Lineage-specific biomarkers predict response to FGFR inhibition

In this issue of Cancer Discovery, Guagnano and colleagues use a large and diverse annotated collection of cancer cell lines, the Cancer Cell Line Encyclopedia, to correlate whole-genome expression and genomic alteration datasets with cell line sensitivity data to the novel pan-fibroblast growth factor receptor (FGFR) inhibitor NVP-BGJ398. Their findings underscore not only the preclinical use of such cell line panels in identifying predictive biomarkers, but also the emergence of the FGFRs as valid therapeutic targets, across an increasingly broad range of malignancies.

Non-aqueous synthesis of hexagonal ZnO nanopyramids : gas sensing properties

Zinc oxide (ZnO) nanopyramids were synthesized by a one-pot route in a non-aqueous and surfactantfree environment. The synthesized metal oxide was characterized using SEM, XRD, and TEM to investigate the surface morphology and crystallographic phase of the nanostructures. It was observed that the ZnO nanopyramids were of uniform size and symmetrical, with a hexagonal base and height of ∼100 nm. Gas sensing characterization of the ZnO nanopyramids when deposited as thin-film onto conductometric transducers were performed towards NOx and C2H5OH vapor of different concentrations over a temperature range of 22–350 ◦C. It was observed that the sensors responded towards NO2 (10 ppm) and C2H5OH(250 ppm) analytes best at temperatures of 200 and 260 ◦C with a sensor response of 14.5 and 5.72, respectively. The sensors showed satisfactory sensitivity, repeatability as well as fast response and recovery towards both the oxidizing and the reducing analyte. The good performance was attributed to the low amount of organic impurities, large surface-to-volume ratio and high crystallinity of the solvothermally synthesized ZnO nanopyramids.

Numerical study of impact forces on railway sleepers under wheel flat

Wheel-rail interaction is one of the most important research topics in railway engineering. It includes track vibration, track impact response and safety of the track. Track structure failures caused by impact forces can lead to significant economic loss for track owners through damage to rails and to the sleepers beneath. The wheel-rail impact forces occur because of imperfections on the wheels or rails such as wheel flats, irregular wheel profile, rail corrugation and differences in the height of rails connected at a welded joint. The vehicle speed and static wheel load are important factors of the track design, because they are related to the impact forces under wheel-rail defects. In this paper, a 3-Dimensional finite element model for the study of wheel flat impact is developed by use of the FEA software package ANSYS. The effects of the wheel flat to impact force on sleepers with various speeds and static wheel loads under a critical wheel flat size are investigated. It has found that both wheel-rail impact force and impact force on sleeper induced by wheel flat are varying nonlinearly by increasing the vehicle speed; both impact forces are nonlinearly and monotonically increasing by increasing the static wheel load. The relationships between both of impact forces induced by wheel flat and vehicles speed or static load are important to the track engineers to improve the design and maintenance methods in railway industry.

A non-linear dynamic analysis of heavy haul track line sleepers under wheel flat

Significant wheel-rail dynamic forces occur because of imperfections in the wheels and/or rail. One of the key responses to the transmission of these forces down through the track is impact force on the sleepers. Dynamic analysis of nonlinear systems is very complicated and does not lend itself easily to a classical solution of multiple equations. Trying to deduce the behaviour of track components from experimental data is very difficult because such data is hard to obtain and applies to only the particular conditions of the track being tested. The finite element method can be the best solution to this dilemma. This paper describes a finite element model using the software package ANSYS for various sized flat defects in the tread of a wheel rolling at a typical speed on heavy haul track. The paper explores the dynamic response of a prestressed concrete sleeper to these defects.

A dynamic wheel-rail impact analysis of railway track under wheel flat by Finite Element Analysis

Wheel–rail interaction is one of the most important research topics in railway engineering. It involves track impact response, track vibration and track safety. Track structure failures caused by wheel–rail impact forces can lead to significant economic loss for track owners through damage to rails and to the sleepers beneath. Wheel–rail impact forces occur because of imperfections in the wheels or rails such as wheel flats, irregular wheel profiles, rail corrugations and differences in the heights of rails connected at a welded joint. A wheel flat can cause a large dynamic impact force as well as a forced vibration with a high frequency, which can cause damage to the track structure. In the present work, a three-dimensional (3-D) finite element (FE) model for the impact analysis induced by the wheel flat is developed by use of the finite element analysis (FEA) software package ANSYS and validated by another validated simulation. The effect of wheel flats on impact forces is thoroughly investigated. It is found that the presence of a wheel flat will significantly increase the dynamic impact force on both rail and sleeper. The impact force will monotonically increase with the size of wheel flats. The relationships between the impact force and the wheel flat size are explored from this finite element analysis and they are important for track engineers to improve their understanding of the design and maintenance of the track system.

