http://lod.bco-dmo.org/id/dataset/885923
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2022-12-28
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project)
2022-12-28
publication
2022-12-28
revision
Woods Hole Oceanographic Institution (WHOI BCO-DMO)
2025-12-01
publication
https://doi.org/10.26008/1912/bco-dmo.885923.1
Jeffrey Guasto
Tufts University
principalInvestigator
Richard J. Henshaw
Tufts University
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Richard J. Henshaw (2025) Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2022-12-28 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.885923.1 [access date]
Dynamic mode structure of active turbulence Dataset Description: Methods and Sampling: <p><strong>Culturing:</strong></p>
<p>Wild-type&nbsp;Bacillus subtilis&nbsp;bacteria (strain OI1084) were taken from −80°C frozen stock and streaked onto 1.5% agar plates prepared with Terrific Broth (TB, Sigma). Plates were incubated at 25°C for 24 hours, after which time a single colony from the plate was used to inoculate an overnight liquid TB culture at 30°C with shaking (200 rpm). The bacterial suspension was then subcultured (1.5 ml of cell culture into 60 ml of pre-warmed TB) and grown at 35°C and 200 rpm for 6 hours to mid-log phase (OD600&nbsp;≈ 0.2). Immediately prior to experiments, dense cell suspensions (∼ 1010&nbsp;cells.ml−1) were prepared by centrifugation at 5000 g for 5 minutes, and the pellet was resuspended with 2&nbsp;μl of fresh TB media.</p>
<p>&nbsp;</p>
<p><strong>Microfluidics and image analysis</strong></p>
<p>Polydimethlysiloxane (PDMS) microfluidic channels were fabricated through soft lithography&nbsp;and plasma bonded to No. 1 thickness glass coverslips. The PDMS channels were thinly cast to ensure ample diffusion of oxygen to the bacterial suspension and prolonged cell activity. Dense cell suspensions were gently loaded into the microfluidic devices via pipette, and the channel inlet and outlet were sealed with wax to prevent residual flows. For all experiments, bacterial suspensions were imaged with brightfield illumination on an inverted microscope (Nikon Ti-E) using a sCMOS camera (Zyla 5.5, Andor Technology). Time-resolved velocity fields,&nbsp;u(x,&nbsp;t), of the bacterial suspensions were measured by performing Particle Image Velocimetry (PIV) using PIVLab&nbsp;implemented in MATLAB. The subsequent velocity fields were then lightly smoothed using a Guassian kernel with a standard deviation of one PIV pixel (1.73&nbsp;μm) in space and one frame (9.5 ms) in time. Vorticity fields,&nbsp;ω(x,&nbsp;t), were computed from measured velocity fields using central differencing (MATLAB).</p>
<p>&nbsp;</p>
<p><strong>Bulk suspensions with decaying activity (quasi-2D)</strong></p>
<p>To capture the bulk dynamics of dense bacterial suspensions in the absence of lateral wall effects, large microfluidic chambers (2 mm ×2 mm ×26&nbsp;μm) were prepared, where the side length corresponds to ≈80 correlation lengths of the turbulent bacterial suspension. Imaging was performed with a 20 × objective (0.45 NA) at 105.5 fps. Bacterial activity was varied using a previously established approach.&nbsp;Briefly, the cell suspensions were imaged periodically over the course of 30 minutes, during which time the cell swimming speed naturally decayed due to oxygen depletion, where the decay rate of cell activity was controlled via the thickness of the PDMS device. Without the need to manipulate the cell suspension, this approach ensured that the bacterial concentration was constant across varying activity levels. Seven data sets were captured in total, which consisted of 6, 300 frames each (≈ 1 min per video) and a 2 − 3 min delay between acquisitions. Data analysis was restricted to the central portion of the chamber, ≈ 30 correlation lengths from the lateral boundaries.</p>
<p>&nbsp;</p>
<p><strong>Cell suspensions under varying confinement</strong></p>
