Few-Shot Learning for Low-Data Drug Discovery.

The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of conventional machine learning techniques to this problem domain. This work explores few-shot machine learning for hit discovery and lead optimization.

The effect of alloying elements on the properties of pressed and non-pressed biodegradable Fe-Mn-Ag powder metallurgy alloys.

Current trends in the biodegradable scaffold industry call for powder metallurgy methods in which compression cannot be applied due to the nature of the scaffold template itself and the need to retain the shape of an underlying template throughout the fabrication process. Iron alloys have been shown to be good candidates for biomedical applications where load support is required. Fe-Mn alloys were researched extensively for this purpose.

Influence of cold rolling on cytotoxicity and electrochemical behaviour of an Fe-Mn-C biodegradable alloy in physiological solutions.

The properties of cold-worked Fe-13Mn-1.2C steel, as candidate material for scaffolding and stenting applications, have been investigated. The study of the electrochemical corrosion susceptibility of Fe-13Mn-1.

Biocalcifying Bacillus subtilis cells effectively consolidate deteriorated Globigerina limestone.

Microbially induced calcite precipitation occurs naturally on ancient limestone surfaces in Maltese hypogea. We exploited this phenomenon and treated deteriorated limestone with biocalcifying bacteria. The limestone was subjected to various mechanical and physical tests to present a statistically robust data set to prove that treatment was indeed effective.