The Venice Strips

Based on the MUD Intermediate Studio’s visit to Venice, Italy, in November 2006, Professor Roy Strickland asked students to develop concepts and create narrative strips for the entire city: “In ten years, Venice will host a year-long celebration of its history, urban form, architecture, and culture. The city is to create the venue for interventions that highlight Venice’s past, present, and future. More than showcasing the city’s history, these interventions will also suggest means of expanding Venice’s economy beyond tourism and re-introducing a live-work population. Concepts consist of both temporary and permanent interventions, accommodating the millions of international tourists who are expected to attend the celebration, as well as the tens of thousands of people who will return to Venice to live and work during and after the event.”http://deepblue.lib.umich.edu/bitstream/2027.42/120327/1/SchemperWinch_UrbanDesignStudio2006-Venice.pd

Renate Wagner-Rieger (1921 ‒ 1980) — University Professor, Historicism Researcher, and Advocate for the Preservation of Vienna’s Townscape

On the occasion of the 100th birthday of the art and architecture historian Renate Wagner-Rieger, the University of Vienna in cooperation with the Austrian Academy of Sciences organized an international conference in November 2021.1 Wagner-Rieger, who died in 1980, was the first female full professor at the Viennese Institute of Art History. On one hand, the conference showed the impact of her scholarly work in her time, and on the other hand her legacy and reception in current research. A second conference in early July 2022 focused on the genre of 19th-century sculpture, which as an art form closely related to architecture and was also an essential research interest of Wagner-Rieger. The 2021 conference was a further contribution of the Institute of Art History to its past. The focus of the institute’s self-reflection so far had contained several main representatives of the Viennese School, such as Rudolf Eitelberger, Alois Riegl, Julius Schlosser and Josef Strzygowski

Explained variation of excess hazard models.

The availability of longstanding collection of detailed cancer patient information makes multivariable modelling of cancer-specific hazard of death appealing. We propose to report variation in survival explained by each variable that constitutes these models. We adapted the ranks explained (RE) measure to the relative survival data setting, ie, when competing risks of death are accounted for through life tables from the general population. RE is calculated at each event time. We introduce weights for each death reflecting its probability to be a cancer death. RE varies between -1 and +1 and can be reported at given times in the follow-up and as a time-varying measure from diagnosis onward. We present an application for patients diagnosed with colon or lung cancer in England. The RE measure shows reasonable properties and is comparable in both relative and cause-specific settings. One year after diagnosis, RE for the most complex excess hazard models reaches 0.56, 95% CI: 0.54 to 0.58 (0.58 95% CI: 0.56-0.60) and 0.69, 95% CI: 0.68 to 0.70 (0.67, 95% CI: 0.66-0.69) for lung and colon cancer men (women), respectively. Stage at diagnosis accounts for 12.4% (10.8%) of the overall variation in survival among lung cancer patients whereas it carries 61.8% (53.5%) of the survival variation in colon cancer patients. Variables other than performance status for lung cancer (10%) contribute very little to the overall explained variation. The proportion of the variation in survival explained by key prognostic factors is a crucial information toward understanding the mechanisms underpinning cancer survival. The time-varying RE provides insights into patterns of influence for strong predictors

Depositional systems, lithofacies, and geochemistry of the Jurassic (Oxfordian) Smackover and Buckner Formations in Van Zandt County, Texas : a type-core section

