A computational method for simulating growth patterns in unicell propagation

AbstractAdvances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

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Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100068096 ◽

1970 ◽

Vol 28 ◽

pp. 220-221

Author(s):

Gerald Fine ◽

Azorides R. Morales

Keyword(s):

Cardiac Myxoma ◽

Osteogenic Sarcoma ◽

Diagnostic Value ◽

Soft Tissue Tumors ◽

Amelanotic Melanoma ◽

Growth Patterns ◽

Light Microscopic Level ◽

Mesenchymal Tumors ◽

Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

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Episodic whole plant growth patterns in Ligustrum

Physiologia Plantarum ◽

10.1034/j.1399-3054.1993.890106.x ◽

1993 ◽

Vol 89 (1) ◽

pp. 33-39 ◽

Cited By ~ 1

Author(s):

Jeff S. Kuehny ◽

Mary C. Halbrooks

Keyword(s):

Plant Growth ◽

Growth Patterns ◽

Whole Plant

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Larval Behavior and Protective Implications for Sea Scallops and Surf Clams within the Middle Atlantic Bight: Part Two, Variability of Physical Environment and Larval Behaviors

Earth & Environmental Science Research & Reviews ◽

10.33140/eesrr.02.02.04 ◽

2019 ◽

Vol 2 (2) ◽

Keyword(s):

Physical Environment ◽

Climate Changes ◽

Larval Growth ◽

Growth Patterns ◽

Larval Behavior ◽

Larval Release ◽

Physical Conditions ◽

Climatic Variations ◽

Middle Atlantic ◽

Sea Scallops

Larval growth and settlement rates are important larval behaviors for larval protections. The variability of larval growthsettlement rates and physical conditions for 2006-2012 and in the future with potential climate changes was studied using the coupling ROMS-IMBs, and new temperature and current indexes. Forty-four experimental cases were conducted for larval growth patterns and release mechanisms, showing the spatial, seasonal, annual, and climatic variations of larval growthsettlement rates and physical conditions, demonstrating that the slight different larval temperature-adaption and larval release strategies made difference in larval growth-settlement rates, and displaying that larval growth and settlement rates highly depended upon physical conditions and were vulnerable to climate changes.

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