Transient receptor potential ankyrin 1 channel localized to non-neuronal airway cells promotes non-neurogenic inflammation

The transient receptor potential ankyrin 1 (TRPA1) channel, localized to airway sensory nerves, has been proposed to mediate airway inflammation evoked by allergen and cigarette smoke (CS) in rodents, via a neurogenic mechanism. However the limited clinical evidence for the role of neurogenic inflammation in asthma or chronic obstructive pulmonary disease raises an alternative possibility that airway inflammation is promoted by non-neuronal TRPA1.

Insulin-like growth factor binding protein-3 mediates vascular repair by enhancing nitric oxide generation

Insulin-like growth factor binding protein (IGFBP)-3 modulates vascular development by regulating endothelial progenitor cell (EPC) behavior, specifically stimulating EPC cell migration. This study was undertaken to investigate the mechanism of IGFBP-3 effects on EPC function and how IGFBP-3 mediates cytoprotection following vascular injury.

A bridge-monitoring tool based on bridge and vehicle accelerations

Previous research on damage detection based on the response of a structure to a moving load has reported decay in accuracy with increasing load speed. Using a 3D vehicle – bridge interaction model, this paper shows that the area under the filtered acceleration response of the bridge increases with increasing damage, even at highway load speeds. Once a datum reading is established, the area under subsequent readings can be monitored and compared with the baseline reading, if an increase is observed it may indicate the presence of damage. The sensitivity of the proposed approach to road roughness and noise is tested in several damage scenarios. The possibility of identifying damage in the bridge by analysing the acceleration response of the vehicle traversing it is also investigated. While vehicle acceleration is shown to be more sensitive to road roughness and noise and therefore less reliable than direct bridge measurements, damage is successfully identified in favourable scenarios.

The use of a dynamic truck-trailer drive-by system to monitor bridge damping

Bridge structures are continuously subject to degradation due to the environment, ageing and excess loading. Periodic monitoring of bridges is therefore a key part of any maintenance strategy as it can give early warning if a bridge becomes unsafe. This article investigates an alternative method for the monitoring of bridge dynamic behaviour: a truck-trailer vehicle system, with accelerometers fitted to the axles of the trailer. The method aims to detect changes in the damping of a bridge, which may indicate the existence of damage. A simplified vehicle-bridge interaction model is used in theoretical simulations to assess the effectiveness of the method in detecting those changes. The influence of road profile roughness on the vehicle vibration is overcome by recording accelerations from both axles of a trailer and then analysing the spectra of the difference in the accelerations between the two axles. The effectiveness of the approach in detecting damage simulated as a loss in stiffness is also investigated. In addition, the sensitivity of the approach to the vehicle speed, road roughness class, bridge span length, changes in the equal axle properties and noise is investigated.

Dynamic Axle Force and Road Profile Identification Using a Moving Vehicle

In the interaction between vehicles, pavements and bridges, it is essential to aim towards a reduction of vehicle axle forces to promote longer pavement life spans and to prevent bridges loads becoming too high. Moreover, as the road surface roughness affects the vehicle dynamic forces, an efficient monitoring of pavement condition is also necessary to achieve this aim. This paper uses a novel algorithm to identify the dynamic interaction forces and pavement roughness from vehicle accelerations in both theoretical simulations and a laboratory experiment; moving force identification theory is applied to a vehicle model for this purpose. Theoretical simulations are employed to evaluate the ability of the algorithm to predict forces over a range of bridge spans and to evaluate the influence of road roughness level on the accuracy of the results. Finally, in addressing the challenge for the real-world problem, the effects of vehicle configuration and speed on the predicted road roughness are also investigated in a laboratory experiment.

