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In JoVE (1)
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Articles by Anirban Chakraborty in JoVE
JoVE Applied Physics
Micropunching الطباعة الحجرية لتوليد الصغرى، وأنماط Submicron، على ركائز بوليمر
Mechanical and Aerospace Engineering, University of Texas at Arlington
ووضع نهج الطباعة الحجرية micropunching لتوليد صغيرة وأنماط submicron، على القمة، وجدار والأسطح السفلي من ركائز البوليمر. أنها تتغلب على العقبات من الزخرفة إجراء البوليمرات وتوليد أنماط جدار. هذا الأسلوب يسمح تلفيق السريع للميزات متعددة و مجانية والكيمياء العدوانية.
Other articles by Anirban Chakraborty on PubMed
Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of a Developing Plant Meristem
Automated segmentation and tracking of cells in actively developing tissues can provide high-throughput and quantitative spatiotemporal measurements of a range of cell behaviors; cell expansion and cell-division kinetics leading to a better understanding of the underlying dynamics of morphogenesis. Here, we have studied the problem of constructing cell lineages in time-lapse volumetric image stacks obtained using Confocal Laser Scanning Microscopy (CLSM). The novel contribution of the work lies in its ability to segment and track cells in densely packed tissue, the shoot apical meristem (SAM), through the use of a close-loop, adaptive segmentation, and tracking approach. The tracking output acts as an indicator of the quality of segmentation and, in turn, the segmentation can be improved to obtain better tracking results. We construct an optimization function that minimizes the segmentation error, which is, in turn, estimated from the tracking results. This adaptive approach significantly improves both tracking and segmentation when compared to an open loop framework in which segmentation and tracking modules operate separately.
Computational Tools for Quantitative Analysis of Cell Growth Patterns and Morphogenesis in Actively Developing Plant Stem Cell Niches
Pattern formation in developmental fields involves precise spatial arrangement of different cell types in a dynamic landscape wherein cells exhibit a variety of behaviors, such as cell division, cell expansion, and cell migration [Reddy (Curr Opin Plant Biol 11:88-931, 2008) and Meyerowitz (Cell 88:299-3082, 2007)]. The information is exchanged between multiple cell layers through cell-cell communication processes to regulate gene expression and cell behaviors in specifying distinct cell types. Therefore, a quantitative and dynamic understanding of the spatial and temporal organization of gene expression and cell behavioral patterns within multilayered and actively growing developmental fields is crucial to model the process of development. The quantification of spatiotemporal dynamics of cell behaviors requires computational tools in image analysis, statistical modeling, pattern recognition, machine learning, and dynamical system identification. Here, we give a brief account of recently developed methods in analyzing both local and global growth patterns in Arabidopsis shoot apical meristems. The computational toolkit can be used to gain new insights into causal relationships among cell growth, cell division, changes in gene expression patterns, and organ development by analyzing various mutants that affect these processes. This may allow us to develop function space models that capture variations in several growth parameters both at local/single-cell level and at global/organ level. In the long run, this may enable clustering of molecular pathways that mediate distinct cell behaviors.
