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6,234 results for “phenotypes”
Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering (data)
<p>MIBI-TOF data for lymph node dataset reported in Liu et al., Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering</p> <p>1. mibi_single_channel_tifs.zip: Single-channel MIBI-TOF images</p> <p>Folders are labeled according to the field-of-view (FOV) number. Each folder contains single-channel TIFFs for each marker in the panel. Images are 1024x1024 pixels, 500 um. See paper for details.</p> <p>2. segmentation.zip: Segmentation output of MIBI-TOF images</p> <p>Cell segmentation was performed using Mesmer (Greenwald NF, Nature Biotechnology 2021). Output of Mesmer that delineates the single cells in each of the images is included.</p> <p>3. source_data.zip: Source data files for figures</p> <ul> <li>pixel_ccs_allpreprocessing.csv: Cluster consistency score (CCS) for all pixels using all preprocessing steps, related to Fig. 2d-f, Supp. Fig. 4,5,9,10</li> <li>pixel_ccs_nopixelnorm.csv: CCS for all pixels where pixel normalization was left out, related to Fig. 2f, Supp. Fig. 6</li> <li>pixel_ccs_nochannelnorm.csv: CCS for all pixels where channel normalization was left out, related to Fig. 2f, Supp. Fig. 8</li> <li>pixel_ccs_passes1.csv: CCS for all pixels where 1 pass was used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_passes100.csv: CCS for all pixels where 100 passes were used for SOM training, related to Supp. Fig. 10g</li> <li>pixel_ccs_sigma0.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma1.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma3.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing, related to Supp. Fig. 5d</li> <li>pixel_ccs_sigma0_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 0 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma1_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 1 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma2_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 2 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_sigma3_reps100.csv: CCS for all pixels where a Gaussian blur sigma of 3 was used for preprocessing and 100 replicates were used for CCS calculation, related to Supp. Fig. 5e</li> <li>pixel_ccs_nodes15.csv: CCS for all pixels where 15 nodes were used for SOM training, related to Supp. Fig. 9e</li> <li>pixel_ccs_threshold80.csv: CCS for all pixels where a threshold of 80% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_ccs_threshold98.csv: CCS for all pixels where a threshold of 98% was used for CCS calculation, related to Supp. Fig. 4b</li> <li>pixel_info_comparison_table.csv: Number of pixels that were assigned to a cluster outside of cell segmentation masks, related to Fig. 3d</li> <li>single_cell_pixel_composition_table.csv: Pixel composition information for each single cell, related to Fig. 5, Supp. Fig 16</li> <li>single_cell_integrated_expression_table.csv: Integrated expression per cell, output by Mesmer, related to Fig. 5, Supp. Fig. 16</li> <li>cell_silhouette_scores.csv: Silhouette scores for comparing integrated expression and pixel composition, related to Fig. 5d</li> <li>cell_silhouette_scores_undefinedremoved.csv: Silhouette scores for comparing integrated expression and pixel composition where undefined cells were removed, related to Fig. 16g</li> <li>cell_silhouette_scores_preprocessed.csv: Silhouette scores for comparing integrated expression and pixel composition where pixels were preprocessed before integrating expression, related to Fig. 17d</li> <li>cell_silhouette_scores_ilastik_cellprofiler.csv: Silhouette scores for comparing integrated expression and pixel composition where segmentation masks were obtained using Ilastik/CellProfiler, related to Fig. 18b</li> <li>cell_ccs_pixel_composition.csv: CCS for all cells using pixel composition for clustering, related to Supp. Fig. 16e, 17c</li> <li>cell_ccs_integrated_expression.csv: CCS for all cells using integrated expression for clustering, related to Supp. Fig 16e-f</li> <li>cell_ccs_integrated_expression_preprocessed.csv: CCS for all cells using integrated expression for clustering