The Late Jurassic Smackover Formation is part of a prolific petroleum system along the northern rim of the Gulf of Mexico with a long history of exploration. Most investigations into the Smackover have taken place in sub basins east of the East Texas basin and primarily focus on upper Smackover oolitic grainstone reservoirs, but only a few studies have been completed on the lower Smackover source rocks. Because of this, there is a lack of understanding related to the conditions under which the lower Smackover was deposited. This study provides a more complete understanding of the Smackover deposits in the East Texas through investigation of a long continuous 713-ft core drilled from the Sun Oil Travis GU No. 1 well in Van Zandt County, Texas. It contains a continuous section of Smackover lithofacies from lower Smackover mudstones to upper Smackover grainstones and contains a large portion of the overlying Buckner Anhydrite. The goal of this investigation is to lithologically and chemically characterize the Smackover Formation in order to create a type section for future studies and create a refined depositional model for East Texas Smackover deposition. High-resolution geochemical data composed of x-ray diffraction analysis (XRD), total organic carbon (TOC) and Rock-Eval, x-ray fluorescence (XRF) analysis, and isotope data was taken to fully characterize the section chemically. This geochemical dataset provides insight into controls on organic matter accumulation and destruction, diagenetic fluids, and local and global isotope signatures. With understandings of lithofacies distribution, depositional controls for organic accumulation and porosity development, and diagenesis the unconventional and conventional potential of the Smackover can be assessed.Geological Science

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Weighted Cox Regression Using the R Package coxphw

Cox's regression model for the analysis of survival data relies on the proportional hazards assumption. However, this assumption is often violated in practice and as a consequence the average relative risk may be under- or overestimated. Weighted estimation of Cox regression is a parsimonious alternative which supplies well interpretable average effects also in case of non-proportional hazards. We provide the R package coxphw implementing weighted Cox regression. By means of two biomedical examples appropriate analyses in the presence of non-proportional hazards are exemplified and advantages of weighted Cox regression are discussed. Moreover, using package coxphw, time-dependent effects can be conveniently estimated by including interactions of covariates with arbitrary functions of time

Conditional Transformation Models

The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This goal is, however, seldom achieved because most established regression models only estimate the conditional mean as a function of the explanatory variables and assume that higher moments are not affected by the regressors. The underlying reason for such a restriction is the assumption of additivity of signal and noise. We propose to relax this common assumption in the framework of transformation models. The novel class of semiparametric regression models proposed herein allows transformation functions to depend on explanatory variables. These transformation functions are estimated by regularised optimisation of scoring rules for probabilistic forecasts, e.g. the continuous ranked probability score. The corresponding estimated conditional distribution functions are consistent. Conditional transformation models are potentially useful for describing possible heteroscedasticity, comparing spatially varying distributions, identifying extreme events, deriving prediction intervals and selecting variables beyond mean regression effects. An empirical investigation based on a heteroscedastic varying coefficient simulation model demonstrates that semiparametric estimation of conditional distribution functions can be more beneficial than kernel-based non-parametric approaches or parametric generalised additive models for location, scale and shape

Erythropoiesis-stimulating agents significantly delay the onset of a regular transfusion need in nontransfused patients with lower-risk myelodysplastic syndrome

Background The EUMDS registry is an unique prospective, longitudinal observational registry enrolling newly diagnosed patients with lower‐risk myelodysplastic syndrome (MDS) from 17 European countries from both university hospitals and smaller regional hospitals. Objective The aim of this study was to describe the usage and clinical impact of erythropoiesis‐stimulating agents (ESAs) in 1696 patients enrolled between 2008 and 2014. Methods The effects of ESAs on outcomes were assessed using proportional hazards models weighting observations by propensity to receive ESA treatment within a subset of anaemic patients with or without a regular transfusion need. Results ESA treatment (median duration of 27.5 months, range 0–77 months) was administered to 773 patients (45.6%). Outcomes were assessed in 897 patients (484 ESA treated and 413 untreated). ESA treatment was associated with a nonsignificant survival benefit (HR 0.82, 95% CI: 0.65–1.04, P = 0.09); this benefit was larger amongst patients without prior transfusions (P = 0.07). Amongst 539 patients for whom response to ESA treatment could be defined, median time to first post‐ESA treatment transfusion was 6.1 months (IQR: 4.3–15.9 months) in those transfused before ESA treatment compared to 23.3 months (IQR: 7.0–47.8 months) in patients without prior transfusions (HR 2.4, 95% CI: 1.7–3.3, P < 0.0001). Responding patients had a better prognosis in terms of a lower risk of death (HR 0.65, 95% CI: 0.45–0.893, P = 0.018), whereas there was no significant effect on the risk of progression to acute myeloid leukaemia (HR 0.71, 95% CI: 0.39–1.29, P = 0.27). Conclusion Appropriate use of ESAs can significantly delay the onset of a regular transfusion need in patients with lower‐risk MDS

