Bayesian splines versus fractional polynomials in network meta-analysis

BACKGROUND: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. METHODS: In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. RESULTS: We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. CONCLUSIONS: The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles

Bayesian Deconvolution and Quantification of Metabolites from J-Resolved NMR Spectroscopy

Two-dimensional (2D) nuclear magnetic resonance (nmr) methods have become increasingly popular in metabolomics, since they have considerable potential to accurately identify and quantify metabolites within complex biological samples. 2D 1 H J-resolved (jres) nmr spectroscopy is a widely used method that expands overlapping resonances into a second dimension. However, existing analytical processing methods do not fully exploit the information in the jres spectrum and, more importantly, do not provide measures of uncertainty associated with the estimates of quantities of interest, such as metabolite concentration. Combining the data-generating mechanisms and the extensive prior knowledge available in online databases, we develop a Bayesian method to analyse 2D jres data, which allows for automatic deconvolution, identification and quantification of metabolites. The model extends and improves previous work on one-dimensional nmr spectral data. Our approach is based on a combination of B-spline tight wavelet frames and theoretical templates, and thus enables the automatic incorporation of expert knowledge within the inferential framework. Posterior inference is performed through specially devised Markov chain Monte Carlo methods. We demonstrate the performance of our approach via analyses of datasets from serum and urine, showing the advantages of our proposed approach in terms of identification and quantification of metabolites

Enabling of AUTOSAR system design using Eclipse-based tooling

International audienceAUTOSAR is a development partnership for standardisation of software architectures for the development of complex E/E systems. The software configuration process specified by AUTOSAR involves the handling of large amounts of data describing the E/E system. An efficient application of the process requires good and continuous toolsupport.In this paper we propose an approach for AUTOSAR tooling, which is based on the technology and, more important, on the idea of Eclipse. Eclipse is one of the most successful open source projects of the last years with a strong influence on the industry. It provides an open development platform that can easily be extended. On top of Eclipse, the approach provides an open tool basis, which can be extended by special, free or commercial plug-ins.Our AUTOSAR tooling approach is oriented towards the ideas behind Eclipse and focuses on reusing the success factors for a tool approach within the AUTOSAR community

Nitrogen dioxide radical generated by the myeloperoxidase-hydrogen peroxide-nitrite system promotes lipid peroxidation of low density lipoprotein

AbstractMyeloperoxidase, a heme protein secreted by activated phagocytes, is present and enzymatically active in human atherosclerotic lesions. In the current studies, we explored the possibility that reactive nitrogen species generated by myeloperoxidase promote lipid peroxidation of low density lipoprotein (LDL) – a modification that may render the lipoprotein atherogenic. We found that myeloperoxidase, an H2O2-generating system and nitrite (NO2−) peroxidized LDL lipids. The process required NO2− and each component of the enzymatic system; it was inhibited by catalase, cyanide and ascorbate, a potent scavenger of aqueous phase radicals. LDL peroxidation did not require chloride ion, and it was little affected by the hypochlorous acid scavenger taurine. Collectively, these results suggest that lipid peroxidation is promoted by a nitrogen dioxide radical-like species. These observations indicate that myeloperoxidase, by virtue of its ability to form reactive nitrogen intermediates, may promote lipid peroxidation and atherogenesis

Gender agreement on adverbs in Spanish

In this article we explore the exceptional gender agreement of the Spanish adverb mucho (‘much’), when it modifies comparative adjectives inside DPs that contain a particular type of noun (as in muchafem mejor intenciónfem, ‘much better intention’). This phenomenon, which we describe in detail, raises crucial questions both about the mechanisms of agreement and about the nature of gender in a language such as Spanish. We will argue on the basis of our analysis that agreement is not semantically motivated, but blindly triggered by certain formal configurations. We will also argue that –at least in languages such as Spanish– gender information is scattered in two different positions inside the DP.Peer reviewe

Организационные резервы повышения производительности труда на предприятиях нефтегазовой отрасли

Проблема повышения производительности труда в современной экономике России имеет значительную актуальность, поскольку повышение уровня производительности труда является важнейшим условием социально-экономического развития общества, устойчивого экономического роста и повышения конкурентоспособности национальной экономики. Актуальность представленного исследования определяется необходимостью повышения производительности труда на современных предприятиях России, что может быть достигнуто за счет реализации организационных резервов роста. Целью исследования является структурирование факторов, оказывающих влияние на уровень производительности труда, подтверждение значимости организационных резервов роста, апробация методики выявления организационных резервов повышения производительности труда. Методы исследования. В настоящей работе нашли применение методы сбора первичной экономической информации, включая анализ законодательных и нормативно-правовых актов РФ, официальных статистических данных, данных публичной отчетности отечественных предприятий, анализ прочих открытых источников информации, системный подход, методы статистического и сравнительного анализа. The country's labor productivity in modern Russia over recent years has acquired impressive relevance. Since it is a key indicator of overall economic efficiency, strong labor productivity growth has always been a sufficient condition for socio-economic development, economic stability and enhancing competitiveness of the national economy. The relevance of the study is determined by the urgency of the labor productivity growth at the modern Russian factories that can be achieved through realization of organizational reserves. The main aim of the study is identifying and structuring the factors that seem to affect labor productivity level. It is also pointed at corroborating the necessity of labor productivity growth and approbation of organizational reserves seeking method. Methods. In the present work different methods of information gathering and processing were used to obtain the economic data including the analysis of appropriate laws and regulations, official statistics, companies' public statements and reporting and other open available resources. Also system approach, comparative and statistical analysis were adopted in this research

Upper critical field pecularities of superconducting YNi2B2C and LuNi2B2C

We present new upper critical field Hc2(T) data in a broad temperature region from 0.3K to Tc for LuNi2B2C and YNi2B2C single crystals with well characterized low impurity scattering rates. The absolute values for all T, in particular Hc2(0), and the sizeable positive curvature (PC) of Hc2(T) at high and intermediate T are explained quantitatively within an effective two-band model. The failure of the isotropic single band approach is discussed in detail. Supported by de Haas van Alphen data, the superconductivity reveals direct insight into details of the electronic structure. The observed maximal PC near Tc gives strong evidence for clean limit type II superconductors.Comment: 4 pages, 2 figures, Phys. Rev. Lett. accepte

Unique Proteomic Signatures Distinguish Macrophages and Dendritic Cells

Monocytes differentiate into heterogeneous populations of tissue macrophages and dendritic cells (DCs) that regulate inflammation and immunity. Identifying specific populations of myeloid cells in vivo is problematic, however, because only a limited number of proteins have been used to assign cellular phenotype. Using mass spectrometry and bone marrow-derived cells, we provided a global view of the proteomes of M-CSF-derived macrophages, classically and alternatively activated macrophages, and GM-CSF-derived DCs. Remarkably, the expression levels of half the plasma membrane proteins differed significantly in the various populations of cells derived in vitro. Moreover, the membrane proteomes of macrophages and DCs were more distinct than those of classically and alternatively activated macrophages. Hierarchical cluster and dual statistical analyses demonstrated that each cell type exhibited a robust proteomic signature that was unique. To interrogate the phenotype of myeloid cells in vivo, we subjected elicited peritoneal macrophages harvested from wild-type and GM-CSF-deficient mice to mass spectrometric and functional analysis. Unexpectedly, we found that peritoneal macrophages exhibited many features of the DCs generated in vitro. These findings demonstrate that global analysis of the membrane proteome can help define immune cell phenotypes in vivo