PET/CT imaging for target volume delineation in curative intent radiotherapy of non-small cell lung cancer: IAEA consensus report 2014

This document describes best practice and evidence based recommendations for the use of FDG-PET/CT for the purposes of radiotherapy target volume delineation (TVD) for curative intent treatment of non-small cell lung cancer (NSCLC). These recommendations have been written by an expert advisory group, convened by the International Atomic Energy Agency (IAEA) to facilitate a Coordinated Research Project (CRP) aiming to improve the applications of PET based radiation treatment planning (RTP) in low and middle income countries. These guidelines can be applied in routine clinical practice of radiotherapy TVD, for NSCLC patients treated with concurrent chemoradiation or radiotherapy alone, where FDG is used, and where a calibrated PET camera system equipped for RTP patient positioning is available. Recommendations are provided for PET and CT image visualization and interpretation, and for tumor delineation using planning CT with and without breathing motion compensation.

Experimental validation of a drive-by stiffness identification method for bridge monitoring

An experimental investigation is carried out to verify the feasibility of using an instrumented vehicle to detect and monitor bridge dynamic parameters. The low-cost method consists of the use of a moving vehicle fitted with accelerometers on its axles. In the laboratory experiment, the vehicle–bridge interaction model consists of a scaled two-axle vehicle model crossing a simply supported steel beam. The bridge model also includes a scaled road surface profile. The effects of varying the vehicle model configuration and speed are investigated. A finite element beam model is calibrated using the experimental results, and a novel algorithm for the identification of global bridge stiffness is validated. Using measured vehicle accelerations as input to the algorithm, the beam stiffness is identified with a reasonable degree of accuracy.

Identification of Road Irregularities via Vehicle Accelerations

A periodic monitoring of the pavement condition facilitates a cost-effective distribution of the resources available for maintenance of the road infrastructure network. The task can be accurately carried out using profilometers, but such an approach is generally expensive. This paper presents a method to collect information on the road profile via accelerometers mounted in a fleet of non-specialist vehicles, such as police cars, that are in use for other purposes. It proposes an optimisation algorithm, based on Cross Entropy theory, to predict road irregularities. The Cross Entropy algorithm estimates the height of the road irregularities from vehicle accelerations at each point in time. To test the algorithm, the crossing of a half-car roll model is simulated over a range of road profiles to obtain accelerations of the vehicle sprung and unsprung masses. Then, the simulated vehicle accelerations are used as input in an iterative procedure that searches for the best solution to the inverse problem of finding road irregularities. In each iteration, a sample of road profiles is generated and an objective function defined as the sum of squares of differences between the ‘measured’ and predicted accelerations is minimized until convergence is reached. The reconstructed profile is classified according to ISO and IRI recommendations and compared to its original class. Results demonstrate that the approach is feasible and that a good estimate of the short-wavelength features of the road profile can be detected, despite the variability between the vehicles used to collect the data.

Using Instrumented Vehicles To Detect Damage in Bridges

Bridge structures are subject to continuous degradation due to the environment, ageing and excess loading. Monitoring of bridges is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. This paper will theoretically assess the ability of a vehicle fitted with accelerometers on its axles to detect changes in damping of bridges, which may be the result of damage. Two vehicle models are used in this investigation. The first is a two degree-of-freedom quarter-car and the second is a four degree-of-freedom halfcar. The bridge is modelled as a simply supported beam and the interaction between the vehicle and the bridge is a coupled dynamic interaction algorithm. Both smooth and rough road profiles are used in the simulation and results indicate that changes in bridge damping can be detected by the vehicle models for a range of vehicle velocities and bridge spans.

Using Instrumented Vehicles to Detect Damage in Bridges

This paper describes a ‘drive-by’ method of bridge inspection using an instrumented vehicle. Accelerometers on the vehicle are proposed as a means of detecting damage on the bridge in the time it takes for the vehicle to cross the bridge at full highway speed. For a perfectly smooth road profile, the method is shown to be feasible. Changes in bridge damping, which is an indicator of damage, are clearly visible in the acceleration signal of a quarter-car vehicle on a smooth road surface modelled using MatLab. When road profile is considered, the influence of changes in bridge damping on the vehicle acceleration signal is much less clear. However, when a half-car model is used on a road with a rough profile, it is again possible to detect changes in bridge damping, provided the vehicle has two identical axles.

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.