Wild-type Measles Virus Infection Upregulates Poliovirus Receptor-Related 4 and Causes Apoptosis in Brain Endothelial Cells by Induction of Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand

Small numbers of brain endothelial cells (BECs) are infected in children with neurologic complications of measles virus (MV) infection. This may provide a mechanism for virus entry into the central nervous system, but the mechanisms are unclear. Both in vitro culture systems and animal models are required to elucidate events in the endothelium. We compared the ability of wild-type (WT), vaccine, and rodent-adapted MV strains to infect, replicate, and induce apoptosis in human and murine brain endothelial cells (HBECs and MBECs, respectively). Mice also were infected intracerebrally. All MV stains productively infected HBECs and induced the MV receptor PVRL4. Efficient WT MV production also occurred in MBECs. Extensive monolayer destruction associated with activated caspase 3 staining was observed in HBECs and MBECs, most markedly with WT MV. Tumor necrosis factor–related apoptosis-inducing ligand (TRAIL), but not Fas ligand, was induced by MV infection. Treatment of MBECs with supernatants from MV-infected MBEC cultures with an anti-TRAIL antibody blocked caspase 3 expression and monolayer destruction. TRAIL was also expressed in the endothelium and other cell types in infected murine brains. This is the first demonstration that infection of low numbers of BECs with WT MV allows efficient virus production, induction of TRAIL, and subsequent widespread apoptosis.

Patterns of Holocene relative sea level change in the North of Britain and Ireland

Temporal and spatial patterns of relative sea level (RSL) change in the North of Britain and Ireland during the Holocene are examined. Four episodes, each defined by marked changes in the RSL trend, are identified. Each episode is marked by a rise to a culminating shoreline followed by a fall. Episode HRSL-1 dates from the Younger Dryas to early in the Holocene; HRSL-2 to HRSL-4 occurred later in the Holocene. There is extensive evidence for each episode, and on this basis the spatial distribution of the altitude data for three culminating shorelines and a shoreline formed at the time of the Holocene Storegga Slide tsunami (ca 8110 ± 100 cal. BP) is analysed. Ordinary Kriging is used to determine the general pattern, following which Gaussian Trend Surface Analysis is employed. Recognising that empirical measurements of RSL change can be unevenly distributed spatially, a new approach is introduced which enables the developing pattern to be identified. The patterns for the most widely occurring shorelines were analysed and found to be similar and common centre and axis models were developed for all shorelines. The analyses described provide models of the spatial pattern of Holocene RSL change in the area between ca 8100 cal. BP and ca 1000 cal. BP based on 2262 high resolution shoreline altitude measurements. These models fit the data closely, no shoreline altitude measurement lying more than −1.70 m or +1.82 m from the predicted value. The models disclose a similar pattern to a recently published Glacial Isostatic Adjustment model for present RSL change across the area, indicating that the overall spatial pattern of RSL change may not have varied greatly during the last ca 8000 years.

Photosynthetic pigments in soils from northern Victoria Land (continental Antarctica) as proxies for soil algal community structure and function

Although soil algae are among the main primary producers in most terrestrial ecosystems of continental Antarctica, there are very few quantitative studies on their relative proportion in the main algal groups and on how their distribution is affected by biotic and abiotic factors. Such knowledge is essential for understanding the functioning of Antarctic terrestrial ecosystems. We therefore analyzed biological soil crusts from northern Victoria Land to determine their pH, electrical conductivity (EC) water content (W), total and organic C (TC and TOC) and total N (TN) contents, and the presence and abundance of photosynthetic pigments. In particular, the latter were tested as proxies for biomass and coarse-resolution community structure. Soil samples were collected from five sites with known soil algal communities and the distribution of pigments was shown to reflect differences in the relative proportions of Chlorophyta, Cyanophyta and Bacillariophyta in these sites. Multivariate and univariate models strongly indicated that almost all soil variables (EC, W, TOC and TN) were important environmental correlates of pigment distribution. However, a significant amount of variation is independent of these soil variables and may be ascribed to local variability such as changes in microclimate at varying spatial and temporal scales. There are at least five possible sources of local variation: pigment preservation, temporal variations in water availability, temporal and spatial interactions among environmental and biological components, the local-scale patchiness of organism distribution, and biotic interactions. (C) 2009 Elsevier Ltd. All rights reserved.