<p>To quantify POD modes for bacterial suspensions under varying degrees of confinement experiments from Wioland et al (2016). were replicated with minor modifications. Microfluidic devices (19 µm deep) were fabricated, which comprised a series of racetrack geometries connected by narrow inlets (30 µm wide). Nine racetrack widths were chosen between 30 µm ≤ W ≤ 171 µm in fractional increments of our measured characteristic vortex size (38 µm) taken as the minimum point in the spatial ACF in quasi-2D conditions (Fig. 1C, dotted red line). Cell suspensions were imaged at 40× (0.6 NA) magnification and 100 fps for 10 s per channel width. All confined microfluidic geometries were simultaneously loaded with the same cell suspension and imaged within the first 10 min of loading to ensure consistent bacterial activity. Experiments were 8 repeated across multiple days with freshly cultured bacteria to verify repeatability. The net flow ψ was calculated following the definition in [18]: ψ = |(Σu · eˆx) / (Σ||u||)|, where eˆx is the unit vector along the principal channel direction and the sum is over all PIV sub-windows over 5 s (500 frames) of video. ψ = 1 indicates uni-directional circulation flow around the racetrack, and ψ = 0 corresponds to a globally stationary suspension. The stream-wise flow profiles, F(y), were generated from the velocity field by averaging over time and space as F(y) = hu(x, y, t) · eˆxi(x,t) , where eˆx was chosen as defined in [18], such that F(y) is on average positive.</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1829827 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1829827
completed
Jeffrey Guasto
Tufts University
617-627-6838
Department of Mechanical Engineering Robinson Hall, Rm 173, 200 College Avenue
Medford
MA
02155
US
Jeffrey.Guasto@tufts.edu
pointOfContact
Richard J. Henshaw
Tufts University
rhenshaw@ethz.ch
pointOfContact
asNeeded
Dataset Version: 1
Unknown
Experiment_Number
Average_speed
Dataset
Nikon Ti-E Inverted Microscope
theme
None, User defined
experiment id
flow rate
file_name
featureType
BCO-DMO Standard Parameters
Inverted Microscope
instrument
BCO-DMO Standard Instruments
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Collaborative Research: Viral induced chemotaxis mediating cross-trophic microbial interactions and carbon flux
https://osprey.bco-dmo.org/project/783777
Collaborative Research: Viral induced chemotaxis mediating cross-trophic microbial interactions and carbon flux
<p><em>NSF Award Abstract:</em><br />
Drifting photosynthetic microbes in surface ocean waters carry out nearly half of global carbon (C) fixation, both supporting the marine food web and reducing atmospheric carbon dioxide (CO2) levels. The fate of C in ocean ecosystems is controlled by myriad individual interactions within a highly interconnected planktonic food web, the sheer complexity of which has hindered predictive understanding of global C cycling. Chemical cues govern microbial interactions, and during infection, marine viruses manipulate the metabolism of phytoplankton and bacteria, facilitating the release of dissolved organic matter from infected cells. This research aims to determine how viral metabolic reprogramming of and organic matter release from intact, infected phytoplankton influences microbial interactions and C cycling. The interdisciplinary, collaborative nature of the project will enable direct training of two postdoctoral researchers, one graduate student, and undergraduate students in viral ecology, microfluidics, and metabolomics. An educational outreach program that engages middle school students in hands-on, high speed imaging of microbes will be expanded, and the project will culminate in a three-day workshop to advance the application of microfluidic devices and mass spectrometry analyses in microbial ecology.</p>
<p>The overarching hypothesis behind this research is that viral infection alters the chemical landscape of intact, infected picophytoplankton cells, attracting neighboring chemotactic bacteria and protistan zooplankton, and altering C flux pathways. To test this idea, a series of linked multi-scale laboratory-based experiments will be run to 1) Characterize the response of diverse model marine microbes to dissolved organic matter (DOM) released from intact, virus-infected picophytoplankton using microfluidics-based chemotaxis assays, 2) Identify key viral-derived DOM compounds eliciting chemotactic responses using stable isotope labeling, metabolomics analyses, and chemotaxis assays, and 3) Quantify micron-scale cross-trophic encounter dynamics and evaluate their impact on bulk-scale C cycling using liter-scale measurements of C dynamics linked to high spatiotemporal resolution live imaging of microbial food webs. The ultimate goal of the project is to develop a mechanistic understanding of the role of intact, virus-infected cells in oceanic C cycling.</p>
VIC
