Publications by authors named "Chengwei Zhong"

Fetal immune cell functions during congenital infections are poorly understood. Zika virus (ZIKV) can vertically transmit from mother to fetus, causing nervous system infection and congenital ZIKV syndrome (CZS). We identified differential functional roles for fetal monocyte/macrophage cell types and microglia in ZIKV dissemination versus clearance using mouse models.

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Article Synopsis
  • STOMP is a new method for analyzing spatial transcriptomics data that simplifies complex information into meaningful, low-dimensional representations.
  • It utilizes a deep generative model to identify spatial patterns and gene modules that are biologically relevant, enhancing the interpretation of data like lung cancer samples and mouse embryonic development.
  • The method is highly scalable, capable of analyzing datasets with over 500,000 cells, making it versatile for various biological studies.
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Advances in spatial omics technologies now allow multiple types of data to be acquired from the same tissue slice. To realize the full potential of such data, we need spatially informed methods for data integration. Here, we introduce SpatialGlue, a graph neural network model with a dual-attention mechanism that deciphers spatial domains by intra-omics integration of spatial location and omics measurement followed by cross-omics integration.

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Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa.

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Natural siderite (FeCO), simulated synthetic siderite and nZVI/FeCO composite were used as green and easily available iron-based catalysts in peroxydisulfate activation for remediating 2-chlorophenol as the target contaminant and this technique can effectively degrade organic pollutants in the soil. The key reaction parameters such as catalysts dosage, oxidant concentration and pH, were investigated to evaluate the catalytic performance of different materials in catalytic systems. The buffering property of natural soil conduced satisfactory degradation performance in a wide pH range (3-10).

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A novel synergistic oxidation technology based on modified nanoscale zero-valent iron (nZVI) and potassium permanganate (KMnO) was developed for polycyclic aromatic hydrocarbons (PAHs) remediation in actual contaminated soil. In this study, three surfactants were used as dispersants to modify nZVI, including poly acrylic (PAA), sorbitan monolaurate (SPAN-20) and sugar esters (SE). The following parameters were studied to optimize the coupling oxidation process: dispersants/nZVI ratio, dosage of oxidant based on soil oxidation demand (SOD), amount of modified nanomaterials added in the coupling system.

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Lead (Pb) pollution in soil has become one of the most serious environmental problems, and it is more urgent in areas where acid rain is prevalent. Curing agents to solidify heavy metals in soil are efficiently applied to remediate Pb-contaminated soil. In this study, we prepared biochar, biochar loaded with nano-zero-valent iron (BC-nZVI), and biochar loaded with nano-ferroferric oxide (BC-nFeO), and investigated the Pb-immobilizing efficiency in contaminated soil in the condition of acid rain by them.

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Itaconic acid, one of the most promising and flexible bio-based chemicals, is mainly produced by Aspergillus terreus. Previous studies to improve itaconic acid production in A. terreus through metabolic engineering were mainly focused on its biosynthesis pathway, while the itaconic acid-degrading pathway has largely been ignored.

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