Therapeutic effects of PPARα agonists on diabetic retinopathy in type 1 diabetes models

Retinal vascular leakage, inflammation, and neovascularization (NV) are features of diabetic retinopathy (DR). Fenofibrate, a peroxisome proliferator-activated receptor a (PPARa) agonist, has shown robust protective effects against DR in type 2 diabetic patients, but its effects on DR in type 1 diabetes have not been reported. This study evaluated the efficacy of fenofibrate on DR in type 1 diabetes models and determined if the effect is PPARa dependent. Oral administration of fenofibrate significantly ameliorated retinal vascular leakage and leukostasis in streptozotocin-induced diabetic rats and in Akita mice. Favorable effects on DR were also achieved by intravitreal injection of fenofibrate or another specific PPARa agonist. Fenofibrate also ameliorated retinal NV in the oxygen-induced retinopathy (OIR) model and inhibited tube formation and migration in cultured endothelial cells. Fenofibrate also attenuated overexpression of intercellular adhesion molecule-1, monocyte chemoattractant protein-1, and vascular endothelial growth factor (VEGF) and blocked activation of hypoxia-inducible factor-1 and nuclear factor-?B in the retinas of OIR and diabetic models. Fenofibrate's beneficial effects were blocked by a specific PPARa antagonist. Furthermore, Ppara knockout abolished the fenofibrate-induced downregulation of VEGF and reduction of retinal vascular leakage in DR models. These results demonstrate therapeutic effects of fenofibrate on DR in type 1 diabetes and support the existence of the drug target in ocular tissues and via a PPARa-dependent mechanism.

Laser-ion accelerators: State-of-the-art and scaling laws

A significant amount of experimental work has been devoted over the last decade to the development and optimization of proton acceleration based on the so-called Target Normal Sheath acceleration mechanism. Several studies have been dedicated to the determination of scaling laws for the maximum energy of the protons as a function of the parameters of the irradiating pulses, studies based on experimental results and on models of the acceleration process. We briefly summarize the state of the art in this area, and review some of the scaling studies presented in the literature. We also discuss some recent results, and projected scalings, related to a different acceleration mechanism for ions, based on the Radiation Pressure of an ultraintense laser pulse.

Using Mesh-Geometry Relationships to Transfer Analysis Models between CAE Tools

Integrating analysis and design models is a complex task due to differences between the models and the architectures of the toolsets used to create them. This complexity is increased with the use of many different tools for specific tasks during an analysis process. In this work various design and analysis models are linked throughout the design lifecycle, allowing them to be moved between packages in a way not currently available. Three technologies named Cellular Modeling, Virtual Topology and Equivalencing are combined to demonstrate how different finite element meshes generated on abstract analysis geometries can be linked to their original geometry. Establishing the equivalence relationships between models enables analysts to utilize multiple packages for specialist tasks without worrying about compatibility issues or rework.

Non-steroidal anti-inflammatory drug use and brain tumour risk:a case-control study within the Clinical Practice Research Datalink

Purpose: The aetiology of primary brain tumours is largely unknown; the role of non-steroidal anti-inflammatory drugs (NSAIDs) or aspirin use and glioma risk has been inconclusive, but few population-based studies with reliable prescribing data have been conducted, and the association with meningioma risk has yet to be assessed. Methods: The UK Clinical Practice Research Datalink was used to assess the association between aspirin and non-aspirin NSAID use and primary brain tumour risk using a nested case-control study design. Conditional logistic regression analysis was performed on 5,052 brain tumour patients aged 16 years and over, diagnosed between 1987 and 2009 and 42,678 controls matched on year of birth, gender and general practice, adjusting for history of allergy and hormone replacement therapy use in the glioma and meningioma models, respectively.

Results: In conditional logistic regression analysis, excluding drug use in the year preceding the index date, there was no association with non-aspirin NSAID use (OR 0.96, 95 % CI 0.81-1.13) or glioma risk comparing the highest category of daily defined dose to non-users; however, non-aspirin NSAID use was positively associated with meningioma risk (OR 1.35, 95 % CI 1.06-1.71). No association was seen with high- or low-dose aspirin use irrespective of histology.

Conclusions: This large nested case-control study finds no association between aspirin or non-aspirin NSAID use and risk of glioma but a slight increased risk with non-aspirin NSAIDs and meningioma. © 2013 Springer Science+Business Media Dordrecht.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we present AGWAN (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the AGWAN model to real-world graphs and for generating random graphs from the model. Using the Enron “who communicates with whom” social graph, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to the structure of real-world graphs.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

The Engine of Thought Is a Hybrid: Roles of Associative and Structured Knowledge in Reasoning

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.

