87 resultados para diagnostics


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This paper provides a generalisation of the structural time series version of the Almost Ideal Demand System (AIDS) that allows for time-varying coefficients (TVC/AIDS) in the presence of cross-equation constraints. An empirical appraisal of the TVC/AIDS is made using a dynamic AIDS with trending intercept as the baseline model with a data set from the Italian Household Budget Survey (1986-2001). The assessment is based on four criteria: adherence to theoretical constraints, statistical diagnostics on residuals, forecasting performance and economic meaningfulness. No clear evidence is found for superior performance of the TVC/AIDS, apart from improved short-term forecasts.

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This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.

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The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis - the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the self-sensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems. Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system. Copyright © 2004 Royal Meteorological Society

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BACKGROUND: The bacterial biothreat agents Burkholderia mallei and Burkholderia pseudomallei are the cause of glanders and melioidosis, respectively. Genomic and epidemiological studies have shown that B. mallei is a recently emerged, host restricted clone of B. pseudomallei. RESULTS: Using bacteriophage-mediated immunoscreening we identified genes expressed in vivo during experimental equine glanders infection. A family of immunodominant antigens were identified that share protein domain architectures with hemagglutinins and invasins. These have been designated Burkholderia Hep_Hag autotransporter (BuHA) proteins. A total of 110/207 positive clones (53%) of a B. mallei expression library screened with sera from two infected horses belonged to this family. This contrasted with 6/189 positive clones (3%) of a B. pseudomallei expression library screened with serum from 21 patients with culture-proven melioidosis. CONCLUSION: Members of the BuHA proteins are found in other Gram-negative bacteria and have been shown to have important roles related to virulence. Compared with other bacterial species, the genomes of both B. mallei and B. pseudomallei contain a relative abundance of this family of proteins. The domain structures of these proteins suggest that they function as multimeric surface proteins that modulate interactions of the cell with the host and environment. Their effect on the cellular immune response to B. mallei and their potential as diagnostics for glanders requires further study.

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An understanding of the multi-step nature of cancer as it is in the breast, as a series of pivotal genetic/epigenetic modifications is irrefutably a milestone in diagnostics, prognostics and eventually providing a cure. Here we have utilised a variant of analysis of variance (ANOVA) as a model for the identification and tracking of specific mRNA species whose transcription has been significantly altered at each grade in the progression of ductal carcinoma, making it possible to correlate histological progression with the genetic events underlying breast cancer. We show that in the progression of ductal carcinomas, from grade 1 to 3, there is a reduction in the actual number of mRNA species, which are significantly over or under expressed. We also show that this technique can be employed to generate differential gene expression patterns, whereby the combined expression profile of the tailored spectra of genes in the comparison of each ductal grade is sufficient to render them on clearly separate arms of an array-wise hierarchical cluster dendrogram.

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Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

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MS is an important analytical tool in clinical proteomics, primarily in the disease-specific discovery identification and characterisation of proteomic biomarkers and patterns. MS-based proteomics is increasingly used in clinical validation and diagnostic method development. The latter departs from the typical application of MS-based proteomics by exchanging some of the high performance of analysis for the throughput, robustness and simplicity required for clinical diagnostics. Although conventional MS-based proteomics has become an important field in clinical applications, some of the most recent MS technologies have not yet been extensively applied in clinical proteomics. in this review, we will describe the current state of MS in clinical proteomics and look to the future of this field.

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In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S. Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

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There has been significant interest in the methodologies of controlled release for a diverse range of applications spanning drug delivery, biological and chemical sensors, and diagnostics. The advancement in novel substrate-polymer coupling moieties has led to the discovery of self-immolative linkers. This new class of linker has gained popularity in recent years in polymeric release technology as a result of stable bond formation between protecting and leaving groups, which becomes labile upon activation, leading to the rapid disassembly of the parent polymer. This ability has prompted numerous studies into the design and development of self-immolative linkers and the kinetics surrounding their disassembly. This review details the main concepts that underpin self-immolative linker technologies that feature in polymeric or dendritic conjugate systems and outlines the chemistries of amplified self-immolative elimination.

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The mean state, variability and extreme variability of the stratospheric polar vortices, with an emphasis on the Northern Hemisphere vortex, are examined using 2-dimensional moment analysis and Extreme Value Theory (EVT). The use of moments as an analysis to ol gives rise to information about the vortex area, centroid latitude, aspect ratio and kurtosis. The application of EVT to these moment derived quantaties allows the extreme variability of the vortex to be assessed. The data used for this study is ECMWF ERA-40 potential vorticity fields on interpolated isentropic surfaces that range from 450K-1450K. Analyses show that the most extreme vortex variability occurs most commonly in late January and early February, consistent with when most planetary wave driving from the troposphere is observed. Composites around sudden stratospheric warming (SSW) events reveal that the moment diagnostics evolve in statistically different ways between vortex splitting events and vortex displacement events, in contrast to the traditional diagnostics. Histograms of the vortex diagnostics on the 850K (∼10hPa) surface over the 1958-2001 period are fitted with parametric distributions, and show that SSW events comprise the majority of data in the tails of the distributions. The distribution of each diagnostic is computed on various surfaces throughout the depth of the stratosphere, and shows that in general the vortex becomes more circular with higher filamentation at the upper levels. The Northern Hemisphere (NH) and Southern Hemisphere (SH) vortices are also compared through the analysis of their respective vortex diagnostics, and confirm that the SH vortex is less variable and lacks extreme events compared to the NH vortex. Finally extreme value theory is used to statistically mo del the vortex diagnostics and make inferences about the underlying dynamics of the polar vortices.

