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260 results for “PCA”
PCA coordinates describing dorsal colour pattern variation in 723 Morpho butterflies
<p>Species interactions such as mimicry can promote trait convergence but disentangling this effect from those of shared ecology, evolutionary history and niche conservatism is often challenging. Here by focusing on wing color pattern variation within and between three butterfly species living in sympatry in a large proportion of their range, we tested the effect of species interactions on trait diversification. These butterflies display a conspicuous iridescent blue coloration on the dorsal side of their wings and a cryptic brownish colour on the ventral side. Combined with an erratic and fast flight, these color patterns increase the difficulty of capture by predators and contribute to the high escape abilities of these butterflies. We hypothesize that, beyond their direct contribution to predator escape, these wing patterns can be used as signals of escape abilities by predators, resulting in positive frequency-dependent selection favouring convergence in wing pattern in sympatry. To test this hypothesis, we quantified dorsal wing pattern variations of 723 butterflies from the three species sampled throughout their distribution, including sympatric and allopatric situations and compared the phenotypic distances between species, sex and localities. We detected a significant effect of localities on colour pattern, and higher inter-specific resemblance in sympatry as compared to allopatry, consistent with the hypothesis of local convergence of wing patterns. Our results provide support to the existence of escape mimicry in the wild and stress the importance of estimating trait variation within species to understand trait variation between species, and to a larger extent, trait diversification at the macro-evolutionary scale.</p>
Cortocircuitos: CIA, CIV y PCA
<p>Ponencia de Ecocardiografía de los cortocircuitos: CIA, CIV y PCA</p>
Dataset for "AACVD Synthesized Tungsten Oxide-NWs loaded with Osmium oxide as Gas Sensor Array: Enhancing Detection with PCA and ANNs"
<p>Dataset with the gas senisng mesurements for Osmium oxide decorated tunsten oxide gas sensors</p>
DCv2, PCA and k-means on ERA5 covering Europe (1964-2023)
<p>Contains:</p> <ul> <li>DCv2 cluster assignments, feature space embeddings of centroids and samples (12:00 UTC of ERA5) and their respective distances to the cluster centroids.</li> <li>DCv2 time series labels for `k=14`.</li> <li>PCA time series labels for `k=30`.</li> <li>GWL time series labels.</li> <li>PCA for six PCs and the respective k-means clustering result.</li> <li>Performance benchmarking results.</li> </ul>
Figure 3. PCA analysis. A. Fore wing. B. Hind wing. C in Geometric morphometric analysis of Eysarcoris guttiger, E. annamita and E. ventralis (Hemiptera: Pentatomidae)
Figure 3. PCA analysis. A. Fore wing. B. Hind wing. C. Pygophore.
Figure 8. PCA and MANOVA scatterplot representing S. i in Taxonomic status of Tamarinus imperator subgrisescens (Lönnberg, 1940) (Cebidae, Callitrichinae)
Figure 8. PCA and MANOVA scatterplot representing S. i. imperator (green) and S. i. subgrisescens from Peru (pink) and Brazilian Amazon (purple).
Epidural Analgesia (EDA) Versus Patient Controlled Analgesia (PCA) in Laparoscopic Colon Surgery
ClinicalTrials.gov study NCT00508300. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Multicenter, Randomized, Double-Blind, Placebo-Controlled Trial to Evaluate the Efficacy and Safety of the Sufentanil NanoTab PCA System/15 mcg (Zalviso™) for the Treatment of Post-Operative Pain in
ClinicalTrials.gov study NCT01539642. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Differences in Incidence of Common Side Effects Between Young Adults and Elderly Patients While Using IV-PCA
ClinicalTrials.gov study NCT02448862. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PET Imaging in MCI Following ADT for PCa
ClinicalTrials.gov study NCT02061345. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Efficacy and Safety Trial to Evaluate the Sufentanil NanoTab® PCA System/15 mcg (Zalviso™) for Post-Operative Pain in Patients After Knee or Hip Replacement Surgery
ClinicalTrials.gov study NCT01660763. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of Side Effects of Morphine and Hydromorphone Patient-Controlled Analgesia (PCA)
ClinicalTrials.gov study NCT00385541. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Multicenter, Randomized, Open-Label, Parallel-Group Trial to Compare the Efficacy and Safety of the Sufentanil NanoTab PCA System/15 mcg (Zalviso™)to Intravenous Patient-Controlled Analgesia With Mo
ClinicalTrials.gov study NCT01539538. IPD Sharing: Not stated. Countries: 1. Publications: 1.
PCA coordinates describing dorsal colour pattern variation in 723 Morpho butterflies
Open the record for dataset details and reuse information.
Supplementary material 1 from: Oumarou Ngoute C, Kekeunou S, Lecoq M, Nzoko Fiemapong AR, Um Nyobe PCA, Bilong Bilong CF (2020) Effect of anthropogenic pressure on grasshopper (Orthoptera: Acridomorpha) species diversity in three forests in southern Cameroon. Journal of Orthoptera Research 29(1): 25-34. https://doi.org/10.3897/jor.29.33373
: Explanation note: Effect of anthropogenic pressures on floristic composition from the forests of three localities of southern Cameroon.
