Methods, Techniques, and Biological Applications

Morphological Properties of the Two Types of Caudate Interneurons: Kohonen Self-Organizing Maps and Correlation- Comparison Analysis by I Grbatinić, B Krstonošić, D Marić, and N Milošević, Microsc Microanal | doi: 10.1017/S1431927618015337 Te caudate cluster consists of two subclusters of morphologically

different interneurons. Binary images of human caudate interneurons (Figure) were analyzed by using 46 computational neuromorpho- logical parameters to quantify the size of a neuron, neuronal shape, compartmental length, dendritic branching, neuromorphological organization, and dendritic tree complexity. Each individual inter- neuron was assigned to a cluster according to morphological criteria by using the trained Kohonen self-organizing map. Aſter assignment to a cluster, analysis was continued by feature-wise determination of morphological differences found between clusters and then finished by defining correlation-based, mutual, inter-parametric relations for each of the clusters. Te first was performed by using single-factor analysis and the second by correlation-comparison analysis. Single- factor analysis showed 34 significant parameters that distinguish between the clusters. Correlation-comparison analysis extended the results of single-factor analysis by demonstrating significance for 198 inter-parametric correlation pairs, representing 19.13% of mis- matched correlations (significant inter-cluster separation zone). Te two clusters of caudate interneurons were found to be significantly morphologically different, which may be important for their different neurofunctional behavior.

Techniques and Material Applications

Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) with Nonrectangular Scans by Xin Li, Ondrej Dyck, Sergei V. Kalinin, and Stephen Jesse, Microsc Microanal | doi: 10.1017/S143192761801543X Scanning transmission electron microscopy (STEM) has

become the mainstay for materials characterization on the atomic level, with applications ranging from visualization of defects to mapping order parameter fields.

In recent years,

attention has been focused on the potential of STEM to explore beam-induced chemical processes, especially manipulating atomic motion and enabling atom-by-atom fabrication. These applications together with traditional imaging of beam- sensitive materials, necessitate an increase in the dynamic range of STEM in imaging and manipulation modes, increasing the absolute scanning speed that can be achieved by combining sparse sens- ing methods with nonrectangular scanning trajectories. This work presents a general method for real-time reconstruction of sparsely sampled images from high-speed, noninvasive, and diverse scanning pathways, such as spiral and Lissajous scans. This approach is demonstrated on both the synthetic data and experimental STEM data on the beam-sensitive material gra- phene. This work opens the door for comprehensive investiga- tion and optimal design of dose efficient scanning strategies and real-time adaptive inference and control of e-beam induced atomic fabrication.

44 doi:10.1017/S1551929519000415

3D reconstruction of cluster 1 (green) and cluster II (yellow) caudate interneurons. Cluster I interneurons are larger with a more elongated cell body, longer dendrites, and a larger area of spreading. Cluster II interneurons have a more complex dendritic arborization and thicker dendrites relative to the soma size. These structural variations may imply functional specificity.

Schematic diagram showing a real-time sparse spiral scan (top); recon- struction of image of graphene with a silicon dopant (bottom). This result was finished in milliseconds. • 2019 May

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