where data was preprocessed before integrating, related to Supp. Fig 17c</li> <li>cytof_ccs.csv: CCS of the CyTOF dataset used as a benchmark, related to Supp. Fig. 4c,d</li> <li>scrnaseq_ccs.csv: CCS of the scRNA-seq dataset used as a benchmark, related to Supp. Fig. 4c,e</li> <li>pixel_phenotype_maps: TIFFs where pixel value corresponds to pixel cluster number as reported in the paper</li> <li>cell_phenotype_maps: TIFFs where pixel value corresponds to cell cluster number as reported in the paper</li> <li>runtime_analysis_pixel.csv: Runtime analysis of pixel clustering in Pixie, related to Supp. Fig. 22a</li> <li>runtime_analysis_cell.csv: Runtime analysis of cell clustering in Pixie, related to Supp. Fig. 22b</li> <li>runtime_clustering_algorithm.csv: Runtime analysis of different clustering algorithms, related to Supp. Fig. 22c</li> </ul>
Human Phenotype Ontology: Standardized Framework Cataloging Comprehensive Phenotypic Information
<p><strong>ABSTRACT:</strong></p> <p>The Human Phenotype Ontology (HPO) is a standardized and comprehensive framework that catalogs and organizes phenotypic information related to human diseases and genetic disorders. It provides a structured vocabulary of terms to describe observable traits and abnormalities associated with various medical conditions. This dataset showcases the Human Phenotype Ontology (HPO) by including HPO IDs, names, descriptions, and additional references. The HPO IDs serve as unique identifiers for specific phenotypes. The names provide concise labels for the phenotypic traits, while the descriptions offer detailed explanations of their characteristics. In addition to the essential information, the dataset includes references that further describe and support the understanding of diverse phenotypes. These references can be used to explore the scientific literature, studies, or clinical resources associated with each phenotype. By combining HPO IDs, names, descriptions, and references, the dataset offers a comprehensive overview of different phenotypes, facilitating research, diagnostics, and the exploration of genotype-phenotype relationships.</p> <p><strong>Instructions</strong>:</p> <p>The data was downloaded as an .obo file then converted to a .csv file. Unrelated columns were removed and a references column was added. </p> <p><strong>Acknowledgements</strong>:</p> <p><a>Sebastian Köhler</a>, <a>Michael Gargano</a>, <a>Nicolas Matentzoglu</a>, <a>Leigh C Carmody</a>, <a>David Lewis-Smith</a>, <a>Nicole A Vasilevsky</a>, <a>Daniel Danis</a>, <a>Ganna Balagura</a>, <a>Gareth Baynam</a>, <a>Amy M Brower</a>, </p> <p><a>Tiffany J Callahan</a>, <a>Christopher G Chute</a>, <a>Johanna L Est</a>, <a>Peter D Galer</a>, <a>Shiva Ganesan</a>, <a>Matthias Griese</a>, <a>Matthias Haimel</a>, <a>Julia Pazmandi</a>, <a>Marc Hanauer</a>, <a>Nomi L Harris</a>, <a>Michael J Hartnett</a>, <a>Maximilian Hastreiter</a>, <a>Fabian Hauck</a>, <a>Yongqun He</a>, <a>Tim Jeske</a>, <a>Hugh Kearney</a>, <a>Gerhard Kindle</a>, <a>Christoph Klein</a>, <a>Katrin Knoflach</a>, <a>Roland Krause</a>, <a>David Lagorce</a>, <a>Julie A McMurry</a>, <a>Jillian A Miller</a>, <a>Monica C Munoz-Torres</a>, <a>Rebecca L Peters</a>, <a>Christina K Rapp</a>, <a>Ana M Rath</a>, <a>Shahmir A Rind</a>, <a>Avi Z Rosenberg</a>, <a>Michael M Segal</a>, <a>Markus G Seidel</a>, <a>Damian Smedley</a>, <a>Tomer Talmy</a>, <a>Yarlalu Thomas</a>, <a>Samuel A Wiafe</a>, <a>Julie Xian</a>, <a>Zafer Yüksel</a>, <a>Ingo Helbig</a>, <a>Christopher J Mungall</a>, <a>Melissa A Haendel</a>, <a>Peter N Robinson</a></p> <p><em>Nucleic Acids Research</em>, Volume 49, Issue D1, 8 January 2021, Pages D1207–D1217, <a href="https://doi.org/10.1093/nar/gkaa1043">https://doi.org/10.1093/nar/gkaa1043</a></p> <p><strong>Human Phenotype Ontology downloads</strong> <strong>page:</strong> https://hpo.jax.org/app/data/ontology</p> <p><strong>U-BRITE last update: </strong>7/6/23</p> <p> </p>
Supplementary Material for "Automated detection of an insect-infested keystone vegetation phenotype using airborne LiDAR"
