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Abstract The spatial organization and dynamics of a genome are central to gene regulation. While a comprehensive understanding of chromatin organization in the human nucleus has been achieved using fixed-cell methods, measuring the dynamics of specific genomic regions over extended periods in individual living cells remains challenging. Here, we present a robust and fully genetically encoded system for fluorescent labeling and long-term tracking of any accessible non-repetitive genomic locus in live human cells using fluorogenic and replenishable nanobody array fusions of theStaphylococcus aureusdCas9, and compact polycistronic single guide (sg)RNAs. First, we characterize the selectivity and photostability of our probes, enabling genome-wide visualization of chromatin dynamics at locally repetitive elements. Next, through multiplexed expression of 8–10 sgRNAs from polycistronic cassettes, we demonstrate efficient and sustained labeling of non-repetitive loci, enabling high-fidelity tracking of gene-proximal regions at exceptional spatial and temporal resolution. Finally, by correlating chromatin mobility with transcriptional activity at multiple genes, we find that local chromatin dynamics at 20 Hz are gene-specific and not necessarily dependent on transcription. Our approach is versatile, minimally invasive, and scalable, enabling multiplexed imaging of regulatory element dynamics involved in gene control, with broad applicability across diverse biological systems and disease contexts. 

ABSTRACT We develop machine learning models that incorporate both external (deterministic) and internal (voluntaristic) factors affecting firm failure and survival. Using structured and unstructured data, we empirically investigate the external and internal factors that affect the US manufacturing firms’ business failure. We also examine how the interactions between external shocks and firm responses impact business failure. Our findings indicate that while external factors can significantly impact the likelihood that firms fail, specific management responses to these challenges can effectively mitigate the negative effects and contribute to firm survival. 

We develop an efficient approach to evaluate range-separated exact exchange for grid- or plane-wave-based representations within the generalized Kohn–Sham–density functional theory (GKS–DFT) framework. The Coulomb kernel is fragmented in reciprocal space, and we employ a mixed deterministic-stochastic representation, retaining long-wavelength (low-k) contributions deterministically and using a sparse (“fragmented”) stochastic basis for the high-k part. Coupled with a projection of the Hamiltonian onto a subspace of valence and conduction states from a prior local-DFT calculation, this method allows for the calculation of the long-range exchange of large molecular systems with hundreds and potentially thousands of coupled valence states delocalized over millions of grid points. We find that even a small number of valence and conduction states is sufficient for converging the HOMO and LUMO energies of the GKS–DFT. Excellent tuning of long-range separated hybrids (RSH) is easily obtained in the method for very large systems, as exemplified here for the chlorophyll hexamer of Photosystem II with 1320 electrons. 

This study employs supervised machine learning algorithms to test whether locomotive features during exploratory activity in open field arenas can serve as predictors for the genotype of fruit flies. Because of the nonlinearity in locomotive trajectories, traditional statistical methods that are used to compare exploratory activity between genotypes of fruit flies may not reveal all insights. 10-minute-long trajectories of four different genotypes of fruit flies in an open-field arena environment were captured. Turn angles and step size features extracted from the trajectories were used for training supervised learning models to predict the genotype of the fruit flies. Using the first five minute locomotive trajectories, an accuracy of 83% was achieved in differentiating wild-type flies from three other mutant genotypes. Using the final 5 min and the entire ten minute duration decreased the performance indicating that the most variations between the genotypes in their exploratory activity are exhibited in the first few minutes. Feature importance analysis revealed that turn angle is a better predictor than step size in predicting fruit fly genotype. Overall, this study demonstrates that features of trajectories can be used to predict the genotype of fruit flies through supervised machine learning methods.