Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple stages of working memory

Constrained principal component analysis (CPCA) with a finite impulse response (FIR) basis set was used to reveal functionally connected networks and their temporal progression over a multistage verbal working memory trial in which memory load was varied. Four components were extracted, and all showed statistically significant sensitivity to the memory load manipulation. Additionally, two of the four components sustained this peak activity, both for approximately 3 s (Components 1 and 4). The functional networks that showed sustained activity were characterized by increased activations in the dorsal anterior cingulate cortex, right dorsolateral prefrontal cortex, and left supramarginal gyrus, and decreased activations in the primary auditory cortex and "default network" regions. The functional networks that did not show sustained activity were instead dominated by increased activation in occipital cortex, dorsal anterior cingulate cortex, sensori-motor cortical regions, and superior parietal cortex. The response shapes suggest that although all four components appear to be invoked at encoding, the two sustained-peak components are likely to be additionally involved in the delay period. Our investigation provides a unique view of the contributions made by a network of brain regions over the course of a multiple-stage working memory trial.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

The relationship between diffuse spectral reflectance of the soil and its cation exchange capacity is scale-dependent

Diffuse reflectance spectroscopy (DRS) is increasingly being used to predict numerous soil physical, chemical and biochemical properties. However, soil properties and processes vary at different scales and, as a result, relationships between soil properties often depend on scale. In this paper we report on how the relationship between one such property, cation exchange capacity (CEC), and the DRS of the soil depends on spatial scale. We show this by means of a nested analysis of covariance of soils sampled on a balanced nested design in a 16 km × 16 km area in eastern England. We used principal components analysis on the DRS to obtain a reduced number of variables while retaining key variation. The first principal component accounted for 99.8% of the total variance, the second for 0.14%. Nested analysis of the variation in the CEC and the two principal components showed that the substantial variance components are at the > 2000-m scale. This is probably the result of differences in soil composition due to parent material. We then developed a model to predict CEC from the DRS and used partial least squares (PLS) regression do to so. Leave-one-out cross-validation results suggested a reasonable predictive capability (R2 = 0.71 and RMSE = 0.048 molc kg− 1). However, the results from the independent validation were not as good, with R2 = 0.27, RMSE = 0.056 molc kg− 1 and an overall correlation of 0.52. This would indicate that DRS may not be useful for predictions of CEC. When we applied the analysis of covariance between predicted and observed we found significant scale-dependent correlations at scales of 50 and 500 m (0.82 and 0.73 respectively). DRS measurements can therefore be useful to predict CEC if predictions are required, for example, at the field scale (50 m). This study illustrates that the relationship between DRS and soil properties is scale-dependent and that this scale dependency has important consequences for prediction of soil properties from DRS data

Exploring the spatial relations between soil physical properties and apparent electrical conductivity

Relations between the apparent electrical conductivity of the soil (ECa) and top- and sub-soil physical properties were examined for two arable fields in southern England (Crowmarsh Battle Farms and the Yattendon Estate). The spatial variation of ECa and the soil properties was explored geostatistically. The variogram ranges showed that ECa varied on a similar spatial scale to many of the soil physical properties in both fields. Several features in the map of kriged predictions of ECa were also evident in maps of the soil properties. In addition, the correlation coefficients showed a strong relation between ECa and several soil properties. A moving correlation analysis enabled differences in the relations between ECa and the soil properties to be examined within the fields. The results indicated that relations were inconsistent; they were stronger in some areas than others. A regression of ECa on the principal component scores of the leading components for both fields showed that the first two components accounted for a large proportion of the variance in ECa, whereas the others accounted for little or none. For Crowmarsh topsoil sand and clay, loss on ignition and volumetric water measured in the autumn had large correlations on the first component, and for Yattendon they were large for topsoil sand and clay, and autumn and spring volumetric water. The cross-variograms suggested strong coregionalization between ECa and several soil physical properties; in particular subsoil sand and silt at Crowmarsh, and subsoil sand and clay at Yattendon. The structural correlations from the linear model of coregionalization confirmed the strength of the relations between ECa and the subsoil properties. Nevertheless, no one property was consistently important for both fields. Although a map of ECa can indicate the general patterns of spatial variation in the soil, it is not a substitute for information on soil properties obtained by sampling and analysing the soil. Nevertheless, it could be used to guide further sampling. (c) 2005 Elsevier B.V. All rights reserved.

