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260 results for “PCA”
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after salinity increase. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Database PCA: A middle Pleistocene Homo from Nesher Ramla, Israel
<p>It has long been believed that Neanderthals originated and flourished on the European continent. However, recent morphological and genetic studies have suggested that they may have received a genetic contribution from a yet unknown non-European group. Here we report on the recent discovery of archaic Homo fossils from the site of Nesher Ramla, Israel, which we dated to 140,000 to 120,000 years ago. Comprehensive qualitative and quantitative analyses of the parietal bones, mandible, and lower second molar revealed that this Homo group presents a distinctive combination of Neanderthal and archaic features. We suggest that these specimens represent the late survivors of a Levantine Middle Pleistocene paleodeme that was most likely involved in the evolution of the Middle Pleistocene Homo in Europe and East Asia.</p>
FIGURE 4. PCA with the first two principal components. A in Morphometric approach to supports distinction among three species of Paucibranchia Molina-Acevedo, 2018 (Annelida: Eunicidae)
FIGURE 4. PCA with the first two principal components. A) Biplot of the 23 morphological variables; B) distribution of all specimens of P. bellii (B), P. carrerai (C), and P. disjuncta (D).
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 2. Pairwise PCA plots for P. tricornutum endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 5. (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 1. Pairwise PCA plots for T. pseudonana endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after salinity increase. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 in Metabolic adaptation of diatoms to hypersalinity
Fig. 3. Pairwise PCA plots for S. marinoi endometabolome samples extracted 24 (A, B) and 96 (C, D) hours after the salinity stress treatment. PCA plots of all data analyzed together (E, F). Panels A, C, and E show results from GC-MS. Panels B, D, and F result from the analysis of LC-MS data; the number of replicates analyzed is 4–5 (see Experimental 5.13.). (For interpretation of the colours in this figure legend, the reader is referred to the Web version of this article.)
FIGURE 11. Multivariate PCA plot. Blue- L in Description of a new species of Liocheles Sundevall, 1833 (Hormuridae) from India
FIGURE 11. Multivariate PCA plot. Blue- L. nigripes, black- Liocheles schalleri sp. nov., male indicated by solid square and females by solid circles.
PCA of Brazilian Horse Populations Compared to World-Distributed Breeds
Open the record for dataset details and reuse information.
Machine learning-based pulse wave analysis for classification of circle of Willis topology: an in silico study with 30,618 virtual subjects (database: Missing PCoA and PCA P1)
<p>This repository contains the dataset for the Missing PCoA and PCA P1 described in the article with the same name. MATLAB and Python codes for post-processing the dataset and the code for training and testing all machine learning models using the open-source library TensorFlow 2.12, the Keras application programming interface, and the Scikit-learn Python package can be found in here (<a href="https://zenodo.org/records/12519322" target="_blank" rel="noopener">https://zenodo.org/records/12519322</a>).</p>
Summary of shortlisted genes by PCA
<p><strong>Supplementary Table 1. </strong>Enriched ontologies identified in mutants at different developmental stages on the basis of molecular function</p> <p><strong>Supplementary Table 2. </strong>Enriched ontologies identified in mutants at different developmental stages on the basis of biological function</p> <p><strong>Supplementary</strong> <strong>Figure 1.</strong> Comparison of targets of ABI 3 identified in shortlisted genes for <em>abi 3-5</em>, <em>fus 3-3</em>, <em>lec 1-1</em> and <em>abi 3-1</em> mutant lines</p> <p><strong>Additional Data 1</strong>. Functionally more important genes shortlisted by PCA, for <em>abi3-5</em>, <em>fus3-3</em>, <em>lec1-1</em> & <em>abi 3 -1 </em>at 15, 17 and 21 DAF.</p>
FIGURE 2. PCA diagrams for the first two factors, F1 and F2 in Common but new: Bartramia rosamrosiae, a "new" widespread species of apple mosses (Bartramiales, Bryophytina) from the Mediterranean and western North America
FIGURE 2. PCA diagrams for the first two factors, F1 and F2, with the variance explained by each. Italicized numbers correspond to the characters in Table 1. A: Bartramia (Anacolia) laevisphaera; M: Bartramia rosamrosiae sp. nov.; S: Bartramia stricta.
FIGURE 3. The PCA-scatterplot for the 165 in Primula subpyrenaica (Primulaceae) a new species from the Pyrenean range (south-western Europe)
FIGURE 3. The PCA-scatterplot for the 165 individuals of P. subpyrenaica, P. auricula and P. lutea studied.
