Performance assessment of building envelope interfaces: self-adhering flashings

The overall performance of a building envelope is mainly related to the performance of the different materials of building components. The performance of those specific components may be very well known in different research area’s (thermal properties, acoustics, airtightness, watertightness…), experience points out the weakest link is in fact the interface between those different components. There are a number of elements that caused a shift in the way we think about building envelope interfaces. - First of all, an overall increasing life standard causes people to have higher expectations when it comes to newly constructed residential buildings. A few decades ago houses used to be “somewhat warm, somewhat dry and somewhat comfortable” as J. D. Katsaros (Katsaros and Hardman, 2007) puts it. Today people have strict demands and even the slightest mark of water infiltration can give rise to disputes and legal actions. - Due to an increased attention to environmental issues and the spectacular rise of energy prices governments and individual builders invest in higher insulation levels, more airtight buildings and the use of sustainable solutions. - In a lot of countries building regulation codes are evolving from prescriptive codes towards performance based standards (Sjöström 1999, Haberecht et al., 1999). That way those standards will make way for expanding the limits of free trade in a globalizing world. The development of a methodology for performance assessment of building products, components and constructions is crucial for the future implementation of performance based standards. Building envelope interfaces cannot be evaluated using the performance criteria of the adjacent building components, so specific research is needed in order to quantify those interfaces in different areas. As the current building practice in Europe uses more and more self-adhesive flashings there is a major need for performance-based criteria to assess their short- and long-term behavior. This paper looks into different existing methodologies for artificial aging of samples and offers a state-of-the-art concerning research on self-adhering flashings. Initial experiments seem to contradict some of the found results concerning the performance of different types of adhesives (butyl/modified asphalt). Lab experiments also point out that the interaction between certain adhesives and substrates needs further investigation in order to avoid future water infiltration problems. Three types of self-adhering flashings are tested on four different substrates in lab conditions and after artificial aging to investigate the long-term performance of different flashings. Also the influence of the use of primers is analyzed

Fatigue investigation of threaded pipe connections

Threaded pipe connections are used to connect well casing, well tubing, drill pipes and risers. For many of these applications fatigue resistance plays an important role. In this study the fatigue properties of threaded connections are studied using a combination of finite element modelling and experimental testing. Using 2D axisymmetric FE analysis several connections are compared. It is shown that the load distribution over the engaged threads is an important feature. Experimental tests are carried out on three setups. A small scale four-point bending setup is used to develop S-N curves. An S-N curve for a standard API Line Pipe connection is compared to an S-N curve for a connection that showed an improved load distribution over the engaged threads in the FE analysis. On a medium scale four-point bending setup, strains together with crack opening are measured. The strain measurements are compared with the strains obtained by the numerical model. Finally a full scale resonant bending fatigue setup is presented, which will be used in future testing of pipe connections ranging from 168 mm (6”) to 508 mm (20”) in diameter

Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer

The emergence of the precision medicine paradigm in oncology has led to increasing interest in the integration of real-world data (RWD) into cancer clinical research. As sources of real-world evidence (RWE), such data could potentially help address the uncertainties that surround the adoption of novel anticancer therapies into the clinic following their investigation in clinical trials. At present, RWE-generating studies which investigate antitumour interventions seem to primarily focus on collecting and analysing observational RWD, typically forgoing the use of randomisation despite its methodological benefits. This is appropriate in situations where randomised controlled trials (RCTs) are not feasible and non-randomised RWD analyses can offer valuable insights. Nevertheless, depending on how they are designed, RCTs have the potential to produce strong and actionable RWE themselves. The choice of which methodology to employ for RWD studies should be guided by the nature of the research question they are intended to answer. Here, we attempt to define some of the questions that do not necessarily require the conduct of RCTs. Moreover, we outline the strategy of the European Organisation for Research and Treatment of Cancer (EORTC) to contribute to the generation of robust and high-quality RWE by prioritising the execution of pragmatic trials and studies set up according to the trials-within-cohorts approach. If treatment allocation cannot be left up to random chance due to practical or ethical concerns, the EORTC will consider undertaking observational RWD research based on the target trial principle. New EORTC-sponsored RCTs may also feature concurrent prospective cohorts composed of off-trial patients

Defining the role of real-world data in cancer clinical research:The position of the European Organisation for Research and Treatment of Cancer

