Multipolar Spindle Pole Coalescence Is a Major Source of Kinetochore Mis-Attachment and Chromosome Mis-Segregation in Cancer Cells

Many cancer cells display a CIN (Chromosome Instability) phenotype, by which they exhibit high rates of chromosome loss or gain at each cell cycle. Over the years, a number of different mechanisms, including mitotic spindle multipolarity, cytokinesis failure, and merotelic kinetochore orientation, have been proposed as causes of CIN. However, a comprehensive theory of how CIN is perpetuated is still lacking. We used CIN colorectal cancer cells as a model system to investigate the possible cellular mechanism(s) underlying CIN. We found that CIN cells frequently assembled multipolar spindles in early mitosis. However, multipolar anaphase cells were very rare, and live-cell experiments showed that almost all CIN cells divided in a bipolar fashion. Moreover, fixed-cell analysis showed high frequencies of merotelically attached lagging chromosomes in bipolar anaphase CIN cells, and higher frequencies of merotelic attachments in multipolar vs. bipolar prometaphases. Finally, we found that multipolar CIN prometaphases typically possessed γ-tubulin at all spindle poles, and that a significant fraction of bipolar metaphase/early anaphase CIN cells possessed more than one centrosome at a single spindle pole. Taken together, our data suggest a model by which merotelic kinetochore attachments can easily be established in multipolar prometaphases. Most of these multipolar prometaphase cells would then bi-polarize before anaphase onset, and the residual merotelic attachments would produce chromosome mis-segregation due to anaphase lagging chromosomes. We propose this spindle pole coalescence mechanism as a major contributor to chromosome instability in cancer cells

Relative Pose from Deep Learned Depth and a Single Affine Correspondence

We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence. Considering the depth information and affine features, two new constraints on the camera pose are derived. The proposed solver is usable within 1-point RANSAC approaches. Thus, the processing time of the robust estimation is linear in the number of correspondences and, therefore, orders of magnitude faster than by using traditional approaches. The proposed 1AC+D solver is tested both on synthetic data and on 110395 publicly available real image pairs where we used an off-the-shelf monocular depth network to provide up-to-scale depth per pixel. The proposed 1AC+D leads to similar accuracy as traditional approaches while being significantly faster. When solving large-scale problems, e.g., pose-graph initialization for Structure-from-Motion (SfM) pipelines, the overhead of obtaining ACs and monocular depth is negligible compared to the speed-up gained in the pairwise geometric verification, i.e., relative pose estimation. This is demonstrated on scenes from the 1DSfM dataset using a state-of-the-art global SfM algorithm. Source code: https://github.com/eivan/one-ac-pos

HAND-EYE CALIBRATION USING MULTILINEAR CONSTRAINTS

The hand-eye calibration problem is a fairly well studied problem. Most methods are related to those proposed by Tsai and Lenz. Here a novel method is introduced that uses multilinear constraints to eliminate the problem of solving for structure and motion. The method is based on tracked interest points. The method is non-iterative involving only low cost operations. The method has been tested on both simulated and real data

Simplified vehicle calibration using multilinear constraints

An Autonomously Guided Vehicle using both odometry and visual data for navigation needs calibration parameters. These include camera placement as well as parameters relating odometry to vehicle motion. Calibration of these parameters is related to the Hand-Eye calibration problem. Instead of using a calibration target or trying to solve for structure and motion a novel method using the continuous multilinear constraint to test parameter combinations is proposed. A low order polynomial target function is calculated in linear time over the sample size resulting in very fast iterations in the optimisation step. The method is tested on simulated data and increased sample size improves the parameter estimates

A Cost-Effective Automatic 3D Reconstruction Pipeline for Plants Using Multi-view Images

Plant phenotyping involves the measurement, ideally objectively, of characteristics or traits. Traditionally, this is either limited to tedious and sparse manual measurements, often acquired destructively, or coarse image-based 2D measurements. 3D sensing technologies (3D laser scanning, structured light and digital photography) are increasingly incorporated into mass produced consumer goods and have the potential to automate the process, providing a cost-effective alternative to current commercial phenotyping platforms. We evaluate the performance, cost and practicability for plant phenotyping and present a 3D reconstruction method from multi-view images acquired with a domestic quality camera. This method consists of the following steps: (i) image acquisition using a digital camera and turntable; (ii) extraction of local invariant features and matching from overlapping image pairs; (iii) estimation of camera parameters and pose based on Structure from Motion(SFM); and (iv) employment of a patch based multi-view stereo technique to implement a dense 3D point cloud. We conclude that the proposed 3D reconstruction is a promising generalized technique for the non-destructive phenotyping of various plants during their whole growth cycles