@article{7545,
abstract = {Neuronal activity often leads to alterations in gene expression and cellular architecture. The nematode Caenorhabditis elegans, owing to its compact translucent nervous system, is a powerful system in which to study conserved aspects of the development and plasticity of neuronal morphology. Here we focus on one pair of sensory neurons, termed URX, which the worm uses to sense and avoid high levels of environmental oxygen. Previous studies have reported that the URX neuron pair has variable branched endings at its dendritic sensory tip. By controlling oxygen levels and analyzing mutants, we found that these microtubule-rich branched endings grow over time as a consequence of neuronal activity in adulthood. We also find that the growth of these branches correlates with an increase in cellular sensitivity to particular ranges of oxygen that is observable in the behavior of older worms. Given the strengths of C. elegans as a model organism, URX may serve as a potent system for uncovering genes and mechanisms involved in activity-dependent morphological changes in neurons and possible adaptive changes in the aging nervous system.},
author = {Cohn, Jesse A. and Cebul, Elizabeth R. and Valperga, Giulio and Brose, Lotti and de Bono, Mario and Heiman, Maxwell G. and Pierce, Jonathan T.},
issn = {0012-1606},
journal = {Developmental Biology},
publisher = {Elsevier},
title = {{Long-term activity drives dendritic branch elaboration of a C. elegans sensory neuron}},
doi = {10.1016/j.ydbio.2020.01.005},
year = {2020},
}
@article{7546,
abstract = {The extent to which behavior is shaped by experience varies between individuals. Genetic differences contribute to this variation, but the neural mechanisms are not understood. Here, we dissect natural variation in the behavioral flexibility of two Caenorhabditis elegans wild strains. In one strain, a memory of exposure to 21% O2 suppresses CO2-evoked locomotory arousal; in the other, CO2 evokes arousal regardless of previous O2 experience. We map that variation to a polymorphic dendritic scaffold protein, ARCP-1, expressed in sensory neurons. ARCP-1 binds the Ca2+-dependent phosphodiesterase PDE-1 and co-localizes PDE-1 with molecular sensors for CO2 at dendritic ends. Reducing ARCP-1 or PDE-1 activity promotes CO2 escape by altering neuropeptide expression in the BAG CO2 sensors. Variation in ARCP-1 alters behavioral plasticity in multiple paradigms. Our findings are reminiscent of genetic accommodation, an evolutionary process by which phenotypic flexibility in response to environmental variation is reset by genetic change.},
author = {Beets, Isabel and Zhang, Gaotian and Fenk, Lorenz A. and Chen, Changchun and Nelson, Geoffrey M. and Félix, Marie-Anne and de Bono, Mario},
issn = {0896-6273},
journal = {Neuron},
number = {1},
pages = {106--121.e10},
publisher = {Cell Press},
title = {{Natural variation in a dendritic scaffold protein remodels experience-dependent plasticity by altering neuropeptide expression}},
doi = {10.1016/j.neuron.2019.10.001},
volume = {105},
year = {2020},
}
@unpublished{7553,
abstract = {Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.},
author = {Mlynarski, Wiktor F and Hledik, Michal and Sokolowski, Thomas R and Tkačik, Gašper},
booktitle = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
title = {{Statistical analysis and optimality of biological systems}},
year = {2020},
}
@article{7554,
abstract = {Slicing a Voronoi tessellation in ${R}^n$ with a $k$-plane gives a $k$-dimensional weighted Voronoi tessellation, also known as a power diagram or Laguerre tessellation. Mapping every simplex of the dual weighted Delaunay mosaic to the radius of the smallest empty circumscribed sphere whose center lies in the $k$-plane gives a generalized discrete Morse function. Assuming the Voronoi tessellation is generated by a Poisson point process in ${R}^n$, we study the expected number of simplices in the $k$-dimensional weighted Delaunay mosaic as well as the expected number of intervals of the Morse function, both as functions of a radius threshold. As a by-product, we obtain a new proof for the expected number of connected components (clumps) in a line section of a circular Boolean model in ${R}^n$.},
author = {Edelsbrunner, Herbert and Nikitenko, Anton},
issn = {10957219},
journal = {Theory of Probability and its Applications},
number = {4},
pages = {595--614},
publisher = {SIAM},
title = {{Weighted Poisson–Delaunay mosaics}},
doi = {10.1137/S0040585X97T989726},
volume = {64},
year = {2020},
}
@article{7563,
abstract = {We introduce “state space persistence analysis” for deducing the symbolic dynamics of time series data obtained from high-dimensional chaotic attractors. To this end, we adapt a topological data analysis technique known as persistent homology for the characterization of state space projections of chaotic trajectories and periodic orbits. By comparing the shapes along a chaotic trajectory to those of the periodic orbits, state space persistence analysis quantifies the shape similarity of chaotic trajectory segments and periodic orbits. We demonstrate the method by applying it to the three-dimensional Rössler system and a 30-dimensional discretization of the Kuramoto–Sivashinsky partial differential equation in (1+1) dimensions.
One way of studying chaotic attractors systematically is through their symbolic dynamics, in which one partitions the state space into qualitatively different regions and assigns a symbol to each such region.1–3 This yields a “coarse-grained” state space of the system, which can then be reduced to a Markov chain encoding all possible transitions between the states of the system. While it is possible to obtain the symbolic dynamics of low-dimensional chaotic systems with standard tools such as Poincaré maps, when applied to high-dimensional systems such as turbulent flows, these tools alone are not sufficient to determine symbolic dynamics.4,5 In this paper, we develop “state space persistence analysis” and demonstrate that it can be utilized to infer the symbolic dynamics in very high-dimensional settings.},
author = {Yalniz, Gökhan and Budanur, Nazmi B},
issn = {1089-7682},
journal = {Chaos},
number = {3},
publisher = {AIP Publishing},
title = {{Inferring symbolic dynamics of chaotic flows from persistence}},
doi = {10.1063/1.5122969},
volume = {30},
year = {2020},
}