Towards a new method for kinematic quantification of bradykinesia in patients with parkinson's disease using triaxial accelerometry

We propose a new kinematic analysis procedure using triaxial accelerometers mounted to the wrist in the assessment of bradykinesia in patients with Parkinson's disease (PD). The deviation of the magnitude of the accelerometer vector signal from the magnitude of the gravitational acceleration is taken as a measure for effective magnitude of the acceleration at the position of the triaxial accelerometer. For low acceleration, two of the three angles describing the orientation of the lower arm can be derived from the accelerometer signal

Periodic fever associated with intermittent rhytmic delta activity : a syndrome of hypothalamic origin?

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Stilstaan bij beweging : over de hardware en software problemen van het bewegen

Niet UB, maar tijdelijk ter bevordering van de PDF bestanden in het Leids Repositorium

Inverse design of curvature-sensing antiviral peptides

Viral diseases constitute one of the major challenges in modern medicine. Membrane-targeting have broad-spectrum potential and could distinguish viral membranes from the host cell plasma membrane by the difference in membrane curvature. In this thesis, we used a combination of molecular dynamics simulations and an evolutionary algorithm to inverse design such curvature-sensing antiviral peptides, purely from physical principles.Supramolecular & Biomaterials Chemistr

Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature “sensors” challenges our understanding of how they differ from general membrane “binders” that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known sensors and binders. We illustrate that sensing and binding are phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors