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Unravelling the geometry of data matrices: effects of water stress regimes on winemaking.

ABSTRACT: A new method is proposed for unravelling the patterns between a set of experiments and the features that characterize those experiments. The aims are to extract these patterns in the form of a coupling between the rows and columns of the corresponding data matrix and to use this geometry as a support for model testing. These aims are reached through two key steps, namely application of an iterative geometric approach to couple the metric spaces associated with the rows and columns, and use of statistical physics to generate matrices that mimic the original data while maintaining their inherent structure, thereby providing the basis for hypothesis testing and statistical inference. The power of this new method is illustrated on the study of the impact of water stress conditions on the attributes of 'Cabernet Sauvignon' Grapes, Juice, Wine and Bottled Wine from two vintages. The first step, named data mechanics, de-convolutes the intrinsic effects of grape berries and wine attributes due to the experimental irrigation conditions from the extrinsic effects of the environment. The second step provides an analysis of the associations of some attributes of the bottled wine with characteristics of either the matured grape berries or the resulting juice, thereby identifying statistically significant associations between the juice pH, yeast assimilable nitrogen, and sugar content and the bottled wine alcohol level.

SUBMITTER: Fushing H

PROVIDER: S-EPMC4614510 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Unravelling the geometry of data matrices: effects of water stress regimes on winemaking.

Fushing Hsieh H Hsueh Chih-Hsin CH Heitkamp Constantin C Matthews Mark A MA Koehl Patrice P

Journal of the Royal Society, Interface 20151001 111

A new method is proposed for unravelling the patterns between a set of experiments and the features that characterize those experiments. The aims are to extract these patterns in the form of a coupling between the rows and columns of the corresponding data matrix and to use this geometry as a support for model testing. These aims are reached through two key steps, namely application of an iterative geometric approach to couple the metric spaces associated with the rows and columns, and use of st ...[more]

PMID: 26468072

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Project description:BackgroundGrowing evidence suggests that soil microbes can improve plant fitness under drought. However, in potato, the world's most important non-cereal crop, the role of the rhizosphere microbiome under drought has been poorly studied. Using a cultivation independent metabarcoding approach, we examined the rhizosphere microbiome of two potato cultivars with different drought tolerance as a function of water regime (continuous versus reduced watering) and manipulation of soil microbial diversity (i.e., natural (NSM), vs. disturbed (DSM) soil microbiome).ResultsWater regime and soil pre-treatment showed a significant interaction with bacterial community composition of the sensitive (HERBST) but not the resistant cultivar (MONI). Overall, MONI had a moderate response to the treatments and its rhizosphere selected Rhizobiales under reduced watering in NSM soil, whereas Bradyrhizobium, Ammoniphilus, Symbiobacterium and unclassified Hydrogenedensaceae in DSM soil. In contrast, HERBST response to the treatments was more pronounced. Notably, in NSM soil treated with reduced watering, the root endophytic fungus Falciphora and many Actinobacteriota members (Streptomyces, Glycomyces, Marmoricola, Aeromicrobium, Mycobacterium and others) were largely represented. However, DSM soil treatment resulted in no fungal taxa and fewer enrichment of these Actinobacteriota under reduced watering. Moreover, the number of bacterial core amplicon sequence variants (core ASVs) was more consistent in MONI regardless of soil pre-treatment and water regimes as opposed to HERBST, in which a marked reduction of core ASVs was observed in DSM soil.ConclusionsBesides the influence of soil conditions, our results indicate a strong cultivar-dependent relationship between the rhizosphere microbiome of potato cultivars and their capacity to respond to perturbations such as reduced soil moisture. Our study highlights the importance of integrating soil conditions and plant genetic variability as key factors in future breeding programs aiming to develop drought resistance in a major food crop like potato. Elucidating the molecular mechanisms how plants recruit microbes from soil which help to mitigate plant stress and to identify key microbial taxa, which harbour the respective traits might therefore be an important topic for future research.

Project description:Understanding the correlation structure associated with multiple brain measurements informs about potential "functional groupings" and network organization. The correlation structure can be conveniently captured in a matrix format that summarizes the relationships among a set of brain measurements involving two regions, for example. Such functional connectivity matrix is an important component of many types of investigation focusing on network-level properties of the brain, including clustering brain states, characterizing dynamic functional states, performing participant identification (so-called "fingerprinting") understanding how tasks reconfigure brain networks, and inter-subject correlation analysis. In these investigations, some notion of proximity or similarity of functional connectivity matrices is employed, such as their Euclidean distance or Pearson correlation (by correlating the matrix entries). Here, we propose the use of a geodesic distance metric that reflects the underlying non-Euclidean geometry of functional correlation matrices. The approach is evaluated in the context of participant identification (fingerprinting): given a participant's functional connectivity matrix based on resting-state or task data, how effectively can the participant be identified? Using geodesic distance, identification accuracy was over 95% on resting-state data, and exceeded the Pearson correlation approach by 20%. For whole-cortex regions, accuracy improved on a range of tasks by between 2% and as much as 20%. We also investigated identification using pairs of subnetworks (say, dorsal attention plus default mode), and particular combinations improved accuracy over whole-cortex participant identification by over 10%. The geodesic distance also outperformed Pearson correlation when the former employed a fourth of the data as the latter. Finally, we suggest that low-dimensional distance visualizations based on the geodesic approach help uncover the geometry of task functional connectivity in relation to that during resting-state. We propose that the use of the geodesic distance is an effective way to compare the correlation structure of the brain across a broad range of studies.

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Unravelling the geometry of data matrices: effects of water stress regimes on winemaking.

Publications

Unravelling the geometry of data matrices: effects of water stress regimes on winemaking.

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