Computationally efficient inference for center effects based on restricted mean survival time

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/1/SIM_8356-Supp-0001-Supp_Info_revised_Xin_paper_2_SIM_30MAY2019.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/2/sim8356_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151995/3/sim8356.pd

Secondary prevention following coronary artery bypass surgery: a pilot study for improved patient education

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An intraductal human-in-mouse transplantation model mimics the subtypes of ductal carcinoma in situ

Introduction: Human models of noninvasive breast tumors are limited, and the existing in vivo models do not mimic inter- and intratumoral heterogeneity. Ductal carcinoma in situ (DCIS) is the most common type (80%) of noninvasive breast lesions. The aim of this study was to develop an in vivo model whereby the natural progression of human DCIS might be reproduced and studied. To accomplish this goal, the intraductal human-in-mouse (HIM) transplantation model was developed. The resulting models, which mimicked some of the diversity of human noninvasive breast cancers in vivo, were used to show whether subtypes of human DCIS might contain distinct subpopulations of tumor-initiating cells.Methods The intraductal models were established by injection of human DCIS cell lines (MCF10DCIS.COM and SUM-225), as well as cells derived from a primary human DCIS (FSK-H7), directly into the primary mouse mammary ducts via cleaved nipple. Six to eight weeks after injections, whole-mount, hematoxylin and eosin, and immunofluorescence staining were performed to evaluate the type and extent of growth of the DCIS-like lesions. To identify tumor-initiating cells, putative human breast stem/progenitor subpopulations were sorted from MCF10DCIS.COM and SUM-225 with flow cytometry, and their in vivo growth fractions were compared with the Fisher's Exact test. Results: Human DCIS cells initially grew within the mammary ducts, followed by progression to invasion in some cases into the stroma. The lesions were histologically almost identical to those of clinical human DCIS. This method was successful for growing DCIS cell lines (MCF10DCIS.COM and SUM-225) as well as a primary human DCIS (FSK-H7). MCF10DCIS.COM represented a basal-like DCIS model, whereas SUM-225 and FSK-H7 cells were models for HER-2[super]+ DCIS. With this approach, we showed that various subtypes of human DCIS appeared to contain distinct subpopulations of tumor-initiating cells. Conclusions: The intraductal HIM transplantation model provides an invaluable tool that mimics human breast heterogeneity at the noninvasive stages and allows the study of the distinct molecular and cellular mechanisms of breast cancer progression

A modified approach for obtaining sieve bootstrap prediction intervals for time series

The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres assumes that the underlying distribution of the innovations is Gaussian. It is well known that deviations from this assumption can lead to prediction intervals with poor coverage. Nonparametric bootstrap-based procedures for obtaining prediction intervals overcome this handicap, but many early versions of such intervals for autoregressive moving average (ARMA) processes assume that the autoregressive and moving average orders, p, q respectively, are known, The sieve bootstrap, first introduced by Bühlmann in 1997, sidesteps this assumption for invertible time series by approximating the ARMA process by a finite autoregressive model whose order is estimated by using a model procedure such as the AICC. Existing sieve bootstrap methods in general, however, produces liberal prediction intervals due to several factors, including the use of residuals that underestimate the actual variance of the innovations and the failure of the methods to capture variations due to sampling error of some parameter estimates. In this dissertation, a modified sieve bootstrap approach, that corrects these deficiencies, is implemented to obtain prediction intervals for both univariate and multivariate time series. Monte Carlo simulations results show that the modifications provide prediction intervals that achieve nominal or near nominal coverage probabilities. Asymptotic results for the univariate series also establish the validity of the modified approach --Abstract, page iii

Prediction Intervals for Time Series: a Modified Sieve Bootstrap Approach

Traditional Box-Jenkins prediction intervals perform poorly when the innovations are not Gaussian. Nonparametric bootstrap procedures overcome this handicap, but most existing methods assume that the AR and MA orders of the process are known. The sieve bootstrap approach requires no such assumption but produces liberal coverage due to the use of residuals that underestimate the actual variance of the innovations and the failure of the methods to capture variations due to sampling error of the mean. A modified approach, that corrects these deficiencies, is implemented. Monte Carlo simulations results show that the modified version achieves nominal or near nominal coverage

Obtaining Prediction Intervals for Farima Processes Using the Sieve Bootstrap

The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) processes are constructed using resamples of residuals obtained by fitting a finite degree autoregressive approximation to the time series. The advantage of this approach is that it does not require the knowledge of the orders, p and q, associated with the ARMA(p, q) model. Up until recently, the application of this method has been limited to ARMA processes whose autoregressive polynomials do not have fractional unit roots. The authors, in a 2012 publication, introduced a version of the SB suitable for fractionally integrated autoregressive moving average (FARIMA (p,d,q)) processes with

Study of the tensile behavior of AISI type 316 stainless steel using acoustic emission and infrared thermography techniques

Acoustic emission (AE) and infrared thermography technique (IRT) have been used to study the tensile behavior of AISI type 316 stainless steel. Strain rates of tensile testing were varied from 1.4 × 10−3 s−1 to 1.4 × 10−2 s−1. AE root mean square voltage increases with increase in strain rate due to the increase in source activation. Dominant frequency of the AE signals generated during different regions of tensile deformation has also been used to compare the results for different strain rates. The dominant frequency increases from elastic region to around 590 kHz during work hardening and 710 kHz around ultimate tensile strength (UTS) for all the strain rates. Temperature changes during different regions of deformation are monitored using infrared thermography. The temperature rise in the work hardening region is found to approximately increase linearly with time and from the slopes of the linear regression analyses the rate of temperature rise in the work-hardening region is obtained which is found to be very sensitive to strain rates. From the experimental results an empirical equation that relates the rate of temperature increase with strain rate and thermal hardening coefficient is obtained. The correlation between the variation of AE dominant frequency and temperature rise during different deformation regions provided better insight into the tensile behavior of AISI type 316 SS for different strain rates