Multivariate relationships between groundwater chemistry and toxicity in an urban aquifer

Multivariate statistical methods were used to investigate file Causes of toxicity and controls on groundwater chemistry from 274 boreholes in an Urban area (London) of the United Kingdom. The groundwater was alkaline to neutral, and chemistry was dominated by calcium, sodium, and Sulfate. Contaminants included fuels, solvents, and organic compounds derived from landfill material. The presence of organic material in the aquifer caused decreases in dissolved oxygen, sulfate and nitrate concentrations. and increases in ferrous iron and ammoniacal nitrogen concentrations. Pearson correlations between toxicity results and the concentration of individual analytes indicated that concentrations of ammoinacal nitrogen, dissolved oxygen, ferrous iron, and hydrocarbons were important where present. However, principal component and regression analysis suggested no significant correlation between toxicity and chemistry over the whole area. Multidimensional Scaling was used to investigate differences in sites caused by historical use, landfill gas status, or position within the sample area. Significant differences were observed between sites with different historical land use and those with different gas status. Examination of the principal component matrix revealed that these differences are related to changes in the importance of reduced chemical species.

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.

The mean evolution and variability of the Asian summer monsoon: Comparison of ECMWF and NCEP-NCAR reanalyses

The behavior of the Asian summer monsoon is documented and compared using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) and the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) Reanalysis. In terms of seasonal mean climatologies the results suggest that, in several respects, the ERA is superior to the NCEP-NCAR Reanalysis. The overall better simulation of the precipitation and hence the diabatic heating field over the monsoon domain in ERA means that the analyzed circulation is probably nearer reality. In terms of interannual variability, inconsistencies in the definition of weak and strong monsoon years based on typical monsoon indices such as All-India Rainfall (AIR) anomalies and the large-scale wind shear based dynamical monsoon index (DMI) still exist. Two dominant modes of interannual variability have been identified that together explain nearly 50% of the variance. Individually, they have many features in common with the composite flow patterns associated with weak and strong monsoons, when defined in terms of regional AIR anomalies and the large-scale DMI. The reanalyses also show a common dominant mode of intraseasonal variability that describes the latitudinal displacement of the tropical convergence zone from its oceanic-to-continental regime and essentially captures the low-frequency active/break cycles of the monsoon. The relationship between interannual and intraseasonal variability has been investigated by considering the probability density function (PDF) of the principal component of the dominant intraseasonal mode. Based on the DMI, there is an indication that in years with a weaker monsoon circulation, the PDF is skewed toward negative values (i,e., break conditions). Similarly, the PDFs for El Nino and La Nina years suggest that El Nino predisposes the system to more break spells, although the sample size may limit the statistical significance of the results.

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

Observations of the diurnal cycle of outgoing longwave radiation from the Geostationary Earth Radiation Budget instrument

The Geostationary Earth Radiation Budget instrument on Meteosat-8, located over Africa, provides unprecedented temporal sampling (~17 minutes) of the broadband emitted thermal and reflected solar radiances. We analyse the diurnal cycle of the outgoing longwave radiation (OLR) fluxes derived from the thermal radiances for July 2006. Principal component (PC) analysis separates the signals of the surface temperature response to solar heating and of the development of convective clouds. The first two PCs explain most of the OLR variations: PC1 (surface heating) explains 82.3% of the total variance and PC2 (cloud development) explains 12.8% of the variance. Convection is initiated preferentially over mountainous regions and the cloud then advects downstream in the ambient flow. Diurnal variations are much weaker over the oceans, but a coherent signal over the Gulf of Guinea suggests that the cloudiness is modulated by the diurnally varying contrast between the Gulf and the adjacent land mass.