largerWorkCitation
project
eng; USA
oceans
2019-09-30
2022-05-01
Culture-based work
0
BCO-DMO catalogue of parameters from Dynamic Mode Structure of Active Turbulence Modeling Results from 2019-2022 (VIC project)
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/910919.rdf
Name: Experiment_Number
Units: unitless
Description: <p>ID representing a unique experiment. These IDs correspond with the POD data files available for download in the Supplemental Files section of the BCO-DMO metadata page for this representative dataset (<a href="https://www.bco-dmo.org/dataset/885923">https://www.bco-dmo.org/dataset/885923</a>).</p>
http://lod.bco-dmo.org/id/dataset-parameter/910920.rdf
Name: Average_speed
Units: um/s
Description: <p>Average flow speeds in um/s of each of the flow fields in each of the experimental PODFiles zip files.</p>
http://lod.bco-dmo.org/id/dataset-parameter/910921.rdf
Name: Dataset
Units: unites
Description: <p>Name of corresponding experimental POD zip file</p>
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
https://doi.org/10.26008/1912/bco-dmo.885923.1
download
onLine
dataset
<p><strong>Culturing:</strong></p>
<p>Wild-type&nbsp;Bacillus subtilis&nbsp;bacteria (strain OI1084) were taken from −80°C frozen stock and streaked onto 1.5% agar plates prepared with Terrific Broth (TB, Sigma). Plates were incubated at 25°C for 24 hours, after which time a single colony from the plate was used to inoculate an overnight liquid TB culture at 30°C with shaking (200 rpm). The bacterial suspension was then subcultured (1.5 ml of cell culture into 60 ml of pre-warmed TB) and grown at 35°C and 200 rpm for 6 hours to mid-log phase (OD600&nbsp;≈ 0.2). Immediately prior to experiments, dense cell suspensions (∼ 1010&nbsp;cells.ml−1) were prepared by centrifugation at 5000 g for 5 minutes, and the pellet was resuspended with 2&nbsp;μl of fresh TB media.</p>
<p>&nbsp;</p>
<p><strong>Microfluidics and image analysis</strong></p>
<p>Polydimethlysiloxane (PDMS) microfluidic channels were fabricated through soft lithography&nbsp;and plasma bonded to No. 1 thickness glass coverslips. The PDMS channels were thinly cast to ensure ample diffusion of oxygen to the bacterial suspension and prolonged cell activity. Dense cell suspensions were gently loaded into the microfluidic devices via pipette, and the channel inlet and outlet were sealed with wax to prevent residual flows. For all experiments, bacterial suspensions were imaged with brightfield illumination on an inverted microscope (Nikon Ti-E) using a sCMOS camera (Zyla 5.5, Andor Technology). Time-resolved velocity fields,&nbsp;u(x,&nbsp;t), of the bacterial suspensions were measured by performing Particle Image Velocimetry (PIV) using PIVLab&nbsp;implemented in MATLAB. The subsequent velocity fields were then lightly smoothed using a Guassian kernel with a standard deviation of one PIV pixel (1.73&nbsp;μm) in space and one frame (9.5 ms) in time. Vorticity fields,&nbsp;ω(x,&nbsp;t), were computed from measured velocity fields using central differencing (MATLAB).</p>
<p>&nbsp;</p>
<p><strong>Bulk suspensions with decaying activity (quasi-2D)</strong></p>
<p>To capture the bulk dynamics of dense bacterial suspensions in the absence of lateral wall effects, large microfluidic chambers (2 mm ×2 mm ×26&nbsp;μm) were prepared, where the side length corresponds to ≈80 correlation lengths of the turbulent bacterial suspension. Imaging was performed with a 20 × objective (0.45 NA) at 105.5 fps. Bacterial activity was varied using a previously established approach.&nbsp;Briefly, the cell suspensions were imaged periodically over the course of 30 minutes, during which time the cell swimming speed naturally decayed due to oxygen depletion, where the decay rate of cell activity was controlled via the thickness of the PDMS device. Without the need to manipulate the cell suspension, this approach ensured that the bacterial concentration was constant across varying activity levels. Seven data sets were captured in total, which consisted of 6, 300 frames each (≈ 1 min per video) and a 2 − 3 min delay between acquisitions. Data analysis was restricted to the central portion of the chamber, ≈ 30 correlation lengths from the lateral boundaries.</p>
<p>&nbsp;</p>
<p><strong>Cell suspensions under varying confinement</strong></p>
