Author

T. Nichols

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Assay:imaging· 1 Assay:metabolomics· 1 Assay:metagenomics· 1 Organism:mouse· 1

Frequent Co-Authors

A. Carlson· 1 shared A. Didier· 1 shared A. J. Buros Stein· 1 shared A. Kashina· 1 shared A. Lehmkuhl· 1 shared A. Staskevicius· 1 shared A. Zolty· 1 shared B. Kumar· 1 shared

Activity by Year

2016-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperMicrobiology

A. Carlson

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Prospective Fecal Microbiomics Biomarkers for Chronic Wasting Disease

A. Didier, M. Bourner, G. Kleks, A. Zolty, B. Kumar, T. Nichols, K. Durynski, S. Bender, M. Gibison, L. Murphy, J. C. Ellis, D. W. Dong, A. Kashina

This study identifies fecal microbiome and metabolomics biomarkers that can differentiate CWD-positive from CWD-negative deer with 70% accuracy.

bioRxiv · Aug 21, 2023Microbiology

Estimating relative CWD susceptibility and disease progression in farmed whitetail deer with rare PRNP alleles

N. J. Haley, K. Merrett, A. J. Buros Stein, D. Simpson, A. Carlson, G. Mitchell, A. Staskevicius, T. Nichols, A. Lehmkuhl, B. Thomsen

The paper assesses CWD susceptibility and progression in farmed whitetail deer with rare PRNP alleles by analyzing infection status and disease stage in over 2100 deer.

bioRxiv · Oct 14, 2019Microbiology

Scanning the Horizon: Towards transparent and reproducible neuroimaging research

R. Poldrack, C. I. Baker, J. Durnez, K. Gorgolewski, P. M. Matthews, M. Munafo, T. Nichols, J.-B. Poline, E. Vul, T. Yarkoni

The paper discusses issues in functional neuroimaging research, such as low statistical power and lack of replication, and suggests best practices for more transparent and reproducible studies.

bioRxiv · Aug 1, 2016Scientific Communication & Education