English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The ocean is filled with microscopic microalgae called phytoplankton, whichtogether are responsible for as much photosynthesis as all plants on landcombined. Our ability to predict their response to the warming ocean relies onunderstanding how the dynamics of phytoplankton populations is influenced bychanges in environmental conditions. One powerful technique to study thedynamics of phytoplankton is flow cytometry, which measures the opticalproperties of thousands of individual cells per second. Today, oceanographersare able to collect flow cytometry data in real-time onboard a moving ship,providing them with fine-scale resolution of the distribution of phytoplanktonacross thousands of kilometers. One of the current challenges is to understandhow these small and large scale variations relate to environmental conditions,such as nutrient availability, temperature, light and ocean currents. In thispaper, we propose a novel sparse mixture of multivariate regressions model toestimate the time-varying phytoplankton subpopulations while simultaneouslyidentifying the specific environmental covariates that are predictive of theobserved changes to these subpopulations. We demonstrate the usefulness andinterpretability of the approach using both synthetic data and realobservations collected on an oceanographic cruise conducted in the north-eastPacific in the spring of 2017.

Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions