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Cells sense and transduce mechanical forces to regulate diverse biological processes, yet the mechanical stimuli that initiate these processes remain poorly understood. In particular, how nuclear and cytoplasmic deformations respond to external forces is unclear. Here, we developed a microscopy-based technique to quantify the extensional uniaxial strains of the nucleus and cytoplasm during cell stretching, enabling direct measurement of their bulk mechanical responses. Using this approach, we identified a previously unrecognized inverse relationship between nuclear and cytoplasmic deformation in epithelial monolayers. We demonstrate that nucleo-cytoskeletal coupling, mediated by the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, regulates this anti-correlation (Pearson correlation coefficient approx. 0.3). Disrupting LINC abolished this relationship, revealing its fundamental role in intracellular deformation partitioning. Furthermore, we found that cytoplasmic deformation is directly correlated with stretch-induced nuclear shrinkage, suggesting a mechanotransduction pathway in which cytoplasmic mechanics influence nuclear responses. Lastly, multivariable analyses established that intracellular deformation can be inferred from cell morphology, providing a predictive framework for cellular mechanical behaviour. These findings refine our understanding of nucleo-cytoskeletal coupling in governing intracellular force transmission and mechanotransduction. 

Algorithms provide powerful tools for detecting and dissecting human bias and error. Here, we develop machine learning methods to to analyze how humans err in a particular high-stakes task: image interpretation. We leverage a unique dataset of 16,135,392 human predictions of whether a neighborhood voted for Donald Trump or Joe Biden in the 2020 US election, based on a Google Street View image. We show that by training a machine learning estimator of the Bayes optimal decision for each image, we can provide an actionable decomposition of human error into bias, variance, and noise terms, and further identify specific features (like pickup trucks) which lead humans astray. Our methods can be applied to ensure that human-in-the-loop decision-making is accurate and fair and are also applicable to black-box algorithmic systems. 

Abstract Understanding brain function requires monitoring local and global brain dynamics. Two-photon imaging of the brain across mesoscopic scales has presented trade-offs between imaging area and acquisition speed. We describe a flexible cellular resolution two-photon microscope capable of simultaneous video rate acquisition of four independently targetable brain regions spanning an approximate five-millimeter field of view. With this system, we demonstrate the ability to measure calcium activity across mouse sensorimotor cortex at behaviorally relevant timescales.