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.

Extensive and intensive globalizations: explicating the low connectivity puzzle of U.S. cities using a city-dyad analysis

This article reports an experiment in world city network analysis focusing on city-dyads. Results are derived from an unusual principal components analysis of 27,966 city-dyads across 5 advanced producer service sectors. A 2-component solution is found that identifies different forms of globalization: extensive and intensive. The latter is characterized by very high component scores and describes the more important city-dyads focused upon London-New York (NYLON). The extensive globalization component heavily features London and New York but with each linked to less important cities. U.S. cities score relatively high on the intensive globalization component and we use this finding to explain the low connectivities of U.S. cities in previous studies of the world city network. The two components are tentatively interpreted in world-systems terms: intensive globalization is the process of core-making through city-dyads; extensive globalization is the process of linking core with non-core through city-dyads.

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

Making 'dirty' nations look clean? The nation state and the problem of selecting and weighting indices as tools for measuring progress towards sustainability

Pressing global environmental problems highlight the need to develop tools to measure progress towards "sustainability." However, some argue that any such attempt inevitably reflects the views of those creating such tools and only produce highly contested notions of "reality." To explore this tension, we critically assesses the Environmental Sustainability Index (ESI), a well-publicized product of the World Economic Forum that is designed to measure 'sustainability' by ranking nations on league tables based on extensive databases of environmental indicators. By recreating this index, and then using statistical tools (principal components analysis) to test relations between various components of the index, we challenge ways in which countries are ranked in the ESI. Based on this analysis, we suggest (1) that the approach taken to aggregate, interpret and present the ESI creates a misleading impression that Western countries are more sustainable than the developing world; (2) that unaccounted methodological biases allowed the authors of the ESI to over-generalize the relative 'sustainability' of different countries; and, (3) that this has resulted in simplistic conclusions on the relation between economic growth and environmental sustainability. This criticism should not be interpreted as a call for the abandonment of efforts to create standardized comparable data. Instead, this paper proposes that indicator selection and data collection should draw on a range of voices, including local stakeholders as well as international experts. We also propose that aggregating data into final league ranking tables is too prone to error and creates the illusion of absolute and categorical interpretations. (c) 2004 Elsevier Ltd. All rights reserved.

The effects of irrigation, nitrogen fertilizer and grain size on Hagberg falling number, specific weight and blackpoint of winter wheat

The effects of irrigation and nitrogen (N) fertilizer on Hagberg falling number (HFN), specific weight (SW) and blackpoint (BP) of winter wheat (Triticum aestivum L) were investigated. Mains water (+50 and +100 mm month(-1), containing 44 mg NO3- litre(-1) and 28 mg SO42- litre(-1)) was applied with trickle irrigation during winter (17 January-17 March), spring (21 March-20 May) or summer (24 May-23 July). In 1999/2000 these treatments were factorially combined with three N levels (0, 200, 400 kg N ha(-1)), applied to cv Hereward. In 2000/01 the 400 kg N ha(-1) treatment was replaced with cv Malacca given 200 kg N ha(-1). Irrigation increased grain yield, mostly by increasing grain numbers when applied in winter and spring, and by increasing mean grain weight when applied in summer. Nitrogen increased grain numbers and SW, and reduced BP in both years. Nitrogen increased HFN in 1999/2000 and reduced HFN in 2000/01. Effects of irrigation on HFN, SW and BP were smaller and inconsistent over year and nitrogen level. Irrigation interacted with N on mean grain weight: negatively for winter and spring irrigation, and positively for summer irrigation. Ten variables derived from digital image analysis of harvested grain were included with mean grain weight in a principal components analysis. The first principal component ('size') was negatively related to HFN (in two years) and BP (one year), and positively related to SW (two years). Treatment effects on dimensions of harvested grain could not explain all of the effects on HFN, BP and SW but the results were consistent with the hypothesis that water and nutrient availability, even when they were affected early in the season, could influence final grain quality if they influenced grain numbers and size. (C) 2004 Society of Chemical Industry

Identifying and understanding factors influencing the uptake of new technologies on dairy farms in SW England using the theory of reasoned action

Whilst much is known of new technology adopters, little research has addressed the role of their attitudes in adoption decisions; particularly, for technologies with evident economic potential that have not been taken up by farmers. This paper presents recent research that has used a new approach which examines the role that adopters' attitudes play in identifying the drivers of and barriers to adoption. The study was concerned with technologies for livestock farming systems in SW England, specifically oestrus detection, nitrogen supply management, and, inclusion of white clover. The adoption behaviour is analysed using the social-psychology theory of reasoned action to identify factors that affect the adoption of technologies, which are confirmed using principal components analysis. The results presented here relate to the specific adoption behaviour regarding the Milk Development Council's recommended observation times for heat detection. The factors that affect the adoption of this technology are: cost effectiveness, improved detection and conception rates as the main drivers, whilst the threat to demean the personal knowledge and skills of a farmer in 'knowing' their cows is a barrier. This research shows clearly that promotion of a technology and transfer of knowledge for a farming system need to take account of the beliefs and attitudes of potential adopters. (C) 2006 Elsevier Ltd. All rights reserved.