How to perform PCA on single-cell RNA-Seq data in three simple steps
<p>Video on YouTube: <a href="https://www.youtube.com/watch?v=IOe0X-q7FtE">https://www.youtube.com/watch?v=IOe0X-q7FtE</a></p> <p>My Twitter: <a href="https://twitter.com/flo_compbio">https://twitter.com/flo_compbio</a></p> <p>Savannah Bertrand’s fundraiser: <a href="https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1">https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1</a></p> <p>-------------------<br> References:</p> <p>Batson, Joshua, Loïc Royer, and James Webber. “Molecular Cross-Validation for Single-Cell RNA-Seq.” BioRxiv, September 30, 2019, 786269. <a href="https://doi.org/10.1101/786269">https://doi.org/10.1101/786269</a>.</p> <p>Grün, Dominic, Lennart Kester, and Alexander van Oudenaarden. “Validation of Noise Models for Single-Cell Transcriptomics.” Nature Methods 11, no. 6 (June 2014): 637–40.<a href="https://doi.org/10.1038/nmeth.2930"> https://doi.org/10.1038/nmeth.2930</a>.</p> <p>Hafemeister, Christoph, and Rahul Satija. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20, no. 1 (23 2019): 296. <a href="https://doi.org/10.1186/s13059-019-1874-1">https://doi.org/10.1186/s13059-019-1874-1</a>.</p> <p>Hsu, Lauren L., and Aedin C. Culhane. “Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.” Frontiers in Oncology 10 (2020). <a href="https://doi.org/10.3389/fonc.2020.00973">https://doi.org/10.3389/fonc.2020.00973</a>.</p> <p>Sun, Shiquan, Jiaqiang Zhu, Ying Ma, and Xiang Zhou. “Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single-Cell RNA-Seq Analysis.” Genome Biology 20, no. 1 (10 2019): 269. <a href="https://doi.org/10.1186/s13059-019-1898-6">https://doi.org/10.1186/s13059-019-1898-6</a>.</p> <p>Townes, F. William, Stephanie C. Hicks, Martin J. Aryee, and Rafael A. Irizarry. “Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model.” Genome Biology 20, no. 1 (23 2019): 295. <a href="https://doi.org/10.1186/s13059-019-1861-6">https://doi.org/10.1186/s13059-019-1861-6</a>.</p> <p>Tsuyuzaki, Koki, Hiroyuki Sato, Kenta Sato, and Itoshi Nikaido. “Benchmarking Principal Component Analysis for Large-Scale Single-Cell RNA-Sequencing.” Genome Biology 21, no. 1 (20 2020): 9. <a href="https://doi.org/10.1186/s13059-019-1900-3">https://doi.org/10.1186/s13059-019-1900-3</a>.</p> <p>Wagner, Florian, Dalia Barkley, and Itai Yanai. “Accurate Denoising of Single-Cell RNA-Seq Data Using Unbiased Principal Component Analysis.” BioRxiv, June 17, 2019, 655365.<a href="https://doi.org/10.1101/655365"> https://doi.org/10.1101/655365</a>.</p> <p>Wagner, Florian. “Monet: An Open-Source Python Package for Analyzing and Integrating ScRNA-Seq Data Using PCA-Based Latent Spaces.” BioRxiv, 2020. <a href="https://doi.org/10.1101/2020.06.08.140673">https://doi.org/10.1101/2020.06.08.140673</a>.</p>
Figure 4. PCA axes 1–2 in Hybridization patterns in two contact zones of grass snakes reveal a new Central European snake species
Figure 4. PCA axes 1–2 for microsatellite data. Samples are coloured according to mitochondrial lineages (top) or STRUCTURE clusters (bottom). Admixed individuals were identified according to HYBRIDLAB results. PCAs for the yellow and red lineages correspond to the samples from Fig. 3c. Non-native samples were excluded. The oval outlines represent 95% confidential intervals. For helvetica and the eastern lineages (left) the x axis explains 16.6% and the y axis 4.5% of variation. For the eastern lineages (right) the x axis explains 3.8% and the y axis 2.9% of variation. Analyses along axes 1–3 produced nearly identical results (see Supplementary Fig. S4).
FIGURE 6. Size-corrected PCA for components 1 and 2 in A new species of Limatulichthys Isbrücker & Nijssen (Loricariidae, Loricariinae) from the western Guiana Shield
FIGURE 6. Size-corrected PCA for components 1 and 2, showing differentiation between morphological characters of the head region. On PC1: head width (HW), snout width at anterior margin of nares (SW), and width of head at upper lip (LW) loaded most positively. PC2: Opercular length (OL), snout length (SL), and distance from tip of snout to nasal loaded most positively (SN), while interorbital distance (ID) loaded most negatively. Symbols as in Figure 5; Limatulichthys cf. griseus "Beni" (red circles).
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.)
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.)
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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
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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
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