Response to “Topical agent therapy for prevention and treatment of radiodermatitis : a meta-analysis”

To the Editor; It was with interest that I read the recent article by Zhang et al. published in Supportive Care in Cancer [1]. This paper highlighted the importance of radiodermatitis (RD) being an unresolved and distressing clinical issue in patients with cancer undergoing radiation therapy. However, I am concerned with a number of clinical and methodological issues within this paper: (i) the clinical and operational definition of prophylaxis and treatment of RD; (ii) the accuracy of the identification of trials; and (iii) the appropriateness of the conduct of the meta-analyses...

A known-groups evaluation of the Response Bias Scale in a neuropsychological setting

We evaluated the Minnesota Multiphasic Personality Inventory-Second Edition (MMPI-2) Response Bias Scale (RBS). Archival data from 83 individuals who were referred for neuropsychological assessment with no formal diagnosis (n = 10), following a known or suspected traumatic brain injury (n = 36), with a psychiatric diagnosis (n = 20), or with a history of both trauma and a psychiatric condition (n = 17) were retrieved. The criteria for malingered neurocognitive dysfunction (MNCD) were applied, and two groups of participants were formed: poor effort (n = 15) and genuine responders (n = 68). Consistent with previous studies, the difference in scores between groups was greatest for the RBS (d = 2.44), followed by two established MMPI-2 validity scales, F (d = 0.25) and K (d = 0.23), and strong significant correlations were found between RBS and F (rs = .48) and RBS and K (r = −.41). When MNCD group membership was predicted using logistic regression, the RBS failed to add incrementally to F. In a separate regression to predict group membership, K added significantly to the RBS. Receiver-operating curve analysis revealed a nonsignificant area under the curve statistic, and at the ideal cutoff in this sample of >12, specificity was moderate (.79), sensitivity was low (.47), and positive and negative predictive power values at a 13% base rate were .25 and .91, respectively. Although the results of this study require replication because of a number of limitations, this study has made an important first attempt to report RBS classification accuracy statistics for predicting poor effort at a range of base rates.

Peripheral compartment innate immune response to Haemophilus influenzae and Streptococcus pneumoniae in chronic obstructive pulmonary disease patients.

Alterations in innate immunity that predispose to chronic obstructive pulmonary disease (COPD) exacerbations are poorly understood. We examined innate immunity gene expression in peripheral blood polymorphonuclear leukocytes (PMN) and monocytes stimulated by Haemophilus influenzae and Streptococcus pneumoniae. Thirty COPD patients (15 rapid and 15 non-rapid lung function decliners) and 15 smokers without COPD were studied. Protein expression of IL-8, IL-6, TNF-α and IFN-γ (especially monocytes) increased with bacterial challenge. In monocytes stimulated with S. pneumoniae, TNF-α protein expression was higher in COPD (non-rapid decliners) than in smokers. In co-cultures of monocytes and PMN, mRNA expression of TGF-β1 and MYD88 was up-regulated, and CD14, TLR2 and IFN-γ down-regulated with H. influenzae challenge. TNF-α mRNA expression was increased with H. influenzae challenge in COPD. Cytokine responses were similar between rapid and non-rapid decliners. TNF-α expression was up-regulated in non-rapid decliners in response to H. influenzae (monocytes) and S. pneumoniae (co-culture of monocytes and PMN). Exposure to bacterial pathogens causes characteristic innate immune responses in peripheral blood monocytes and PMN in COPD. Bacterial exposure significantly alters the expression of TNF-α in COPD patients, although not consistently. There did not appear to be major differences in innate immune responses between rapid and non-rapid decliners.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Flotation process response of Ok Tedi fluorine bearing minerals

Orebodies at Ok Tedi contain a number of different fluorine bearing minerals. Some of these minerals report to concentrate and are responsible for the presence of the penalty element, fluorine, within the concentrate. Previous analytical work has tended to examine geological samples for content, rather than determine the metallurgical behaviour of the different mineralogical species. This investigation utilised X-Ray Diffraction combined with Scanning Electron Microscope/Electron Microprobe to identify the fluorine bearing minerals in flotation test products. Seven fluorine bearing minerals were identified, viz., talc, phlogopite, amphibole (tremolite and actinolite), sphene, apatite, biotite and clay. Talc was found exclusively in the skarn ore type. Phlogopite and amphiboles (tremolite and actinolite) were found to occur in both skarn and porphyry ores, while sphene, apatite, biotite and clay were found only in the porphyry ores. Of the fluorine bearing minerals observed, only talc exhibited natural hydrophobicity to any significant degree. Phlogopite and the amphibole minerals were found to be hydrophillic, whilst the remaining minerals occurred in insufficient quantities to determine the flotation behaviour. Ok Tedi copper concentrate fluorine content prior to skarn ore treatment in the mill (typically 350ppm) was previously identified as deriving from phlogopite, while talc was believed to be the source of intermittent high concentrate fluorine contents when skarn ores were treated. This paper provides supporting evidence for this belief, and reports the nature of fluorine bearing mineral flotation behaviour.

