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In situ cryogenic electron tomography (cryoET) promises to reveal the distribution and structures of macromolecular complexes across the cell with minimal disturbance to their native context. There have been several proof-of-principle studies but the routine application of this technology is limited by the relatively noisy data, the crowded cellular environment, and the size of the datasets that can be collected. The problem is ideally suited to AI which can learn from the large datasets and give bias-free interpretations of tomograms. There are nevertheless issues with generalisability of trained models and useability by research scientists. In this proposal, we aim to look into AI techniques for 3D particle classification and identification from in situ tomograms. Specifically, we wish to establish a collaboration with the group of Min Xu at Carnegie Mellon University, who has worked in this area for more than 10 years. We will benchmark a selection of his methods on simulated and real datasets, considering factors from accuracy through to ease-of-use. Within the CCP-EM project, we are developing software pipelines for cryoET, and so we are particularly looking for AI tools that can enhance these pipelines. Part of our evaluation will be to quantify the improvement in downstream results, for example higher resolution sub-tomogram averages, providing essential feedback to Xu. We also aim to strengthen our collaboration with Zachary Freyberg at the University of Pittsburgh, with whom we are processing in situ cryoET data on disease-associated cell lines and tissues. These datasets will be used to help benchmark the AI tools, while potentially leading to important research outcomes in their own right. By integrating novel AI tools in our CCP-EM tomography pipelines, this work will have a much larger impact. This depends partly on practicalities such as the robustness of the software and the ease with which we can make trained models available, and this will form an important part of the project. Briefly, we will carry out three tasks: (1) Install selected modules from Xu's AITom package and benchmark on simulated and real datasets, (2) Integrate these tools into the CCP-EM tomography pipeline, and investigate how to optimise the tools in the context of a full investigation, and (3) look into the practicalities of making the software available for general usage, compare with similar tools, and host a workshop for dissemination. There is obviously a significant amount of work needed to develop in situ cryoET into a routine techqniue. This proposal focusses on one specific aspect, namely the application and adaptation of AI approaches to improve the quality of information that can be obtained. As a proposal to the IPAP scheme, we look to expand our existing network of UK and European collaborators to bring in leading US groups. While the CCP-EM consortium is also developing AI tools, the expertise of Xu's group is complementary, covering different specific AI approaches and with a stronger focus on in situ tomography.

United States of America

Magnetic hyperthermia is a promising treatment for brain and prostate cancers due to the localised nature of the treatment compared to chemo or radiotherapy. Brain cancer in particular is difficult to treat with conventional therapies due to the sensitivity of the surrounding tissue with only a 14% survival rate after 10 years in the UK. Magnetic nanoparticles used in magnetic hyperthermia must be biocompatible and provide efficient and reliable heating, yet their physical complexity is limiting progress towards their clinical use. Complexity arises due to the small size of the particles (10-100 nm) leading to a range of physical properties such as surface and bulk atomic defects, finite size and thermal effects, multiple oxide phases and surface functionalization. All of these properties contribute to the overall magnetic properties but are extremely difficult to predict theoretically or with simple model approaches. Previous simulations have considered only simple approaches to the magnetic properties of individual magnetic nanoparticles and give limited insight into the properties of real nanoparticles. Yet there is an urgent need to understand the relative importance of these effects so that experimental effort can be focused on their control and optimisation to accelerate development of this potentially life saving treatment. This proposal will address this challenge by developing a realistic model of magnetic nanoparticles to understand the role of the surface on the particle properties and the resulting magnetization dynamics used to generate heat during magnetic hyperthermia. The aim of the project is to develop a novel atomic scale magnetic model of magnetite nanocrystals to understand the effects of size, shape and the surface on their equilibrium and dynamic magnetic properties. We will use this information to model and understand how the magnetic particles reverse in an applied magnetic field which is directly related to the amount of heat generated during magnetic hyperthermia. Using atomistic spin dynamics we will be able to simulate the effects of thermal fluctuations at the surface on the effective magnetic properties and their importance in determining the reversal mechanism. The interactions between particles can also play a critical role in the overall magnetic properties, and so we will use our model to simulate the interaction of small clusters of particles with atomic resolution giving new insight into their importance. Finally, we will develop an atomistic model of functional core-shell oxide nanoparticles to determine the optimal magnetic properties for magnetic hyperthermia. The computational methods developed in this project will significantly advance the ability to accurately model magnetic composite materials with wide application in the fields of magnetism and spintronics and made freely available to the community within the open source vampire software package. The results from this project will improve our understanding of the properties of magnetite nanocrystals, guide future research on magnetic hyperthermia and accelerate the development of this critical treatment.

United States