Statistical inference of OH concentrations and air mass dilution rates from successive observations of non-methane hydrocarbons in single air masses

Bayesian inference has been used to determine rigorous estimates of hydroxyl radical concentrations () and air mass dilution rates (K) averaged following air masses between linked observations of nonmethane hydrocarbons (NMHCs) spanning the North Atlantic during the Intercontinental Transport and Chemical Transformation (ITCT)-Lagrangian-2K4 experiment. The Bayesian technique obtains a refined (posterior) distribution of a parameter given data related to the parameter through a model and prior beliefs about the parameter distribution. Here, the model describes hydrocarbon loss through OH reaction and mixing with a background concentration at rate K. The Lagrangian experiment provides direct observations of hydrocarbons at two time points, removing assumptions regarding composition or sources upstream of a single observation. The estimates are sharpened by using many hydrocarbons with different reactivities and accounting for their variability and measurement uncertainty. A novel technique is used to construct prior background distributions of many species, described by variation of a single parameter . This exploits the high correlation of species, related by the first principal component of many NMHC samples. The Bayesian method obtains posterior estimates of , K and following each air mass. Median values are typically between 0.5 and 2.0 × 106 molecules cm−3, but are elevated to between 2.5 and 3.5 × 106 molecules cm−3, in low-level pollution. A comparison of estimates from absolute NMHC concentrations and NMHC ratios assuming zero background (the “photochemical clock” method) shows similar distributions but reveals systematic high bias in the estimates from ratios. Estimates of K are ∼0.1 day−1 but show more sensitivity to the prior distribution assumed.

Shifts in rhizosphere microbial communities and enzyme activity of Poa alpina across an alpine chronosequence

This study quantifies the influence of Poa alpina on the soil microbial community in primary succession of alpine ecosystems, and whether these effects are controlled by the successional stage. Four successional sites representative of four stages of grassland development (initial, 4 years (non-vegetated); pioneer, 20 years; transition, 75 years; mature, 9500 years old) on the Rotmoos glacier foreland, Austria, were sampled. The size, composition and activity of the microbial community in the rhizosphere and bulk soil were characterized using the chloroform-fumigation extraction procedure, phospholipid fatty acid (PLFA) analysis and measurements of the enzymes beta-glucosidase, beta-xylosidase, N-acetyl-beta-glucosaminidase, leucine aminopeptidase, acid phosphatase and sulfatase. The interplay between the host plant and the successional stage was quantified using principal component (PCA) and multidimensional scaling analyses. Correlation analyses were applied to evaluate the relationship between soil factors (C-org, N-t, C/N ratio, pH, ammonium, phosphorus, potassium) and microbial properties in the bulk soil. In the pioneer stage microbial colonization of the rhizosphere of P. alpina was dependent on the reservoir of microbial species in the bulk soil. As a consequence, the rhizosphere and bulk soil were similar in microbial biomass (ninhydrin-reactive nitrogen (NHR-N)), community composition (PLFA), and enzyme activity. In the transition and mature grassland stage, more benign soil conditions stimulated microbial growth (NHR-N, total amount of PLFA, bacterial PLFA, Gram-positive bacteria, Gram-negative bacteria), and microbial diversity (Shannon index H) in the rhizosphere either directly or indirectly through enhanced carbon allocation. In the same period, the rhizosphere microflora shifted from a G(-) to a more G(+), and from a fungal to a more bacteria-dominated community. Rhizosphere beta-xylosidase, N-acetyl-beta-glucosaminidase, and sulfatase activity peaked in the mature grassland soil, whereas rhizosphere leucine aminopeptidase, beta-glucosidase, and phosphatase activity were highest in the transition stage, probably because of enhanced carbon and nutrient allocation into the rhizosphere due to better growth conditions. Soil organic matter appeared to be the most important driver of microbial colonization in the bulk soil. The decrease in soil pH and soil C/N ratio mediated the shifts in the soil microbial community composition (bacPLFA, bacPLFA/fungPLFA, G(-), G(+)/G(-)). The activities of beta-glucosidase, beta-xylosidase and phosphatase were related to soil ammonium and phosphorus, indicating that higher decomposition rates enhanced the nutrient availability in the bulk soil. We conclude that the major determinants of the microllora vary along the successional gradient: in the pioneer stage the rhizosphere microflora was primarily determined by the harsh soil environment; under more favourable environmental conditions, however, the host plant selected for a specific microbial community that was related to the dynamic interplay between soil properties and carbon supply. (C) 2004 Elsevier Ltd. All rights reserved.

