Publications by authors named "Julie Chih-Yu Chen"

Neutralization assays are important for understanding and quantifying neutralizing antibody responses toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 lentivirus surrogate neutralization assay (SCLSNA) can be used in biosafety level 2 (BSL-2) laboratories and has been shown to be a reliable alternative approach to the plaque reduction neutralization test (PRNT). In this study, we optimized and validated the SCLSNA to assess its ability as a comparator and prescreening method to support the PRNT.

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The high-risk human papillomaviruses are oncogenic viruses associated with almost all cases of cervical carcinomas, and increasing numbers of anal, and oral cancers. Two oncogenic HPV proteins, E6 and E7, are capable of immortalizing keratinocytes and are required for HPV associated cell transformation. Currently, the influence of these oncoproteins on the global regulation of the host proteome is not well defined.

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Stool culture is the gold standard method to diagnose enteric bacterial infections; however, many clinical laboratories are transitioning to syndromic multiplex PCR panels. PCR is rapid, accurate, and affordable, yet does not yield subtyping information critical for foodborne disease surveillance. A metagenomics-based stool testing approach could simultaneously provide diagnostic and public health information.

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There remains an urgent need for assays to quantify humoral protective immunity to SARS-CoV-2 to understand the immune responses of COVID-19 patients, evaluate efficacy of vaccine candidates in clinical trials, and conduct large-scale epidemiological studies. The plaque-reduction neutralization test (PRNT) is the reference-standard for quantifying antibodies capable of neutralizing SARS-CoV-2. However, the PRNT is logistically demanding, time-consuming, and requires containment level-3 facilities to safely work with live virus.

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Background: The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately when the origin has been previously sampled. Using metagenomic datasets provided by the 2019 CAMDA challenge, we evaluated the influence of variable technical, analytical and machine learning approaches for result interpretation and novel source prediction.

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Genome-scale metabolic network reconstruction can be used for simulating cellular behaviors by simultaneously monitoring thousands of biochemical reactions, and is therefore important for systems biology studies in microbes. However, the labor-intensive and time-consuming reconstruction process has hindered the progress of this important field. Here we present a web server, MrBac (Metabolic network Reconstructions for Bacteria), to streamline the network reconstruction process for draft genome-scale metabolic networks and to provide annotation information from multiple databases for further curation of the draft reconstructions.

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