Testing preference formation in learning design contingent valuation (LDCV) using advanced information and repetitive treatments

Policymakers have largely replaced Single Bounded Discrete Choice (SBDC) valuation by the more statistically efficient repetitive methods; Double Bounded Discrete Choice (DBDC) and Discrete Choice Experiments (DCE) . Repetitive valuation permits classification into rational preferences: (i) a priori well-formed; (ii) consistent non-arbitrary values “discovered” through repetition and experience; (Plott, 1996; List 2003) and irrational preferences; (iii) consistent but arbitrary values as “shaped” by preceding bid level (Tufano, 2010; Ariely et al., 2003) and (iv) inconsistent and arbitrary values. Policy valuations should demonstrate behaviorally rational preferences. We outline novel methods for testing this in DBDC applied to renewable energy premiums in Chile.

Increased expression of the RIα subunit of the cAMP-dependent protein kinase A is associated with advanced stage ovarian cancer

The primary element in the cAMP signal transduction pathway is the cAMP-dependent protein kinase (PKA). Expression of the RIα subunit of type I PKA is elevated in a variety of human tumours and cancer cell lines. The purpose of this study was to assess the prognostic importance of RIα expression in patients with ovarian cancer. We have evaluated the expression of RIα in a panel of human ovarian tumours (n = 40) and five human ovarian cancer cell lines using quantitative reverse transcription polymerase chain reaction (RT-PCR) and Western blot analysis. The human ovarian cell lines OAW42 and OTN14 express high endogenous levels of RIα mRNA and protein (at significantly higher mRNA levels than high tissue expressors, P < 0.05). The ovarian cell line A2780 expresses low endogenous levels of RIα mRNA and protein (also at higher mRNA levels than low tissue expressors, P < 0.05). Quantitative RT-PCR revealed no significant difference in RIα mRNA expression between different ovarian histological subtypes in this study. No associations were found between RIα mRNA expression and differentiation state. RIα mRNA expression was significantly associated with tumour stage (P = 0.0036), and this remained significant in univariate analysis (P = 0.0002). A trend emerged between RIα mRNA expression levels and overall survival in univariate analysis (P = 0.051), however, by multivariate analysis, stage remained the major determinant of overall survival (P = 0.0001). This study indicates that in ovarian epithelial tumours high RIα mRNA expression is associated with advanced stage disease. RIα expression may be of predictive value in ovarian cancer and may be associated with dysfunctional signalling pathways in this cancer type.

Open Source Telecommunications Framework for Time-Series Data from Smart Grid Sensors.

This paper presents a framework for a telecommunications interface which allows data from sensors embedded in Smart Grid applications to reliably archive data in an appropriate time-series database. The challenge in doing so is two-fold, firstly the various formats in which sensor data is represented, secondly the problems of telecoms reliability. A prototype of the authors' framework is detailed which showcases the main features of the framework in a case study featuring Phasor Measurement Units (PMU) as the application. Useful analysis of PMU data is achieved whenever data from multiple locations can be compared on a common time axis. The prototype developed highlights its reliability, extensibility and adoptability; features which are largely deferred from industry standards for data representation to proprietary database solutions. The open source framework presented provides link reliability for any type of Smart Grid sensor and is interoperable with existing proprietary database systems, and open database systems. The features of the authors' framework allow for researchers and developers to focus on the core of their real-time or historical analysis applications, rather than having to spend time interfacing with complex protocols.

Advanced materials for recognition and capture of whole cells and microorganisms

Selective cell recognition and capture has recently attracted significant interest due to its potential importance for clinical, diagnostic, environmental, and security applications. Current methods for cell isolation from complex samples are largely dependent on cell size and density, with limited application scope as many of the target cells do not exhibit appreciable differences in this respect. The most recent and forthcoming developments in the area of selective recognition and capture of whole cells, based on natural receptors, as well as synthetic materials utilising physical and chemical properties of the target cell or microorganism, are highlighted. Particular focus is given to the development of cell complementary surfaces using the cells themselves as templating agents, by means of molecular imprinting, and their combination with sensing platforms for rapid cell detection in complex media. The benefits and challenges of each approach are discussed and a perspective of the future of this research area is given.

The effect of manufacture and assembly joining methods on stiffened panel performance

This paper describes the simulation of representative aircraft wing stiffened panels under axial compression loading, to determine the effects of varying the manufacturing shape and assembly joining methods on stiffened panel performance. T-stiffened and Z-stiffened panels are modelled in Abaqus simulating integral, co-cured and mechanically fastened joints. The panels are subject to an edge compressive displacement along the stiffener axis until failure and the ultimate failure load and buckling performance is assessed for each. Integral panels consistently offer the highest performance. Co-cured panels demonstrate reduced performance (3-5% reduction in ultimate load relative to integral) caused by localised cohesive failure and skin-stiffener separation. The mechanically fastened panels are consistently the weakest joint (19-25% reduction in ultimate load relative to integral) caused primarily by inter-rivet buckling between fasteners

Promoter methylation correlates with reduced Smad4 expression in advanced prostate cancer

BACKGROUND: Transforming growth factor-beta (TGF-beta) is a potent growth inhibitor in a wide range of cell types. A transducer of TGF-beta signaling known as Mothers against decapentaplegic homologue 4 (Smad4) is a known tumor suppressor found on chromosome 18q21.1 and is typically inactivated by deletion or mutation in pancreatic and colorectal cancers. The purpose of the article is to investigate Smad4 expression, gene copy number and methylation status in advanced cases of prostate cancer.

METHODS: We have employed Methylation Specific PCR (MSP) to identify methylation sites within the Smad4 promoter and combined this with quantitative real-time PCR to look for correlates between methylation status and Smad4 expression and to examine androgen receptor (AR) expression. Bacterial artificial chromosome-comparative genomic hybridization (BAC-CGH) has been used to look for genomic amplifications and deletions which may also contribute to expression changes.

RESULTS: We fail to find evidence of genomic deletions or amplifications affecting the Smad4 locus on chromosome 18 but show a correlation between promoter methylation and the loss of Smad4 expression in the same material. We confirm that the AR locus on the X chromosome is amplified in 30% of the advanced clinical samples and that this correlates with increased transcript levels as previously reported by other groups.

CONCLUSION: This indicates that epigenetic changes affect the expression of the Smad4 protein in prostate cancer and points to methylation of the promoter as a novel marker of and contributor to the disease warranting further study.