Refinement of the Clinical Scenario Evaluation Framework for Assessment of Competing Development Strategies with an Application to Multiple Sclerosis

When competing strategies for development programs, clinical trial designs, or data analysis methods exist, the alternatives need to be evaluated in a systematic way to facilitate informed decision making. Here we describe a refinement of the recently proposed clinical scenario evaluation framework for the assessment of competing strategies. The refinement is achieved by subdividing key elements previously proposed into new categories, distinguishing between quantities that can be estimated from preexisting data and those that cannot and between aspects under the control of the decision maker from those that are determined by external constraints. The refined framework is illustrated by an application to a design project for an adaptive seamless design for a clinical trial in progressive multiple sclerosis.

Actors and objects: A socio-technical networks approach for construction management research

We explore the contribution of socio-technical networks approaches to construction management research. These approaches are distinctive for their analysis of actors and objects as mutually constituted within socio-technical networks. They raise questions about the ways in which the content, meaning and use of technology is negotiated in practice, how particular technical configurations are elaborated in response to specific problems and why certain paths or solutions are adopted rather than others. We illustrate this general approach with three case studies: a historical study of the development of reinforced concrete in France, the UK and the US, the recent introduction of 3D-CAD software into four firms and an analysis of the uptake of environmental assessment technologies in the UK since 1990. In each we draw out the ways in which various technologies shaped and were shaped by different socio-technical networks. We conclude with a reflection on the contributions of socio-technical network analysis for more general issues including the study of innovation and analyses of context and power.

Evaluation of peak-picking algorithms for protein mass spectrometry

Peak picking is an early key step in MS data analysis. We compare three commonly used approaches to peak picking and discuss their merits by means of statistical analysis. Methods investigated encompass signal-to-noise ratio, continuous wavelet transform, and a correlation-based approach using a Gaussian template. Functionality of the three methods is illustrated and discussed in a practical context using a mass spectral data set created with MALDI-TOF technology. Sensitivity and specificity are investigated using a manually defined reference set of peaks. As an additional criterion, the robustness of the three methods is assessed by a perturbation analysis and illustrated using ROC curves.

N-nitroso compound exposure-associated transcriptomic profiles are indicative of an increased risk for colorectal cancer

Endogenous formation of N-nitroso compounds (NOCs), which are known animal carcinogens, could contribute to human carcinogenesis but definitive evidence is still lacking. To investigate the relevance of NOCs in human colorectal cancer (CRC) development, we analyzed whole genome gene expression modifications in human colon biopsies in relation to fecal NOC exposure. We had a particular interest in patients suffering from intestinal inflammation as this may stimulate endogenous NOC formation, and consequently predispose to CRC risk. Inflammatory bowel disease (IBD) patients diagnosed with ulcerative colitis and irritable bowel syndrome patients without inflammation, serving as controls, were therefore recruited. Fecal NOC were demonstrated in the majority of subjects. By associating gene expression levels of all subjects to fecal NOC levels, we identified a NOC exposure-associated transcriptomic response that suggests that physiological NOC concentrations may potentially induce genotoxic responses and chromatin modifications in human colon tissue, both of which are linked to carcinogenicity. In a network analysis, chromatin modifications were linked to 11 significantly modulated histone genes, pointing towards a possible epigenetic mechanism that may be relevant in comprehending NOC-induced carcinogenesis. In addition, pro-inflammatory transcriptomic modifications were identified in visually non-inflamed regions of the IBD colon. However, fecal NOC levels were slightly but not significantly increased in IBD patients, suggesting that inflammation did not strongly stimulate NOC formation. We conclude that NOC exposure is associated with gene expression modifications in the human colon that may suggest a potential role of these compounds in CRC development.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

Closed loop design for a simple nonlinear aircraft model using eigenstructure assignment

Aircraft systems are highly nonlinear and time varying. High-performance aircraft at high angles of incidence experience undesired coupling of the lateral and longitudinal variables, resulting in departure from normal controlled flight. The aim of this work is to construct a robust closed-loop control that optimally extends the stable and decoupled flight envelope. For the study of these systems nonlinear analysis methods are needed. Previously, bifurcation techniques have been used mainly to analyze open-loop nonlinear aircraft models and investigate control effects on dynamic behavior. In this work linear feedback control designs calculated by eigenstructure assignment methods are investigated for a simple aircraft model at a fixed flight condition. Bifurcation analysis in conjunction with linear control design methods is shown to aid control law design for the nonlinear system.

Synthesis, structural and emission studies of a bis (carbamoyl methyl) sulfone complex of uranyl nitrate

A new tri-functional ligand (Bu2NCOCH2SO2CH2CONBu2)-Bu-i-Bu-i (L) was prepared and characterized. The coordination chemistry of this ligand with uranyl nitrate was studied with IR, (HNMR)-H-1, ES-MS, TG and elemental analysis methods. The structure of the compound [UO2(NO3)(2)L] was determined by single crystal X-ray diffraction techniques. In the structure the uranium(VI) ion is surrounded by eight oxygen atoms in a hexagonal bi-pyramidal geometry. Four oxygen atoms from two nitrate groups and two oxygen atoms from the ligand form a planar hexagon. The ligand acts as a bidentate chelate and bonds through both the carbamoyl groups to the uranyl nitrate. An ES-MS spectrum shows that the complex retains the bonding in solution. The compound displayed vibronically coupled fluorescence emission.

