Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.

In this paper, the fault detection problem for a multi-packet transmission network control system with limited communication and random delay is studied. The communication sequence method is introduced to deal with the limited communication problems in the system, and a multi-packet transmission is equivalent to a Markov jump process. A fault detection filter based on delta domain is established for the system model to generate the residual signal. The residual and fault signals are further used to generate the residual error so that the fault signal can be detected intuitively. Through linear matrix inequality (LMI) method and Lyapunov-Krasinskii stability theory, the designed H∞fault detection filter's stability conditions are gained. Finally, a numerical simulation example is shown to demonstrate the availability of the proposed method.