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opennotspecifiedJan 2023View details →

Fig. 7 in Multivariate analysis of chemical and genetic diversity of wild Humulus lupulus L. (hop) collected in situ in northern France

Fig. 7. Results of the statistical treatment of data for the quantitation of xanthohumol, co-, n-, ad-humulone and co-, n-, ad-lupulone. This quantitation has been performed on the 63 crude hydro-ethanolic extracts of hop cone powder from Northern France, including 10 commercial varieties, 3 heirloom varieties and 50 wild hops (Fig. 1, Table 2). A. PCA biplot of quantitation data with score plot and loading plot of variables. Individuals were colored by collection site for a given observation. Variable contribution to component was represented by arrows length. B. Dendrogram of the hierarchical cluster analysis among the 63 hops based on the quantitation similarity (Ward's method, distance scale) (N = 3).

Fig. 5 in Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Fig. 5. Multivariate statistical analysis results of 9 different types of Panax notoginseng samples. (a) Score scatter plot of 2D PCA model, (b) Score scatter plot of 3D PCA model, (c) Score scatter plot of OPLS-DA model, (d) Bar plot with OPLS-DA model of VIP.

Fig. 4 in Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Fig. 4. Tandem MS analysis of characteristic ions in Panax notoginseng samples. (a) MS3 spectrum of m/z 1143 → 1107→, (b) MS3 spectrum of m/z 1193 → 1149→, (c) MS3 spectrum of m/z 885 → 841, (d) MS3 spectrum of m/z 1031 → 987, (e) MS3 spectrum of m/z 1245 → 1209, (f) MS3 spectrum of m/z 968 → 931.

Fig. 3 in Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Fig. 3. Mass spectra of different types of Panax notoginseng samples analyzed by iEESI-MS. (a) Mass spectrum of Panax notoginseng from Kunming, (b) Mass spectrum of Panax notoginseng from Qujing, (c) Mass spectrum of Panax notoginseng from Hongjiaozhou, (d) Mass spectrum of Panax notoginseng from Wenshan (1 year), (e) Mass spectrum of Panax notoginseng from Wenshan (2 year), (f) Mass spectrum of Panax notoginseng from Wenshan (3 year), (g) Mass spectrum of Panax notoginseng from Hongjiaozhou (black soil), (h) Mass spectrum of Panax notoginseng from Hongjiaozhou (white soil), (i) Mass spectrum of Panax notoginseng from Hongjiaozhou (red soil). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Fig. 2. Chemical structures of eight ginsenosides used in this study as reference standards. G, ginsenoside; NG, notoginsenoside; glc, glucoside; rha, rhamnoside; xyl, xyloside; ara, arabinoside.

Fig. 1 in Molecular differentiation of Panax notoginseng grown under different conditions by internal extractive electrospray ionization mass spectrometry and multivariate analysis

Fig. 1. Schematic illustration of iEESI-MS for direct analysis of Panax notoginseng samples. (a) Analytical procedure of Panax notoginseng analysis by iEESI-MS. Approximately 0.1 mg of Panax notoginseng tissue was loaded into the sample chamber by punching without sample pretreatment, (b) Disposable iEESI device and its components, (c) Photo of iEESI-MS interface.

Fig. 1 in Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 1. Samplings for the study series showing subtribes of Eupatorieae with relative sizes (in number of species) both inside and outside the Rio Grande do Sul State territory. Fleishmanninae, Hebeclininae, Hofmeisterinae, Liatrinae, Neomirandeinae, Oaxacaninae, and Trichocoroniinae have no representatives in the area. For other subtribes, the number of species in the area and its proportion to the total number in the subtribe is represented in light green. (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 Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 5. Compositional PCA of the chemical composition of VO of species from the subtribe Disynaphiinae (according to Rivera et al., 2016) sampled in Rio Grande do Sul, Southern Brazil. S. itatiayensis was transferred to an uncertain place, Grazielia was transferred to Neocabreria and Campovassouria was merged into Disynaphia. Samples were colored according to the newly proposed genera. Ten variables contributing the most to variability are depicted. Upper Left: PC1 and PC2; Upper Right: PC2 and PC3. The exclusion of S. itatiayensis from Symphyopappus is well-supported by the chemical data. Samples from R. tremula (green spheres in top-left (bottom panel), have a very similar chemistry to S. itatiayensis (yellow cube). Two samples of R. crenulata from the same area previously published by our group were included in the analysis (de Souza et al., 2007). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 4. Compositional robust PCA of the chemical composition of VO of species from the subtribe Disynaphiinae (according to Rivera et al., 2016) sampled in Rio Grande do Sul, Southern Brazil. S. itatiayensis was transferred to an uncertain place while Grazielia was transferred to Neocabreria. Samples are identified by species and colored according to the newly proposed genera. PC1 and PC2. Two samples of R. crenulata from the same area previously published by our group were included in the analysis (de Souza et al., 2007).

Fig. 3 in Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 3. Compositional PCA of the chemical composition of VO of species from the subtribe Disynaphiinae (King and Robinson, 1987) sampled in Rio Grande do Sul, Southern Brazil. Samples are colored according to genera. Ten variables contributing the most to variability are depicted. Top: PC2 and PC3; Bottom: PC2, PC3, and PC4 with Mahalanobis distances (Aconthostyles buniifolius was excluded for clarity). Two samples of R. crenulata from the same area previously published by our group were included in the analysis (de Souza et al., 2007).

