Pairwise comparison matrices and the error-free property of the decision maker

Pairwise comparison is a popular assessment method either for deriving criteria-weights or for evaluating alternatives according to a given criterion. In real-world applications consistency of the comparisons rarely happens: intransitivity can occur. The aim of the paper is to discuss the relationship between the consistency of the decision maker—described with the error-free property—and the consistency of the pairwise comparison matrix (PCM). The concept of error-free matrix is used to demonstrate that consistency of the PCM is not a sufficient condition of the error-free property of the decision maker. Informed and uninformed decision makers are defined. In the first stage of an assessment method a consistent or near-consistent matrix should be achieved: detecting, measuring and improving consistency are part of any procedure with both types of decision makers. In the second stage additional information are needed to reveal the decision maker’s real preferences. Interactive questioning procedures are recommended to reach that goal

MRI of the lung (3/3)-current applications and future perspectives

BACKGROUND: MRI of the lung is recommended in a number of clinical indications. Having a non-radiation alternative is particularly attractive in children and young subjects, or pregnant women. METHODS: Provided there is sufficient expertise, magnetic resonance imaging (MRI) may be considered as the preferential modality in specific clinical conditions such as cystic fibrosis and acute pulmonary embolism, since additional functional information on respiratory mechanics and regional lung perfusion is provided. In other cases, such as tumours and pneumonia in children, lung MRI may be considered an alternative or adjunct to other modalities with at least similar diagnostic value. RESULTS: In interstitial lung disease, the clinical utility of MRI remains to be proven, but it could provide additional information that will be beneficial in research, or at some stage in clinical practice. Customised protocols for chest imaging combine fast breath-hold acquisitions from a "buffet" of sequences. Having introduced details of imaging protocols in previous articles, the aim of this manuscript is to discuss the advantages and limitations of lung MRI in current clinical practice. CONCLUSION: New developments and future perspectives such as motion-compensated imaging with self-navigated sequences or fast Fourier decomposition MRI for non-contrast enhanced ventilation- and perfusion-weighted imaging of the lung are discussed. Main Messages • MRI evolves as a third lung imaging modality, combining morphological and functional information. • It may be considered first choice in cystic fibrosis and pulmonary embolism of young and pregnant patients. • In other cases (tumours, pneumonia in children), it is an alternative or adjunct to X-ray and CT. • In interstitial lung disease, it serves for research, but the clinical value remains to be proven. • New users are advised to make themselves familiar with the particular advantages and limitations

A four-helix bundle stores copper for methane oxidation

Methane-oxidising bacteria (methanotrophs) require large quantities of copper for the membrane-bound (particulate) methane monooxygenase (pMMO). Certain methanotrophs are also able to switch to using the iron-containing soluble MMO (sMMO) to catalyse methane oxidation, with this switchover regulated by copper. MMOs are Nature’s primary biological mechanism for suppressing atmospheric levels of methane, a potent greenhouse gas. Furthermore, methanotrophs and MMOs have enormous potential in bioremediation and for biotransformations producing bulk and fine chemicals, and in bioenergy, particularly considering increased methane availability from renewable sources and hydraulic fracturing of shale rock. We have discovered and characterised a novel copper storage protein (Csp1) from the methanotroph Methylosinus trichosporium OB3b that is exported from the cytosol, and stores copper for pMMO. Csp1 is a tetramer of 4-helix bundles with each monomer binding up to 13 Cu(I) ions in a previously unseen manner via mainly Cys residues that point into the core of the bundle. Csp1 is the first example of a protein that stores a metal within an established protein-folding motif. This work provides a detailed insight into how methanotrophs accumulate copper for the oxidation of methane. Understanding this process is essential if the wide-ranging biotechnological applications of methanotrophs are to be realised. Cytosolic homologues of Csp1 are present in diverse bacteria thus challenging the dogma that such organisms do not use copper in this location

DNA adducts in fish following an oil spill exposure

On 12 December 1999, one third of the load of the Erika tanker, amounting to about 10,000 t crude oil flowed into sea waters close to the French Atlantic Coast. This oil contained polycyclic aromatic compounds (PAC) that are known to be genotoxic. Genotoxic effects induce DNA adducts formation, which can thus be used as pollution biomarkers. Here, we assessed the genotoxic impact of the “Erika” oil spill by DNA adducts detection in the liver of immature fishes (Solea solea) from four locations of the French Brittany coasts. Two months after the spill, a high amount of DNA adducts was found in samples from all locations, amounting to 92–290 DNA adduct per 109 nucleotides. Then total DNA adduct levels decreased to reach about 50 adducts per 109 nucleotides nine months after the spill. In vitro experiments using human cell cultures and fish liver microsomes evidence the genotoxicity of the Erika fuel. They also prove the formation of reactive species able to create DNA adducts. Furthermore, in vitro and in vivo DNA adducts fingerprints are similar, thus confirming that DNA adducts are a result of the oil spill

