEVAR, arclength deformation was increased at the ascending aorta from 5.9 \ub1 3.1 % to 8.8 \ub1 4.4 % (P < 0.05), and decreased at the stented aorta from 7.5 \ub1 5.1 % to 2.7 \ub1 2.5 % (P < 0.05). Longitudinal curvature and diametric deformations were reduced at the stented aorta. Centerline curvature, inner surface curvature, and cross-sectional eccentricity deformations were increased at the distal ascending aorta. Conclusions: Deformations were reduced in the stented thoracic aorta after TEVAR, but increased in the ascending aorta near the aortic arch, possibly as a compensatory mechanism to maintain overall thoracic compliance in the presence of reduced deformation in the stiffened stented aorta

Practical large-scale spatio-temporal modeling of particulate matter concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988--2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of $PM_{10}$ for the full time period and $PM_{2.5}$ for a subset of the period. For the earlier part of the period, 1988--1998, few $PM_{2.5}$ monitors were operating, so we develop a simple extension to the model that represents $PM_{2.5}$ conditionally on $PM_{10}$ model predictions. In the epidemiological analysis, model predictions of $PM_{10}$ are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space--time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS204 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Function of a Spindle Checkpoint Gene bub-1 in C. elegans Development

BACKGROUND:The serine/threonine kinase BUB1 (Budding Uninhibited by Benzimidazole 1) was originally identified in yeast as a checkpoint protein, based on its mutant's incapacity of delaying the cell cycle in response to loss of microtubules. Our understanding of its function is primarily from studies carried out in yeast S. cerevisiae. It has been shown that it is a component of the mitotic spindle checkpoint and regulates the separation of sister chromatids through its downstream molecules. However, its roles in multi-cellular organisms remain unclear. METHODS AND FINDINGS:In nematode C. elegans, rapid cell divisions primarily occur in embryos and in germline of postembryonic larvae and adults. In addition, a select set of cells undergo a few rounds of cell division postembryonically. One common phenotype associated with impaired cell division is described as Stu (Sterile and Uncoordinated) [1], [2]. We conducted a genetic screen for zygotic mutants that displayed Stu phenotype in C. elegans. We isolated seven Stu mutants that fell into five complementation groups. We report here that two mutations, FanWang5 (fw5) and FanWang8 (fw8) affect the bub-1 gene, a homolog of yeast BUB1. Both mutant alleles of fw5 and fw8 exhibited variable behavioral defects, including developmental arrest, uncoordination and sterility. The number of postembryonically born neurons in the ventral cord decreased and their axon morphology was abnormal. Also, the decrease of neurons in the ventral cord phenotype could not be suppressed by a caspase-3 loss-of-function mutant. In addition, bub-1(fw5 and fw8) mutants showed widespread effects on postembryonic development in many cell lineages. We found that bub-1 functioned maternally in several developmental lineages at the embryonic stage in C. elegans. Studies in yeast have shown that BUB1 functions as a spindle checkpoint protein by regulating the anaphase promoting complex/cyclosome (APC/C). We performed double mutant analysis and observed that bub-1 genetically interacted with several downstream genes, including fzy-1/CDC20, mat-2/APC1 and emb-27/APC6. CONCLUSIONS:Our results demonstrate a conserved role of bub-1 in cell-cycle regulation and reveal that C. elegans bub-1 is required both maternally and zygotically. Further, our genetic analysis is consistent with that the function of bub-1 in C. elegans is likely similar to its yeast and mammalian homologs

Influence of Thoracic Endovascular Aortic Repair on True Lumen Helical Morphology for Stanford Type B Dissections

Objective: Thoracic endovascular aortic repair (TEVAR) can change the morphology of the flow lumen in aortic dissections, which may affect aortic hemodynamics and function. This study characterizes how the helical morphology of the true lumen in type B aortic dissections is altered by TEVAR. Methods: Patients with type B aortic dissection who underwent computed tomography angiography before and after TEVAR were retrospectively reviewed. Images were used to construct three-dimensional stereolithographic surface models of the true lumen and whole aorta using custom software. Stereolithographic models were segmented and co-registered to determine helical morphology of the true lumen with respect to the whole aorta. The true lumen region covered by the endograft was defined based on fiducial markers before and after TEVAR. The helical angle, average helical twist, peak helical twist, and cross-sectional eccentricity, area, and circumference were quantified in this region for pre- and post-TEVAR geometries. Results: Sixteen patients (61.3 \ub1 8.0 years; 12.5% female) were treated successfully for type B dissection (5 acute and 11 chronic) with TEVAR and scans before and after TEVAR were retrospectively obtained (follow-up interval 52 \ub1 91 days). From before to after TEVAR, the true lumen helical angle (–70.0 \ub1 71.1 to –64.9 \ub1 75.4\ub0; P =.782), average helical twist (–4.1 \ub1 4.0 to –3.7 \ub1 3.8\ub0/cm; P =.674), and peak helical twist (–13.2 \ub1 15.2 to –15.4 \ub1 14.2\ub0/cm; P =.629) did not change. However, the true lumen helical radius (1.4 \ub1 0.5 to 1.0 \ub1 0.6 cm; P <.05) and eccentricity (0.9 \ub1 0.1 to 0.7 \ub1 0.1; P <.05) decreased, and the cross-sectional area (3.0 \ub1 1.1 to 5.0 \ub1 2.0 cm2; P <.05) and circumference (7.1 \ub1 1.0 to 8.0 \ub1 1.4 cm; P <.05) increased significantly from before to after TEVAR. The distinct bimodal distribution of chiral and achiral native dissections disappeared after TEVAR, and subgroup analyses showed that the true lumen circumference of acute dissections increased with TEVAR, although it did not for chronic dissections. Conclusions: The unchanged helical angle and average and peak helical twists as a result of TEVAR suggest that the angular positions of the true lumen are constrained and that the endografts were helically conformable in the angular direction. The decrease of helical radius indicated a straightening of the corkscrew shape of the true lumen, and in combination with more circular and expanded lumen cross-sections, TEVAR produced luminal morphology that theoretically allows for lower flow resistance through the endografted portion. The impact of TEVAR on dissection flow lumen morphology and the interaction between endografts and aortic tissue can provide insight for improving device design, implantation technique, and long-term clinical outcomes

