HiSV: A control-free method for structural variation detection from Hi-C data.

Structural variations (SVs) play an essential role in the evolution of human genomes and are associated with cancer genetics and rare disease. High-throughput chromosome capture (Hi-C) technology probed all genome-wide crosslinked chromatin to study the spatial architecture of chromosomes. Hi-C read pairs can span megabases, making the technology useful for detecting large-scale SVs. So far, the identification of SVs from Hi-C data is still in the early stages with only a few methods available. Especially, no algorithm has been developed that can detect SVs without control samples. Therefore, we developed HiSV (Hi-C for Structural Variation), a control-free method for identifying large-scale SVs from a Hi-C sample. Inspired by the single image saliency detection model, HiSV constructed a saliency map of interaction frequencies and extracted saliency segments as large-scale SVs. By evaluating both simulated and real data, HiSV not only detected all variant types, but also achieved a higher level of accuracy and sensitivity than existing methods. Moreover, our results on cancer cell lines showed that HiSV effectively detected eight complex SV events and identified two novel SVs of key factors associated with cancer development. Finally, we found that integrating the result of HiSV helped the WGS method to identify a total number of 94 novel SVs in two cancer cell lines.

Kekulé Moiré Superlattices.

In this paper, we discuss a new moiré system where the long moiré periodicity emerges from two dissimilar van der Waals layers with vastly different lattice constants. We reconstruct the first layer using a 3 by 3 supercell resembling the Kekulé distortion in graphene, and such reconstruction becomes nearly commensurate with the second layer. We term this construction a Kekulé moiré superlattice, which enables coupling between moiré bands from remote valleys in momentum space. Kekulé moiré superlattices can be realized in heterostructures of transition metal dichalcogenides and metal phosphorus trichalcogenides such as MoTe2/MnPSe3. By first-principles calculations, we demonstrate that the antiferromagnetic MnPSe3 strongly couples the otherwise degenerate Kramers' valleys of MoTe2, resulting in valley pseudospin textures that depend on the Néel vector direction, stacking geometry, and external fields. With one hole per moiré supercell, the system becomes a Chern insulator with highly tunable topological phases.

CCIP: predicting CTCF-mediated chromatin loops with transitivity.

MOTIVATION: CTCF-mediated chromatin loops underlie the formation of topological associating domains and serve as the structural basis for transcriptional regulation. However, the formation mechanism of these loops remains unclear, and the genome-wide mapping of these loops is costly and difficult. Motivated by the recent studies on the formation mechanism of CTCF-mediated loops, we studied the possibility of making use of transitivity-related information of interacting CTCF anchors to predict CTCF loops computationally. In this context, transitivity arises when two CTCF anchors interact with the same third anchor by the loop extrusion mechanism and bring themselves close to each other spatially to form an indirect loop. RESULTS: To determine whether transitivity is informative for predicting CTCF loops and to obtain an accurate and low-cost predicting method, we proposed a two-stage random-forest-based machine learning method, CTCF-mediated Chromatin Interaction Prediction (CCIP), to predict CTCF-mediated chromatin loops. Our two-stage learning approach makes it possible for us to train a prediction model by taking advantage of transitivity-related information as well as functional genome data and genomic data. Experimental studies showed that our method predicts CTCF-mediated loops more accurately than other methods and that transitivity, when used as a properly defined attribute, is informative for predicting CTCF loops. Furthermore, we found that transitivity explains the formation of tandem CTCF loops and facilitates enhancer-promoter interactions. Our work contributes to the understanding of the formation mechanism and function of CTCF-mediated chromatin loops. AVAILABILITY AND IMPLEMENTATION: The source code of CCIP can be accessed at: https://github.com/GaoLabXDU/CCIP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Twist-Angle-Dependent Ultrafast Charge Transfer in MoS2-Graphene van der Waals Heterostructures.

Vertically stacked transition metal dichalcogenide-graphene heterostructures provide a platform for novel optoelectronic applications with high photoresponse speeds. Photoinduced nonequilibrium carrier and lattice dynamics in such heterostructures underlie these applications but have not been understood. In particular, the dependence of these photoresponses on the twist angle, a key tuning parameter, remains elusive. Here, using ultrafast electron diffraction, we report the simultaneous visualization of charge transfer and electron-phonon coupling in MoS2-graphene heterostructures with different stacking configurations. We find that the charge transfer timescale from MoS2 to graphene varies strongly with twist angle, becoming faster for smaller twist angles, and show that the relaxation timescale is significantly shorter in a heterostructure as compared to a monolayer. These findings illustrate that twist angle constitutes an additional tuning knob for interlayer charge transfer in heterobilayers and deepen our understanding of fundamental photophysical processes in heterostructures, of importance for future applications in optoelectronics and light harvesting.

MSTD: an efficient method for detecting multi-scale topological domains from symmetric and asymmetric 3D genomic maps.

The chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. However, existing methods for domain detection were only designed based on symmetric Hi-C maps, ignoring long-range interaction structures between domains. To this end, we proposed a generic and efficient method to identify multi-scale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets. We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multi-scale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the-state-of-art methods in terms of accuracy, flexibility and efficiency.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Express delivery logistics with high-speed railway: a perspective of payment scheme and forecast information sharing.