<p>To quantify POD modes for bacterial suspensions under varying degrees of confinement experiments from Wioland et al (2016). were replicated with minor modifications. Microfluidic devices (19 µm deep) were fabricated, which comprised a series of racetrack geometries connected by narrow inlets (30 µm wide). Nine racetrack widths were chosen between 30 µm ≤ W ≤ 171 µm in fractional increments of our measured characteristic vortex size (38 µm) taken as the minimum point in the spatial ACF in quasi-2D conditions (Fig. 1C, dotted red line). Cell suspensions were imaged at 40× (0.6 NA) magnification and 100 fps for 10 s per channel width. All confined microfluidic geometries were simultaneously loaded with the same cell suspension and imaged within the first 10 min of loading to ensure consistent bacterial activity. Experiments were 8 repeated across multiple days with freshly cultured bacteria to verify repeatability. The net flow ψ was calculated following the definition in [18]: ψ = |(Σu · eˆx) / (Σ||u||)|, where eˆx is the unit vector along the principal channel direction and the sum is over all PIV sub-windows over 5 s (500 frames) of video. ψ = 1 indicates uni-directional circulation flow around the racetrack, and ψ = 0 corresponds to a globally stationary suspension. The stream-wise flow profiles, F(y), were generated from the velocity field by averaging over time and space as F(y) = hu(x, y, t) · eˆxi(x,t) , where eˆx was chosen as defined in [18], such that F(y) is on average positive.</p>
Specified by the Principal Investigator(s)
<div class="field" id="form_data_processing">
<p>Flow fields were calculated using PIV (PIVLab, MATLAB), publicly available here.&nbsp;https://www.mathworks.com/matlabcentral/fileexchange/27659-pivlab-particle-image-velocimetry-piv-tool-with-gui . Box sizes of 32 pixel then 16 pixel (50%) overlap were used.</p>
<p>The subsequent velocity fields were then lightly smoothed using a Guassian kernel with a standard deviation of one PIV pixel (1.73&nbsp;μm) in space and one frame (9.5 ms) in time.</p>
<p>An example script of the POD analysis is included in the submitted data files - this makes uses of the in-built "svd" function in MATLAB, and should be compatible with all versions.</p>
<p>&nbsp;</p>
</div>
<div class="field" id="form_problems">&nbsp;</div>
Specified by the Principal Investigator(s)
Primary data file set to a csv representation of POD Flow Field Averages from each of the seven experiments.
Spaces removed from parameter names in primary data file (csv).
Specified by BCO-DMO Data Managers
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
Nikon Ti-E Inverted Microscope
Nikon Ti-E Inverted Microscope
PI Supplied Instrument Name: Nikon Ti-E Inverted Microscope PI Supplied Instrument Description:For all experiments, bacterial suspensions were imaged with brightfield illumination on an inverted microscope (Nikon Ti-E) using a sCMOS camera (Zyla 5.5, Andor Technology). Instrument Name: Inverted Microscope Instrument Short Name: Instrument Description: An inverted microscope is a microscope with its light source and condenser on the top, above the stage pointing down, while the objectives and turret are below the stage pointing up. It was invented in 1850 by J. Lawrence Smith, a faculty member of Tulane University (then named the Medical College of Louisiana).
Inverted microscopes are useful for observing living cells or organisms at the bottom of a large container (e.g. a tissue culture flask) under more natural conditions than on a glass slide, as is the case with a conventional microscope. Inverted microscopes are also used in micromanipulation applications where space above the specimen is required for manipulator mechanisms and the microtools they hold, and in metallurgical applications where polished samples can be placed on top of the stage and viewed from underneath using reflecting objectives.
The stage on an inverted microscope is usually fixed, and focus is adjusted by moving the objective lens along a vertical axis to bring it closer to or further from the specimen. The focus mechanism typically has a dual concentric knob for coarse and fine adjustment. Depending on the size of the microscope, four to six objective lenses of different magnifications may be fitted to a rotating turret known as a nosepiece. These microscopes may also be fitted with accessories for fitting still and video cameras, fluorescence illumination, confocal scanning and many other applications. Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/LAB05/