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Although microtubules in plant cells have been extensively studied, the mechanisms that regulate the spatial organization of microtubules are poorly understood. We hypothesize that the interaction between microtubules and cytoplasmic flow plays an important role in the assembly and orientation of microtubules. To test this hypothesis, we developed a new computational modeling framework for microtubules based on theory and methods from the fluid-structure interaction. We employed the immersed boundary method to track the movement of microtubules in cytoplasmic flow. We also incorporated details of the encounter dynamics when two microtubules collide with each other. We verified our computational model through several numerical tests before applying it to the simulation of the microtubule-cytoplasm interaction in a growing plant cell. Our computational investigation demonstrated that microtubules are primarily oriented in the direction orthogonal to the axis of cell elongation. We validated the simulation results through a comparison with the measurement from laboratory experiments. We found that our computational model, with further calibration, was capable of generating microtubule orientation patterns that were qualitatively and quantitatively consistent with the experimental results. The computational model proposed in this study can be naturally extended to many other cellular systems that involve the interaction between microstructures and the intracellular fluid. 

Recent advances in high-resolution connectomics provide researchers access to accurate reconstructions of vast neuronal circuits and brain networks for the first time. Neuroscientists anticipate analyzing these networks to gain a better understanding of information processing in the brain. In particular, scientists are interested in identifying specific network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to the detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings of the involved neurons and synapses. To reduce visual clutter and simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by continuous visual abstractions [MAAB∗18] that allows the user to transition from the highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly to form a longer synaptic chain. We evaluate Vimo in a user study with seven domain experts and an in-depth case study on motifs in the central complex (CX) of the fruit fly brain. 

Analog compute‐in‐memory (CIM) systems are promising candidates for deep neural network (DNN) inference acceleration. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential security vulnerability is identified wherein an adversary can reconstruct the user's private input data from a power side‐channel attack even without knowledge of the stored DNN model. An attack approach using a generative adversarial network is developed to achieve high‐quality data reconstruction from power leakage measurements. The analyses show that the attack methodology is effective in reconstructing user input data from power leakage of the analog CIM accelerator, even at large noise levels and after countermeasures. To demonstrate the efficacy of the proposed approach, an example of CIM inference of U‐Net for brain tumor detection is attacked, and the original magnetic resonance imaging medical images can be successfully reconstructed even at a noise level of 20% standard deviation of the maximum power signal value. This study highlights a potential security vulnerability in emerging analog CIM accelerators and raises awareness of needed safety features to protect user privacy in such systems.