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Biology subjects

Lott, R.

Publications and source records attributed to Lott, R..

2 recordsLinked to original sources

Cells use molecular working memory to navigate in changing chemoattractant fields

In order to migrate over large distances, cells within tissues and organisms rely on sensing local gradient cues. These cues however are multifarious, irregular or conflicting, changing both in time and space. Here we find that single cells utilize a molecular mechanism akin to a working memory, to generate persistent directional migration when signals are disrupted by temporally memorizing their position, while still remaining adaptive to spatial and temporal changes of the signal source. Using dynamical systems theory, we derive that these information processing capabilities are inherent for protein networks whose dynamics is maintained away from steady state through organization at criticality. We demonstrate experimentally using the Epidermal growth factor receptor (EGFR) signaling network, that the memory is maintained in the prolonged receptors activity via a slow-escaping remnant, a dynamical "ghost" of the attractor of the polarized signaling state, that further results in memory in migration. As this state is metastable, it also enables continuous adaptation of the migration direction when the signals vary in space and time. We therefore show that cells implement real-time computations without stable-states to navigate in changing chemoattractant fields by memorizing position of disrupted signals while maintaining sensitivity to novel chemical cues.

biophysics↗

Random Forest Regression for Optimizing Variable Planting Rates for Corn and Soybean Using High-Resolution Topographical and Soil Data

In recent years, planting machinery that enables precise control of the planting rates has become available for corn (Zea mays L.) and soybean (Glycine max L.). With increasingly available topographical and soil information, there is a growing interest in developing variable rate planting strategies to exploit variation in the agri-landscape in order to maximize production. A random forest regression-based approach was developed to model the interactions between planting rate, topography, and soil characteristics and their effects on yield based on on-farm variable rate planting trials for corn and soybean conducted at 27 sites in New York between 2014 and 2018 (57 site-years) in collaboration with the New York Corn and Soybean Growers Association. Planting rate ranked highly in terms of random forest regression variable importance while explaining relatively minimal yield variation in the linear context, indicating that yield response to planting rate likely depends on complex interactions with agri-landscape features. Models were moderately predictive of yield within site-years and across years at a particular site, while the ability to predict yield across sites was low. Relatedly, variable importance measures for the topographical and soil features varied considerably across sites. Together, these results suggest that local testing may provide the most accurate optimized planting rate designs due to the unique set of conditions at each site. The proposed method was extended to identify the optimal variable rate planting design for maximizing yield at each site given the topographical and soil data, and empirical validation of the resulting designs is currently underway.

plant biology↗