<p>Ecologists, foresters and conservation practitioners need "biodiversity scanners" to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using airborne LiDAR remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these "extended phenotypes" could serve as environmental landmarks of insect presence in LiDAR data. To test the feasibility of this approach, we studied the symbiotic ants that alter canopy shapes of whistling thorn acacia, a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol of LiDAR data collection, training data preparation (including a customizable tree-segmentation algorithm) and convolutional neural network-based classification for the detection of ant-infested, within-species acacia tree phenotypic variations. Surveying ant occupancy of 402 hectares of 9,680 acacia trees took 1,000 work hours, while surveyed patterns of ant distribution was replicated by trained classifier based on an hour-long airborne LiDAR collection. We suggest that large scale surveys of insect occupancy (or insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.</p>
Data from: Phenotypic rate and state are decoupled in response to river-to-lake transitions in cichlid fishes
<p>Geographic access to isolated ecosystems is an important catalyst of adaptive radiation. Cichlid fishes repeatedly colonized rift, crater, and volcanic lakes from surrounding rivers. We test the "lake effect" on the phenotypic rate and state across 253 cichlid species. The rate of evolution was consistently higher (~10-fold) in lakes, and consistent across different dimensions of the phenotype. Rate shifts tended to occur coincident with or immediately following river-to-lake transitions, generally resulting in 2- to 5-fold faster rates than in the founding riverine lineage. By contrast, river- and lake-dwelling cichlids exhibit considerable overlap in phenotypes, generally with less disparity in lakes, but often different evolutionary optima. Taken together, these results suggest that lake radiations rapidly expand into niches largely already represented by ancestral riverine lineages, albeit in different frequencies. Lakes may provide ecological opportunity via ecological release (e.g., from predators/competitors), but need not be coupled with access to novel ecological niches.</p>
Fig. 3 in An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits
Fig. 3. Biplot from Principal Component Analysis for the phytochemical components (flavonols and crocins) for fifteen wild Crocus samples and saffron. All components were used for the analysis, but only the ones with the longest vectors were represented in the graph. See Table 1 for the coding of populations (S = saffron) and Tables 4 and 5 for the coding of phytochemicals.
Fig. 4 in An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits
Fig. 4. Wild Crocus taxa collected in Southern Italy: Crocus biflorus; C. longiflorus; C. siculus; C. thomasii; C. neapolitanus.
Fig. 1 in An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits
Fig. 1. Biplot from Principal Component Analysis for the floral traits in the sixteen Crocus populations. Lops and Lips: outer and inner perigone segments; Lsb: length of style-branches; Lpt: Length of perigone tube; Lanth: length of anther of the 16 Crocus samples. See Table 1 for the coding of populations.
Fig. 2 in An integrated approach for the characterization of wild Crocus species adopting phenotypical and phytochemical traits
Fig. 2. General molecular structures of flavonoids (flavonols) and apocarotenoids (crocins) characterised in the stigmas of wild Crocus samples and saffron.
Fig. 1 in Comprehensive evaluation of Bletilla striata and its substitutes by combining phenotypic characteristic, chemical composition, and anti-melanogenic activity
Fig. 1. Plant morphological characteristics of B. striata (A1 aerial part, A2 pseudobulb), B. ochracea (B1 aerial part, B2 pseudobulb) and B. formosana (C1 aerial part, C2 pseudobulb).
Fig. 3 in Comprehensive evaluation of Bletilla striata and its substitutes by combining phenotypic characteristic, chemical composition, and anti-melanogenic activity
Fig. 3. UPLC chromatographic fingerprints of 27 Bletillae Rhizoma samples (S1–S27) and the reference fingerprint (R). The peaks marked with 1–19 represent the 19 common peaks.
