Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization. (14), and single-cell (15) and in situ Hi-C (16)], close chromatin contacts can now be identified at increasing resolution, providing new insight into genome organization. These methods measure the relative frequencies of chromosome interactions averaged over a large population of cells. However, individual 3D genome structures can vary dramatically from cell to cell even within an isogenic sample, especially with respect to long-range interactions (15,17,18). This structural variability poses a great challenge to the interpretation of ensemble-averaged Hi-C data (14,(19)(20)(21)(22)(23) and prevents the direct detection of cooperative interactions co-occurring in the same cell. This problem is particularly evident for long-range (cis) and interchromosomal (trans) interactions, which are generally observed at relatively low frequencies and are therefore present only in a small subset of individual cells at any given time (3,11,15). Despite their low frequencies, long-range and interchromosome interaction patterns are not random noise. In fact, these interactions are more informative than short-range interactions in determining the global genome architectures in cells and are often functionally relevant-interactions between transcriptionally active regions are often interchromosomal in nature (14). Owing to their variable nature, long-range and trans interactions can be part of alternative, structurally different conformations, which makes their interpretation in form of consensus structures impossible. However, inferring which of the long-range interactions co-occur in the same cell from ensemble Hi-C data remains a major challenge.These challenges cannot be easily overcome even by the new single-cell Hi-C techno...

Soybean is an important legume crop that displays the classic shade avoidance syndrome (SAS), including exaggerated stem elongation, which leads to lodging and yield reduction under density farming conditions. Here, we compared the effects of two shade signals, low red light to far-red light ratio (R:FR) and low blue light (LBL), on soybean status and revealed that LBL predominantly induces excessive stem elongation. We used CRISPR-Cas9-engineered Gmcry mutants to investigate the functions of seven cryptochromes (GmCRYs) in soybean and found that the four GmCRY1s overlap in mediating LBL-induced SAS. Lightactivated GmCRY1s increase the abundance of the bZIP transcription factors STF1 and STF2, which directly upregulate the expression of genes encoding GA2 oxidases to deactivate GA 1 and repress stem elongation. Notably, GmCRY1b overexpression lines displayed multiple agronomic advantages over the wild-type control under both dense planting and intercropping conditions. Our study demonstrates the integration of GmCRY1-mediated signals with the GA metabolic pathway in the regulation of LBL-induced SAS in soybean. It also provides a promising option for breeding lodging-resistant, high-yield soybean cultivars in the future.

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Three-dimensional (3D) genome structures vary from cell to cell even in an isogenic sample. Unlike protein structures, genome structures are highly plastic, posing a significant challenge for structure-function mapping. Here we report an approach to comprehensively identify 3D chromatin clusters that each occurs frequently across a population of genome structures, either deconvoluted from ensemble-averaged Hi-C data or from a collection of single-cell Hi-C data. Applying our method to a population of genome structures (at the macrodomain resolution) of lymphoblastoid cells, we identify an atlas of stable inter-chromosomal chromatin clusters. A large number of these clusters are enriched in binding of specific regulatory factors and are therefore defined as ‘Regulatory Communities.' We reveal two major factors, centromere clustering and transcription factor binding, which significantly stabilize such communities. Finally, we show that the regulatory communities differ substantially from cell to cell, indicating that expression variability could be impacted by genome structures.

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AI plays an important role in COVID-19 identification. Computer vision and deep learning techniques can assist in determining COVID-19 infection with Chest X-ray Images. However, for the protection and respect of the privacy of patients, the hospital's specific medical-related data did not allow leakage and sharing without permission. Collecting such training data was a major challenge. To a certain extent, this has caused a lack of sufficient data samples when performing deep learning approaches to detect COVID-19. Federated Learning is an available way to address this issue. It can effectively address the issue of data silos and get a shared model without obtaining local data. In the work, we propose the use of federated learning for COVID-19 data training and deploy experiments to verify the effectiveness. And we also compare performances of four popular models (MobileNet, ResNet18, MoblieNet, and COVID-Net) with the federated learning framework and without the framework. This work aims to inspire more researches on federated learning about COVID-19.

New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

Previously, it was reported that miR396s interact with growth-regulating factors (GRFs) to modulate plant growth, development, and stress resistance. In soybean, 11 gma-miR396 precursors (Pre-miR396a–k) were found, and 24 GmGRFs were predicted as targets of seven mature gma-miR396s (gma-miR396a/b/c/e/h/i/k). To explore the roles of the miR396–GRF module in low water availability response of soybean, we analyzed the expression of Pre-miR396a–k, and found that Pre-miR396a/i/bdgk/e/h were up-regulated in leaves and down-regulated in roots; on the contrary, GmGRF5/6/7/8/15/17/21 were down-regulated in leaves and GmGRF1/2/17/18/19/20/21/22/23/24 were up-regulated in roots of low water potential stressed soybean. Any one of gma-miR396a/b/c/e/h/i/k was able to interact with 20 GmGRFs (GmGRF1/2/6–11/13–24), confirming that this module represents a multi-to-multi network interaction. We generated Arabidopsis plants over-expressing each of the 11 gma-miR396 precursors (Pre-miR396a–k), and seven of them (miR396a/b/c/e/h/i/k-OE transgenic Arabidopsis) showed altered development. The low water availability of miR396a/b/c/e/h/i/k-OE was enhanced in leaves but reduced in seeds and roots. Contrary to previous reports, miR396a/b/c/i-OE seedlings showed lower survival rate than WT when recovering after rewatering under soil drying. In general, we believe our findings are valuable to understand the role of gma-miR396 family in coordinating development and low water availability responses, and can provide potential strategies and directions for soybean breeding programs to improve seed yield and plant drought tolerance.