Forceps minor control of social behaviour.

The PRISM project, funded by the EU's Innovative Medicines Initiative, has identified a transdiagnostic, pathophysiological relationship between the integrity of the default mode network (DMN) and social dysfunction. To explore the causal link between DMN integrity and social behaviour, we employed a preclinical back-translation approach, using focal demyelination of the forceps minor to disrupt DMN connectivity in mice. By applying advanced techniques such as functional ultrasound imaging and automated analysis of social behaviour, we demonstrated that reduced DMN connectivity leads to impaired social interactions and increased anxiety in mice.

Inferring protein fitness landscapes from laboratory evolution experiments.

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory.

Coherent characterisation of a single molecule in a photonic black box.

Extinction spectroscopy is a powerful tool for demonstrating the coupling of a single quantum emitter to a photonic structure. However, it can be challenging in all but the simplest of geometries to deduce an accurate value of the coupling efficiency from the measured spectrum. Here we develop a theoretical framework to deduce the coupling efficiency from the measured transmission and reflection spectra without precise knowledge of the photonic environment.

Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data.

Programmed Sequential Additions to Halogenated Mucononitriles.

Dihalomucononitriles were synthesized and their reactivity evaluated to assess their ability to function as linchpin reagents. Bis(2-chloroacrylonitrile) and bis(2-bromoacrylonitrile) were synthesized from 2,1,3-benzothiadiazole and undergo conjugate addition/elimination reactions with both nitrogen (40-95% yield) and carbon nucleophiles (72-93% yield). Secondary amines undergo monosubstitutions, while carbon nucleophiles are added twice.