Browsing by Author "Miller, Nick"

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    Counting Photobleach Steps and the Dynamics of Bacterial Predators
    (Office of the Vice Chancellor for Research, 2016-04-08) Jashnsaz, Hossein; Tsekouras, Konstantinos; Al Juboori, Mohammed; Weistuch, Corey; Miller, Nick; Nguyen, Tyler; McCoy, Bryan; Perkins, Stephanie; Anderson, Gregory; Presse, Steve
    Photobleach (PB) counting is used to enumerate proteins by monitoring how the light intensity in some regions decreases by quanta as individual fluorophores photobleach. While it is straightforward in theory, PB counting is often difficult because fluorescence traces are noisy. In this work, we quantify the sources of noise that arise during photobleach counting to construct a principled likelihood function of observing the data given a model. Noise in the signal could arise from background fluorescence, variable fluorophore emission, and fluorophore blinking. In addition, in a completely different direction, we explore the role of hydrodynamic interactions on the dynamics of bacterial predators. Our study shows that Bdellovibrio (BV) - a model predatory bacterium - is susceptible to self-generated hydrodynamic forces. Near surfaces and defects, these hydrodynamic interactions co-localize BV with its prey, and this may enhance BV’s hunting efficiency.
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    Molecular Analysis of Thymoma
    (Public Library of Science, 2012) Badve, Sunil; Goswami, Chirayu; Gökmen–Polar, Yesim; Nelson, Robert P., Jr.; Henley, John; Miller, Nick; Zaheer, Narjis A.; Sledge, George W., Jr.; Li, Lang; Kesler, Kenneth A.; Loehrer, Patrick J.; Pathology and Laboratory Medicine, School of Medicine
    Histologic classification of thymomas has significant limitations with respect to both subtype definitions and consistency. In order to better understand the biology of the disease processes, we performed whole genome gene expression analysis. RNA was extracted from fresh frozen tumors from 34 patients with thymomas and followup data was available. Using the Illumina BeadStudio® platform and Human Ref-8 Beadchip, gene expression data was analyzed with Partek Genomics Suite®, and Ingenuity Pathways Analysis (IPA). Unsupervised clustering of gene expression data, representing one of the largest series in literature, resulted in identification of four molecular clusters of tumors (C1-C4), which correlated with histology (P = 0.002). However, neither histology nor clusters correlated with clinical outcomes. Correlation of gene expression data with clinical data showed that a number of genes were associated with either advanced stage at diagnosis or development of recurrence or metastases. The top pathways associated with metastases were amino acid metabolisms, biosynthesis of steroids and glycosphingolipids, cell cycle checkpoint proteins and Notch signaling. The differential expression of some of the top genes related to both metastases and stage was confirmed by RT-PCR in all cases of metastases and matched nonmetastatic cases. A number of potential candidates for therapeutics were also identified.