Spatial clustering and common regulatory elements correlate with coordinated gene expression

Many cellular responses to surrounding cues require temporally concerted transcriptional regulation of multiple genes. In prokaryotic cells, a single-input-module motif with one transcription factor regulating multiple target genes can generate coordinated gene expression. In eukaryotic cells, transcriptional activity of a gene is affected by not only transcription factors but also the epigenetic modifications and three-dimensional chromosome structure of the gene. To examine how local gene environment and transcription factor regulation are coupled, we performed a combined analysis of time-course RNA-seq data of TGF-\b{eta} treated MCF10A cells and related epigenomic and Hi-C data. Using Dynamic Regulatory Events Miner (DREM), we clustered differentially expressed genes based on gene expression profiles and associated transcription factors. Genes in each class have similar temporal gene expression patterns and share common transcription factors. Next, we defined a set of linear and radial distribution functions, as used in statistical physics, to measure the distributions of genes within a class both spatially and linearly along the genomic sequence. Remarkably, genes within the same class despite sometimes being separated by tens of million bases (Mb) along genomic sequence show a significantly higher tendency to be spatially close despite sometimes being separated by tens of Mb along the genomic sequence than those belonging to different classes do. Analyses extended to the process of mouse nervous system development arrived at similar conclusions. Future studies will be able to test whether this spatial organization of chromosomes contributes to concerted gene expression.Comment: 30 pages, 9 figures, accepted in PLoS Computational Biolog

Molecular Mechanism Study on the Effect of Nonionic Surfactants with Different Degrees of Ethoxylation on the Wettability of Anthracite

A serious risk to the production safety of coal mines is coal dust. The wettability of coal may be successfully changed by adding surfactants to water. However, the creation of very effective dust suppressants is constrained by the lack of knowledge about the microscopic interaction mechanism between coal dust and surfactants. In this investigation, we explained macroscopic experimental phenomena from a molecular perspective. The lauryl polyoxyethylene ethers (C12 (EO)n, n = 7,15,23) were selected. The macromolecular model of anthracite with 55 different components was constructed. Surface tension experiments and hydrophilic lipophilic balance (HLB) calculations showed that the ability of surface hydrophilicization followed the order of C12 (EO)7(EO)15(EO)23. Contact angle experiment, XPS and FTIR experiments proved that after the surfactants were adsorbed on the surface of anthracite, the content of carbon element decreased and the content of oxygen element increased, indicating the enhanced surface hydrophilicity. The simulation results showed that with the degree of ethoxylation increases, the adsorption strength of surfactants becomes stronger, and the hydrophilic head group of surfactant on anthracite surface is more uniformly distributed. The greater the degree of ethoxylation, the more powerfully the modified coal surface can bind to water molecules

Molecular Mechanism Study on the Effect of Nonionic Surfactants with Different Degrees of Ethoxylation on the Wettability of Anthracite

A serious risk to the production safety of coal mines is coal dust. The wettability of coal may be successfully changed by adding surfactants to water. However, the creation of very effective dust suppressants is constrained by the lack of knowledge about the microscopic interaction mechanism between coal dust and surfactants. In this investigation, we explained macroscopic experimental phenomena from a molecular perspective. The lauryl polyoxyethylene ethers (C12 (EO)n, n = 7,15,23) were selected. The macromolecular model of anthracite with 55 different components was constructed. Surface tension experiments and hydrophilic lipophilic balance (HLB) calculations showed that the ability of surface hydrophilicization followed the order of C12 (EO)712 \u3e(EO)1512 \u3e(EO)23. Contact angle experiment, XPS and FTIR experiments proved that after the surfactants were adsorbed on the surface of anthracite, the content of carbon element decreased and the content of oxygen element increased, indicating the enhanced surface hydrophilicity. The simulation results showed that with the degree of ethoxylation increases, the adsorption strength of surfactants becomes stronger, and the hydrophilic head group of surfactant on anthracite surface is more uniformly distributed. The greater the degree of ethoxylation, the more powerfully the modified coal surface can bind to water molecules

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popularity for its three outstanding advantages: 1) A large amount of storage space saving by a \emph{Block Sparse Row} matrix. 2) Excellent performance at a high sparsity. 3) Significant speedups on CPUs with Advanced Vector Extensions. Recent work requires selecting and fine-tuning 1$\times$N sparse weights based on dense pre-trained weights, leading to the problems such as expensive training cost and memory access, sub-optimal model quality, as well as unbalanced workload across threads (different sparsity across output channels). To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch. Specifically, our approach tends to repeatedly allow pruned blocks to regrow to the network based on block angular redundancy and importance sampling in a uniform manner throughout the training process. It not only makes the model less dependent on pre-training, reduces the model redundancy and the risk of pruning the important blocks permanently but also achieves balanced workload. Empirically, on ImageNet, comprehensive experiments across various CNN architectures show that our SUBP consistently outperforms existing 1$\times$N and structured sparsity methods based on pre-trained models or training from scratch. Source codes and models are available at \url{https://github.com/JingyangXiang/SUBP}.Comment: 14 pages, 4 figures, Accepted by 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure

Full velocities and propagation directions of coronal mass ejections inferred from simultaneous full-disk imaging and Sun-as-a-star spectroscopic observations

Coronal mass ejections (CMEs) are violent ejections of magnetized plasma from the Sun, which can trigger geomagnetic storms, endanger satellite operations and destroy electrical infrastructures on the Earth. After systematically searching Sun-as-a-star spectra observed by the Extreme-ultraviolet Variability Experiment (EVE) onboard the Solar Dynamics Observatory (SDO) from May 2010 to May 2022, we identified eight CMEs associated with flares and filament eruptions by analyzing the blue-wing asymmetry of the O III 52.58 nm line profiles. Combined with images simultaneously taken by the 30.4 nm channel of the Atmospheric Imaging Assembly onboard SDO, the full velocity and propagation direction for each of the eight CMEs are derived. We find a strong correlation between geomagnetic indices (Kp and Dst) and the angle between the CME propagation direction and the Sun-Earth line, suggesting that Sun-as-a-star spectroscopic observations at EUV wavelengths can potentially help to improve the prediction accuracy of the geoeffectiveness of CMEs. Moreover, an analysis of synthesized long-exposure Sun-as-a-star spectra implies that it is possible to detect CMEs from other stars through blue-wing asymmetries or blueshifts of spectral lines.Comment: Accepted by Ap