Nonrandom missing data can bias PCA inference of population genetic structure
<p>Population genetic studies in non-model systems increasingly use next-generation sequencing to obtain more loci, but such methods also generate more missing data that may affect downstream analyses. Here we focus on the Principal Component Analysis (PCA) which has been widely used to explore and visualize population structure with mean-imputed missing data. We simulated data of different population models with various total missingness (1%, 10%, 20%) introduced either randomly or biased among individuals or populations. We found that individuals biased with missing data would be dragged away from their real population clusters to the origin of PCA plots, making them indistinguishable from true admixed individuals and potentially leading to misinterpreted population structure. We also generated empirical data of the big brown bat (<i>Eptesicus fuscus</i>) using restriction site-associated DNA sequencing (RADseq). We filtered three data sets with 19.12%, 9.87%, and 1.35% total missingness, all showing nonrandom missing data with biased individuals dragged towards the PCA origin, consistent with results from simulations. We highlight the importance of considering missing data effects on PCA in non-model systems where nonrandom missing data are common due to varying sample quality. To help detect missing data effects, we suggest to 1) plot PCA with a color gradient showing per sample missingness, 2) interpret samples close to the PCA origin with extra caution, 3) explore filtering parameters with and without the missingness-biased samples, and 4) use complementary analyses (e.g., model-based methods) to cross-validate PCA results and help interpret population structure.</p>
FIGURE 98. PCA Biplot. Principle Component 1 in Three new cricket species and a new subgenus of Endecous Saussure, 1878 (Grylloidea: Phalangopsidae) from caves in northeastern Brazil
FIGURE 98. PCA Biplot. Principle Component 1 (PC1); principle Component 2 (PC2); main variables: leg I length (Leg I), leg II length (Leg II), head width, pronotum width, number of teeth of the stridulatory file (Stridens), intraocular distance (Intraocular dist.), body length (Body), compound eye length (Comp. eye).
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations in Ecomorphological diversity in the Patagonian assemblage of bats from Argentina
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations
FIG. 4. PCA plot depicting the winter-type categories developed for winter environmental data for a in Hoplodactylus tohu Scarsbrook & Walton & Rawlence & Hitchmough 2023, n. sp.
FIG. 4. PCA plot depicting the winter-type categories developed for winter environmental data for a Massasauga population located in southern Ontario, Canada. We analyzed local annual winter (November–April) environmental data: total precipitation, rainfall, snow, and the number of days mean temperature is below zero (Environment and Climate Change Canada, 2016).
GAN, PCA, and Statistical Shape Models for the Creation of Synthetic Craniosynostosis Distance Maps
<p>This dataset is part of the publication "Classification of Craniosynostosis Trained Only On Synthetic Data Using GANs, PCA, and Statistical Shape Models".</p> <p><strong>dataset28.zip</strong> includes 2D distance maps constructed of surface scans of craniosynostosis patients: sagittal suture fusion (scaphocephaly), metopic suture fusion (trigonocephaly), coronal suture fusion (brachycephaly and anterior plagiocephaly), and a control model (normocephaly and positional plagiocephaly).<br> <br> <strong>synthetic_1000.zip</strong> contains are random 1000 samples per class created from each individual synthetic data source (GAN, PCA, statistical shape model).<br> <br> This repository contains only the images. To synthesize your own data, please use the github repository.</p>
Fig. 3. PCA scores scatter plots and HCA dendrograms for S in A chemometric assessment of essential oil variation of three Salvia species indigenous to South Africa
Fig. 3. PCA scores scatter plots and HCA dendrograms for S. africana-lutea (A and B), S. lanceolata (C and D) and S. chamelaeagnea (E and F) samples from various localities. The inserts are the PCA scores plots coloured according to the HCA dendrograms.
Results - Exploratory behavior and PCA
<p>These data shows the results obtained from the analysis of the exploratory behaviour variables (DO) and the principal components (PCA), for each of the participants (12 in total) in the study, in the four situations analysed (ISE=IWOM; ICE=IWM; PSE=PWOM; PCE=PWM).</p>
Influence of Physostigmine on Patient-Controlled Analgesia (PCA) in Postoperative Intensive Care Patients
ClinicalTrials.gov study NCT00608621. IPD Sharing: Not stated. Countries: 1. Publications: 5.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.