Analyzing Collective Motion with Machine Learning and Topology

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based in topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the one that is based on traditional order parameters.Comment: Published in Chaos 29, 123125 (2019), DOI: 10.1063/1.512549

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

A novel approach to investigate transitions in tutor tissue architecture using computational topology

Non UBCUnreviewedAuthor affiliation: Brown UniversityGraduat

Morphology based cell classification : unsupervised machine learning approach

Individual cells adapt their morphology as a function of their differentiation status and in response to environmental cues and selective pressures. While it known that the great majority of these cues and pressures are mediated by changes in intracellular signal transduction, the precise regulatory mechanisms that govern cell shape, size and polarity are not well understood. Systematic investigation of cell morphology involves experimentally perturbing biochemical pathways and observing changes in phenotype. In order to facilitate this work, experimental biologists need software capable of analyzing a large number of microscopic images to classify cells and recognize cell types. Furthermore, automatic cell classification enables pathologists to rapidly diagnose diseases like leukemia that are marked by cell shape deformation. This thesis describes a methodology to identify cells in microscopy images and compute quantitative descriptors that characterize their morphology. Phase-contrast microscopy data is used for the purpose of demonstration. Cells are identified with minimal user input using advanced image segmentation methods. Features (e.g. area, perimeter, curvature, circularity, convexity, etc.) are extracted from segmented cell boundary to quantify cell morphology. Correlated features are combined to reduce dimensionality and the resulting feature set is clustered to identify distinct cell morphologies. Clustering results obtained from different combinations of features are compared to identify a minimal set of features without compromising classification accuracy.Science, Faculty ofMathematics, Department ofGraduat

Dual role for LIM-homeodomain gene Lhx2 in the formation of the lateral olfactory tract

The development of the olfactory system in vertebrates is a multistep process, in which several regulatory molecules are required at different stages. The development of the olfactory sensory epithelium and its projection to the olfactory bulb are both known to require the LIM-homeodomain transcription factor Lhx2. We examined whether Lhx2 plays a role in the development of the OB itself, as well as its projection to the olfactory cortex. Although there is no morphological OB protuberance in the Lhx2 mutant, mitral cells are normally specified and cluster in a displaced olfactory bulb-like structure (OBLS). The OBLS is not able to pioneer the lateral olfactory tract (LOT) projection in vivo or when provided control (host) telencephalic territory in an in vitro assay. Strikingly, the mutant OBLS is capable of projecting along the LOT if provided with an existing normal LOT in the host explant. This is the first report of a role for a transcription factor expressed in the OB that selectively affects the axon guidance but not the specification of mitral cells. Furthermore, the Lhx2 mutant lateral telencephalon does not support growth of an LOT projection from control OB explants. The defect correlates with the disruption of a cellular mechanism that is thought to be critical for LOT pathfinding: a specialized cell population, the "lot cells," is mislocalized in the Lhx2 mutant. In addition, the expression of Sema6A is aberrantly upregulated. Together, these findings reveal a dual role for Lhx2, in the OB as well as in the lateral telencephalon, for establishing the LOT projection

Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion

Abstract Different cell types aggregate and sort into hierarchical architectures during the formation of animal tissues. The resulting spatial organization depends (in part) on the strength of adhesion of one cell type to itself relative to other cell types. However, automated and unsupervised classification of these multicellular spatial patterns remains challenging, particularly given their structural diversity and biological variability. Recent developments based on topological data analysis are intriguing to reveal similarities in tissue architecture, but these methods remain computationally expensive. In this article, we show that multicellular patterns organized from two interacting cell types can be efficiently represented through persistence images. Our optimized combination of dimensionality reduction via autoencoders, combined with hierarchical clustering, achieved high classification accuracy for simulations with constant cell numbers. We further demonstrate that persistence images can be normalized to improve classification for simulations with varying cell numbers due to proliferation. Finally, we systematically consider the importance of incorporating different topological features as well as information about each cell type to improve classification accuracy. We envision that topological machine learning based on persistence images will enable versatile and robust classification of complex tissue architectures that occur in development and disease