SERS substrate for detection of explosives

A novel gold coated femtosecond laser nanostructured sapphire surface – an “optical nose” - based on surface-enhanced Raman spectroscopy (SERS) for detecting vapours of explosive substances was investigated. Four different nitroaromatic vapours at room temperature were tested. Sensor responses were unambiguous and showed response in the range of 0.05 – 15 uM at 25 °C. The laser fabricated substrate nanostructures produced up to an eight-fold increase in Raman signal over that observed on the unstructured portions of the substrate. This work demonstrates a simple sensing system that is compatible with commercial manufacturing practices to detect taggants in explosives which can undertake as part of an integrated security or investigative mission.

Mechanical properties of graphene : effects of layer number, temperature and isotope

Herein the mechanical properties of graphene, including Young’s modulus, fracture stress and fracture strain have been investigated by molecular dynamics simulations. The simulation results show that the mechanical properties of graphene are sensitive to the temperature changes but insensitive to the layer numbers in the multilayer graphene. Increasing temperature exerts adverse and significant effects on the mechanical properties of graphene. However, the adverse effect produced by the increasing layer number is marginal. On the other hand, isotope substitutions in graphene play a negligible role in modifying the mechanical properties of graphene.

Managing the health effects of temperature in response to climate change : challenges ahead

BACKGROUND: Although many studies have shown that high temperatures are associated with an increased risk of mortality and morbidity, there has been little research on managing the process of planned adaptation to alleviate the health effects of heat events and climate change. In particular, economic evaluation of public health adaptation strategies has been largely absent from both the scientific literature and public policy discussion. OBJECTIVES: his paper aims to discuss how public health organizations should implement adaptation strategies, and how to improve the evidence base for policies to protect health from heat events and climate change. DISCUSSION: Public health adaptation strategies to cope with heat events and climate change fall into two categories: reducing the heat exposure and managing the health risks. Strategies require a range of actions, including timely public health and medical advice, improvements to housing and urban planning, early warning systems, and the assurance that health care and social systems are ready to act. Some of these actions are costly, and the implementation should be based on the cost-effectiveness analysis given scarce financial resources. Therefore, research is required not only on the temperature-related health costs, but also on the costs and benefits of adaptation options. The scientific community must ensure that the health co-benefits of climate change policies are recognized, understood and quantified. CONCLUSIONS: The integration of climate change adaptation into current public health practice is needed to ensure they increase future resilience. The economic evaluation of temperature-related health costs and public health adaptation strategies are particularly important for policy decisions.

Porohyperelastic analysis to explore mechanical properties of chondrocytes using numerical modeling and experiments : a finite element study

There are many continuum mechanical models have been developed such as liquid drop models, solid models, and so on for single living cell biomechanics studies. However, these models do not give a fully approach to exhibit a clear understanding of the behaviour of single living cells such as swelling behaviour, drag effect, etc. Hence, the porohyperelastic (PHE) model which can capture those aspects would be a good candidature to study cells behaviour (e.g. chondrocytes in this study). In this research, an FEM model of single chondrocyte cell will be developed by using this PHE model to simulate Atomic Force Microscopy (AFM) experimental results with the variation of strain rate. This material model will be compared with viscoelastic model to demonstrate the advantages of PHE model. The results have shown that the maximum value of force applied of PHE model is lower at lower strain rates. This is because the mobile fluid does not have enough time to exude in case of very high strain rate and also due to the lower permeability of the membrane than that of the protoplasm of chondrocyte. This behavior is barely observed in viscoelastic model. Thus, PHE model is the better model for cell biomechanics studies.

Digital Copyright Exchange Feasibility Study : Response to the Hooper Consultation from the Big Innovation Centre

In this submission, we provide evidence for our view that copyright policy in the UK must encourage new digital business models which meet the changing needs of consumers and foster innovation in the UK both within, and beyond, the creative industries. We illustrate our arguments using evidence from the music industry. However, we believe that our key points on the relationship between the copyright system and innovative digital business models apply across the UK creative industries.