Infrared study of defects in nitrogen-doped electron irradiated silicon

Nitrogen is a key dopant in Czochralski silicon widely used to control properties of Si wafers for applications in the microelectronics industry. Most of these properties are affected by defects and their processes. Here we employ Fourier transform infrared spectroscopy to investigate the existence of radiation induced N-related defects in Si. Besides well-known signals of substitutional (Ns) at 653 cm−1, interstitial (Ni) at 691 cm−1, N2 at 766 cm−1, N–O complexes at 801, 996 and 1026 cm−1 and N2O at 973 and 996 cm−1 we determined some additional signals. The pair of bands at 646 and 663 cm−1 has been tentatively correlated with the Ns V pair in agreement with previous theoretical calculations. Similarly the pair of bands at 725 and 778 cm−1 has been tentatively correlated with the N2 V complex and another pair of bands at 930 and 953 cm−1 may be related with the N2SiI complex. Additionally, oxygen-vacancy defects such as the vacancy-oxygen pair (A-center or VO) are common in electron irradiated Si and can impact the material and electronic properties of Si. We investigate and compare the effect of N doping on oxygen-vacancy defects in electron irradiated Si. It is determined that nitrogen reduces the production of VO defects

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Multimodal discrimination of immune cells using a combination of Raman spectroscopy and digital holographic microscopy

This work was supported by the UK Engineering and Physical Sciences Research Council under grant EP/J01771X/1, A European Union FAMOS project (FP7 ICT, 317744), and the ’BRAINS’ 600th anniversary appeal, and Dr. E. Killick. We would also like to thank The RS Macdonald Charitable Trust for funding support. KD acknowledges support of a Royal Society Leverhulme Trust Senior Fellowship. This work was also supported by the PreDiCT-TB consortium [IMI Joint undertaking grant agreement number 115337, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution (www.imi.europa.eu)]The ability to identify and characterise individual cells of the immune system under label-free conditions would be a significant advantage in biomedical and clinical studies where untouched and unmodified cells are required. We present a multi-modal system capable of simultaneously acquiring both single point Raman spectra and digital holographic images of single cells. We use this combined approach to identify and discriminate between immune cell populations CD4+ T cells, B cells and monocytes. We investigate several approaches to interpret the phase images including signal intensity histograms and texture analysis. Both modalities are independently able to discriminate between cell subsets and dual-modality may therefore be used a means for validation. We demonstrate here sensitivities achieved in the range of 86.8% to 100%, and specificities in the range of 85.4% to 100%. Additionally each modality provides information not available from the other providing both a molecular and a morphological signature of each cell.Publisher PDFPeer reviewe

Glucanocellulosic ethanol: The undiscovered biofuel potential in energy crops and marine biomass

Converting biomass to biofuels is a key strategy in substituting fossil fuels to mitigate climate change. Conventional strategies to convert lignocellulosic biomass to ethanol address the fermentation of cellulose-derived glucose. Here we used super-resolution fluorescence microscopy to uncover the nanoscale structure of cell walls in the energy crops maize and Miscanthus where the typical polymer cellulose forms an unconventional layered architecture with the atypical (1, 3)-β-glucan polymer callose. This raised the question about an unused potential of (1, 3)-β-glucan in the fermentation of lignocellulosic biomass. Engineering biomass conversion for optimized (1, 3)-β-glucan utilization, we increased the ethanol yield from both energy crops. The generation of transgenic Miscanthus lines with an elevated (1, 3)-β-glucan content further increased ethanol yield providing a new strategy in energy crop breeding. Applying the (1, 3)-β-glucan-optimized conversion method on marine biomass from brown macroalgae with a naturally high (1, 3)-β-glucan content, we not only substantially increased ethanol yield but also demonstrated an effective co-fermentation of plant and marine biomass. This opens new perspectives in combining different kinds of feedstock for sustainable and efficient biofuel production, especially in coastal regions

Recycled gabbro signature in hotspot magmas unveiled by plume–ridge interactions

Lavas erupted within plate interiors above upwelling mantle plumes have chemical signatures that are distinct from midocean ridge lavas. When a plume interacts with a mid-ocean ridge, the compositions of both their lavas changes, but there is no consensus as to how this interaction occurs1–3. For the past 15 Myr, the Pacific–Antarctic mid-ocean ridge has been approaching the Foundation hotspot4 and erupted lavas have formed seamounts. Here we analyse the noble gas isotope and trace element signature of lava samples collected from the seamounts. We find that both intraplate and on-axis lavas have noble gas isotope signatures consistent with the contribution from a primitive plume source. In contrast, nearaxis lavas show no primitive noble gas isotope signatures, but are enriched in strontium and lead, indicative of subducted former oceanic lower crust melting within the plume source5–7. We propose that, in a near-ridge setting, primitive, plumesourced magmas formed deep in the plume are preferentially channelled to and erupted at the ridge-axis. The remaining residue continues to rise and melt, forming the near-axis seamounts. With the deep melts removed, the geochemical signature of subduction contained within the residue becomes apparent. Lavas with strontium and lead enrichments are found worldwide where plumes meet mid-ocean ridges6–8, suggesting that subducted lower crust is an important but previously unrecognised plume component