@article {70384, title = {Ousiometrics: The essence of meaning aligns with a power-danger-structure framework instead of valence-arousal-dominance.}, journal = {Sci Adv}, volume = {12}, year = {2026}, month = {2026 May 06}, pages = {eadr4039}, abstract = {

From work emerging through the middle of the 20th century, the essence of meaning has become widely accepted as being described by the three orthogonal dimensions of valence, arousal, and dominance. These essential dimensions have become the cornerstone of sentiment analysis across many fields. By reexamining first types and then tokens for the English language, and through the use of automatically annotated histograms-"ousiograms"-we find here that the essence of meaning conveyed by words is instead best described by a goodness-power-aggression-danger-structure (GPADS) circumplex framework; that large-scale English language corpora reveal a systematic bias toward safe, low-danger words; and that the power-danger-structure framework is the minimal framework that represents essential meaning. We find remarkable congruences between the GPADS framework and other spaces including mental states and fictional archetypes, and we construct and demonstrate a prototype ousiometer.

}, keywords = {Arousal, Emotions, Humans, Language}, issn = {2375-2548}, doi = {10.1126/sciadv.adr4039}, author = {Dodds, Peter Sheridan and Alshaabi, Thayer and Fudolig, Mikaela Irene and Zimmerman, Julia Witte and Lovato, Juniper and Beaulieu, Shawn and Minot, Joshua R and Arnold, Michael V and Reagan, Andrew J and Danforth, Christopher M} } @article {70132, title = {Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens.}, journal = {Nat Methods}, year = {2025}, month = {2025 Oct 01}, abstract = {

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOVIFT (adaptive optical vision Fourier transformer)-a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOVIFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOVIFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOVIFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Preprint: https://doi.org/10.48550/arXiv.2503.12593

}, issn = {1548-7105}, doi = {10.1038/s41592-025-02844-7}, url = {https://doi.org/10.1038/s41592-025-02844-7}, author = {Alshaabi, Thayer and Milkie, Daniel E and Liu, Gaoxiang and Shirazinejad, Cyna and Hong, Jason L and Achour, Kemal and G{\"o}rlitz, Frederik and Milunovic-Jevtic, Ana and Simmons, Cat and Abuzahriyeh, Ibrahim S and Hong, Erin and Williams, Samara Erin and Harrison, Nathanael and Huang, Evan and Bae, Eun Seok and Killilea, Alison N and Swinburne, Ian A and Drubin, David G and Upadhyayula, Srigokul and Betzig, Eric} }