Experimentally measuring rolling and sliding in three-dimensional dense granular packings

We experimentally measure a three-dimensional (3D) granular system's reversibility under cyclic compression. We image the grains using a refractive-index-matched fluid, then analyze the images using the artificial intelligence of variational autoencoders. These techniques allow us to track all the grains' translations and 3D rotations with accuracy sufficient to infer sliding and rolling displacements. Our observations reveal unique roles played by 3D rotational motions in granular flows. We find that rotations and contact-point motion dominate the dynamics in the bulk, far from the perturbation's source. Furthermore, we determine that 3D rotations are irreversible under cyclic compression. Consequently, contact-point sliding, which is dissipative, accumulates throughout the cycle. Using numerical simulations whose accuracy our experiment supports, we discover that much of the dissipation occurs in the bulk, where grains rotate more than they translate. Our observations suggest that the analysis of 3D rotations is needed for understanding granular materials' unique and powerful ability to absorb and dissipate energy

Effects of interparticle friction on the response of 3D cyclically compressed granular material

We numerically study the effect of inter-particle friction coefficient on the response to cyclical pure shear of spherical particles in three dimensions. We focus on the rotations and translations of grains and look at the spatial distribution of these displacements as well as their probability distribution functions. We find that with increasing friction, the shear band becomes thinner and more pronounced. At low friction, the amplitude of particle rotations is homogeneously distributed in the system and is therefore mostly independent from both the affine and non-affine particle translations. In contrast, at high friction, the rotations are strongly localized in the shear zone. This work shows the importance of studying the effects of inter-particle friction on the response of granular materials to cyclic forcing, both for a better understanding of how rotations correlate to translations in sheared granular systems, and due to the relevance of cyclic forcing for most real-world applications in planetary science and industry

High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies.

To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure

Selected timelapse phase contrast images of iPS cells exposed to varying levels of fluorescence excitation.

From right to left: 0x, 1x, 1.4x, 2.1x and 3.6x light dose. Time is shown as h:min. (MP4)</p

Time-lapse images of exp0 showing the phase contrast channel (greyscale) and overlayed with the inferenced and tracked nuclei (color).

Individual tracked cells are identified as objects that maintain the same color from one frame to the next. The initial and final cell counts were 3983 and 11358, respectively. Debris on the detector can be seen to create occasional spurious nuclear objects by the 2D UNET; detecting such imperfections in the imaging system and correcting them is important for minimizing inaccuracies. Upon removal, the generation of spurious objects due to the debris was eliminated. Time is shown as h:min. (MP4)</p

Schematic of the workflow progression for the analysis of time lapse Zernike phase images.

(A) Tiled, time-lapse images are acquired and pre-processed by focal plane selection and image stitching. (B) Stitched phase contrast images are inferenced with a 2D U-net, then object separation is performed with the Fogbank algorithm for nuclear segmentation. (C) Nuclear objects are tracked. (D) Time-lapse, stitch phase contrast images are inferenced with a 3D U-Net for mitosis event detection and daughter nuclei identification and detection. (E) Tracked objects from (C) are linked with mitosis events and daughter nuclei from (D). (F) Tracked nuclei and lineages are output. See Methods and S1 Text for details.</p