A dynamic model of neurovascular coupling: Implications for blood vessel dilation and constriction

Neurovascular coupling in response to stimulation of the rat barrel cortex was investigated using concurrent multichannel electrophysiology and laser Doppler flowmetry. The data were used to build a linear dynamic model relating neural activity to blood flow. Local field potential time series were subject to current source density analysis, and the time series of a layer IV sink of the barrel cortex was used as the input to the model. The model output was the time series of the changes in regional cerebral blood flow (CBF). We show that this model can provide excellent fit of the CBF responses for stimulus durations of up to 16 s. The structure of the model consisted of two coupled components representing vascular dilation and constriction. The complex temporal characteristics of the CBF time series were reproduced by the relatively simple balance of these two components. We show that the impulse response obtained under the 16-s duration stimulation condition generalised to provide a good prediction to the data from the shorter duration stimulation conditions. Furthermore, by optimising three out of the total of nine model parameters, the variability in the data can be well accounted for over a wide range of stimulus conditions. By establishing linearity, classic system analysis methods can be used to generate and explore a range of equivalent model structures (e.g., feed-forward or feedback) to guide the experimental investigation of the control of vascular dilation and constriction following stimulation.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution π by simulating a Markov chain with transition kernel P such that π is invariant under P. However, there are many situations for which it is impractical or impossible to draw from the transition kernel P. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace P by an approximation Pˆ. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how ’close’ the chain given by the transition kernel Pˆ is to the chain given by P . We apply these results to several examples from spatial statistics and network analysis.

Centennial changes in the solar wind speed and in the open solar flux

We use combinations of geomagnetic indices, based on both variation range and hourly means, to derive the solar wind flow speed, the interplanetary magnetic field strength at 1 AU and the total open solar flux between 1895 and the present. We analyze the effects of the regression procedure and geomagnetic indices used by adopting four analysis methods. These give a mean interplanetary magnetic field strength increase of 45.1 ± 4.5% between 1903 and 1956, associated with a 14.4 ± 0.7% rise in the solar wind speed. We use averaging timescales of 1 and 2 days to allow for the difference between the magnetic fluxes threading the coronal source surface and the heliocentric sphere at 1 AU. The largest uncertainties originate from the choice of regression procedure: the average of all eight estimates of the rise in open solar flux is 73.0 ± 5.0%, but the best procedure, giving the narrowest and most symmetric distribution of fit residuals, yields 87.3 ± 3.9%.

Nonlinear regional warming with increasing CO₂ concentration

When considering adaptation measures and global climate mitigation goals, stakeholders need regional-scale climate projections, including the range of plausible warming rates. To assist these stakeholders, it is important to understand whether some locations may see disproportionately high or low warming from additional forcing above targets such as 2 K (ref. 1). There is a need to narrow uncertainty2 in this nonlinear warming, which requires understanding how climate changes as forcings increase from medium to high levels. However, quantifying and understanding regional nonlinear processes is challenging. Here we show that regional-scale warming can be strongly superlinear to successive CO2 doublings, using five different climate models. Ensemble-mean warming is superlinear over most land locations. Further, the inter-model spread tends to be amplified at higher forcing levels, as nonlinearities grow—especially when considering changes per kelvin of global warming. Regional nonlinearities in surface warming arise from nonlinearities in global-mean radiative balance, the Atlantic meridional overturning circulation, surface snow/ice cover and evapotranspiration. For robust adaptation and mitigation advice, therefore, potentially avoidable climate change (the difference between business-as-usual and mitigation scenarios) and unavoidable climate change (change under strong mitigation scenarios) may need different analysis methods.

Extracting planetary waves from geomagnetic time series using Empirical Mode Decomposition

Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

An investigation of the required skills for the delivery of low and zero carbon buildings within a region

The United Kingdom is committed to a raft of requirements to create a low-carbon economy. Buildings consume approximately 40% of UK energy demand. Any improvement on the energy performance of buildings therefore can significantly contribute to the delivery of a low-carbon economy. The challenge for the construction sector and its clients is how to meet the policy requirements to deliver low and zero carbon (LZC) buildings, which spans broader than the individual building level, to requirements at the local and regional levels, and wider sustainability pressures. Further, the construction sector is reporting skills shortages coupled with the need for ‘new skills’ for the delivery of LZC buildings. The aim of this paper is to identify, and better understand, the skills required by the construction sector and its clients for the delivery of LZC buildings within a region. The theoretical framing for this research is regional innovation system (RIS) using a socio-technical network analysis (STNA) methodology. A case study of a local authority region is presented. Data is drawn from a review of relevant local authority documentation, observations and semi-structured interviews from one (project 1) of five school retrofit projects within the region. The initial findings highlight the complexity surrounding the form and operation of the LZC network for project 1. The skills required by the construction sector and its clients are connected to different actor roles surrounding the delivery of the project. The key actors involved and their required skills are: project management and energy management skills required by local authority; project management skills (in particular project planning), communication and research skills required by school end-users; and a ‘technical skill’ relating to knowledge of a particular energy efficient measure (EEM) and use of equipment to implement the EEM is required by the EEM contractors.

The Ocean Reanalysis Intercomparison project (ORA-IP)

Uncertainty in ocean analysis methods and deficiencies in the observing system are major obstacles for the reliable reconstruction of the past ocean climate. The variety of existing ocean reanalyses is exploited in a multi-reanalysis ensemble to improve the ocean state estimation and to gauge uncertainty levels. The ensemble-based analysis of signal-to-noise ratio allows the identification of ocean characteristics for which the estimation is robust (such as tropical mixed-layer-depth, upper ocean heat content), and where large uncertainty exists (deep ocean, Southern Ocean, sea ice thickness, salinity), providing guidance for future enhancement of the observing and data assimilation systems.