Inferring murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification.

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a process whose dysregulation is linked to neurodegenerative diseases like Alzheimer's. Traditional approaches like particle tracking velocimetry (PTV) are limited by their reliance on single-plane two-dimensional measurements, which fail to capture the complex dynamics of CSF flow fully. To overcome these limitations, we employ artificial intelligence velocimetry (AIV) to reconstruct three-dimensional velocities, infer pressure and wall shear stress and quantify flow rates.

Trapping and Sympathetic Cooling of Conformationally Selected Molecular Ions.

We report the generation, trapping, and sympathetic cooling of individual conformers of molecular ions with the example of cis- and trans- meta-aminostyrene. Following conformationally selective photoionization, the incorporation of the conformers into a Coulomb crystal of laser-cooled calcium ions was confirmed by fluorescence imaging, mass spectrometry, and molecular dynamics simulations. We deduce the molecules to be stable in the trap environment for more than ten minutes.

Rotational-state-selected Carbon Astrochemistry.

The addition of individual quanta of rotational excitation to a molecule has been shown to markedly change its reactivity by significantly modifying the intermolecular interactions. So far, it has only been possible to observe these rotational effects in a very limited number of systems due to lack of rotational selectivity in chemical reaction experiments. The recent development of rotationally controlled molecular beams now makes such investigations possible for a wide range of systems.

Decoding speech sounds from neurophysiological data: Practical considerations and theoretical implications.

Machine learning techniques have proven to be a useful tool in cognitive neuroscience. However, their implementation in scalp-recorded electroencephalography (EEG) is relatively limited. To address this, we present three analyses using data from a previous study that examined event-related potential (ERP) responses to a wide range of naturally-produced speech sounds.