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Little is known about how distance between homologous chromosomes are controlled during the cell cycle. Here, we show that the distribution of centromere components display two discrete clusters placed to either side of the centrosome and apical/basal axis from prophase to G1 interphase. 4- Dimensional live cell imaging analysis of centromere and centrosome tracking reveals that centromeres oscillate largely within one cluster, but do not cross over to the other cluster. We propose a model of an axis-dependent ipsilateral restriction of chromosome oscillations throughout mitosis. 

Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications. Prior approaches achieve this goal by employing a two-stage completion framework, generating a coarse yet complete seed point cloud through an encoder-decoder network, followed by refinement and upsampling. However, the encoded features suffer from information loss of the missing portion, leading to an inability of the decoder to reconstruct seed points with detailed geometric clues. To tackle this issue, we propose a novel Orthogonal Dictionary Guided Shape Completion Network (ODGNet). The proposed ODGNet consists of a Seed Generation U-Net, which leverages multi-level feature extraction and concatenation to significantly enhance the representation capability of seed points, and Orthogonal Dictionaries that can learn shape priors from training samples and thus compensate for the information loss of the missing portions during inference. Our design is simple but to the point, extensive experiment results indicate that the proposed method can reconstruct point clouds with more details and outperform previous state-of-the-art counterparts. The implementation code is available at https://github.com/corecai163/ODGNet. 

3D Point Cloud Data (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But the measured PCD is often incomplete and sparse due to the sensor occlusion and poor lighting conditions. To automatically reconstruct complete PCD from the incomplete ones, we propose DeepPCD, a deep-learning-based system that reconstructs both geometric and color information for large indoor environments. For geometric reconstruction, DeepPCD uses a novel patch based technique that splits the PCD into multiple parts, approximates, extends, and independently reconstructs the parts by 3D planes, and then merges and refines them. For color reconstruction, DeepPCD uses a conditional Generative Adversarial Network to infer the missing color of the geometrically reconstructed PCD by using the color feature extracted from incomplete color PCD. We experimentally evaluate DeepPCD with several real PCD collected from large, diverse indoor environments and explore the feasibility of PCD autocompletion in various ubiquitous sensing applications. 

3D Point Cloud (PCD) is an efficient machine representation for surrounding environments and has been used in many applications. But a fast reconstruction of complete PCD for large environments remains a challenge. We propose AutoPCD, a machine-learning model that reconstructs complete PCDs, under sensor occlusion and poor lighting conditions. AutoPCD splits the PCD into multiple parts, approximates them by several 3D planes, and independently learns the plane features for reconstruction. We have experimentally evaluated AutoPCD in a large indoor hallway environment.