Definition of Tubular Anatomic Structures from Arbitrary Stereo Lithographic Surface

An accurate description of anatomies and dynamics of vessels is crucial to understand their characteristics and improve surgical techniques, thus it is the basis, in addition to surgeon experience, on which stent design and operation procedures rely. The process of producing this description is user intensive, and recent improvement in image processing of medical3D imaging allows for a more automated workflow. However, there is a need to bridge the gap from a processed geometry to a robust mathematical computational grid. By sequentially segmenting a tubular anatomic structure, here defined by a stereo lithographic (STL) surface, an initial centerline is formed by connecting centroids of orthogonal cross-sectional contours along the length of the structure. Relying on the initial centerline, a set of non-overlapping 2D cross sectional contours are defined along the centerline, a centerline which is updated after the 2D contours are produced. After a second iteration of producing 2D contours and updating the centerline, a full description of the structure is created. Our method for describing vessel geometry shows good coherence to existing method. The main advantages of our method include the possibility of having arbitrary triangulated STL surface input, automated centerline definition, safety against intersecting cross-sectional contours and automatic clean-up of local kinks and wrinkles

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

Background: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007

Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States

Background: Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 μm; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5–10 μm; PM10–2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10–2.5 concentrations for the northeastern and midwestern United States. Methods: For PM2.5, we developed models for two periods: 1988–1998 and 1999–2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1). PM10–2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model. Results: Predictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 μg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10–2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 μg/m3). PM10–2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10–2.5. Conclusions: We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10–2.5 for estimating exposures of populations living in the northeastern and midwestern United States

Design space exploration and optimization of path oblivious RAM in secure processors

Keeping user data private is a huge problem both in cloud computing and computation outsourcing. One paradigm to achieve data privacy is to use tamper-resistant processors, inside which users' private data is decrypted and computed upon. These processors need to interact with untrusted external memory. Even if we encrypt all data that leaves the trusted processor, however, the address sequence that goes off-chip may still leak information. To prevent this address leakage, the security community has proposed ORAM (Oblivious RAM). ORAM has mainly been explored in server/file settings which assume a vastly different computation model than secure processors. Not surprisingly, naïvely applying ORAM to a secure processor setting incurs large performance overheads. In this paper, a recent proposal called Path ORAM is studied. We demonstrate techniques to make Path ORAM practical in a secure processor setting. We introduce background eviction schemes to prevent Path ORAM failure and allow for a performance-driven design space exploration. We propose a concept called super blocks to further improve Path ORAM's performance, and also show an efficient integrity verification scheme for Path ORAM. With our optimizations, Path ORAM overhead drops by 41.8%, and SPEC benchmark execution time improves by 52.4% in relation to a baseline configuration. Our work can be used to improve the security level of previous secure processors.National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 1122374)American Society for Engineering Education. National Defense Science and Engineering Graduate FellowshipUnited States. Defense Advanced Research Projects Agency (Clean-slate design of Resilient, Adaptive, Secure Hosts Contract N66001-10-2-4089

Chronic Fine and Coarse Particulate Exposure, Mortality, and Coronary Heart Disease in the Nurses’ Health Study

Background: The relationship of fine particulate matter < 2.5 μm in diameter (PM2.5) air pollution with mortality and cardiovascular disease is well established, with more recent long-term studies reporting larger effect sizes than earlier long-term studies. Some studies have suggested the coarse fraction, particles between 2.5 and 10 μm (PM10–2.5), may also be important. With respect to mortality and cardiovascular events, questions remain regarding the relative strength of effect sizes for chronic exposure to fine and coarse particles. Objectives: We examined the relationship of chronic PM2.5 and PM10–2.5 exposures with all-cause mortality and fatal and nonfatal incident coronary heart disease (CHD), adjusting for time-varying covariates. Methods: The current study included women from the Nurses’ Health Study living in metropolitan areas of the northeastern and midwestern United States. Follow-up was from 1992 to 2002. We used geographic information systems–based spatial smoothing models to estimate monthly exposures at each participant’s residence. Results: We found increased risk of all-cause mortality [hazard ratio (HR), 1.26; 95% confidence interval (CI), 1.02–1.54] and fatal CHD (HR = 2.02; 95% CI, 1.07–3.78) associated with each 10-μg/m3 increase in annual PM2.5 exposure. The association between fatal CHD and PM10–2.5 was weaker. Conclusions: Our findings contribute to growing evidence that chronic PM2.5 exposure is associated with risk of all-cause and cardiovascular mortality