The emergence of the precision medicine paradigm in oncology has led to increasing interest in the integration of real-world data (RWD) into cancer clinical research. As sources of real-world evidence (RWE), such data could potentially help address the uncertainties that surround the adoption of novel anticancer therapies into the clinic following their investigation in clinical trials. At present, RWE-generating studies which investigate antitumour interventions seem to primarily focus on collecting and analysing observational RWD, typically forgoing the use of randomisation despite its methodological benefits. This is appropriate in situations where randomised controlled trials (RCTs) are not feasible and non-randomised RWD analyses can offer valuable insights. Nevertheless, depending on how they are designed, RCTs have the potential to produce strong and actionable RWE themselves. The choice of which methodology to employ for RWD studies should be guided by the nature of the research question they are intended to answer. Here, we attempt to define some of the questions that do not necessarily require the conduct of RCTs. Moreover, we outline the strategy of the European Organisation for Research and Treatment of Cancer (EORTC) to contribute to the generation of robust and high-quality RWE by prioritising the execution of pragmatic trials and studies set up according to the trials-within-cohorts approach. If treatment allocation cannot be left up to random chance due to practical or ethical concerns, the EORTC will consider undertaking observational RWD research based on the target trial principle. New EORTC-sponsored RCTs may also feature concurrent prospective cohorts composed of off-trial patients

TP53 outperforms other androgen receptor biomarkers to predict abiraterone or enzalutamide outcome in metastatic castration-resistant prostate cancer

Purpose: To infer the prognostic value of simultaneous androgen receptor (AR) and TP53 profiling in liquid biopsies from patients with metastatic castration-resistant prostate cancer (mCRPC) starting a new line of AR signaling inhibitors (ARSi). Experimental Design: Between March 2014 and April 2017, we recruited patients with mCRPC (n = 168) prior to ARSi in a cohort study encompassing 10 European centers. Blood samples were collected for comprehensive profiling of Cell Search-enriched circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). Targeted CTC RNA sequencing (RNA-seq) allowed the detection of eight AR splice variants (ARV). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss of heterozygosity, mutations, and structural rearrangements in ctDNA. Clinical or radiologic progression-free survival (PFS) was estimated by Kaplan-Meier analysis, and independent associations were determined using multivariable Cox regression models. Results: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumor burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value [HR 1.88; 95% confidence interval (CI), 1.18-3.00; P = 0.008], and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs. 7.51 vs. 2.62 months; P < 0.0001), which was validated in an independent mCRPC cohort (n = 202) starting first-line ARSi (median, 14.3 vs. 6.39 vs. 2.23 months; P < 0.0001). Conclusions: In an all-comer cohort, tumor burden estimates and TP53 outperform any AR perturbation to infer prognosis. See related commentary by Rebello et al., p. 169

Development and Reporting of Prediction Models: Guidance for Authors From Editors of Respiratory, Sleep, and Critical Care Journals

Prediction models aim to use available data to predict a health state or outcome that has not yet been observed. Prediction is primarily relevant to clinical practice, but is also used in research, and administration. While prediction modeling involves estimating the relationship between patient factors and outcomes, it is distinct from casual inference. Prediction modeling thus requires unique considerations for development, validation, and updating. This document represents an effort from editors at 31 respiratory, sleep, and critical care medicine journals to consolidate contemporary best practices and recommendations related to prediction study design, conduct, and reporting. Herein, we address issues commonly encountered in submissions to our various journals. Key topics include considerations for selecting predictor variables, operationalizing variables, dealing with missing data, the importance of appropriate validation, model performance measures and their interpretation, and good reporting practices. Supplemental discussion covers emerging topics such as model fairness, competing risks, pitfalls of “modifiable risk factors”, measurement error, and risk for bias. This guidance is not meant to be overly prescriptive; we acknowledge that every study is different, and no set of rules will fit all cases. Additional best practices can be found in the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, to which we refer readers for further details

Altered spin state equilibrium in the T309V mutant of cytochrome P450 2D6: a spectroscopic and computational study

Cytochrome P450 2D6 (CYP2D6) is one of the most important cytochromes P450 in humans. Resonance Raman data from the T309V mutant of CYP2D6 show that the substitution of the conserved I-helix threonine situated in the enzyme’s active site perturbs the heme spin equilibrium in favor of the six-coordinated low-spin species. A mechanistic hypothesis is introduced to explain the experimental observations, and its compatibility with the available structural and spectroscopic data is tested using quantum-mechanical density functional theory calculations on active-site models for both the CYP2D6 wild type and the T309V mutant

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe