Pathway-Based Genomics Prediction using Generalized Elastic Net.

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach

Surfacing teacher perceptions

Schools must learn to deal with dynamics of educational change

Deletion of Tsc2 in nociceptors reduces target innervation, ion channel expression, and sensitivity to heat

AbstractThe mechanistic target of rapamycin complex 1 (mTORC1) is known to regulate cellular growth pathways, and its genetic activation is sufficient to enhance regenerative axon growth following injury to the central or peripheral nervous systems. However, excess mTORC1 activation may promote innervation defects, and mTORC1 activity mediates injury-induced hypersensitivity, reducing enthusiasm for the pathway as a therapeutic target. While mTORC1 activity is required for full expression of some pain modalities, the effects of pathway activation on nociceptor phenotypes and sensory behaviors are currently unknown. To address this, we genetically activated mTORC1 in mouse peripheral sensory neurons by conditional deletion of its negative regulator Tuberous Sclerosis Complex 2 (Tsc2). Consistent with the well-known role of mTORC1 in regulating cell size, soma size and axon diameter of C-nociceptors were increased in Tsc2-deleted mice. Glabrous skin and spinal cord innervation by C-fiber neurons were also disrupted. Transcriptional profiling of nociceptors enriched by fluorescence-associated cell sorting (FACS) revealed downregulation of multiple classes of ion channels as well as reduced expression of markers for peptidergic nociceptors in Tsc2-deleted mice. In addition to these changes in innervation and gene expression, Tsc2-deleted mice exhibited reduced noxious heat sensitivity and decreased injury-induced cold hypersensitivity, but normal baseline sensitivity to cold and mechanical stimuli. Together, these data show that excess mTORC1 activity in sensory neurons produces changes in gene expression, neuron morphology and sensory behavior.</jats:p

Elicitation and characterization of monoclonal anti-idiotypic antibodies reactive with the ligand binding sites of monoclonal kinin antibodies

This document only includes an excerpt of the corresponding thesis or dissertation. To request a digital scan of the full text, please contact the Ruth Lilly Medical Library's Interlibrary Loan Department ([email protected])

Bayesian off-line detection of multiple change-points corrupted by multiplicative noise : application to SAR image edge detection

This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented

Imaging non-radiative point defects buried in quantum wells using cathodoluminescence

Crystallographic point defects (PDs) can dramatically decrease the efficiency of optoelectronic semiconductor devices, many of which are based on quantum well (QW) heterostructures. However, spatially resolving individual non-radiative PDs buried in such QWs has so far not been demonstrated. Here, using high-resolution cathodoluminescence (CL) and a specific sample design, we spatially resolve, image, and analyse non-radiative PDs in InGaN/GaN QWs. We identify two different types of PD by their contrasting behaviour with temperature, and measure their densities from $10^{14}$ cm$^{-3}$ to as high as $10^{16}$ cm$^{-3}$. Our CL images clearly illustrate the interplay between PDs and carrier dynamics in the well: increasing PD concentration severely limits carrier diffusion lengths, while a higher carrier density suppresses the non-radiative behaviour of PDs. The results in this study are readily interpreted directly from CL images, and represent a significant advancement in nanoscale PD analysis.Comment: Main text: 8 pages, 6 figures. Supplementary: 11 pages, 8 figure

Moving Towards the Post p < 0.05 Era via the Analysis of Credibility

It is now widely accepted that the techniques of null hypothesis significance testing (NHST) are routinely misused and misinterpreted by researchers seeking insight from data. There is, however, no consensus on acceptable alternatives, leaving researchers with little choice but to continue using NHST, regardless of its failings. I examine the potential for the Analysis of Credibility (AnCred) to resolve this impasse. Using real-life examples, I assess the ability of AnCred to provide researchers with a simple but robust framework for assessing study findings that goes beyond the standard dichotomy of statistical significance/nonsignificance. By extracting more insight from standard summary statistics while offering more protection against inferential fallacies, AnCred may encourage researchers to move toward the post p < 0.05 era