Diffusion-based generation of gene regulatory network from scRNA-seq data with DigNet.

Gene regulatory network (GRN) intricately encodes the interconnectedness of identities and functionalities of genes within cells, ultimately shaping to cellular specificity. Despite decades of endeavors, reverse engineering of GRN from gene expression profiling data remains a profound challenge, particularly when it comes to reconstructing cell specific GRN that are tailored to precise cellular and genetic contexts. For alternatively approaching network reconstruction from data, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRN from high-throughput single-cell RNA sequencing (scRNA-seq) data.

Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment.

Effects of suspended particles and dispersants on marine oil snow formation of crude oil/diesel oil.

Marine oil snow (MOS) potentially forms after an oil spill. To fully understand the mechanism of its formation, we investigated the effects of suspended particles (SP) and dispersants on MOS formation of crude oil and diesel oil by laboratory experiments. In the crude oil experiment, the SP concentration of 0.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

Motivation: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China.

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian "7.

Tar ball pollution under the influence of ocean circulation in the Bohai Sea, China.

The deposition of tar balls of unknown sources on the coast poses a great threat to the fishery, tourism and coastal biodiversity in the Bohai Sea. This work aimed to identify the sources of tar balls by using oil fingerprint technique. Tar ball samples were collected from the seashore of two islands of the western Bohai Sea and divided into two groups (Group I and Group II).

Source identification and weathering processes of tar ball deposited Qinhuangdao coast along the Bohai Sea, China.

Deposition of tar balls on the Qinhuangdao beaches along the coasts of the Bohai Sea (China) could affect people's leisure activities and tourism, and even affect the marine ecosystem. In 2020, representative tar balls collected from Qinhuangdao beaches, fingerprint analysis based on diagnostic ratios suggested that these tar balls were all very similar and may belong to the same source. Source identification by cross plot and principal component analysis (PCA), showed that the tar balls were likely from Penglai 19-3 crude oil.

Evaluating Gene Regulatory Network Activity From Dynamic Expression Data by Regularized Constraint Programming.

By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g.

Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization.

The development of single-cell RNA sequencing (scRNA-seq) technology has made it possible to measure gene expression levels at the resolution of a single cell, which further reveals the complex growth processes of cells such as mutation and differentiation. Recognizing cell heterogeneity is one of the most critical tasks in scRNA-seq research. To solve it, we propose a non-negative matrix factorization framework based on multi-subspace cell similarity learning for unsupervised scRNA-seq data analysis (MscNMF).

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR).

Sparse robust graph-regularized non-negative matrix factorization based on correntropy.

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC).

Effects of Artificial Islands Construction on the Spatial Distribution and Risk Assessment of Heavy Metals in the Surface Sediments from a Semi-closed Bay (Longkou Bay), China.

Artificial islands construction can significantly influence the spatial distribution of heavy metals in inshore sediments. In this study, the distribution and contamination of heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn, As and Hg) in inshore sediments of the Longkou Bay and artificial island adjacent areas were investigated in 2013 and 2014, respectively. Results showed that the contents of heavy metals increased in the Longkou Bay and decreased in the west of the artificial island in 2014 compared with 2013.

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework.

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data.

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification.

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can learn part-based representations effectively. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF).

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis.

Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality. Gene expression data usually have some noise and outliers, while the original NMF loss function is very sensitive to non-Gaussian noise.

Effects of diesel oil spill on macrobenthic assemblages at the intertidal zone: A mesocosm experiment in situ.

The oil spill accidents may drastically impact the environment and ecosystem at intertidal zones. The spilled oil will penetrate the sediments and accumulate to cause lethal or sublethal effects on the benthic invertebrates. An M-BACI experiment was manipulated in situ to assess the ecological responses of benthic macrofauna to different degrees of diesel oil spill.

Biodegradation of marine oil spill residues using aboriginal bacterial consortium based on Penglai 19-3 oil spill accident, China.

Bioremediation, mainly by indigenous bacteria, has been regarded as an effective way to deal with the petroleum pollution after an oil spill accident. The biodegradation of crude oil by microorganisms co-incubated from sediments collected from the Penglai 19-3 oil platform, Bohai Sea, China, was examined. The relative susceptibility of the isomers of alkylnaphthalenes, alkylphenanthrenes and alkyldibenzothiophene to biodegradation was also discussed.

In situ microbial remediation of crude oil-soaked marine sediments using zeolite carrier with a polymer coating.

Marine oil spill pollution is an important environmental problem in the world, especially crude oil-soaked marine sediments, because they are difficult to be remediated. In this study, in situ bioremediation of oil-soaked sediment was performed in the middle of the Bohai Sea. Oil-degrading bacteria were adsorbed on powdery zeolite (PZ)/granular zeolites (GZ) surfaces and then wrapped with poly-γ glutamic acid (γ-PGA).

Oil mixing behavior after an oil spill: identification conflicts of different fingerprints.

Clearing up whether spilled oil is mixed or not can strengthen the accuracy of oil spill identification. In the present study, the biomarkers in spilled oil samples were detected. The weathering modes of different types of diagnostic ratio and carbon isotope values of individual n-alkanes were also analyzed.

Petroleum hydrocarbons in a water-sediment system from Yellow River estuary and adjacent coastal area, China: Distribution pattern, risk assessment and sources.

Aliphatic hydrocarbons (AHs), biomarker and polycyclic aromatic hydrocarbons (PAHs) concentrations of surface water and sediment samples collected from Yellow River Estuary and adjacent coastal area in China were measured to determine their spatial distributions, analyze their sources and evaluate the ecological risk of PAHs in the water-sediment system. The spatial distributions of n-alkane in sediments are mainly controlled by the mixing inputs of terrigenous and marine components. In comparison with AHs, the total concentrations of Σ16PAHs in surface sediments from a transect of the offshore area were noticeably higher than that of the riverine and estuary areas.

Assessing pollution-related effects of oil spills from ships in the Chinese Bohai Sea.

An analysis of the effects of potential oil spills will provide data in support of decisions related to improving the response to oil spills and its emergency management. We selected the Chinese Bohai Sea, especially the Bohai Strait, as our investigation region to provide an assessment of the effects of pollution from ship-related oil spills on adjacent coastal zones. Ship-related accidents are one of the major factors causing potential oil spills in this area.

New diagnostic ratios based on phenanthrenes and anthracenes for effective distinguishing heavy fuel oils from crude oils.

The heavy fuel oils (HFOs) and crude oils are the main oil types in the marine oil spill accidents in China. It is usually a challenge to distinguish the HFOs from crude oils due to the highly similar physicochemical characteristics. In this paper, the distributions of phenanthrene (Phe), anthracene (Ant), methyl-phenanthrene (MP) and methyl-anthracene (MA) in hundreds of HFOs and crude oils samples which were collected from all over the world were characterized.