The Research on Coordinated Decision-Making Method Tax System Based on Subject Data

Academically, the research of subject database of tax system aims to set up an efficient, harmonious virtual data application environment. Subject data, in application and management, has been on demand polymerized and autonomously collaborated and has reached a balance between instantaneity and accuracy. This paper defines the connotation and characteristics enterprise informationization, designs a value system of enterprise informationization which is subject database oriented, and builds a model for the import of the subject database of enterprise informationization. Meantime, this paper describes the structure of the subject database based information import model and forges the model’s theoretical basis of subject data import in tax system. Using the model can make an analysis on the information of data warehouse, storage information, and tax information to provide decision support for the tax administrators

Development of a network visualization and analysis system for malignant tumors based on transcriptome data

Shiny technology has developed rapidly in recent years, as an R package for developing interactive app, through which we can package the written R code into a web app, which can not only save user time, but also accelerate the development of the speed of user-end communication, analyze the transcriptome data of related malignant tumors, and construct a ceRNA network diagram of desired malignant tumors. The code utilizing shiny technology package can facilitate users to map the ceRNA network associated with malignant tumors only through screen operation, significantly improving the efficiency and accuracy of clinical decision support in primary hospitals

HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative

AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast

A Critical Role of Perinuclear Filamentous Actin in Spatial Repositioning and Mutually Exclusive Expression of Virulence Genes in Malaria Parasites

SummaryMany microbial pathogens, including the malaria parasite Plasmodium falciparum, vary surface protein expression to evade host immune responses. P. falciparium antigenic variation is linked to var gene family-encoded clonally variant surface protein expression. Mututally exclusive var gene expression is partially controlled by spatial positioning; silent genes are retained at distinct perinuclear sites and relocated to transcriptionally active locations for monoallelic expression. We show that var introns can control this process and that var intron addition relocalizes episomes from a random to a perinuclear position. This var intron-regulated nuclear tethering and repositioning is linked to an 18 bp nuclear protein-binding element that recruits an actin protein complex. Pharmacologically induced F-actin formation, which is restricted to the nuclear periphery, repositions intron-carrying episomes and var genes and disrupts mutually exclusive var gene expression. Thus, actin polymerization relocates var genes from a repressive to an active perinuclear compartment, which is crucial for P. falciparium phenotypic variation and pathogenesis

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery

Bidirectional Interplay between Vimentin Intermediate Filaments and Contractile Actin Stress Fibers

The actin cytoskeleton and cytoplasmic intermediate filaments contribute to cell migration and morphogenesis, but the interplay between these two central cytoskeletal elements has remained elusive. Here, we find that specific actin stress fiber structures, transverse arcs, interact with vimentin intermediate filaments and promote their retrograde flow. Consequently, myosin-II-containing arcs are important for perinuclear localization of the vimentin network in cells. The vimentin network reciprocally restricts retrograde movement of arcs and hence controls the width of flat lamellum at the leading edge of the cell. Depletion of plectin recapitulates the vimentin organization phenotype of arc-deficient cells without affecting the integrity of vimentin filaments or stress fibers, demonstrating that this cytoskeletal crosslinker is required for productive interactions between vimentin and arcs. Collectively, our results reveal that plectin-mediated interplay between contractile actomyosin arcs and vimentin intermediate filaments controls the localization and dynamics of these two cytoskeletal systems and is consequently important for cell morphogenesis.Peer reviewe