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Consider the problem of determining the effect of a compound on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug of interest to that cell type. This approach is not scalable: given a large number of different actions (compounds) and a large number of different contexts (cell types), it is infeasible to run an experiment for every action-context pair. In such cases, one would ideally like to predict the outcome for every pair while only needing outcome data for a small _subset_ of pairs. This task, which we label "causal imputation", is a generalization of the causal transportability problem. To address this challenge, we extend the recently introduced _synthetic interventions_ (SI) estimator to handle more general data sparsity patterns. We prove that, under a latent factor model, our estimator provides valid estimates for the causal imputation task. We motivate this model by establishing a connection to the linear structural causal model literature. Finally, we consider the prominent CMAP dataset in predicting the effects of compounds on gene expression across cell types. We find that our estimator outperforms standard baselines, thus confirming its utility in biological applications. 

In this short review, we provide an overview of our efforts in developing a family of anodically coloring electrochromic (EC) molecules that are fully transparent and colorless in the charge neutral state, and that can rapidly switch to a vibrantly colored state upon oxidation. We employ molecules with reduced conjugation lengths to center the neutral state absorption of the electrochrome in the ultraviolet, as desired for highly transparent and colorless materials. Oxidation creates radical cations that absorb light in the visible and near infrared regions of the electromagnetic spectrum, thus providing a host of accessible colors. Combining a density functional theory (DFT) computational approach fed back to the synthetic effort, target molecules are proposed, synthesized and studied, directing us to develop a complete color palette based on these high contrast ACE molecules. Utilizing pendant phosphonic acid binding substituents in concert with high surface area mesoporous indium tin oxide (ITO) electrodes, the electrochromes can be distributed throughout the oxide film, bringing high extent of light absorption and color density. 

Abstract Atomic nuclei are self-organized, many-body quantum systems bound by strong nuclear forces within femtometre-scale space. These complex systems manifest a variety of shapes^1–3, traditionally explored using non-invasive spectroscopic techniques at low energies^4,5. However, at these energies, their instantaneous shapes are obscured by long-timescale quantum fluctuations, making direct observation challenging. Here we introduce the collective-flow-assisted nuclear shape-imaging method, which images the nuclear global shape by colliding them at ultrarelativistic speeds and analysing the collective response of outgoing debris. This technique captures a collision-specific snapshot of the spatial matter distribution within the nuclei, which, through the hydrodynamic expansion, imprints patterns on the particle momentum distribution observed in detectors^6,7. We benchmark this method in collisions of ground-state uranium-238 nuclei, known for their elongated, axial-symmetric shape. Our findings show a large deformation with a slight deviation from axial symmetry in the nuclear ground state, aligning broadly with previous low-energy experiments. This approach offers a new method for imaging nuclear shapes, enhances our understanding of the initial conditions in high-energy collisions and addresses the important issue of nuclear structure evolution across energy scales.