The relative impact of co-occurring stressors on the abundance of benthic species examined with three-way correspondence analysis

This paper presents a novel application of three-way correspondence analysis as a technique to analyse three-way contingency tables with abundance scores of several species. The example data analysis presented was taken from a previous mesocosm experiment and consists of a two-factor experimental design with physical disturbance and organic enrichment as factors, applied to sediment collected from the Oslofjørd, Norway. The focus of the original research was to evaluate the influence of the two factors and their interactions on the abundance of the species present in the sediment. In the current paper we demonstrate that by using a three-way correspon�dence approach it is possible to undertake simultaneous analysis of the species, identifying and evaluating their relative sensitivity to the environmental factors thus adding additional insight than was possible in the original analysis. In particular, this new approach allowed even relatively scarce species to be included in the analysis and evaluated together with abundant species. This paper demonstrates how three-way correspondence analysis can be a useful analytical tool in teasing out effects and interactions from multi-factorial studies

Motor function in Parkinson's disease and supranuclear palsy: simultaneous factor analysis of a clinical scale in several populations

BACKGROUND: In order to better understand the similarities and differences in the motor behaviour of different groups of patients, their scores on the Motor Examination section of the Unified Parkinson's Disease Rating Scale (UPDRS) were analysed simultaneously. The three groups consisted, respectively, of patients with Parkinson's disease (PD) on medication, patients with Parkinson's disease withdrawn from anti-parkinsonian medication for at least 12 hours, and patients diagnosed with a specific Parkinsonism syndrome: Progressive Supranuclear Palsy (PSP). METHODS: A total of 669 consecutively sampled patients from three separate hospital-based clinics participated (294 PD on medication; 200 PD off medication: 175 PSP). The Motor Examination section of the UPDRS was administered by neurologists at the three participating clinics. The patient scores on each item were recorded. To assess similarities and differences among the components of the UPDRS in these samples, we performed simultaneous or multigroup factor analysis on the covariance matrices of the three groups. In addition, it was investigated whether a single model for the Motor Examination section of the UPDRS could be developed which would be valid for all three groups at the same time. RESULTS: A single six-dimensional factor solution was found that fitted all groups, although this was not straightforward due to differences between the tremor-at-rest variables. The factors were identified as Tremor-at-rest, Postural Tremor, Axial Dysfunctioning, Rigidity, Left Bradykinesia and Right Bradykinesia. The analysis also pointed to a somewhat lower lateralization in bradykinesia for PSP patients. The groups differed in intensity of motor impairment, especially with respect to Tremor-at-Rest, but the overall relationships between the variables were shared by the three groups. In addition, apart from the common factor structure evidence of differences in body part-specific and motor-specific variances was found. CONCLUSION: From a clinical point of view, the analyses showed that using the Motor Examination section of the UPDRS is also appropriate for patients with PSP, because the correlational structure of the items is directly comparable to that of Parkinson's patients. Methodologically, the analysis of all groups together showed that it is possible to evaluate similarities and differences between factor structures in great detail

Tradeoff between Biomass and Flavonoid Accumulation in White Clover Reflects Contrasting Plant Strategies

An outdoor study was conducted to examine relationships between plant productivity and stress-protective phenolic plant metabolites. Twenty-two populations of the pasture legume white clover were grown for 4½ months during spring and summer in Palmerston North, New Zealand. The major phenolic compounds identified and quantified by HPLC analysis were glycosides of the flavonoids quercetin and kaempferol. Multivariate analysis revealed a trade-off between flavonoid accumulation and plant productivity attributes. White clover populations with high biomass production, large leaves and thick tap roots showed low levels of quercetin glycoside accumulation and low quercetin:kaempferol ratios, while the opposite was true for less productive populations. The latter included stress-resistant ecotypes from Turkey and China, and the analysis also identified highly significant positive relationships of quercetin glycoside accumulation with plant morphology (root:shoot ratio). Importantly, a high degree of genetic variation was detected for most of the measured traits. These findings suggest merit for considering flavonoids such as quercetin as potential selection criteria in the genetic improvement of white clover and other crops

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p