Identifying important endemic areas using ecoregions: birds and mammals in the Indo-Pacific

Concentrations of large numbers of endemic species have been singled out in prioritization exercises as significant areas for global biodiversity conservation. This paper describes bird and mammal endemicity in Indo-Pacific ecoregions. An ecoregion is a relatively large unit of land or water that contains a distinct assemblage of natural communities. We prioritize 133 ecoregions according to their levels of endemicity, and explain how variables such as biome type, whether the ecoregion is on an island or continental mass, montane or non-montane, correlate with the proportion of the total species assemblage that are endemic. Following an exploratory principal components analysis we classify all ecoregions according to the relationship between numbers of endemics and overall species richness. Endemicity is negatively correlated with species richness. We show that plotting the logit transformation of the endemicity of birds and mammals against log of species richness is a more effective and useful way of identifying important ecoregions than simply ordering ecoregions by the proportion of endemic species, or any other single measure. The plot, divided into 16 regions corresponding to the quartiles of the two variables, was used to identify ecoregions of high conservation value. These are the ecoregions with the highest endemicity and lowest species richness. Further analysis shows that island and montane ecoregions, regardless of their biome type, are by far the most important for endemic species.

Quantitative and qualitative differences in morphological traits revealed between diploid Fragaria species

Background and Aims: The aims of this investigation were to highlight the qualitative and quantitative diversity apparent between nine diploid Fragaria species and produce interspecific populations segregating for a large number of morphological characters suitable for quantitative trait loci analysis. Methods: A qualitative comparison of eight described diploid Fragaria species was performed and measurements were taken of 23 morphological traits from 19 accessions including eight described species and one previously undescribed species. A principal components analysis was performed on 14 mathematically unrelated traits from these accessions, which partitioned the species accessions into distinct morphological groups. Interspecific crosses were performed with accessions of species that displayed significant quantitative divergence and, from these, populations that should segregate for a range of quantitative traits were raised. Key Results: Significant differences between species were observed for all 23 morphological traits quantified and three distinct groups of species accessions were observed after the principal components analysis. Interspecific crosses were performed between these groups, and F2 and backcross populations were raised that should segregate for a range of morphological characters. In addition, the study highlighted a number of distinctive morphological characters in many of the species studied. Conclusions: Diploid Fragaria species are morphologically diverse, yet remain highly interfertile, making the group an ideal model for the study of the genetic basis of phenotypic differences between species through map-based investigation using quantitative trait loci. The segregating interspecific populations raised will be ideal for such investigations and could also provide insights into the nature and extent of genome evolution within this group.

A metabolic system-wide characterisation of the pig: a model for human physiology

The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.

Developing a brand performance measure for financial services brands

With no universal approach for measuring brand performance, we show how a consumer-based brand measure was developed for corporate financial services brands. Churchill's paradigm was adopted. A literature review and 20 depth interviews with experts suggested that brand loyalty, consumer satisfaction and reputation constitute the brand performance measure. Ten financial services organisations provided access to their consumers. Following a postal survey, 600 questionnaires were analysed through principal components analysis to identify the consumer-based measure. Further testing revealed this to be a valid and reliable brand performance measure.

Promoting the use of BaP as a marker for PAH exposure in UK soils

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants that frequently accumulate in soils. There is therefore a requirement to determine their levels in contaminated environments for the purposes of determining impacts on human health. PAHs are a suite of individual chemicals, and there is an ongoing debate as to the most appropriate method for assessing the risk to humans from them. Two methods predominate: the surrogate marker approach and the toxic equivalency factor. The former assumes that all chemicals in a mixture have an equivalent toxicity. The toxic equivalency approach estimates the potency of individual chemicals relative to the usually most toxic Benzo(a)pyrene. The surrogate marker approach is believed to overestimate risk and the toxic equivalency factor to underestimate risk. When analysing the risks from soils, the surrogate marker approach is preferred due to its simplicity, but there are concerns because of the potential diversity of the PAH profile across the range of impacted soils. Using two independent data sets containing soils from 274 sites across a diverse range of locations, statistical analysis was undertaken to determine the differences in the composition of carcinogenic PAH between site locations, for example, rural versus industrial. Following principal components analysis, distinct population differences were not seen between site locations in spite of large differences in the total PAH burden between individual sites. Using all data, highly significant correlations were seen between BaP and other carcinogenic PAH with the majority of r2 values > 0.8. Correlations with the European Food Standards Agency (EFSA) summed groups, that is, EFSA2, EFSA4 and EFSA8 had even higher correlations (r2 > 0.95). We therefore conclude that BaP is a suitable surrogate marker to represent mixtures of PAH in soil during risk assessments.