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Chaolin Zhang

Ningxia Medical University

ORCID: 0000-0002-0013-5314

Publishes on Coal Properties and Utilization, Rock Mechanics and Modeling, RNA Research and Splicing. 113 papers and 12.4k citations.

113Publications
12.4kTotal Citations

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Top publicationsby citations

An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex
Ye Zhang, Kenian Chen, Steven A. Sloan et al.|Journal of Neuroscience|2014
Cited by 5.3kOpen Access

The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain.

The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems
Fei Chen, Hiroyuki Kusaka, Robert Bornstein et al.|International Journal of Climatology|2010
Cited by 1.2k

Abstract To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi‐layer urban canopy model with an indoor–outdoor exchange sub‐model that directly interacts with the atmospheric boundary layer, (2) coupling to fine‐scale computational fluid dynamic Reynolds‐averaged Navier–Stokes and Large‐Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high‐resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high‐resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary‐layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate‐modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society