Gene expression of PMP22 is an independent prognostic factor for disease-free and overall survival in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Gene expression of peripheral myelin protein 22 (<it>PMP22</it>) and the epithelial membrane proteins (<it>EMPs</it>) was found to be differentially expressed in invasive and non-invasive breast cell lines in a previous study. We want to evaluate the prognostic impact of the expression of these genes on breast cancer.</p> <p>Methods</p> <p>In a retrospective multicenter study, gene expression of <it>PMP22 </it>and the <it>EMPs </it>was measured in 249 primary breast tumors by real-time PCR. Results were statistically analyzed together with clinical data.</p> <p>Results</p> <p>In univariable Cox regression analyses PMP22 and the EMPs were not associated with disease-free survival or tumor-related mortality. However, multivariable Cox regression revealed that patients with higher than median <it>PMP22 </it>gene expression have a 3.47 times higher risk to die of cancer compared to patients with equal values on clinical covariables but lower <it>PMP22 </it>expression. They also have a 1.77 times higher risk to relapse than those with lower <it>PMP22 </it>expression. The proportion of explained variation in overall survival due to <it>PMP22 </it>gene expression was 6.5% and thus PMP22 contributes equally to prognosis of overall survival as nodal status and estrogen receptor status. Cross validation demonstrates that 5-years survival rates can be refined by incorporating <it>PMP22 </it>into the prediction model.</p> <p>Conclusions</p> <p><it>PMP22 </it>gene expression is a novel independent prognostic factor for disease-free survival and overall survival for breast cancer patients. Including it into a model with established prognostic factors will increase the accuracy of prognosis.</p

Different competing risks models applied to data from the Australian Orthopaedic Association National Joint Replacement Registry

Purpose: Here we describe some available statistical models and illustrate their use for analysis of arthroplasty registry data in the presence of the competing risk of death, when the influence of covariates on the revision rate may be different to the influence on the probability (that is, risk) of the occurrence of revision. Patients and methods: Records of 12,525 patients aged 75–84 years who had received hemiarthroplasty for fractured neck of femur were obtained from the Australian Orthopaedic Association National Joint Replacement Registry. The covariates whose effects we investigated were: age, sex, type of prosthesis, and type of fixation (cementless or cemented). Extensions of competing risk regression models were implemented, allowing the effects of some covariates to vary with time. Results: The revision rate was significantly higher for patients with unipolar than bipolar prostheses (HR = 1.38, 95% CI: 1.01–1.89) or with monoblock than bipolar prostheses (HR = 1.45, 95% CI: 1.08–1.94). It was significantly higher for the younger age group (75–79 years) than for the older one (80–84 years) (HR = 1.28, 95% CI: 1.05–1.56) and higher for males than for females (HR = 1.37, 95% CI: 1.09–1.71). The probability of revision, after correction for the competing risk of death, was only significantly higher for unipolar prostheses than for bipolar prostheses, and higher for the younger age group. The effect of fixation type varied with time; initially, there was a higher probability of revision for cementless prostheses than for cemented prostheses, which disappeared after approximately 1.5 years. Interpretation: When accounting for the competing risk of death, the covariates type of prosthesis and sex influenced the rate of revision differently to the probability of revision. We advocate the use of appropriate analysis tools in the presence of competing risks and when covariates have time-dependent effects.Marianne H Gillam, Amy Salter, Philip Ryan, and Stephen E Grave