On sea level change in the North Sea influenced by the North Atlantic Oscillation: local and remote steric effects

In this study, contributions of both local steric and remote baroclinic effects (i.e., steric variations external to the region of interest) to the inter-annual variability of winter sea level in the North Sea, with respect to the North Atlantic Oscillation (NAO), for the period of 1953–2010 are investigated. On inter-annual time scales in this period, the NAO is significantly correlated to sea level variations in the North Sea only in the winter months (December–March), while its correlation to sea temperature over much of the North Sea is only significant in January and February. The discrepancy in sea level between observations and barotropic tide and surge models forced by tides and local atmospheric forcing, i.e., local atmospheric pressure effects and winds, in the present study are found to be consistent with previous studies. In the North Sea, local thermosteric effects caused by thermal expansion play a minor role on winter-mean NAO related sea level variability compared with atmospheric forcing. This is particularly true in the southeastern North Sea where water depths are mostly less than 25 m. Our calculations demonstrate that the discrepancy can be mostly explained by remote baroclinic effects, which appear as water mass exchanges on the continental shelf and are therefore only apparent in ocean bottom pressure. In the North Sea, NAO related sea level variations seem to be a hybrid of barotropic and baroclinic processes. Hence, they can only be adequately modelled with three-dimensional baroclinic ocean models that include contributions of baroclinic effects and large-scale atmospheric forcing external to the region of interest.

A review of radio channel models for body centric communications

The human body is an extremely challenging environment for the operation of wireless communications systems, not least because of the complex antenna-body electromagnetic interaction effects which can occur. This is further compounded by the impact of movement and the propagation characteristics of the local environment which all have an effect upon body centric communications channels. As the successful design of body area networks (BANs) and other types of body centric system is inextricably linked to a thorough understanding of these factors, the aim of this paper is to conduct a survey of the current state of the art in relation to propagation and channel models primarily for BANs but also considering other types of body centric communications. We initially discuss some of the standardization efforts performed by the Institute of Electrical and Electronics Engineers 802.15.6 task group before focusing on the two most popular types of technologies currently being considered for BANs, namely narrowband and Ultrawideband (UWB) communications. For narrowband communications the applicability of a generic path loss model is contended, before presenting some of the scenario specific models which have proven successful. The impacts of human body shadowing and small-scale fading are also presented alongside some of the most recent research into the Doppler and time dependencies of BANs. For UWB BAN communications, we again consider the path loss as well as empirical tap delay line models developed from a number of extensive channel measurement campaigns conducted by research institutions around the world. Ongoing efforts within collaborative projects such as Committee on Science and Technology Action IC1004 are also described. Finally, recent years have also seen significant developments in other areas of body centric communications such as off-body and body-to-body communications. We highlight some of the newest relevant research in these areas as well as discussing some of the advanced topics which are currently being addressed in the field of body centric communications. Key Points Channel models for body centric comms ©2014. The Authors.

Using mesh-geometry relationships to transfer analysis models between CAE tools

Integrating analysis and design models is a complex task due to differences between the models and the architectures of the toolsets used to create them. This complexity is increased with the use of many different tools for specific tasks using an analysis process. In this work various design and analysis models are linked throughout the design lifecycle, allowing them to be moved between packages in a way not currently available. Three technologies named Cellular Modeling, Virtual Topology and Equivalencing are combined to demonstrate how different finite element meshes generated on abstract analysis geometries can be linked to their original geometry. Cellular models allow interfaces between adjacent cells to be extracted and exploited to transfer analysis attributes such as mesh associativity or boundary conditions between equivalent model representations. Virtual Topology descriptions used for geometry clean-up operations are explicitly stored so they can be reused by downstream applications. Establishing the equivalence relationships between models enables analysts to utilize multiple packages for specialist tasks without worrying about compatibility issues or substantial rework.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

Statin Use and Survival from Lung Cancer: A Population-Based Cohort Study

Background: Preclinical evidence from lung cancer cell lines and animal models suggest that statins could have anticancer properties. We investigated whether statin users had reduced risk of cancer-specific mortality in a population based cohort of lung cancer patients.
Methods: Newly diagnosed lung cancer patients, from 1998 to 2009, were identified from English cancer registry data and linked to the UK Clinical Practice Research Datalink, providing prescription records, and to Office of National Statistics mortality data up to 2012. Cox regression models were used to calculate hazard ratios (HR) for cancer-specific mortality and 95% confidence intervals (CIs) by statin use before and after diagnosis and to adjust these HRs for potential confounders.
Results: In 3,638 lung cancer patients, there was some evidence that statin use after diagnosis was associated with reduced lung cancer-specific mortality (adjusted HR=0.89, 95% CI 0.78, 1.02; P=0.09). Associations were more marked after 12 prescriptions (adjusted HR=0.81, 95% CI 0.67, 0.98; P=0.03) and when lipophilic statins were investigated (adjusted HR=0.81, 95% CI 0.70, 0.94; P=0.01) but were attenuated in some sensitivity analyses. Furthermore, in 11,051 lung cancer patients, statin use before diagnosis was associated with reduced lung cancer-specific mortality (adjusted HR=0.88, 95% CI, 0.83, 0.93; P<0.001).
Conclusions: There was some evidence that lung cancer patients who used statins, and particularly simvastatin, had reduced rates of cancer-specific mortality.
 Impact: These findings should first be confirmed in observational studies, but provide some support for conducting randomized controlled trials of simvastatin as adjuvant cancer therapy in lung cancer patients.