This paper investigates the payment scheme and forecast information sharing issues in the express delivery logistics with the high-speed railway. The HSR carriers need to coordinate the transportation capacity between passenger and freight. It is widely recognized that the advance payment scheme (APS) using as deposit is a beneficial way for the HSR carriers to make decisions on the transportation capacity preserved for express delivery. However, the express service providers, who possess private forecast information of express delivery demand, may share inaccurate information with the HSR carriers to acquire sufficient preserved transportation capacity. This paper discusses what payment scheme is preferred by the HSR carrier, the express service provider through discussing the deposit decisions with or without forecast information sharing. We show that sharing demand forecast information can reduce the prereserved capacity and increase the profits of the HSR carrier. With the delayed payment scheme (DPS), the express service provider has no motivation to share the information; while with the APS, the HSR carrier can reasonably choose the deposit to encourage the express service provider to share the demand information. Our analysis also shows that the HSR carrier's profits with the APS is restricted by the investment returns and the express service provider's information sharing decisions. We also analyze the value range of the deposit, which is a proportion of the overall payment, that allows both the HSR carrier and the express service provider to prefer the APS, as well as to encourage the express service provider to share the demand information.

Deciphering Hierarchical Chromatin Domains and Preference of Genomic Position Forming Boundaries in Single Mouse Embryonic Stem Cells.

The exploration of single-cell 3D genome maps reveals that chromatin domains are indeed physical structures presenting in single cells, and domain boundaries vary from cell to cell. However, systematic analysis of the association between regulatory factor binding and elements and the formation of chromatin domains in single cells has not yet emerged. To this end, a hierarchical chromatin domain structure identification algorithm (named as HiCS) is first developed from individual single-cell Hi-C maps, with superior performance in both accuracy and efficiency. The results suggest that in addition to the known CTCF-cohesin complex, Polycomb, TrxG, pluripotent protein families, and other multiple factors also contribute to shaping chromatin domain boundaries in single embryonic stem cells. Different cooperation patterns of these regulatory factors drive genomic position categories with differential preferences forming boundaries, and the most extensive six types of retrotransposons are differentially distributed in these genomic position categories with preferential localization. The above results suggest that these different retrotransposons within genomic regions interplay with regulatory factors navigating the preference of genomic positions forming boundaries, driving the formation of higher-order chromatin structures, and thus regulating cell functions in single mouse embryonic stem cells.

scHi-CSim: a flexible simulator that generates high-fidelity single-cell Hi-C data for benchmarking.

Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells. However, high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis. Here, we developed a single-cell Hi-C simulator (scHi-CSim) that generates high-fidelity data for benchmarking. scHi-CSim merges neighboring cells to overcome the sparseness of data, samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells, and estimates the empirical distribution of restriction fragments to generate simulated data. We demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw data. Furthermore, scHi-CSim is flexible to change sequencing depth and the number of simulated replicates. We showed that increasing sequencing depth could improve the accuracy of detecting topologically associating domains. We also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.

Philanthropic sales in live-streaming shopping: The impact of online interaction on consumer impulse buying.

As philanthropic sales via live-streaming shopping have played an important role in alleviating the huge backlog of agricultural products during the outbreak of the COVID-19 pandemic, this paper aims to study how online interaction in philanthropic marketing exerts influence on consumer impulse buying behaviors. We empirically explore four major dimensions of online interactions in philanthropic live-streaming sales, i.e., the live streamers' image, the herd effect of consumers, the responsiveness of sellers, and the mutual trust between consumers. The results reveal that the herd effect of consumers and the responsiveness of sellers could promote consumers' empathy ability toward the growers of the products sold lively, whereas the live streamers' image and the mutual trust between consumers have little effect on empathy promotions. Meanwhile, both the consumers' empathy ability and the live streamers' image positively affect consumers' impulse buying behavior, which suggests a partial moderating role of consumers' empathy ability. Lastly, by taking both social and business perspectives, we provide managerial implications for improving the effectiveness and efficiency of philanthropic live-streaming sales to alleviate social and economic pressure in emergencies.

Critical Decision-Making Issues in Disaster Relief Supply Management: A Review.

This paper comprehensively reviews the literature related to disaster relief supply management in recent years by taking the perspectives of three critical decision-making issues, i.e., coordination issues, facility location decisions, and inventory decisions. For each decision-making issue discussed, we clarify the barriers of current research papers and identify the major challenges and critical factors that should be considered. In the following, we present the perspectives on the road of coordination between multiple relief actors, characterize the location decisions of relief facilities with a variety of optimization objectives, and emphasize the importance of relief supply varieties and critical factors in the decisions of disaster relief inventories. Future research directions are recommended for further discussions.

Long-range transport of 2D excitons with acoustic waves.

