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27 results for “Phenotypic measurements”
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Phenotypic differences between interfertile Chlamydomonas species- measurements, Cellprofiler
<p>This repository contains 2D morphology measurements from timelapse microscopy data of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile <i>Chlamydomonas</i> species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Measurements collected with Cellprofiler of timelapse data of <i>C. reinhardtii </i>or C<i>. smithii </i>cells in agar microchamber wells are shared here.</p><h4>Reference</h4><p><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile <i>Chlamydomonas </i>species</a></p><h4>Notes</h4><p>Directory and subdirectories containing csv files of measurements of algal cells segmented from images.<br><br>Directory structure: experiments_csv/{experiment}/{video_length}/objects/{species}/{microchamber AKA "pool ID"}/measurements/measurementschlamy.csv</p><p>"Cr" indicates <i>Chlamydomonas reinhardtii</i></p><p>"Cs" indicates <i>Chlamydomonas smithii</i></p>
Phenotypic differences between interfertile Chlamydomonas species- focus-filtered timelapse data and measurements
<p>This repository contains focus-filtered timelapse microscopy data of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile <i>Chlamydomonas</i> species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Focus-filtered timelapse data of <i>C. reinhardtii </i>or C<i>. smithii </i>cells in agar microchamber wells are shared here. The code for focus-filtering and collection of measurements can be found in the <a href="https://github.com/Arcadia-Science/chlamy-comparison">associated Github repository</a>.</p><h4>Reference</h4><p><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile <i>Chlamydomonas </i>species</a></p><h4>Notes</h4><p>In addition to the raw data, the dataset includes sample images that are intermediates in the image processing pipeline, as well as 2D morphology measurements of the cells in a csv file.</p><p>"Cr" indicates <i>Chlamydomonas reinhardtii</i></p><p>"Cs" indicates <i>Chlamydomonas smithii</i></p><p>Frame rate: 20 frames per second (fps)</p><p>Pixel size: 0.6398 microns/pixel</p>
Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
<p>Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of 'hidden' shape nuances which are otherwise not detected.</p>
Phenotypic measurements of STERILE APETALA mutants in Mimulus verbenaceus
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Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
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Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus
<p>For sustainable biomass production of <em>Miscanthus × giganteus</em> (hereafter miscanthus), understanding the impact of stand age and nitrogen (N) fertilization on biomass yield is crucial. This study investigated the effects of varying N fertilization rates (0, 56, 112, and 168 kg N ha<sup>-1</sup>) on yield components (tiller height, density, and weight) and their correlations with end-of-season biomass yield in miscanthus. We also explored end-of-season biomass yield prediction using in-season traits (canopy height, leaf area index (LAI), and leaf chlorophyll content (LCC)). The study was conducted at two sites in Illinois: a previously unfertilized 10-year-old miscanthus research stand at Urbana and a 16-year-old commercial stand at Pesotum with a history of annual 56N application. Results from 2018-2021 in Urbana and 2020-2021 in Pesotum showed increased biomass yields with N fertilization, varying by rate, year, and location. Biomass yield in Pesotum peaked at 56N, while in Urbana, it increased significantly at 112 kg N ha<sup>-1</sup>. Biomass yield was strongly correlated with tiller height and weight measured at Urbana across N rates. Morphological traits measured every 2-3 weeks during the 2020 and 2021 growing seasons showed that canopy height was the strongest single predictor of miscanthus biomass yield, followed by LCC. Mid-August to September measurements of these traits were the best predictors of biomass yield. Multiple regressions involving the canopy height and LCC further improved yield predictions. We conclude that while N enhances biomass yields of aging miscanthus, the optimum rate depends on the site, environmental conditions, and management.</p>
Phenotype images of Gryllus personatus and five resulting morphological measurements
<p class="MsoNormal">Sexual size dimorphism (SSD) and sexual shape dimorphism (SShD) are of interest to evolutionary ecology, but the two phenomena can very easily be conflated by not taking a multivariate approach to measuring size. In our study we draw attention to this problem by measuring four body size dimensions (maxillae span, head width, pronotum length and mean hind femur length) in lab-reared individuals of the badlands cricket (Orthoptera, Gryllinae, <em>Gryllus personatus</em>) and conducting a variety of multivariate analyses to test whether there is SSD and/or SShD.</p> <p class="MsoNormal">We found that males had wider heads and maxillae than females, and females had longer pronota and hind femora than males. This difference in the direction of sexual dimorphism indicates SShD. However, multivariate methods failed to detect SSD, instead confirming that the sexes primarily differ in body shape. We suggest that orthopterists studying sexual dimorphism minimally measure head width, pronotum length and hind femur length as a standard that will allow a more repeatable and generalizable assessment of the prevalence and direction of both SSD and SShD.</p>
Data From: Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes
<p class="p1"><strong>Introduction: </strong>Light response curves are widely used to quantify phenotypic expression of photosynthesis by measuring a single sample and sequentially altering light intensity within a chamber (sequential method) or by measuring different samples that are each acclimated to a different light level (nonsequential method). Both methods are often conducted in controlled environments to achieve steady-state results, and neither method involves equilibrating the entire plant to the speci<span class="s1">fi</span>c light level.</p> <p class="p1"><strong>Methods: </strong>Here, we compare sequential and non-sequential methods in controlled (greenhouse), semi-controlled (plant grown in growth chamber and acclimated to <span class="s1">fi</span>eld conditions 2-3 days before measurements), and <span class="s1">fi</span>eld environments. We selected seven diverse rice genotypes (<span class="s1">fi</span>ve genotypes from the USDA rice minicore collection: 310588, 310723, 311644, 311677, 311795; and 2 additional genotypes: Nagina 22 and Zhe 733) to understand (1) the limitations of different methods, and (2) phenotypic plasticity of photosynthesis in rice grown under different environments.</p> <p class="p1"><strong>Results:</strong> Our results show that the non-sequential method was time-ef<span class="s1">fi</span>cient and captured more variability of <span class="s1">fi</span>eld conditions than the sequential method, but the model parameters were generally similar between the two methods except for the maximum photosynthesis rate (A<sub>max</sub>). A<span class="s2"><sub>max</sub> </span>was signi<span class="s1">fi</span>cantly lower across all genotypes under greenhouse conditions compared to the growth chamber and <span class="s1">fi</span>eld conditions consistent with prior work, but surprisingly the apparent quantum yield (α) and the mitochondrial respiration (R<sub><span class="s2">d</span></sub>) were generally not different among growing environments or measurement methods.</p> <p class="p1"><strong>Discussion: </strong>Our results suggest that <span class="s1">fi</span>eld conditions are best suited to quantify phenotypic differences across different genotypes, and the nonsequential method was better at capturing the variability in photosynthesis.</p>
Data From: Effects of measurement methods and growing conditions on phenotypic expression of photosynthesis in seven diverse rice genotypes
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Data from: On measurements of phenotypic parallel evolution
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Phenotype images of Gryllus personatus and five resulting morphological measurements
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Data from: Biomass yield, yield components and growing season phenotypic measurements of Miscanthus
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FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l in Phenotypic variation and vocal divergence reveals a species complex in White-eared Ground-sparrows (Cabanis) (Aves: Passerellidae)
FIGURE 4. Mean reflectance spectra for ten body regions measured from three Melozone leucotis subspecies, M. l. leucotis (solid lines, N = 13), M. l. nigrior (dotted lines, N = 13), and M. l. occipitalis (dashed lines, N = 8). The gray area around each line represents standard error of the mean calculated at every 1nm.
Thoroughbred horse inbreeding measures and racing phenotypes
<p>We quantified inbreeding based on runs of homozygosity (ROH) using 297K SNP genotypes from 6,128 horses born in Europe and Australia, of which 13.2% were unraced. </p>
Inbreeding measures (FROH) and racing phenotypes in North American thoroughbred horses
<p>We quantified inbreeding based on runs of homozygosity (FROH) using 333K SNP genotypes from 768 Thoroughbred horses born in North America to evaluate the effect of inbreeding on racing traits. Among North American horses, FROH was not associated (P = 0.518) with the probability of ever racing but was significantly associated with the number of race starts (P = 0.002). Among raced horses, those with a 10% higher <em>F</em><sub>ROH </sub>than the mean inbreeding coefficient were predicted to have 3.5 fewer race starts compared to horses with a mean inbreeding coefficient.</p>
Deep Phenotyping of Hearing Instability Disorders: Cohort Establishment, Biomarker Identification, Development of Novel Phenotyping Measures, and Discovery of Therapeutic Targets
ClinicalTrials.gov study NCT04806282. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Inbreeding measures (FROH) and racing phenotypes in North American thoroughbred horses
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Measuring phenotypes in fluctuating environments
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Thoroughbred horse inbreeding measures and racing phenotypes
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