Effect of temperature and iron-oxide nano-particle inclusions on the ultrasound vaporization pressure of perfluorocarbon droplets for disease detection and therapy

Introduction: The longer circulating time and larger particle count of perfluorocarbon (PFC) droplets make them more effective as targeted contrast agents compared to microbubbles. It has also been shown that conversion of PFC droplets to microbubbles improves ultrasound (US) sensitivity to emulsions by 10 orders of magnitude. PFC droplets, with their higher boiling point and smaller particle size are more desirable to prevent spontaneous aporization and maximum accumulation of emulsions. However, the high US energy required to induce phase conversion in these emulsions is not clinically feasible. We hypothesized that by increasing the temperature and using Iron-oxide nanoparticles(IONP) as nucleation sites within the more stable and submicron droplets, the US energy required to vaporize them may be lowered. Material and method: A sample of 60%w/v iron-oxide loaded PFC droplets with mean diameter of 200nm was manufactured and characterized. A phantom was designed to allow for the interaction between the US energy and droplets. A high intensity focused ultrasound system (HIFU) was used to generate US pressures and a heat exchanger pump was used to control the temperature while emulsions of PFHB and PFP with or without IONP were circulated through the chamber. A harmonic imaging system was also used to detect the generated microbubbles. Discussion and result: The effect of temperature and IONP on the vaporization rate and threshold of PFC emulsions were determined. It was shown that presence of IONP and higher temperature increase the rate and decreases the vaporization threshold of PFC emulsion

Embedding optimization in computational science workflows

Workflows support the automation of scientific processes, providing mechanisms that underpin modern computational science. They facilitate access to remote instruments, databases and parallel and distributed computers. Importantly, they allow software pipelines that perform multiple complex simulations (leveraging distributed platforms), with one simulation driving another. Such an environment is ideal for computational science experiments that require the evaluation of a range of different scenarios "in silico" in an attempt to find ones that optimize a particular outcome. However, in general, existing workflow tools do not incorporate optimization algorithms, and thus whilst users can specify simulation pipelines, they need to invoke the workflow as a stand-alone computation within an external optimization tool. Moreover, many existing workflow engines do not leverage parallel and distributed computers, making them unsuitable for executing computational science simulations. To solve this problem, we have developed a methodology for integrating optimization algorithms directly into workflows. We implement a range of generic actors for an existing workflow system called Kepler, and discuss how they can be combined in flexible ways to support various different design strategies. We illustrate the system by applying it to an existing bio-engineering design problem running on a Grid of distributed clusters