Shifts in rhizosphere microbial communities and enzyme activity of Poa alpina across an alpine chronosequence

This study quantifies the influence of Poa alpina on the soil microbial community in primary succession of alpine ecosystems, and whether these effects are controlled by the successional stage. Four successional sites representative of four stages of grassland development (initial, 4 years (non-vegetated); pioneer, 20 years; transition, 75 years; mature, 9500 years old) on the Rotmoos glacier foreland, Austria, were sampled. The size, composition and activity of the microbial community in the rhizosphere and bulk soil were characterized using the chloroform-fumigation extraction procedure, phospholipid fatty acid (PLFA) analysis and measurements of the enzymes beta-glucosidase, beta-xylosidase, N-acetyl-beta-glucosaminidase, leucine aminopeptidase, acid phosphatase and sulfatase. The interplay between the host plant and the successional stage was quantified using principal component (PCA) and multidimensional scaling analyses. Correlation analyses were applied to evaluate the relationship between soil factors (C-org, N-t, C/N ratio, pH, ammonium, phosphorus, potassium) and microbial properties in the bulk soil. In the pioneer stage microbial colonization of the rhizosphere of P. alpina was dependent on the reservoir of microbial species in the bulk soil. As a consequence, the rhizosphere and bulk soil were similar in microbial biomass (ninhydrin-reactive nitrogen (NHR-N)), community composition (PLFA), and enzyme activity. In the transition and mature grassland stage, more benign soil conditions stimulated microbial growth (NHR-N, total amount of PLFA, bacterial PLFA, Gram-positive bacteria, Gram-negative bacteria), and microbial diversity (Shannon index H) in the rhizosphere either directly or indirectly through enhanced carbon allocation. In the same period, the rhizosphere microflora shifted from a G(-) to a more G(+), and from a fungal to a more bacteria-dominated community. Rhizosphere beta-xylosidase, N-acetyl-beta-glucosaminidase, and sulfatase activity peaked in the mature grassland soil, whereas rhizosphere leucine aminopeptidase, beta-glucosidase, and phosphatase activity were highest in the transition stage, probably because of enhanced carbon and nutrient allocation into the rhizosphere due to better growth conditions. Soil organic matter appeared to be the most important driver of microbial colonization in the bulk soil. The decrease in soil pH and soil C/N ratio mediated the shifts in the soil microbial community composition (bacPLFA, bacPLFA/fungPLFA, G(-), G(+)/G(-)). The activities of beta-glucosidase, beta-xylosidase and phosphatase were related to soil ammonium and phosphorus, indicating that higher decomposition rates enhanced the nutrient availability in the bulk soil. We conclude that the major determinants of the microllora vary along the successional gradient: in the pioneer stage the rhizosphere microflora was primarily determined by the harsh soil environment; under more favourable environmental conditions, however, the host plant selected for a specific microbial community that was related to the dynamic interplay between soil properties and carbon supply. (C) 2004 Elsevier Ltd. All rights reserved.