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Fusing Artificial Cell Compartments and Lipid Domains Using Optical Traps: A Tool to Modulate Membrane Composition and Phase Behaviour.

ABSTRACT: New technologies for manipulating biomembranes have vast potential to aid the understanding of biological phenomena, and as tools to sculpt novel artificial cell architectures for synthetic biology. The manipulation and fusion of vesicles using optical traps is amongst the most promising due to the level of spatiotemporal control it affords. Herein, we conduct a suite of feasibility studies to show the potential of optical trapping technologies to (i) modulate the lipid composition of a vesicle by delivering new membrane material through fusion events and (ii) manipulate and controllably fuse coexisting membrane domains for the first time. We also outline some noteworthy morphologies and transitions that the vesicle undergoes during fusion, which gives us insight into the mechanisms at play. These results will guide future exploitation of laser-assisted membrane manipulation methods and feed into a technology roadmap for this emerging technology.

SUBMITTER: Vivek A

PROVIDER: S-EPMC7230983 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m2m−2]) and especially over long-time gaps (R2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m2m−2]). A second assessment is focused on crop-specific regions, clustering pixels fulfilling specific model conditions where the synergy is profitable. Results reveal the MOGP performance is crop type and crop stage dependent. For long time gaps, best R2 are obtained over maize, ranging from 0.1 (tillering) to 0.36 (development) up to 0.81 (maturity); for moderate time gap, R2 = 0.93 (maturity) is obtained. Crops such as wheat, oats, rye and barley, can profit from the LAI-RVI synergy, with R2 varying between 0.4 and 0.6. For beet or potatoes, MOGP provides poorer results, but alternative descriptors to RVI should be tested for these specific crops in the future before discarding synergy real benefits. In conclusion, active-passive sensor fusion with MOGP represents a novel and promising approach to cope with crop monitoring over cloud-dominated areas.

Dataset Information

Fusing Artificial Cell Compartments and Lipid Domains Using Optical Traps: A Tool to Modulate Membrane Composition and Phase Behaviour.

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