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Quantum many-body scar states are highly excited eigenstates of many-body systems that exhibit atypical entanglement and correlation properties relative to typical eigenstates at the same energy density. Scar states also give rise to infinitely long-lived coherent dynamics when the system is prepared in a special initial state having finite overlap with them. Many models with exact scar states have been constructed, but the fate of scarred eigenstates and dynamics when these models are perturbed is difficult to study with classical computational techniques. In this work, we propose state preparation protocols that enable the use of quantum computers to study this question. We present protocols both for individual scar states in a particular model, as well as superpositions of them that give rise to coherent dynamics. For superpositions of scar states, we present both a system-size-linear depth unitary and a finite-depth nonunitary state preparation protocol, the latter of which uses measurement and postselection to reduce the circuit depth. For individual scarred eigenstates, we formulate an exact state preparation approach based on matrix product states that yields quasipolynomial-depth circuits, as well as a variational approach with a polynomial-depth ansatz circuit. We also provide proof of principle state-preparation demonstrations on superconducting quantum hardware. 

The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

 

Three recent publications on BEND3 firmly establish its role as a novel sequence‐specific transcription factor that is essential for PRC2 recruitment and maintenance of pluripotency. Here, we briefly review our current understanding of the BEND3‐PRC2 axis in the regulation of pluripotency and also explore the possibility of a similar connection in cancer.

 

BEN domain–containing proteins are emerging rapidly as an important class of factors involved in modulating gene expression, yet the molecular basis of how they regulate chromatin function and transcription remains to be established. BEND3 is a quadruple BEN domain–containing protein that associates with heterochromatin and functions as a transcriptional repressor. We find that BEND3 is highly expressed in pluripotent cells, and the induction of differentiation results in the down-regulation of BEND3. The removal of BEND3 from pluripotent cells results in cells exhibiting upregulation of the differentiation-inducing gene expression signature. We find that BEND3 binds to the promoters of differentiation-associated factors and key cell cycle regulators, including CDKN1A , encoding the cell cycle inhibitor p21, and represses the expression of differentiation-associated genes by enhancing H3K27me3 decoration at these promoters. Our results support a model in which transcription repression mediated by BEND3 is essential for normal development and to prevent differentiation. 

Heterochromatic domains are enriched with repressive histone marks, including histone H3 lysine 9 methylation, written by lysine methyltransferases (KMTs). The pre-replication complex protein, origin recognition complex-associated (ORCA/LRWD1), preferentially localizes to heterochromatic regions in post-replicated cells. Its role in heterochromatin organization remained elusive. ORCA recognizes methylated H3K9 marks and interacts with repressive KMTs, including G9a/GLP and Suv39H1 in a chromatin context-dependent manner. Single-molecule pull-down assays demonstrate that ORCA-ORC (Origin Recognition Complex) and multiple H3K9 KMTs exist in a single complex and that ORCA stabilizes H3K9 KMT complex. Cells lacking ORCA show alterations in chromatin architecture, with significantly reduced H3K9 di- and tri-methylation at specific chromatin sites. Changes in heterochromatin structure due to loss of ORCA affect replication timing, preferentially at the late-replicating regions. We demonstrate that ORCA acts as a scaffold for the establishment of H3K9 KMT complex and its association and activity at specific chromatin sites is crucial for the organization of heterochromatin structure.