Fig. 2 in Comprehensive evaluation of Bletilla striata and its substitutes by combining phenotypic characteristic, chemical composition, and anti-melanogenic activity
Fig. 2. Microscopic characteristics of B. striata (A), B. ochracea (B), and B. formosana (C). (1.Epidermal cell 2. Raphid of calcium oxalate 3. Mucilage cell 4. Vessel 5. Fiber 6. Starch granule).
Fig. 9 in Comprehensive evaluation of Bletilla striata and its substitutes by combining phenotypic characteristic, chemical composition, and anti-melanogenic activity
Fig. 9. The docking complex of two compounds with tyrosinase and adenylate cyclase. (A) Coelonin in active pocket site of tyrosinase. (B) Batatasin III in an active pocket site of adenylate cyclase. (C) Coelonin formed 2 hydrogen bonds with the amino acid residues of tyrosinase. (D) Batatasin III formed 2 hydrogen bonds with the amino acid residues of adenylate cyclase.
Fig. 8 in Comprehensive evaluation of Bletilla striata and its substitutes by combining phenotypic characteristic, chemical composition, and anti-melanogenic activity
Fig. 8. Total optical density of melanin(A) and anti-melanogenic effect(B) comparison of Control, Winona, B. striata (SR), B. formosana (FS), and B. ochracea (OS). Values given are the means ± SD (n = 30). *P <0.05, ***P <0.001 versus control group.
FIGURE 5. Tillandsia alcatrazensis phenotypical variation. A–C in Tillandsia alcatrazensis (Bromeliaceae), a new endemic species from Alcatrazes Island in southeastern Brazil
FIGURE 5. Tillandsia alcatrazensis phenotypical variation. A–C. Front view of the inflorescences. D–F. Lateral view of the inflorescences. G–I. Habit. Bars: A–F = 1 cm; G–I = 2 cm. Photos by G.P. Sabino based on G.P. Sabino et al. 772 (A,D,G), GPS et al. 773 (B,E,H) and GPS et al. 774 (C,F,I).
Data for: Genotypes selected for early and late avian lay date differ in their phenotype, but not fitness, in the wild
<p><span>Global warming has shifted phenological traits in many species, but whether species are able to track further increasing temperatures depends on the fitness consequences of additional shifts in phenological traits. To test this, we conducted a genomic selection experiment on wild great tits from the Hoge Veluwe study population (The Netherlands) to obtain great tits (<em>Parus</em> <em>major</em>) with genotypes for extremely early and late egg lay dates and measured phenology and fitness of selected great tit females under wild conditions.</span></p> <p><span>Females with early genotypes advanced lay dates relative to females with late genotypes, but not relative to non-selected females. Females with early and late genotypes did not differ in the number of fledglings produced, in line with the weak effect of lay date on the number of fledglings produced by non-selected females in the years of the experiment. Our study is the first application of genomic selection in the wild and led to an asymmetric phenotypic response that indicates the presence of constraints toward early, but not late, lay dates.</span></p>
Assessment of four in vitro phenotypic biofilm detection methods in relation to antimicrobial resistance in aerobic clinical bacterial isolates
<p><strong>Introduction</strong>: The lack of standardized methods for detecting biofilms continues to pose a challenge to microbiological diagnostics since biofilm-mediated infections induce persistent and recurrent infections in humans that often defy treatment with common antibiotics. This study aimed to evaluate diagnostic parameters of four in vitro phenotypic biofilm detection assays in relation to antimicrobial resistance in aerobic clinical bacterial isolates.</p> <p><strong>Methods</strong>: In this cross-sectional study, bacterial strains from clinical samples were isolated and identified following the standard microbiological guidelines. The antibiotic resistance profile was assessed through the Kirby-Bauer disc diffusion method. Biofilm formation was detected by gold standard tissue culture plate method (TCPM), tube method (TM), Congo red agar (CRA), and modified Congo red agar (MCRA). Statistical analyses were performed using SPSS version 17.0, with a significant association considered at p<0.05.</p> <p><strong>Result</strong>: Among the total isolates (n=226), TCPM detected 140 (61.95%) biofilm producers, with CoNS (9/9) (p<0.001) as the predominant biofilm former. When compared to TCPM, TM (n=119) (p<0.001) showed 90.8% sensitivity and 70.1% specificity, CRA (n=88) (p=0.123) showed 68.2% sensitivity and 42% specificity, and MCRA (n=86) (p=0.442) showed 65.1% sensitivity and 40% specificity. Juxtaposed to CRA, colonies formed on MCRA developed more intense black pigmentation from 24 to 96 hours. There were 77 multi-drug-resistant (MDR)-biofilm formers and 39 extensively drug-resistant (XDR)-biofilm formers, with 100% resistance to ampicillin and ceftazidime, respectively.</p> <p><strong>Conclusion</strong>: It is suggested that TM be used for biofilm detection. Unlike MCRA, black pigmentation in colonies formed on CRA declined with time. MDR- and XDR-biofilm formers were frequent among the clinical isolates.</p>
Image datasets used in the paper "Revealing invisible cell phenotypes with conditional generative modeling"
<p>- BBBC021_selection_128 is a selection of the BBBC021 image dataset from the Broad Bioimage Benchmarck Collection from the Broad Institute</p> <p>- golgi_256_subset is a subset (one plate) of the Golgi Dataset we used (which is about 3 times larger). It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- translocation_256 is the translocation Dataset we used. It was generated by the Biophenics platform in Institut Curie, Paris, France</p> <p>- LRKK2_256 is the Parkinson LRKK2 mutation dataset we used. It was generated by Ksilink, Strasbourg, France</p> <p>- smala_256 is the Malaria dataset we used. It was generated by IRD, Paris, France and acquired by the Histopathology Platform at Institut Pasteur in Paris, France. </p>
Fig. 4 in Comparing essential oils from Australia's 'Victorian Christmas Bush' (Prostanthera lasianthos Labill., Lamiaceae) to closely allied new species: Phenotypic plasticity and taxonomic variability
Fig. 4. The PCA scores plot with individual clusters of chemical profiles highlighted. Clustering shows consistent patterns against taxa or chemotype: LL = P. lasianthos subsp. lasianthos; LS = P. lasianthos subsp. subcoriaceae; Eu = P. eungella; BM = P. sp. Bald Mountain; MK = P. sp. Mount Kaputar; PL= P. sp. Point Lookout; SG = P. sp. Schofields Gap; WG = P. sp. Wollomombi Gorge.
Fig. 3 in Comparing essential oils from Australia's 'Victorian Christmas Bush' (Prostanthera lasianthos Labill., Lamiaceae) to closely allied new species: Phenotypic plasticity and taxonomic variability
Fig. 3. PCA Analysis of essential oils from taxa currently circumscribed as P. lasianthos or P. eungella. LL = P. lasianthos subsp. lasianthos; LS = P. lasianthos subsp. subcoriaceae; Eu = P. eungella; BM = P. sp. Bald Mountain; MK = P. sp. Mount Kaputar; PL= P. sp. Point Lookout; SG = P. sp. Schofields Gap; WG = P. sp. Wollomombi Gorge. A) The PCA scores plot, conveying the pronounced grouping of essential oil profiles according to the presence and absence (or trace amount) of 1,8-cineole. B) The loading plot, showing principle components that influenced chemometric clusters. See Fig. 4 for a clearer clustering pattern of individual taxa.
Fig. 2 in Comparing essential oils from Australia's 'Victorian Christmas Bush' (Prostanthera lasianthos Labill., Lamiaceae) to closely allied new species: Phenotypic plasticity and taxonomic variability
Fig. 2. Row fusion dendrogram -: LL = P. lasianthos subsp. lasianthos; LS = P. lasianthos subsp. subcoriaceae; Eu = P. eungella; BM = P. sp. Bald Mountain; MK = P. sp. Mount Kaputar; PL= P. sp. Point Lookout; SG = P. sp. Schofields Gap; WG = P. sp. Wollomombi Gorge.
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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)
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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.