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The stratospheric climate and variability from simulations of sixteen chemistry‐climate models is evaluated. On average the polar night jet is well reproduced though its variability is less well reproduced with a large spread between models. Polar temperature biases are less than 5 K except in the Southern Hemisphere (SH) lower stratosphere in spring. The accumulated area of low temperatures responsible for polar stratospheric cloud formation is accurately reproduced for the Antarctic but underestimated for the Arctic. The shape and position of the polar vortex is well simulated, as is the tropical upwelling in the lower stratosphere. There is a wide model spread in the frequency of major sudden stratospheric warnings (SSWs), late biases in the breakup of the SH vortex, and a weak annual cycle in the zonal wind in the tropical upper stratosphere. Quantitatively, “metrics” indicate a wide spread in model performance for most diagnostics with systematic biases in many, and poorer performance in the SH than in the Northern Hemisphere (NH). Correlations were found in the SH between errors in the final warming, polar temperatures, the leading mode of variability, and jet strength, and in the NH between errors in polar temperatures, frequency of major SSWs, and jet strength. Models with a stronger QBO have stronger tropical upwelling and a colder NH vortex. Both the qualitative and quantitative analysis indicate a number of common and long‐standing model problems, particularly related to the simulation of the SH and stratospheric variability.

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Extra-tropical cyclones are identified and compared using data from four recent re-analyses for the winter periods in both hemispheres. Results show the largest differences occur between the older lower resolution JRA25 re-analysis when compared with the newer high resolution re-analyses, in particular in the Southern Hemisphere (SH). Spatial differences between the newest re-analyses are small in both hemispheres and generally not significant except some common regions associated with cyclogenesis close to orography. Intensities are generally related to spatial resolution except NASA-MERRA which has larger intensities for several different measures. Matching storms between re-analyses shows the number matched between ERA-Interim and the other re-analyses are similar in the Northern Hemisphere (NH). In the SH the number matched between JRA25 and ERA-Interim is lower than in the NH, but for NASA-MERRA and NCEP-CFSR the number matched is similar to the NH. The mean separation of the identically same cyclones is typically less than 20 geodesic in both hemispheres for the latest re-analyses, whereas JRA25 compared with the other re-analyses has a broader distribution in the SH indicating greater uncertainty. The instantaneous intensity differences for matched storms shows narrow distributions for pressure while for winds and vorticity the distributions are much broader indicating larger uncertainty typical of smaller scale fields. Composite cyclone diagnostics show that cyclones are very similar between the re-analyses, with differences being related to the intensities, consistent with the intensity results. Overall, results show NH cyclones correspond well between re-analyses, with a significant improvement in the SH for the latest re-analyses, indicating a convergence between re-analyses for cyclone properties.

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Sting jets are transient mesoscale jets of air that descend from the tip of the cloud head towards the top of the boundary layer in severe extratropical cyclones and can lead to damaging surface wind gusts. This recently identified jet is distinct from the well-documented jets associated with the cold and warm conveyor belts. One mechanism proposed for their development is the release of conditional symmetric instability (CSI). Here the spatial distribution and temporal evolution of several CSI diagnostics in four severe storms are analysed. A sting jet has been identified in three of these storms; for comparison, we also analysed one storm that did not have a sting jet, even though it hadmany of the apparent features of sting-jet storms. The sting-jet storms are distinct from the non-sting-jet storms by having much greater andmore extensive conditional instability (CI) and CSI. CSI is released by ascending air parcels in the cloud head in two of the sting-jet storms and by descending air parcels in the other sting-jet storm. By contrast, only weak CI to ascending air parcels is present at the cloud-head tip in the non-sting-jet storm. CSI released by descending air parcels, as diagnosed by decaying downdraught slantwise convective available potential energy (DSCAPE), is collocated with the sting jets in all three sting-jet storms and has a localisedmaximum in two of them. Consistent evolutions of saturated moist potential vorticity are found.We conclude that CSI release has a role in the generation of the sting jet, that the sting jet may be driven by the release of instability to both ascending and descending parcels, and that DSCAPE could be used as a discriminating diagnostic for the sting jet based on these four case-studies.

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The structure and evolution of the Arctic stratospheric polar vortex is assessed during opposing phases of, primarily, the El Niño–Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO), but the 11 year solar cycle and winters following large volcanic eruptions are also examined. The analysis is performed by taking 2-D moments of vortex potential vorticity (PV) fields which allow the area and centroid of the vortex to be calculated throughout the ERA-40 reanalysis data set (1958–2002). Composites of these diagnostics for the different phases of the natural forcings are then considered. Statistically significant results are found regarding the structure and evolution of the vortex during, in particular, the ENSO and QBO phases. When compared with the more traditional zonal mean zonal wind diagnostic at 60°N, the moment-based diagnostics are far more robust and contain more information regarding the state of the vortex. The study details, for the first time, a comprehensive sequence of events which map the evolution of the vortex during each of the forcings throughout an extended winter period.

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Biomaterials are often soft materials. There is now growing interest in designing, synthesizing and characterising soft materials that mimic the properties of biological materials such as tissue, proteins, DNA or cells. Research on biomimetic soft matter is therefore a developing theme with important emerging applications in biomedicine including tissue engineering, diagnostics, gene therapy, drug delivery and many others. There are also important basic science questions concerning the use of concepts from colloid and polymer science to understand the self-assembly of biomimetic soft materials. This issue of Soft Matter presents a selection of extremely topical articles on a diversity of biomimetic soft matter systems. I thank the contributors for this quite remarkable collection of papers, which report many fascinating discoveries and insights.