Intelligent modeling with physics-informed machine learning for petroleum engineering problems

The advancement in big data and artificial intelligence has enabled a novel exploration mode for the study of petroleum engineering. Unlike theory-based solution methods, the data-driven intelligent approaches demonstrate superior flexibility, computational efficiency and accuracy for dealing with complex multi-scale, and multi-physics problems. However, these intelligent models often disregard physical laws in pursuit of error minimization, which leads to certain uncertainties. Therefore, physics-informed machine learning approaches have been developed based on data, guided by physics, and supported by machine learning models. This study summarizes four embedding mechanisms for introducing physical information into machine learning models, including input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding mechanism. These “data + physics” dualdriven intelligent models not only exhibit higher prediction accuracy while adhering to physic laws, but also accelerate the convergence to improve computational efficiency. This paradigm will facilitate the guide developments in solving petroleum engineering problems toward a more comprehensive and efficient direction.Cited as: Xie, C., Du, S., Wang, J., Lao, J., Song, H. Intelligent modeling with physics-informed machine learning for petroleum engineering problems. Advances in Geo-Energy Research, 2023, 8(2): 71-75. https://doi.org/10.46690/ager.2023.05.0

Histone Lysine Methyltransferase SDG8 Is Involved in Brassinosteroid-Regulated Gene Expression in Arabidopsis thaliana

Citation: Wang, X., Chen, J., Xie, Z., Liu, S., Nolan, T., Ye, H., et al. (2014). Histone lysine methyltransferase SDG8 is involved in brassinosteroid- regulated gene expression in arabidopsis thaliana.The plant steroid hormones, brassinosteroids (BRs), play important roles in plant growth, development and responses to environmental stresses. BRs signal through receptors localized to the plasma membrane and other signaling components to regulate the BES1/BZR1 family of transcription factors, which modulates the expression of 4,000-5,000 genes. How BES1/BZR1 and their interacting proteins function to regulate the large number of genes are not completely understood. Here we report that histone lysine methyltransferase SDG8, implicated in Histone 3 lysine 36 di- and tri-methylation (H3K36me2 and me3), is involved in BR-regulated gene expression. BES1 interacts with SDG8, directly or indirectly through IWS1, a transcription elongation factor involved in BR-regulated gene expression. The knockout mutant sdg8 displays a reduced growth phenotype with compromised BR responses. Global gene expression studies demonstrated that SDG8 plays a major role in BR-regulated gene expression as more than half of BR-regulated genes are differentially affected in sdg8 mutant. A Chromatin Immunoprecipitation (ChIP) experiment showed that H3K36me3 is reduced in BR-regulated genes in the sdg8 mutant. Based on these results, we propose that SDG8 plays an essential role in mediating BR-regulated gene expression. Our results thus reveal a major mechanism by which histone modifications dictate hormonal regulation of gene expression

N6-(2-hydroxyethyl)-Adenosine Induces Apoptosis via ER Stress and Autophagy of Gastric Carcinoma Cells In Vitro and In Vivo

Gastric cancer is the most common malignant tumor of the digestive tract and is great challenge in clinical treatment. N6-(2-Hydroxyethyl)-adenosine (HEA), widely present in various fungi, is a natural adenosine derivative with many biological and pharmacological activities. Here, we assessed the antineoplastic effect of HEA on gastric carcinoma. HEA exerted cytotoxic effects against gastric carcinoma cells (SGC-7901 and AGS) in a dose and time-dependent manner. Additionally, we found that HEA induced reactive oxygen species production and mitochondrial membrane potential depolarization. Moreover, it could trigger caspase-dependent apoptosis, promoting intracellular Ca2+-related endoplasmic reticulum (ER) stress and autophagy. On the other hand, HEA could significantly inhibit the growth of transplanted tumors in nude mice and induce apoptosis of tumor tissues cells in vivo. In conclusion, HEA induced apoptosis of gastric carcinoma cells in vitro and in vivo, demonstrating that HEA is a potential chemotherapeutic agent for gastric carcinoma

Image Retrieval Based on Multiview Constrained Nonnegative Matrix Factorization and Gaussian Mixture Model Spectral Clustering Method

Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF) and Gaussian mixture model- (GMM-) based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme

A Hybrid Bat Algorithm with Path Relinking for Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is an NP-hard problem with wide engineering and theoretical background. In this paper, a hybrid bat algorithm with path relinking (HBA-PR) is proposed to solve CVRP. The HBA-PR is constructed based on the framework of continuous bat algorithm; the greedy randomized adaptive search procedure (GRASP) and path relinking are effectively integrated into bat algorithm. Moreover, in order to further improve the performance, the random subsequences and single-point local search are operated with certain loudness (probability). In order to verify the validity of the method in this paper, and it's efficiency and with other existing methods, several classical CVRP instances from three classes of CVRP benchmarks are selected to tested. Experimental results and comparisons show that the HBA-PR is effective for CVRP

A Hybrid Metaheuristic for Multiple Runways Aircraft Landing Problem Based on Bat Algorithm

The aircraft landing problem (ALP) is an NP-hard problem; the aim of ALP is to minimize the total cost of landing deviation from predefined target time under the condition of safe landing. In this paper, the multiple runways case of the static ALP is considered and a hybrid metaheuristic based on bat algorithm is presented to solve it. Moreover, four types of landing time assignment strategies are applied to allocate the scheduling time, and a constructed initialization is used to speed up the convergence rate. The computational results show that the proposed algorithm can obtain the high-quality and comparable solutions for instances up to 500 aircrafts, and also it is capable of finding the optimal solutions for many instances in a short time

Silkworm Pupa Protein Hydrolysate Induces Mitochondria-Dependent Apoptosis and S Phase Cell Cycle Arrest in Human Gastric Cancer SGC-7901 Cells

Silkworm pupae (Bombyx mori) are a high-protein nutrition source consumed in China since more than 2 thousand years ago. Recent studies revealed that silkworm pupae have therapeutic benefits to treat many diseases. However, the ability of the compounds of silkworm pupae to inhibit tumourigenesis remains to be elucidated. Here, we separated the protein of silkworm pupae and performed alcalase hydrolysis. Silkworm pupa protein hydrolysate (SPPH) can specifically inhibit the proliferation and provoke abnormal morphologic features of human gastric cancer cells SGC-7901 in a dose- and time-dependent manner. Moreover, flow cytometry indicated that SPPH can induce apoptosis and arrest the cell-cycle in S phase. Furthermore, SPPH was shown to provoke accumulation of reactive oxygen species (ROS) and depolarization of mitochondrial membrane potential. Western blotting analysis indicated that SPPH inhibited Bcl-2 expression and promoted Bax expression, and subsequently induced apoptosis-inducing factor and cytochrome C release, which led to the activation of initiator caspase-9 and executioner caspase-3, cleavage of poly (ADP-ribose) polymerase (PARP), eventually caused cell apoptosis. Moreover, SPPH-induced S-phase arrest was mediated by up-regulating the expression of E2F1 and down-regulating those of cyclin E, CDK2 and cyclin A2. Transcriptome sequencing and gene set enrichment analysis (GSEA) also revealed that SPPH treatment could affect gene expression and pathway regulation related to tumourigenesis, apoptosis and cell cycle. In summary, our results suggest that SPPH could specifically suppress cell growth of SGC-7901 through an intrinsic apoptotic pathway, ROS accumulation and cell cycle arrest, and silkworm pupae have a potential to become a source of anticancer agents in the future

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results. Availability & Implementation The source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval