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5,371 results for “Flow”
Tracing evolutionary trajectories in the presence of gene flow in South American temperate lizards (Squamata: Liolaemus kingii group)
<p>Evolutionary processes behind lineage divergence often involve multidimensional differentiation. However, in the context of recent divergences, the signals exhibited by each dimension may not converge. In such scenarios, incomplete lineage sorting, gene flow, and scarce phenotypic differentiation are pervasive. Here, we integrated genomic (RAD loci of 90 individuals), phenotypic (linear and geometric traits of 823 and 411 individuals, respectively), spatial, and climatic data to reconstruct the evolutionary history of a speciation continuum of liolaemid lizards (<em>Liolaemus kingii</em> group). Specifically, we (i) inferred the population structure of the group and contrasted it with the phenotypic variability; (ii) assessed the role of post-divergence gene flow in shaping phylogeographic and phenotypic patterns; and (iii) explored eco-geographic drivers of diversification across time and space. We inferred eight genomic clusters exhibiting leaky genetic borders coincident with geographic transitions. We also found evidence of post-divergence gene flow resulting in transgressive phenotypic evolution in one species. Predicted ancestral niches unveiled suitable areas in southern and eastern Patagonia during glacial and interglacial periods. Our study underscores integrating different data and model-based approaches to determine the underlying causes of diversification, a challenge faced in the study of recently diverged groups. We also highlight <em>Liolaemus</em> as a model system for phylogeographic and broader evolutionary studies.</p>
Supplementary material from: Prediction of the Cold Flow Properties of Biodiesel using the FAME Distribution and Machine Learning Techniques
<p><span>The dataset is divided into three sections within the worksheet.</span></p> <p><span> </span><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></p> <p><span> </span><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></p> <p><span><span>The third and final section describes the measured properties Cloud Point (CP), Cold Filter Plugging Point (CFPP) and Pour Point (PP).</span></span></p>
Discrete Flow Cytometry of Underway Samples from TN427 Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the University of Washington School of Oceanography 2024 undergraduate senior thesis research cruise (TN427) from American Samoa. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: [https://github.com/fribalet/FCSplankton](https://github.com/fribalet/FCSplankton)</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream
<p>Structure from Motion orthoimagery, lidar differencing products, and grain size data to be published with the submission of "Impacts of Post-fire Debris Flows on Fluvial Morphology and Sediment Transport in a California Central Coast Stream" to <em>Journal of Geophysical Research: Earth Surface.</em> </p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Upper Big Creek:</p> <p>J_2016.tif, J_2021.tif, J_2022.tif, K_2016.tif, K_2021.tif, K_2022.tif, L_2016.tif, L_2021.tif, L_2022.tif</p> <p>Files titled K_[year].tif encompass our upstream study reach; files titled J_[year].tif encompass our middle study reach; files titled L_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 orthoimagery for Devil's Creek:</p> <p>G_2016.tif, G_2021.tif, G_2022.tif, _2016.tif, H_2021.tif, H_2022.tif, I_2016.tif, I_2021.tif, I_2022.tif</p> <p>Files labeled G_[year].tif encompass our upstream study reach; files labeled H_[year].tif encompass our middle study reach; files labeled I_[year].tif encompass our downstream study reach.</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Upper Big Creek with units in meters:</p> <p>BC_2016.csv, BC_2021.csv, BC_2022.csv</p> <p> </p> <p>2016, 2021, and 2022 grain size data for Devil's Creek with units in meters:</p> <p>DC_2016.csv, DC_2021.csv, DC_2022.csv</p> <p> </p> <p>Differenced lidar digital terrain models for Big Creek and Devil's Creek with units in meters:</p> <p>DoD_11_22.tif (difference between 2011 and 2022 lidar DTMs), DoD_11_15.tif (difference between 2011 and 2022 lidar DTMs)</p> <p> </p> <p>This work was funded by the Geological Society of America, the National Center for Airborne Laser Mapping, the Washington Section of the American Water Resources Association, the Western Washington University Research and Sponsored Programs Office, and the Western Washington University Geology Department.</p>
Data from: Do genetic loci that cause reproductive isolation in the lab inhibit gene flow in nature?
<p>The genetic dissection of reproductive barriers between diverging lineages provides enticing clues into the origin of species. One strategy uses linkage analysis in experimental crosses to identify genomic locations involved in phenotypes that mediate reproductive isolation. A second framework searches for genomic regions that show reduced rates of exchange across natural hybrid zones. It is often assumed that these approaches will point to the same loci, but this assumption is rarely tested. In this perspective, we discuss the factors that determine whether loci connected to postzygotic reproductive barriers in the laboratory are inferred to reduce gene flow in nature. We synthesize data on the genetics of postzygotic isolation in house mice, one of the most intensively studied systems in speciation genetics. In a rare empirical comparison, we measure the correspondence of loci tied to postzygotic barriers via genetic mapping in the laboratory and loci at which gene flow is inhibited across a natural hybrid zone. We find no evidence that the two sets of loci overlap beyond what is expected by chance. In light of these results, we recommend avenues for empirical and theoretical research to resolve the potential incongruence between the two predominant strategies for understanding the genetics of speciation.</p>
Magnetic field impact on ferronanofluid laminar flow
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Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
SeaFlow data v1: High-resolution abundance, size and biomass of small phytoplankton measured by flow-cytometry
<p>SeaFlow is an underway flow cytometer designed to continuously monitor the optical properties of the smallest phytoplankton from a ship's flow-through seawater system. It collects high-resolution data, generating the equivalent of 1 sample every 3 minutes or every 1 km (for a ship moving at 10 knots).</p> <p>The dataset provides measurements of cell abundance, cell size (equivalent spherical diameter) and carbon biomass for small phytoplankton populations: the cyanobacteria Prochlorococcus, Synechococcus, Crocosphaera, and small eukaryotic phytoplankton (<5 μm ESD). Data processing followed the methods outlined in <a href="https://doi.org/10.1038/s41597-019-0292-2">Ribalet et al. (2019)</a>. For more information, visit the <a href="https://seaflow.netlify.app/">SeaFlow website</a>.</p> <p><strong>New in version 1.6 </strong>The updated dataset includes flow cytometric measurements from 89 cruises, spanning nearly 14,000 hours of observations across 130,000 km of the surface oceans.</p>
FIGURE 3 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 3 Mass–standard length (M–LS) relationships (MLR) determined for exercised () and control () Salmo trutta cohorts over 0–32 weeks from treatment initiation. Each cohort included LS00 individuals (n = 6) as a common origin
FIGURE 1 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 1 (a) Landmark positions () on Salmo trutta parr that were digitised twice and then averaged to minimize measurement error. (b) Shape changes associated with principal components (PCs) 1–3. PCs were derived from a between-group PC analysis of Procrustes superimposed landmarks., Consensus shape with numbered landmark positions;, Shape changes associated with each PC. Shape changes are scaled to observed PC scores: Left hand side shape changes (back outlines) are scaled to the minimum value observed across the sample on each respective PC (shown below the image) and right hand side shape changes (black outlines) are scaled to the maximum value observed across the sample on each respective PC. PC1 describes a change in head size, PC2 describes dorso-ventral arching of the body and PC3 describes changes in overall robustness and body depth
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
FIGURE 2 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 2 (a) Principal component (PC) () C00, () C04, () C10, () C20, () C32, () E04, () E10, () E20, and () E32 and (b) linear discriminant (LD) scores for Salmo trutta treatment groups (C, control; E, exercise) across experimental weeks (i.e., age 00 (control sample before treatment initiation) to 32 (32 weeks of treatment); n = 6 individuals per group). PC1 and PC3, derived from a between-group PC analysis of Procrustes superimposed landmarks corrected for the arching artefact (PC2). LD1 and LD2, derived from a LD analysis on the corrected principal component scores. Ellipses demarcate 95% confidence intervals; O, group centroids. N.B. The change of direction for head size on LD1 resulting from a negative association with PC1 (see Table 2)
FIGURE 4 in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
FIGURE 4 Box plots showing median (), 25th–75th percentiles () and range () of Salmo trutta condition at length (KÞ for exercised () and control () Salmo trutta cohorts across the experimental period (i.e., age) weeks 4–32 after treatment initiation (n = 6 per group). *, significant differences of pairwise least-squares means between exercised and control cohorts; different lower-case letters (black, exercise; grey, control) denote significant differences of pairwise least-squares means within treatments across the experimental period
Figure 5 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)
Figure 5: Sporulation index (SPI) for gametophytes of Ulva rigida after induction of gametogenesis at different temperatures (A) and irradiances (B). Experiment performed according workflow shown in Figure 1. Significant differences among means are indicated by different letters. Error bars represent mean ± standard deviation (n = 3).
