3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila

Abstract

Drosophila is an excellent model organism towards understanding the cognitive function, aging and neurodegeneration in humans. The effects of aging and other long-term dynamics on the behavior serve as important biomarkers in identifying such changes to the brain. In this regard, we are presenting a new imaging technique for lifetime monitoring of Drosophila in 3D at spatial and temporal resolutions capable of resolving the motion of limbs and wings using holographic principles. The developed system is capable of monitoring and extracting various behavioral parameters, such as ethograms and spatial distributions, from a group of flies simultaneously. This technique can image complicated leg and wing motions of flies at a resolution, which allows capturing specific landing responses from the same data set. Overall, this system provides a unique opportunity for high throughput screenings of behavioral changes in 3D over a long term in Drosophila.

Characterization Method for Particle Extraction From Raw-Reconstructed Images Using U-Net

“…The particle fields comprise small objects, such as bubbles, biological cells, droplets. In recent years, 3D imaging has been widely used in particle detection (including shape, location, and motion) across many scientific domains, such as materials [1], chemical engineering [2][3][4], biology [5][6][7], medical sciences [8][9][10], and environmental science [11][12][13]. Digital holography (DH) encodes the 3D information of objects into a 2D hologram using the interference of the reference wave and object wave.…”

Automated droplet size distribution measurements using digital inline holography

“…DIH has been recently demonstrated for the analysis of the motion and morphology of individual aerosol particles (Berg & Holler, 2016;David et al, 2018;Giri et al, 2019). DIH has also been used successfully employed in a diverse array of fields, including the study of social behaviors of flies (Kumar et al, 2016), snowflake size distribution function and morphology (Nemes et al, 2017), atmospheric mixing and cloud formation (Beals et al, 2015), ocean sediment particle size and velocity transport (Graham & Nimmo Smith, 2010), oil droplets in oceans (Li et al, 2017), bubbly wake behind a ventilated supercavity (Shao et al, 2019), and preliminarily (without automated processing), droplets from sprays in compressible flow cross wind (Olinger et al, 2014). Building upon these studies, in this work our goal is to develop an automated and rigorous data analysis approach for DIH to specifically determine the size distribution functions of spray generated droplets.…”

Microalgal swimming signatures and neutral lipids production across growth phases

“…Compared to conventional microscopy methods, DIH-PTV has a much larger depth of focus and is capable of tracking objects in 3D (Yu, Hong, Liu, & Kim, 2014). DIH has been recently applied as particle tracking velocimetry (PTV) technique to study fluid motion and the biolocomotion of biological organism (Sheng et al 2007, Kumar et al 2016, Toloui et al 2017, You et al 2017. Our DIH setup consists of a 532 nm diode laser (Thorlabs CPS532), an optical spatial filter and collimating lens assembly, 5X microscopic objective (Mitutoyo 10X/0.14 NA), and a CCD camera (Flare 2M360-CL) (Figure S2).…”