Mechanistic insights into the role of microRNAs in cancer: influence of nutrient crosstalk

A plethora of studies have described the disruption of key cellular regulatory mechanisms involving non-coding RNAs, specifically microRNAs (miRNA) from the let-7 family, the miR-17 family, miR-21, miR-143, and the miR-200 family, which contribute to aberrant signaling and tumor formation. Certain environmental factors, such as bioactive dietary agents, e.g., folate, curcumin, polyunsaturated fatty acids, are also thought to impact the progression and severity of cancer. In terms of the chemoprotective mechanisms of action, these bioactive dietary agents appear to act, in part, by modulating tissue levels of miR-16, miR-17 family, miR-26b, miR-106b, and miR-200 family miRNAs and their target genes. However, the mechanisms of nutrient action are not yet fully understood. Therefore, additional characterization of the putative underlying mechanisms is needed to further our understanding of the biology, early diagnosis, prevention, and the treatment of cancer. For the purpose of elucidating the epigenetic landscape of cancer, this review will summarize the key findings from recent studies detailing the effect of bioactive dietary agents on miRNA regulation in cancer

Electrostatically Induced Carbon Nanotube Alignment for Polymer Composite Applications

We have developed a non-invasive technique utilizing polarized Raman spectroscopy to measure changes in carbon nanotube (CNT) alignment in situ and in real time in a polymer matrix. With this technique, we have confirmed the prediction of faster alignment for CNTs in higher electric fields. Real-time polarized Raman spectroscopy also allows us to demonstrate the loss of CNT alignment that occurs after the electric field is removed, which reveals the need for fast polymerization steps or the continued application of the aligning force during polymerization to lock in CNT alignment. Through a study on the effect of polymer viscosity on the rate of CNT alignment, we have determined that shear viscosity serves as the controlling mechanism for CNT rotation. This finding matches literature modeling of rigid rod mobility in a polymer melt and demonstrates that the rotational mobility of CNTs can be explained by a continuum model even though the diameters of single-walled CNTs are ~1–2 nm. The viscosity dependence indicates that the manipulation of temperature (and indirectly viscosity) will have a direct effect on the rate of CNT alignment, which could prove useful in expediting the manufacturing of CNT-reinforced composites cured at elevated temperatures. Using real-time polarized Raman spectroscopy, we also demonstrate that electric fields of various strengths lead not only to different speeds of CNT rotation but also to different degrees of alignment. We hypothesize that this difference in achievable alignment results from discrete populations of nanotubes based on their length. The results are then explained by balancing the alignment energy for a given electric field strength with the randomizing thermal energy of the system. By studying the alignment dynamics of different CNT length distributions, we show that different degrees of alignment achieved as a function of the applied electric field strength are directly related to the square of the nanotube length. This finding matches an electrostatic potential energy model for CNT rotation. Lastly, we investigate the effects of conductive carbon fibers on electrostatically induced alignment of CNTs within carbon fiber composites. The relative electric field strength throughout the composite is modeled using COMSOL Multiphysics. We show the ability to generate enhanced electric field gradients within the gaps between carbon fibers for various fiber orientations. Using polarized Raman spectroscopy, increased levels of CNT alignment are observed between carbon fiber tows, which is consistent with the modeled higher electric field strengths in these regions. These findings could potentially lead to the development of carbon fiber composites with CNT additions that selectively enhance the composite properties outside the carbon fiber interphase in the neat epoxy.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136945/1/wchapkin_1.pd

Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets

Quarkonium production in deep-inelastic scattering

We discuss the inclusive production of J/psi mesons in deep-inelastic scattering (DIS) via the electromagnetic, weak neutral, and charged currents within the factorization formalism of nonrelativistic quantum chromodynamics. Theoretical predictions are confronted with experimental data of e p and nu N DIS taken by the H1 Collaboration at DESY HERA and the CHORUS Collaboration at CERN, respectively.Comment: 6 pages (Latex), 6 figures (Postscript); to appear in the Proceedings of the 6th International Symposium on Radiative Corrections: Application of Quantum Field Theory to Phenomenology (RADCOR 2002), Kloster Banz, Germany, September 8-13, 200

Evaluation of fecal mRNA reproducibility via a marginal transformed mixture modeling approach

<p>Abstract</p> <p>Background</p> <p>Developing and evaluating new technology that enables researchers to recover gene-expression levels of colonic cells from fecal samples could be key to a non-invasive screening tool for early detection of colon cancer. The current study, to the best of our knowledge, is the first to investigate and report the reproducibility of fecal microarray data. Using the intraclass correlation coefficient (ICC) as a measure of reproducibility and the preliminary analysis of fecal and mucosal data, we assessed the reliability of mixture density estimation and the reproducibility of fecal microarray data. Using Monte Carlo-based methods, we explored whether ICC values should be modeled as a beta-mixture or transformed first and fitted with a normal-mixture. We used outcomes from bootstrapped goodness-of-fit tests to determine which approach is less sensitive toward potential violation of distributional assumptions.</p> <p>Results</p> <p>The graphical examination of both the distributions of ICC and probit-transformed ICC (PT-ICC) clearly shows that there are two components in the distributions. For ICC measurements, which are between 0 and 1, the practice in literature has been to assume that the data points are from a beta-mixture distribution. Nevertheless, in our study we show that the use of a normal-mixture modeling approach on PT-ICC could provide superior performance.</p> <p>Conclusions</p> <p>When modeling ICC values of gene expression levels, using mixture of normals in the probit-transformed (PT) scale is less sensitive toward model mis-specification than using mixture of betas. We show that a biased conclusion could be made if we follow the traditional approach and model the two sets of ICC values using the mixture of betas directly. The problematic estimation arises from the sensitivity of beta-mixtures toward model mis-specification, particularly when there are observations in the neighborhood of the the boundary points, 0 or 1. Since beta-mixture modeling is commonly used in approximating the distribution of measurements between 0 and 1, our findings have important implications beyond the findings of the current study. By using the normal-mixture approach on PT-ICC, we observed the quality of reproducible genes in fecal array data to be comparable to those in mucosal arrays.</p

Overexpression of Protein Kinase C βII Induces Colonic Hyperproliferation and Increased Sensitivity to Colon Carcinogenesis

Protein kinase C βII (PKC βII) has been implicated in proliferation of the intestinal epithelium. To investigate PKC βII function in vivo, we generated transgenic mice that overexpress PKC βII in the intestinal epithelium. Transgenic PKC βII mice exhibit hyperproliferation of the colonic epithelium and an increased susceptibility to azoxymethane-induced aberrant crypt foci, preneoplastic lesions in the colon. Furthermore, transgenic PKC βII mice exhibit elevated colonic β-catenin levels and decreased glycogen synthase kinase 3β activity, indicating that PKC βII stimulates the Wnt/adenomatous polyposis coli (APC)/β-catenin proliferative signaling pathway in vivo. These data demonstrate a direct role for PKC βII in colonic epithelial cell proliferation and colon carcinogenesis, possibly through activation of the APC/β-catenin signaling pathway