Novel insights into surfactant protein C trafficking revealed through the study of a pathogenic mutant.

BACKGROUND: Alveolar epithelial cell dysfunction plays an important role in the pathogenesis of idiopathic pulmonary fibrosis (IPF), but remains incompletely understood. Some monogenic forms of pulmonary fibrosis are associated with expression of mutant surfactant protein C (SFTPC). The commonest pathogenic mutant, I73T, mislocalises to the alveolar epithelial cell plasma membrane and displays a toxic gain of function. Because the mechanisms explaining the link between this mutant and IPF are incompletely understood, we sought to interrogate SFTPC trafficking in health and disease to understand the functional significance of SFTPC-I73T relocalisation. METHODS: We performed mechanistic analysis of SFTPC trafficking in a cell model that reproduces the in vivo phenotype and validated findings in human primary alveolar organoids. RESULTS: We show that wild-type SFTPC takes an unexpected indirect trafficking route via the plasma membrane and undergoes the first of multiple cleavage events before reaching the multivesicular body (MVB) for further processing. SFTPC-I73T takes this same route, but its progress is retarded both at the cell surface and due to failure of trafficking into the MVB. Unable to undergo onward trafficking, it is recycled to the plasma membrane as a partially cleaved intermediate. CONCLUSION: These data show for the first time that all SFTPC transits the cell surface during normal trafficking, and the I73T mutation accumulates at the cell surface through both retarded trafficking and active recycling. This understanding of normal SFTPC trafficking and how the I73T mutant disturbs it provides novel insight into SFTPC biology in health and disease, and in the contribution of the SFTPC mutant to IPF development.

Neuromorphic computation with a single magnetic domain wall.

Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

All-optical switching in granular ferromagnets caused by magnetic circular dichroism.

Magnetic recording using circularly polarised femto-second laser pulses is an emerging technology that would allow write speeds much faster than existing field driven methods. However, the mechanism that drives the magnetisation switching in ferromagnets is unclear. Recent theories suggest that the interaction of the light with the magnetised media induces an opto-magnetic field within the media, known as the inverse Faraday effect. Here we show that an alternative mechanism, driven by thermal excitation over the anisotropy energy barrier and a difference in the energy absorption depending on polarisation, can create a net magnetisation over a series of laser pulses in an ensemble of single domain grains. Only a small difference in the absorption is required to reach magnetisation levels observed experimentally and the model does not preclude the role of the inverse Faraday effect but removes the necessity that the opto-magnetic field is 10 s of Tesla in strength.