Fig. 2 in Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 2. The number of genera and species recognized in the subtribe Disynaphiinae is given in white, while the number of genera and species reported in the area as well as the number of sampled species is given in black. The number of species in the genus, species in the sampled area, and sampled species are given in gray alongside each genus.

Fig. 6 in Volatile constituents of Eupatorieae (Asteraceae). Compositional multivariate analysis of volatile oils from Southern Brazilian species in the subtribe Disynaphiinae

Fig. 6. Compositional robust PCA of the chemical composition of VO of species from the subtribe Disynaphiinae (according to Rivera et al., 2016) sampled in Rio Grande do Sul, Southern Brazil. Samples are identified at the species level and colored according to the newly proposed genera. Neocabreria serrulata (Critoniinae) and Urolepis hecatantha (Gyptidinae) are included after transference from their respective subtribes. Top panel: Grazielia was merged into Neocabreria. Bottom panel: Neocabreria and Grazielia were merged into Symphyopappus (excluding S. itatiayensis). Two samples of R. crenulata from the same area previously published by our group were included in the analysis (de Souza et al., 2007).

opencc-by-4.0Sep 2023View details →

Extreme Temeperature Datasets Utilized in the Multivariate Logarithmic Modeling of Grain Production in the Yangtze River Basin

Data: gridded to a spatial resolution of 0.5deg *0.5deg, coldwave_frequencies.nc and hwcase19802022.nc contain historical annual terrestral coldwave/heatwave respectively in the timeframe 1980~2022 derived from CPC global unified temperature dataset provided by NOAA PSL. ssp_ete.zip is a compiled set of future coldwave and heatwave frequencies derived from CMIP6 datasets MIROC6 and BCC_CSM2_MR. Said zip archive is utilized for near-future per-hectare grain yield prediction in the original paper. Methods: the 90th percentile of the daily Tmax 1981-2010 climatology distribution defines the minimum heatwave-qualifying temperature of a specific location, and a day will be labeled as 'heatwave day' when daily Tmax exceeds the threshold over at least three consecutive days. Coldwave is determined following similar principles: an event of coldwave contains more than 3 consecutive cold days with Tmin lower than the 10th percentile of the daily Tmin 1981-2010 climatology distribution. cwave.py is an implementation of this method in the determination of heatwaves and coldwaves from SSP experiment datasets.

opencc-by-4.0Sep 2023View details →

Dataset for An efficient multivariate deep learning model for monitoring mooring line tension of floating wind turbine

Reference data needed for mooring line tensions prediction of a 15 MW wind turbine. This dataset contains OpenFAST outputfiles for different design load cases used in the paper. These data files are designed to be used together with the python code, which is available publicly on https://github.com/ramisetti/ 3SDLMooringPrediction

opencc-by-4.0Oct 2023View details →

Association between plasma lipids and ischemic stroke: a multivariable and drug-target Mendelian randomization study

The sensitivity analysis results using the leave-one-out approach revealed that no individual SNP substantially drove the association between plasma lipids and each disease outcome (Figure S1-S4).   The STROBE-MR checklist is also attached.

opencc-by-4.0Sep 2023View details →

Multivariate GWAS of Alzheimer's disease CSF biomarker profiles implies GRIN2D in synaptic functioning: Summary statistics

Summary statistics for a genome-wide association study (GWAS) of Alzheimer's disease CSF biomarkers principal components (PCs). This dataset accompanies the publication "Multivariate GWAS of Alzheimer’s disease CSF biomarker profiles implies GRIN2D in synaptic functioning". The article is currently in press at Genome Medicine. A link will be provided upon publication, but see the medRxiv preprint in the meantime: Neumann, A., Ohlei, O., Küçükali, F., Bos, I. J., Vos, S., Prokopenko, D., ... & Kristel Sleegers & Lars Bertram. (2022). Multivariate GWAS of Alzheimer’s disease CSF biomarker profiles implies GRIN2D in synaptic functioning. medRxiv, 2022-08. https://doi.org/10.1101/2022.08.02.22278185 A GWAS was performed for five different PC biomarkers. PC1: Tau pathology/degeneration PC2: Aβ Pathology PC3: Injury/inflammation PC4: Non-AD inflammation PC5: Non-AD synaptic functioning Each GWAS was performed in either males and females ("all"), in females only ("female"), or in males only ("male"). In addition, we also ran an interaction model with sex as moderator ("interaction"). Each file includes output of the meta-analysis between the EMIF-AD study and ADNI. The files contain following columns: CHROM: Chromosome POS: Position according to Build 37 ID: Chromosome:Position A1: Effect allele A2: Other allele BETA: Effect of one copy of the effect allele on biomarker PCs in SD. In case of sex interaction, the effect specific to females. SE: Standard Error P: p-value D: Direction in ADNI and EMIF-AD respectively Het: Heterogeneity statistics (I2, and corresponding χ2, degrees of freedom and p-value)

controlledIPD-YESFeb 2026View details →

Evaluating Multivariate MRI Maps of Body Awareness

ClinicalTrials.gov study NCT03344081. IPD Sharing: YES. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Multivariable Artificial Pancreas: Detecting and Mitigating Unannounced Physical Activity and Acute Psychological Stress

ClinicalTrials.gov study NCT05145374. IPD Sharing: NO. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →

Development of a Multivariable Prognostic PREdiction Model for 1-year Risk of FALLing in Community-dwelling Older Adults in a Non-clinical Setting

ClinicalTrials.gov study NCT03608709. IPD Sharing: NO. Countries: 1. Publications: 1.