Experimental evaluation of environmental effects on a polymer-coated aluminium structure: a time-series analysis and pattern recognition approach

Temperature variation is an important issue that needs to be considered when trying to develop a reliable Structural Health Monitoring (SHM) strategy. In the case that a data-based approach is chosen for damage detection, environmental fluctuations could be erroneously regarded as an abnormal condition of the structure and could mask the presence of damage. One of the objectives of the current work is to examine a statistical pattern recognition approach for novelty detection under different temperature conditions. A second important issue that could hinder the reliability of a SHM strategy is any kind of nonlinear behaviour, not associated with damage, in a system. For the purposes of this paper, the dynamic behaviour of a polymer-coated aluminium structure with ribs fixed with bolts is examined. The autoregressive parameters are the damage sensitive features and later, it is performed Principal Component Analysis (PCA) for robust novelty detection that takes into account the temperature variation

Evaluation of Embodied Carbon Emissions in UK Supermarket Constructions: A Study on Steel, Brick, and Timber Frameworks with Consideration of End-of-Life Processes

Buildings and the construction sector as a whole are among the chief emitters of carbon, and the structural system of a building contributes substantially to its embodied carbon emissions. Whereas extensive studies exist into carbon missions, a detailed evaluation of real multipart building systems in brick, steel, and timber (glulam) substitutes is lacking. This paper employs whole-life-embedded carbon as a sustainability metric to compare a current UK supermarket building system of steel, brick, and timber. Four construction systems by the supermarket, referred to as CS1, CS2, CS3, and CS4, are used in the investigation. Comparisons are also made between two end-of-life treatment methods (recycle and landfill) along with the benefits that can be realised in future construction projects. The outcome from the comparative assessment reveals that there are minor variations in the embodied carbon of building systems used by the supermarket. CS4, while currently presenting marginal gains (approximately 148,960.68 kgCO2eq.) compared to CS1, loses its advantages when recycled contents for future construction projects are considered. The result indicates that CS4 generates about 18% less carbon emission reduction potential than CS1, whilst CS3 generates approximately 16% less than CS1. The findings of this article can enhance the knowledge of embodied carbon estimation and reduction capabilities of timber, steel, and brick buildings. Also, the detailed method for quantifying embodied carbon used in this article can be adopted in similar projects around the world

Combinations of PARP Inhibitors with Temozolomide Drive PARP1 Trapping and Apoptosis in Ewing's Sarcoma.

Ewing's sarcoma is a malignant pediatric bone tumor with a poor prognosis for patients with metastatic or recurrent disease. Ewing's sarcoma cells are acutely hypersensitive to poly (ADP-ribose) polymerase (PARP) inhibition and this is being evaluated in clinical trials, although the mechanism of hypersensitivity has not been directly addressed. PARP inhibitors have efficacy in tumors with BRCA1/2 mutations, which confer deficiency in DNA double-strand break (DSB) repair by homologous recombination (HR). This drives dependence on PARP1/2 due to their function in DNA single-strand break (SSB) repair. PARP inhibitors are also cytotoxic through inhibiting PARP1/2 auto-PARylation, blocking PARP1/2 release from substrate DNA. Here, we show that PARP inhibitor sensitivity in Ewing's sarcoma cells is not through an apparent defect in DNA repair by HR, but through hypersensitivity to trapped PARP1-DNA complexes. This drives accumulation of DNA damage during replication, ultimately leading to apoptosis. We also show that the activity of PARP inhibitors is potentiated by temozolomide in Ewing's sarcoma cells and is associated with enhanced trapping of PARP1-DNA complexes. Furthermore, through mining of large-scale drug sensitivity datasets, we identify a subset of glioma, neuroblastoma and melanoma cell lines as hypersensitive to the combination of temozolomide and PARP inhibition, potentially identifying new avenues for therapeutic intervention. These data provide insights into the anti-cancer activity of PARP inhibitors with implications for the design of treatment for Ewing's sarcoma patients with PARP inhibitors.Research in the M.J.G. laboratory is supported by grants from the Wellcome Trust (086357 and 102696/Z/13/Z; http://www.wellcome.ac.uk/Funding). Research in the S.P.J. laboratory is funded by Cancer Research UK Program Grant C6/A11224 (http://www.cancerresearchuk.org/funding-for-researchers/our-funding-schemes), the European Research Council (http://erc.europa.eu/funding-and-grants)and the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). Core infrastructure funding was provided by Cancer Research UK Grant C6946/A14492 and Wellcome Trust Grant WT092096. S.P.J. receives a salary from the University of Cambridge, supplemented by Cancer Research UK. J.T. was funded by the European Community Seventh Framework Program grant agreement no. HEALTH-F2-2010-259893 (DDResponse). U.M. is supported by a Cancer Research UK Clinician Scientist Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pone.014098

