Cytokine-mediated activation of cultured CNS microvessels: a system for examining antigenic modulation of CNS endothelial cells, and evidence for long-term expression of the adhesion protein E-selectin

Much of what is known of endothelial responses to cytokines has been derived from in vitro studies using cultured human umbilical vein endothelial cells (EC). Less is known of CNS EC responses and whether intact endothelium responds similarly to cultured cells. We have used techniques by which rat CNS microvessels can be isolated, then cultured in vitro, to study the response of intact endothelium to activation with cytokines. These microvessels are composed of viable EC and perivascular cells, predominantly pericytes. Expression of EC activation antigens in multicellular systems such as cultured microvessels can be assessed quantitatively using immunofluorescence laser cytometry. Interferon gamma increased immunologically reactive major histocompatibility complex class II antigens (< 300 to 2,398 +/- 225 average fluorescence intensity), while tumor necrosis factor alpha induced an increase in vascular cell adhesion molecule-1 (2,167 +/- 171) and E-selectin (1,628 +/- 315). CNS EC appeared to respond similarly to cultured EC with the exception that E-selectin expression was not transiently expressed but was maintained by microvessel EC for 24 and 48 h. Cultured CNS microvessels provide a good system for studying EC activation.     

Preventing and managing pelvic inflammatory disease: key questions, practices, and evidence

Clinicians play a pivotal role in protecting women from pelvic inflammatory disease (PID), one of the most prevalent and serious diseases affecting women of reproductive age. This article examines PID prevention and management by critically addressing five questions: (1) What are the key risk factors for PID? (2) What are the principal microorganisms involved in PID? (3) What are the appropriate diagnostic criteria for PID? (4) What are the best treatment regimens for PID? and (5) What are the effective strategies for preventing PID? In addressing each of these questions, the quality of available evidence and recommended practice is discussed and gaps in the evidence are highlighted.     

Experimental evaluation of hotspot identification methods

Identifying crash "hotspots", "blackspots", "sites with promise", or "high risk" locations is standard practice in departments of transportation throughout the US. The literature is replete with the development and discussion of statistical methods for hotspot identification (HSID). Theoretical derivations and empirical studies have been used to weigh the benefits of various HSID methods; however, a small number of studies have used controlled experiments to systematically assess various methods. Using experimentally derived simulated data--which are argued to be superior to empirical data, three hot spot identification methods observed in practice are evaluated: simple ranking, confidence interval, and Empirical Bayes. Using simulated data, sites with promise are known a priori, in contrast to empirical data where high risk sites are not known for certain. To conduct the evaluation, properties of observed crash data are used to generate simulated crash frequency distributions at hypothetical sites. A variety of factors is manipulated to simulate a host of 'real world' conditions. Various levels of confidence are explored, and false positives (identifying a safe site as high risk) and false negatives (identifying a high risk site as safe) are compared across methods. Finally, the effects of crash history duration in the three HSID approaches are assessed. The results illustrate that the Empirical Bayes technique significantly outperforms ranking and confidence interval techniques (with certain caveats). As found by others, false positives and negatives are inversely related. Three years of crash history appears, in general, to provide an appropriate crash history duration.     

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.     

Statistical shape analysis: clustering, learning, and testing

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a minimum variance type criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.     

Water absorption in internally cured mortar made with water-filled lightweight aggregate

The increased propensity for shrinkage cracking in low water-to-cement ratio (w/c) concrete has inspired the development of new technologies that can reduce the risk of early-age cracking. One of these is internal curing. Internal curing uses saturated lightweight aggregate to supply ‘curing water’ to low w/c paste as it hydrates. Significant research has been performed to determine the effects of internal curing on shrinkage and stress development; however, relatively little detailed information exists about the effects of internal curing on fluid transport properties such as water absorption or diffusivity. This study examines the absorption of water into mortar specimens made with saturated lightweight aggregates (SLWA). These results indicate that the inclusion of SLWA can reduce the water absorption of mortar specimens. This observation was reinforced with electrical conductivity measurements that exhibited similar reductions.     

Trajectory Tracking for Underwater Swimming Manipulators using a Super Twisting Algorithm

The underwater swimming manipulator (USM) is a snake‐like, multi‐articulated, underwater robot that is equipped with thrusters. One of the main purposes of the USM is to act like an underwater floating base manipulator. As such, it is essential to achieve good station‐keeping and trajectory tracking performance for the USM by using the thrusters and by using the joints to attain the desired position and orientation of the head and tail of the USM. In this ‘paper, we propose a sliding mode control (SMC) law, specifically the super‐twisting algorithm with adaptive gains, for the trajectory tracking of the USM's centre of mass. A higher‐order sliding mode observer is proposed for state estimation. Furthermore, we show the ultimate boundedness of the tracking errors. We demonstrate the applicability of the proposed control law and show that it leads to better performance than a linear PD‐controller.