Chitinase and Fizz family members are a generalized feature of nematode infection with selective Upregulation of Ym1 and F10.1 by antigen-presenting cells

Ym1 and Fizz1 are secreted proteins that have been identified in a variety of Th2-mediated inflammatory settings. We originally found Ym1 and Fizz1 as highly expressed macrophage genes in a Brugia malayi infection model. Here, we show that their expression is a generalized feature of nematode infection and that they are induced at the site of infection with both the tissue nematode Litomosoides sigmodontis and the gastrointestinal nematode Nippostrongylus brasiliensis. At the sites of infection with N. brasiliensis, we also observed induction of other chitinase and Fizz family members (ChaFFs): acidic mammalian chitinase (AMCase) and Fizz2. The high expression of both Ym1 and AMCase in the lungs of infected mice suggests that abundant chitinase production is an important feature of Th2 immune responses in the lung. In addition to expression of ChaFFs in the tissues, Ym1 and Fizz1 expression was observed in the lymph nodes. Expression both in vitro and in vivo was restricted to antigen-presenting cells, with the highest expression in B cells and macrophages. ChaFFs may therefore be important effector or wound-repair molecules at the site of nematode infection, with potential regulatory roles for Ym1 and Fizz1 in the draining lymph nodes

Adaptive prior variance calibration in the Bayesian continual reassessment method

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98219/1/sim5621.pd

Variable selection in monotone single‐index models via the adaptive LASSO

We consider the problem of variable selection for monotone single‐index models. A single‐index model assumes that the expectation of the outcome is an unknown function of a linear combination of covariates. Assuming monotonicity of the unknown function is often reasonable and allows for more straightforward inference. We present an adaptive LASSO penalized least squares approach to estimating the index parameter and the unknown function in these models for continuous outcome. Monotone function estimates are achieved using the pooled adjacent violators algorithm, followed by kernel regression. In the iterative estimation process, a linear approximation to the unknown function is used, therefore reducing the situation to that of linear regression and allowing for the use of standard LASSO algorithms, such as coordinate descent. Results of a simulation study indicate that the proposed methods perform well under a variety of circumstances and that an assumption of monotonicity, when appropriate, noticeably improves performance. The proposed methods are applied to data from a randomized clinical trial for the treatment of a critical illness in the intensive care unit. Copyright © 2013 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100172/1/sim5834.pd

A utility approach to individualized optimal dose selection using biomarkers

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non‐negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154301/1/bimj2068.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154301/2/bimj2068_am.pd

Comparison of joint modeling and landmarking for dynamic prediction under an illness‐death model

Dynamic prediction incorporates time‐dependent marker information accrued during follow‐up to improve personalized survival prediction probabilities. At any follow‐up, or “landmark”, time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness‐death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We compare the performance of the landmark models with joint models using simulation studies and cognitive aging data from the PAQUID study. We examine the predicted probabilities produced under both methods using data from a prostate cancer study, where metastatic clinical failure is a time‐dependent covariate for predicting death following radiation therapy.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140034/1/bimj1778.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140034/2/bimj1778_am.pd

Precollege nanotechnology education: A different kind of thinking

The introduction of nanotechnology education into K-12 education has happened so quickly that there has been little time to evaluate the approaches and knowledge goals that are most effective to teach precollege students. This review of nanotechnology education examines the instructional approaches and types of knowledge that frame nanotechnology precollege education. Methods used to teach different forms of knowledge are examined in light of the goal of creating effective and meaningful instruction. The developmental components needed to understand concepts such as surface area to volume relationships as well as the counterintuitive behavior of nanoscale materials are described. Instructional methods used in precollege nanotechnology education and the levels at which different nanoscale topics are introduced is presented and critiqued. Suggestions are made for the development of new nanotechnology educational programs that are developmental, sequenced, and meaningful

Two-Loop ϕ4\phi^4-Diagrams from String Theory

Using the {\em cutting and sewing} procedure we show how to get Feynman diagrams, up to two-loop order, of $\Phi^{4}$-theory with an internal SU(N) symmetry group, starting from tachyon amplitudes of the open bosonic string theory. In a properly defined field theory limit, we easily identify the corners of the string moduli space reproducing the correctly normalized field theory amplitudes expressed in the Schwinger parametrization.Comment: 28 pages, 12 figure

Killed in Cold Blood: An exploration of the efficacy of oncolytic adenoviruses in metastatic breast cancer

https://openworks.mdanderson.org/sumexp21/1182/thumbnail.jp

A Placebo‐Controlled Double‐Blinded Randomized Pilot Study of Combination Phytotherapy in Biochemically Recurrent Prostate Cancer

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136500/1/pros23317_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136500/2/pros23317.pd