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opencc-by-4.0Mar 2023View details →

Multivariate prediction on wake-affected wind turbines using graph neural networks (Eurodyn) database

Database consisting of graphs generated using randomized layouts and PyWake simulations used in 'Multivariate prediction on wake-affected wind turbines using graph neural networks', contribution to Eurodyn 2023.

opencc-by-4.0Apr 2023View details →

Estimating Modal Mineralogy using Raman Spectroscopy: Multivariate Analysis Models and Raman Cross-Section Proxies - Chapter 4 Data

I provide supplementary data for Chapter 4 of my dissertation including electron microprobe analysis data as well as the Raman spectra and associated metadata of the mineral end-members, the mineral-mineral mixtures, and the diamond-mineral mixtures.

opencc-by-sa-4.0Apr 2023View details →

High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors

Data to reproduce figures in submitted manuscript "High-dimensional multivariate autoregressive model estimation of human electrophysiological data using fMRI priors" https://www.biorxiv.org/content/10.1101/2022.11.18.516669v1

opencc-by-4.0Jul 2023View details →

A multivariate physiological model of vagus nerve signalling during metabolic challenges in anaesthetised rats for diabetes treatment

Raw recordings that support the findings of the paper. In the intact recordings, there may be more than one recoridng corresponding to the same animal. For ligated recordings, odd numbers correspond to efferent and even numbers to afferent recordings.

Fig. 7 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 7. Multivariate data analysis (MVDA) of fresh and dried Peruvian ginger samples. A. Principal component analysis (PCA) showed clear separation of fresh and dried ginger samples. B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed clear separation of fresh and dried ginger samples. C. Splot of OPLS-DA. D. VIP scores based on the metabolite data from fresh and dry ginger extracts.

Fig. 9 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 9. [6]-Gingerol (A) and [6]-shogaol (B) contents in μg/g fresh weight (μg/ g FW) among the tested fresh ginger samples using different extraction solvents. Error bars indicate mean ± SE for five replicates. Black asterisks indicate significance difference from the Peruvian samples (*P <0.05, Student's t-test).

Fig. 5 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 5. The influence of geographical distribution of ginger on the gingerols and gingerol-related metabolites. The figure also describes the biosynthetic pathway of [6]-gingerol and hexahydrocurcumin and subsequent transformations. The opposed abundances of the precursors (i.e., phenylalanine and cinnamic acid) and end products (i.e., [6]-gingerol, hexahydrocurcumin, and gingerenone A and B) hypothesize that they have the same biosynthetic pathway.

Fig. 3 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 3. The use of retention time, accurate mass, and co-elution pattern for compound validation. A. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) of [6]-gingerol measured by UPLC/MS in positive and negative ionization modes. B. Different gingerols within the same class show a retention time pattern according to their chain length. Intra-class variability is shown by XIC in positive ionization mode for the 6, 8 and 10-gingerol. C. Scatter plot representation of different gingerols annotated from the tested samples. The m/z of the loss of water from the protonated adducts is given on the x-axis and the observed RT (min) is given in the y-axis. The plot illustrates how the correlation between elution (RT) and chain length in the annotated gingerols can be used for the prediction of other compounds within the same class.

Fig. 4 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 4. Multivariate data analysis (MVDA) of fresh Ginger samples collected from different localities. A. Principal component analysis (PCA) showed clear separation of geographically different fresh ginger samples. B. VIP scores showing the top 25 metabolites discriminating ginger samples C. Dendrogram of the investigated fresh ginger samples based on the metabolites obtained after MS data analysis.

Fig. 2 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 2. Schematic diagram showing the steps for confident compound identification. The feature with the retention time of 12.94 min and 295.191 m/z, representing 6-gingerol was selected. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) were measured by UPLC/MS in the positive ionization mode.

Fig. 1 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Fig. 1. Experimental design for metabolic profiling of fresh ginger rhizomes collected from different geographical sources and the effect of drying.

opencc-by-4.0Jul 2023View details →

High quality figures of "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain"

This repository provides the figures for the publication "Downscaling CORDEX through deep learning to daily 1 km multivariate ensemble in complex terrain" in their original resolution, ensuring clarity and high-quality visual representations for readers.

