Refinement of cryo-EM 3D maps with a self-supervised denoising model: crefDenoiser.

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the reconstructed 3D protein-density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. Here, crefDenoiser is introduced, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines. The crefDenoiser model is trained without the need for `clean' ground-truth target maps. Instead, a custom dataset is employed, composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Competing with the current state-of-the-art, crefDenoiser is designed to optimize for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classic map denoiser and following a network-based sharpening model. Without biasing the map, the proposed denoising method leads to improved visibility of protein structural features, including protein domains, secondary structure elements and modest high-resolution feature restoration.

Threat-dependent scaling of prelimbic dynamics to enhance fear representation.

Promptly identifying threatening stimuli is crucial for survival. Freezing is a natural behavior displayed by rodents toward potential or actual threats. Although it is known that the prelimbic cortex (PL) is involved in both risk evaluation and in fear and anxiety-like behavior expression, here we explored whether PL neuronal activity can dynamically represent different internal states of the same behavioral output (i.e., freezing). We found that freezing can always be decoded from PL activity at a population level. However, the sudden presentation of a fearful stimulus quickly reshaped the PL to a new neuronal activity state, an effect not observed in other cortical or subcortical regions examined. This shift changed PL freezing representation and is necessary for fear memory expression. Our data reveal the unique role of the PL in detecting threats and internally adjusting to distinguish between different freezing-related states in both unconditioned and conditioned fear representations.