Excitons are elementary optical excitation in semiconductors. The ability to manipulate and transport these quasiparticles would enable excitonic circuits and devices for quantum photonic technologies. Recently, interlayer excitons in 2D semiconductors have emerged as a promising candidate for engineering excitonic devices due to their long lifetime, large exciton binding energy, and gate tunability. However, the charge-neutral nature of the excitons leads to weak response to the in-plane electric field and thus inhibits transport beyond the diffusion length. Here, we demonstrate the directional transport of interlayer excitons in bilayer WSe2 driven by the propagating potential traps induced by surface acoustic waves (SAW). We show that at 100 K, the SAW-driven excitonic transport is activated above a threshold acoustic power and reaches 20 µm, a distance at least ten times longer than the diffusion length and only limited by the device size. Temperature-dependent measurement reveals the transition from the diffusion-limited regime at low temperature to the acoustic field-driven regime at elevated temperature. Our work shows that acoustic waves are an effective, contact-free means to control exciton dynamics and transport, promising for realizing 2D materials-based excitonic devices such as exciton transistors, switches, and transducers up to room temperature.

MSTD for Detecting Topological Domains from 3D Genomic Maps.

In this chapter, we introduce a generic and efficient method to identify multiscale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets. We first applied MSTD to detect promoter-anchored interaction domains (PADs) from promoter capture Hi-C datasets across 17 primary blood cell types. The boundaries of PADs are significantly enriched with one or the combination of multiple epigenetic factors. Moreover, PADs between functionally similar cell types are significantly conserved in terms of domain regions and expression states. Cell type-specific PADs involve in distinct cell type-specific activities and regulatory events by dynamic interactions within them. We also employed MSTD to define multiscale domains from typical symmetric Hi-C datasets and illustrated its distinct superiority to the state-of-the-art methods in terms of accuracy, flexibility and efficiency.

Circular Trajectory Reconstruction Uncovers Cell-Cycle Progression and Regulatory Dynamics from Single-Cell Hi-C Maps.

Single-cell Hi-C technology is emerging and will provide unprecedented opportunities to elucidate chromosomal dynamics with high resolution. How to characterize pseudo time-series of single cells using single-cell Hi-C maps is an essential and challenging topic. To this end, a powerful circular trajectory reconstruction tool CIRCLET is developed to resolve cell cycle phases of single cells by considering multiscale features of chromosomal architectures without specifying a starting cell. CIRCLET reveals its best superiority based on the combination of one feature set about global information and another two feature sets about local interactional information in terms of designed evaluation indexes and verification strategies from a collection of cell-cycle Hi-C maps of 1171 single cells. Further division of the reconstructed trajectory into 12 stages helps to accurately characterize the dynamics of chromosomal structures and explain the special regulatory events along cell-cycle progression. Last but not the least, the reconstructed trajectory helps to uncover important regulatory genes related with dynamic substructures, providing a novel framework for discovering regulatory regions even cancer markers at single-cell resolution.

Integrative Analysis of Transcription Factor Combinatorial Interactions Using a Bayesian Tensor Factorization Approach.

Transcription factors play a key role in transcriptional regulation of genes and determination of cellular identity through combinatorial interactions. However, current studies about combinatorial regulation is deficient due to lack of experimental data in the same cellular environment and extensive existence of data noise. Here, we adopt a Bayesian CANDECOMP/PARAFAC (CP) factorization approach (BCPF) to integrate multiple datasets in a network paradigm for determining precise TF interaction landscapes. In our first application, we apply BCPF to integrate three networks built based on diverse datasets of multiple cell lines from ENCODE respectively to predict a global and precise TF interaction network. This network gives 38 novel TF interactions with distinct biological functions. In our second application, we apply BCPF to seven types of cell type TF regulatory networks and predict seven cell lineage TF interaction networks, respectively. By further exploring the dynamics and modularity of them, we find cell lineage-specific hub TFs participate in cell type or lineage-specific regulation by interacting with non-specific TFs. Furthermore, we illustrate the biological function of hub TFs by taking those of cancer lineage and blood lineage as examples. Taken together, our integrative analysis can reveal more precise and extensive description about human TF combinatorial interactions.

Inferring Gene Regulatory Networks Using Conditional Regulation Pattern to Guide Candidate Genes.

Combining path consistency (PC) algorithms with conditional mutual information (CMI) are widely used in reconstruction of gene regulatory networks. CMI has many advantages over Pearson correlation coefficient in measuring non-linear dependence to infer gene regulatory networks. It can also discriminate the direct regulations from indirect ones. However, it is still a challenge to select the conditional genes in an optimal way, which affects the performance and computation complexity of the PC algorithm. In this study, we develop a novel conditional mutual information-based algorithm, namely RPNI (Regulation Pattern based Network Inference), to infer gene regulatory networks. For conditional gene selection, we define the co-regulation pattern, indirect-regulation pattern and mixture-regulation pattern as three candidate patterns to guide the selection of candidate genes. To demonstrate the potential of our algorithm, we apply it to gene expression data from DREAM challenge. Experimental results show that RPNI outperforms existing conditional mutual information-based methods in both accuracy and time complexity for different sizes of gene samples. Furthermore, the robustness of our algorithm is demonstrated by noisy interference analysis using different types of noise.