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              <text>The brown dwarf metallicity correlation was investigated for a set of 48 transiting brown dwarfs, taken from wide field surveys. Host star metallicity was compared to field stars using the Besançon galaxy model, with a metallicity offset between the sets found. Quantifying this using a power law the correlation constant was found to be B= 1.08+0.58 −0.52. Limiting the sample to those brown dwarfs below 42.5 MJ and above 42.5 MJ the correlation constants were found to be B= 1.44+0.87 −0.79 and B= 0.95+0.66 −0.54 respectively. This suggests the population of brown dwarfs with masses below 42.5 MJ form via core accretion whereas those with masses above 42.5 MJ form via gravitational collapse.</text>
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                <text>Vaughn Totterdill </text>
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              <text> Within a biological system, proteins are essential; they are the building blocks of life. Their functions range from performing as antibodies to catalysing reactions as enzymes; so, proteins have an extremely varied and extensive use through a variety of industries. With mutations, their stability and functionality can be modified, with even single-point mutations having a strong effect. Therefore, being able to predict the effect in stability changes of these mutations is essential, but often computationally costly and weak when encountering unseen data. With the rise in popularity of protein language models (PLMs), including ESM, there have been improvements, but these models still encounter the same difficulties. This study explores the effect of ensemble learning on improving protein stability predictions. This is done by combining two base models: a convolutional neural network (CNN) using wild-type and mutant sequences, and a neural network trained on frozen ESM embeddings. They each form two base-learners, where each is trained on half the base training data. To combine their predictions, intermediate meta-learners for each model type and a final meta-learner are used. Results show that ensemble learning improves predictions over base models, showing improvements in all performance metrics used. Notably, even the weaker CNN-based learners contributed to improved ensemble performance, reinforcing the benefit of this type of architecture. Although it is necessary to further optimise hyperparameters and include stronger base models in the future, these results indicate that ensemble learning can lead to more accurate protein stability prediction with minimal additional computational cost.</text>
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                <text>Predicting Protein Stability of Single-Point  Mutations using Ensemble Learning and ESM  Embeddings</text>
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                <text>Victoria Krushovska</text>
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              <text>THE LOW-MASS ISSUE IN COOL WHITE DWARF STARS</text>
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                <text>Vincent Fairchild</text>
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                <text>Pier-Emmanuel Tremblay</text>
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