Fig. 6 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 6. Heatmap highlighting variation of volatile compounds across the 63 hop accessions from Northern France. This heatmap has been generated with normalized data for the top 51 molecules responsible for differences between the chemical profiles. Red and green colors indicate lowest and highest performance of the traits, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 5. Identification of population genetic structure of the 63 accessions of Humulus lupulus L. sampled in Northern France (Hauts-de-France region) using 11 microsatellites. A. Bar plot showing the distribution of individual assignations estimated for K = 2 and K = 6 clusters, from Bayesian inference cluster analysis performed with the 53 Humulus lupulus haplotypes sampled from the 14 locations (from A to K). Each vertical line represents an individual and the length of each colored line corresponds to the membership coefficient (scale at the left of the bar plot) for each cluster. Individuals are grouped according to their sampling locations. B. Frequencies of the 6 clusters (represented by colors) within each sampled location. Colors are same than on Fig. 5A. C. Principle Component Analysis (PCoA) based on genetic distances between each accession. Individuals were colored according to their sample site collection. D. Dendrogram underlying genetic clustering of the 63 hop accessions, including 10 commercial varieties (samples 1 to 10), 3 heirloom varieties (samples 11 to 13) and 50 wild sampled from 11 geographical locations (cf Table 1). 1: Nugget, 2: Strisselspalt, 3: Golding, 4: Challenger, 5: Brewers Gold, 6: Cascade, 7: Magnum, 8: Northern Brewer, 9: Target, 10: Fuggle, 11: Groene Bel, 12: Star, 13: Coigneau, Location A: 14 to 18; Location B: 19 to 23; Location C: 24 to 28; Location D: 29 to 32; Location E: 33 and 34; Location F: 34 and 35; Location G: 37 and 38; Location H: 39 to 42; Location I: 43 to 52; Location J: 53 to 58; Location K: 59 to 63. The tree was constructed using the unweighted neighbor-joining method based on genetic dissimilarity among the haplotypes according to microsatellite markers. Each branch corresponds to a hop genotype and the colors of branches indicate locations from which the genotypes were sampled. The color code is the same as the one on Fig. 5C.

Fig. 2 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 2. Analysis of volatile compounds in hop cones by GC-MS. A. Chemical structure of main volatile compounds found in hop cones. B. GC-MS total ion chromatogram of a hop cone sample (cv. Nugget). Compounds identified correspond to the following compounds: (1) β-myrcene; (2) β-caryophyllene; (3) linalool; (4) 2- undecanone; (5) copaene; (6) α-humulene; (7) γ-muurolene.

Fig. 4 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 4. Results of characterization of soil samples collected close to the root environment of the 63 hops studied. These 63 hops samples are represented by ten commercial varieties: Nugget comes from « comm_1 »; Strisselspalt comes from « comm_2 » and other commercial cultivars come from the same field « comm_3–10 »; three heirloom varieties coming from the same field identified as « old »; and fifty wild hops identified according to Table 1. The characterization of soil samples was based on pH and conductivity measurements, on organic matter content determined by loss of ignition as well as on dosage of the sodium, potassium, calcium and magnesium elements. A. Dendrogram including hierarchical cluster analysis (N = 8) among soil samples determined by soil characterization. B. Heatmap associated to the dendrogram. The 8 clusters of the hierarchical clustering were reported on the heatmap. C. Pictures of soil samples from locations B (Tourbi`ere de Vred, Vred) (1), D (Cap Blanc nez, Wissant) (2) and K (Dunes d'Ecault) (3).

Fig. 1. Main hop prenylated phenolic compounds A in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 1. Main hop prenylated phenolic compounds A. Chemical structure of major chalcones and acylphloroglucinols produced by hops and their molecular weight. B. Chromatogram of a crude hydro-ethanolic extract of hops (cultivar Nugget) at 330 nm. XN: xanthohumol, α1: co-humulone; α2: humulone; α3: ad-humulone; β1: colupulone; β2: lupulone; β3: ad-lupulone.

Fig. 3 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 3. Geographical repartition of the fifty accessions of wild hop (Humulus lupulus L.) collected on the 11 locations A to K in the North of France.

Fig. 8. Untargeted metabolomic analysis A in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 8. Untargeted metabolomic analysis A. Principle component analysis of the 63 chemotypes of hop studied. Each symbol represents a single plant from the different accessions. Commercial varieties (10 accessions), heirloom varieties (3 accessions), wild hops collected on different locations (50 accessions, Fig. 3). B. Principle component analysis of the chemical markers.