Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

The Representative Soil Sampling Scheme of England and Wales: a statistical analysis of topsoil nutrient status and pH between 1971 and 2001

The Representative Soil Sampling Scheme of England and Wales has recorded information on the soil of agricultural land in England and Wales since 1969. It is a valuable source of information about the soil in the context of monitoring for sustainable agricultural development. Changes in soil nutrient status and pH were examined over the period 1971-2001. Several methods of statistical analysis were applied to data from the surveys during this period. The main focus here is on the data for 1971, 1981, 1991 and 2001. The results of examining change over time in general show that levels of potassium in the soil have increased, those of magnesium have remained fairly constant, those of phosphorus have declined and pH has changed little. Future sampling needs have been assessed in the context of monitoring, to determine the mean at a given level of confidence and tolerable error and to detect change in the mean over time at these same levels over periods of 5 and 10 years. The results of a non-hierarchical multivariate classification suggest that England and Wales could be stratified to optimize future sampling and analysis. To monitor soil quality and health more generally than for agriculture, more of the country should be sampled and a wider range of properties recorded.

Effect of internal partitioning on room air quality with mixing ventilation-statistical analysis

In designing modern office buildings, building spaces are frequently zoned by introducing internal partitioning, which may have a significant influence on the room air environment. This internal partitioning was studied by means of model test, numerical simulation, and statistical analysis as the final stage. In this paper, the results produced from the statistical analysis are summarized and presented.

Rapid quality assessment of wheat cultivars registered in Poland using the 2-g mixograph and multivariate statistical analysis

Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Statistical analysis of eight surface ozone measurement series for various sites in Ireland

Data from various stations having different measurement record periods between 1988 and 2007 are analyzed to investigate the surface ozone concentration, long-term trends, and seasonal changes in and around Ireland. Time series statistical analysis is performed on the monthly mean data using seasonal and trend decomposition procedures and the Box-Jenkins approach (autoregressive integrated moving average). In general, ozone concentrations in the Irish region are found to have a negative trend at all sites except at the coastal sites of Mace Head and Valentia. Data from the most polluted Dublin city site have shown a very strong negative trend of −0.33 ppb/yr with a 95% confidence limit of 0.17 ppb/yr (i.e., −0.33 ± 0.17) for the period 2002−2007, and for the site near the city of Cork, the trend is found to be −0.20 ± 0.11 ppb/yr over the same period. The negative trend for other sites is more pronounced when the data span is considered from around the year 2000 to 2007. Rural sites of Wexford and Monaghan have also shown a very strong negative trend of −0.99 ± 0.13 and −0.58 ± 0.12, respectively, for the period 2000−2007. Mace Head, a site that is representative of ozone changes in the air advected from the Atlantic to Europe in the marine planetary boundary layer, has shown a positive trend of about +0.16 ± 0.04 ppb per annum over the entire period 1988−2007, but this positive trend has reduced during recent years (e.g., in the period 2001−2007). Cluster analysis for back trajectories are performed for the stations having a long record of data, Mace Head and Lough Navar. For Mace Head, the northern and western clean air sectors have shown a similar positive trend (+0.17 ± 0.02 ppb/yr for the northern sector and +0.18 ± 0.02 ppb/yr for the western sector) for the whole period, but partial analysis for the clean western sector at Mace Head shows different trends during different time periods with a decrease in the positive trend since 1988 indicating a deceleration in the ozone trend for Atlantic air masses entering Europe.

Estimates of aerosol radiative forcing from the MACC re-analysis

The European Centre for Medium-range Weather Forecast (ECMWF) provides an aerosol re-analysis starting from year 2003 for the Monitoring Atmospheric Composition and Climate (MACC) project. The re-analysis assimilates total aerosol optical depth retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) to correct for model departures from observed aerosols. The reanalysis therefore combines satellite retrievals with the full spatial coverage of a numerical model. Re-analysed products are used here to estimate the shortwave direct and first indirect radiative forcing of anthropogenic aerosols over the period 2003–2010, using methods previously applied to satellite retrievals of aerosols and clouds. The best estimate of globally-averaged, all-sky direct radiative forcing is −0.7±0.3Wm−2. The standard deviation is obtained by a Monte-Carlo analysis of uncertainties, which accounts for uncertainties in the aerosol anthropogenic fraction, aerosol absorption, and cloudy-sky effects. Further accounting for differences between the present-day natural and pre-industrial aerosols provides a direct radiative forcing estimate of −0.4±0.3Wm−2. The best estimate of globally-averaged, all-sky first indirect radiative forcing is −0.6±0.4Wm−2. Its standard deviation accounts for uncertainties in the aerosol anthropogenic fraction, and in cloud albedo and cloud droplet number concentration susceptibilities to aerosol changes. The distribution of first indirect radiative forcing is asymmetric and is bounded by −0.1 and −2.0Wm−2. In order to decrease uncertainty ranges, better observational constraints on aerosol absorption and sensitivity of cloud droplet number concentrations to aerosol changes are required.

Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis

Satellite data are increasingly used to provide observation-based estimates of the effects of aerosols on climate. The Aerosol-cci project, part of the European Space Agency's Climate Change Initiative (CCI), was designed to provide essential climate variables for aerosols from satellite data. Eight algorithms, developed for the retrieval of aerosol properties using data from AATSR (4), MERIS (3) and POLDER, were evaluated to determine their suitability for climate studies. The primary result from each of these algorithms is the aerosol optical depth (AOD) at several wavelengths, together with the Ångström exponent (AE) which describes the spectral variation of the AOD for a given wavelength pair. Other aerosol parameters which are possibly retrieved from satellite observations are not considered in this paper. The AOD and AE (AE only for Level 2) were evaluated against independent collocated observations from the ground-based AERONET sun photometer network and against “reference” satellite data provided by MODIS and MISR. Tools used for the evaluation were developed for daily products as produced by the retrieval with a spatial resolution of 10 × 10 km2 (Level 2) and daily or monthly aggregates (Level 3). These tools include statistics for L2 and L3 products compared with AERONET, as well as scoring based on spatial and temporal correlations. In this paper we describe their use in a round robin (RR) evaluation of four months of data, one month for each season in 2008. The amount of data was restricted to only four months because of the large effort made to improve the algorithms, and to evaluate the improvement and current status, before larger data sets will be processed. Evaluation criteria are discussed. Results presented show the current status of the European aerosol algorithms in comparison to both AERONET and MODIS and MISR data. The comparison leads to a preliminary conclusion that the scores are similar, including those for the references, but the coverage of AATSR needs to be enhanced and further improvements are possible for most algorithms. None of the algorithms, including the references, outperforms all others everywhere. AATSR data can be used for the retrieval of AOD and AE over land and ocean. PARASOL and one of the MERIS algorithms have been evaluated over ocean only and both algorithms provide good results.

Statistical evaluation of alternative models of human evolution

An appropriate model of recent human evolution is not only important to understand our own history, but it is necessary to disentangle the effects of demography and selection on genome diversity. Although most genetic data support the view that our species originated recently in Africa, it is still unclear if it completely replaced former members of the Homo genus, or if some interbreeding occurred during its range expansion. Several scenarios of modern human evolution have been proposed on the basis of molecular and paleontological data, but their likelihood has never been statistically assessed. Using DNA data from 50 nuclear loci sequenced in African, Asian and Native American samples, we show here by extensive simulations that a simple African replacement model with exponential growth has a higher probability (78%) as compared with alternative multiregional evolution or assimilation scenarios. A Bayesian analysis of the data under this best supported model points to an origin of our species approximate to 141 thousand years ago (Kya), an exit out-of-Africa approximate to 51 Kya, and a recent colonization of the Americas approximate to 10.5 Kya. We also find that the African replacement model explains not only the shallow ancestry of mtDNA or Y-chromosomes but also the occurrence of deep lineages at some autosomal loci, which has been formerly interpreted as a sign of interbreeding with Homo erectus.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

On the nature of ULF wave power during nightside auroral activations and substorms: 2. temporal evolution

We present a statistical analysis of the time evolution of ground magnetic fluctuations in three (12–48 s, 24–96 s and 48–192 s) period bands during nightside auroral activations. We use an independently derived auroral activation list composed of both substorms and pseudo-breakups to provide an estimate of the activation times of nightside aurora during periods with comprehensive ground magnetometer coverage. One hundred eighty-one events in total are studied to demonstrate the statistical nature of the time evolution of magnetic wave power during the ∼30 min surrounding auroral activations. We find that the magnetic wave power is approximately constant before an auroral activation, starts to grow up to 90 s prior to the optical onset time, maximizes a few minutes after the auroral activation, then decays slightly to a new, and higher, constant level. Importantly, magnetic ULF wave power always remains elevated after an auroral activation, whether it is a substorm or a pseudo-breakup. We subsequently divide the auroral activation list into events that formed part of ongoing auroral activity and events that had little preceding geomagnetic activity. We find that the evolution of wave power in the ∼10–200 s period band essentially behaves in the same manner through auroral onset, regardless of event type. The absolute power across ULF wave bands, however, displays a power law-like dependency throughout a 30 min period centered on auroral onset time. We also find evidence of a secondary maximum in wave power at high latitudes ∼10 min following isolated substorm activations. Most significantly, we demonstrate that magnetic wave power levels persist after auroral activations for ∼10 min, which is consistent with recent findings of wave-driven auroral precipitation during substorms. This suggests that magnetic wave power and auroral particle precipitation are intimately linked and key components of the substorm onset process.

A general-method for tracking analysis and its application to meteorological data

Recent interest in the validation of general circulation models (GCMs) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

Can knowledge of the state of the stratosphere be used to improve statistical forecasts of the troposphere?

Recent analysis of the Arctic Oscillation (AO) in the stratosphere and troposphere has suggested that predictability of the state of the tropospheric AO may be obtained from the state of the stratospheric AO. However, much of this research has been of a purely qualitative nature. We present a more thorough statistical analysis of a long AO amplitude dataset which seeks to establish the magnitude of such a link. A relationship between the AO in the lower stratosphere and on the 1000 hPa surface on a 10-45 day time-scale is revealed. The relationship accounts for 5% of the variance of the 1000 hPa time series at its peak value and is significant at the 5% level. Over a similar time-scale the 1000 hPa time series accounts for 1% of itself and is not significant at the 5% level. Further investigation of the relationship reveals that it is only present during the winter season and in particular during February and March. It is also demonstrated that using stratospheric AO amplitude data as a predictor in a simple statistical model results in a gain of skill of 5% over a troposphere-only statistical model. This gain in skill is not repeated if an unrelated time series is included as a predictor in the model. Copyright © 2003 Royal Meteorological Society