A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine

N-1 modal interactions of a three-degree-of-freedom system with cubic elastic nonlinearities

In this paper the (Formula presented.) nonlinear modal interactions that occur in a nonlinear three-degree-of-freedom lumped mass system, where (Formula presented.), are considered. The nonlinearity comes from springs with weakly nonlinear cubic terms. Here, the case where all the natural frequencies of the underlying linear system are close (i.e. (Formula presented.)) is considered. However, due to the symmetries of the system under consideration, only (Formula presented.) modes interact. Depending on the sign and magnitude of the nonlinear stiffness parameters, the subsequent responses can be classified using backbone curves that represent the resonances of the underlying undamped, unforced system. These backbone curves, which we estimate analytically, are then related to the forced response of the system around resonance in the frequency domain. The forced responses are computed using the continuation software AUTO-07p. A comparison of the results gives insights into the multi-modal interactions and shows how the frequency response of the system is related to those branches of the backbone curves that represent such interactions

Coronavirus infections from 2002-2021: Neuropsychiatric Manifestations

Coronaviruses have been known to infect humans for several decades and there are four endemic subtypes: HCoV (human coronavirus) -229E, -NL63, -OC43 and -HKU1. These mainly cause a mild upper respiratory illness, but occasionally in vulnerable individuals they can result in more severe respiratory disease and, rarely, CNS involvement. Prior exposure to these viruses has also been associated with an increased odds of having a major psychiatric illness. The severe acute respiratory syndrome (SARS), caused by SARS-CoV, started in 2002 and, as well as causing a more severe respiratory phenotype, was also associated with delirium and affective symptoms acutely. Psychosis occurred in about 1% of individuals and was generally thought to be due to corticosteroid administration. The Middle East respiratory syndrome (MERS), caused by MERS-CoV, revealed similar findings. Survivors of both SARS and MERS reported persistent physical and psychological symptoms at least several months after the acute illness. The reported neuropsychiatric symptoms of COVID-19 range from the common symptoms of systemic and upper respiratory infections to severe and disabling conditions. Delirium has been described using varying terminology; as well as being a possible presenting feature of COVID-19, it has also been shown to be a marker of severe disease. Stroke, both ischaemic and haemorrhagic, have been reported to be more common in COVID-19 than in other medical illnesses. Mood and anxiety disorders are likely to be common at follow-up, while psychosis remains rare and controversial. ‘Long Covid’ is likely to represent a highly clinically and aetiologically heterogeneous group

On an application of probabilistic risk assessment to structural health monitoring

A key motivation for implementing structural health monitoring is to facilitate decision-making regarding the operation of a structure throughout its life. The notion of risk has been used to inform decision-making under uncertainty in industries such as nuclear energy and aerospace - formalised in a procedure known as probabilistic risk assessment. The current paper aims to exploit methods used in probabilistic risk assessment to demonstrate a novel risk-based approach to structural health monitoring. The approach utilises a probabilistic graphical model framework in which information is passed from a probabilistic classifier to an influence diagram representing a decision-process via a Bayesian network representation of a fault tree. The risk-based approach is demonstrated on simulated data from a finite element model of a four bay truss

<i>N </i>- 1 modal interactions of a three-degree-of-freedom system with cubic elastic nonlinearities

In this paper the N-1 nonlinear modal interactions that occur in a nonlinear three-degree-of-freedom lumped mass system, where N=3, are considered. The nonlinearity comes from springs with weakly nonlinear cubic terms. Here, the case where all the natural frequencies of the underlying linear system are close (i.e. wn1 : wn2 : wn3 ≈ 1 : 1 : 1) is considered. However, due to the symmetries of the system under consideration, only N-1 modes interact. Depending on the sign and magnitude of the nonlinear stiffness parameters, the subsequent responses can be classified using backbone curves that represent the resonances of the underlying undamped, unforced system. These backbone curves, which we estimate analytically, are then related to the forced response of the system around resonance in the frequency domain. The forced responses are computed using the continuation software AUTO-07p. A comparison of the results gives insights into the multi-modal interactions and shows how the frequency response of the system is related to those branches of the backbone curves that represent such interactions