Figure 4 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)
Figure 4: Gamete counts of Ulva mutabilis collected from well-mixed culture medium. (A) Fluorescence of a dilution series of gametes measured using a plate reader. (B) For method validation, number of gametes measured by light-scattering flow cytometry (FCM) was compared with number of gametes determined by the Neubauer improved chamber. Error bars represent the mean ± standard deviation (n = 3).
Figure 1 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)
Figure 1: Workflow to determine sporulation index (SPI). (i): (a) Ulva rigida thalli collected from cultivation tank. (b) Thalli dried at 20 °C for 1 h. (c) Three pieces of 1 cm2 U. rigida cut from each specimen used in experiment and left to dry together with thallus. (d) Each specimen photographed using light microscope (and area of known number of cells measured. (ii): (e) Fresh thalli chopped to induce sporulation. (f) Fragments weighed, washed with seawater, and inoculated into incubation flask. (g) After differentiation of thallus cells into gametangia, release of gametes induced through change of culture medium (Vtotal), defined volume (VFCM) of well-mixed culture medium (Vtotal) fixed with 2% glutaraldehyde before measuring number of gametes using flow cytometer and calculating total numbers in Vtotal. (iii): (h) Using cells per unit area and weight of 1 cm2, number of thallus cells in each flask calculated. SPI combined number of gametes discharged with number of thallus cells in incubation flask.
Figure 3 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)
Figure 3: Flow cytometric measurements used to compare active with inactivate gametes of Ulva mutabilis. (i, ii) Mobile gametes were collected at the brightest spot and prepared for flow cytometric measurements. (iii, iv) Gametes were collected at the brightest spot as well. After chlorophyll removal, they were prepared for flow cytometric measurements. Plots present populations of gametes separated by their expected size (i, iii, % of the total counting events is given) and by the measured chlorophyll autofluorescence (ii, iv). The fluorescence measurements correspond to the gametes framed by the red gates in (i, iii). FSC-H, forward scatter height; SSC-H, side scatter height; FL, fluorescence (Fluo).
Figure 2 in Flow cytometric measurements as a proxy for sporulation intensity in the cultured macroalga Ulva (Chlorophyta)
Figure 2: Distinction of gametes of Ulva mutabilis according to their autofluorescence using flow cytometric measurements. (A: i, ii) Gametes released by U. mutabilis were collected from the green layer in the spotlight (i.e., phototactically active gametes). (A: iii, iv) Gametes were collected after the culture medium was well-mixed. Plots present populations of gametes separated by their expected size (i, iii, % of the total counting events is given) and by the measured chlorophyll autofluorescence (ii, iv). The autofluorescence measurements correspond to the gametes framed by the red gates in (i, iii). (B) Percentages of high-level autofluorescence. Error bars represent mean ± standard deviation (n = 3); FSC-H, forward scatter height; SSC-H, side scatter height; FL, fluorescence (Fluo).
Magnetic field influence on heat transfer in inclined laminar ferronanofluid flow
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