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1. Functional consequences of convergently evolved microscopic skin features on snake locomotion

   https://doi.org/10.1073/pnas.2018264118  

   Rieser, Jennifer M. ; Li, Tai-De ; Tingle, Jessica L. ; Goldman, Daniel I. ; Mendelson, Joseph R. ( February 2021 , Proceedings of the National Academy of Sciences)

   null (Ed.)

   The small structures that decorate biological surfaces can significantly affect behavior, yet the diversity of animal–environment interactions essential for survival makes ascribing functions to structures challenging. Microscopic skin textures may be particularly important for snakes and other limbless locomotors, where substrate interactions are mediated solely through body contact. While previous studies have characterized ventral surface features of some snake species, the functional consequences of these textures are not fully understood. Here, we perform a comparative study, combining atomic force microscopy measurements with mathematical modeling to generate predictions that link microscopic textures to locomotor performance. We discover an evolutionary convergence in the ventral skin structures of a few sidewinding specialist vipers that inhabit sandy deserts—an isotropic texture that is distinct from the head-to-tail-oriented, micrometer-sized spikes observed on a phylogenetically broad sampling of nonsidewinding vipers and other snakes from diverse habitats and wide geographic range. A mathematical model that relates structural directionality to frictional anisotropy reveals that isotropy enhances movement during sidewinding, whereas anisotropy improves movement during slithering via lateral undulation of the body. Our results highlight how an integrated approach can provide quantitative predictions for structure–function relationships and insights into behavioral and evolutionary adaptations in biological systems. 

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2. Efficiently Learning the Topology and Behavior of a Networked Dynamical System Via Active Queries

   Rosenkrantz, D. J. ; Adiga, A. ; Marathe, M. ; Qiu, Z. ; Ravi, S. S. ; Stearns, R. ; Vullikanti, A. ( June 2022 , International Conference on Machine Learning)

   Many papers have addressed the problem of learning the behavior (i.e., the local interaction function at each node) of a networked system through active queries, assuming that the network topology is known. We address the problem of inferring both the network topology and the behavior of such a system through active queries. Our results are for systems where the state of each node is from {0, 1} and the local functions are Boolean. We present inference algorithms under both batch and adaptive query models for dynamical systems with symmetric local functions. These algorithms show that the structure and behavior of such dynamical systems can be learnt using only a polynomial number of queries. Further, we establish a lower bound on the number of queries needed to learn such dynamical systems. We also present experimental results obtained by running our algorithms on synthetic and real-world networks. 

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3. A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects’ age

   https://doi.org/10.1038/s42003-021-02451-0  

   McPherson, Brent C. ; Pestilli, Franco ( August 2021 , Communications Biology)

   Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.

    

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4. Optimization of non-equilibrium self-assembly protocols using Markov state models

   https://doi.org/10.1063/5.0130407  

   Trubiano, Anthony ; Hagan, Michael F. ( December 2022 , The Journal of Chemical Physics)

   The promise of self-assembly to enable the bottom-up formation of materials with prescribed architectures and functions has driven intensive efforts to uncover rational design principles for maximizing the yield of a target structure. Yet, despite many successful examples of self-assembly, ensuring kinetic accessibility of the target structure remains an unsolved problem in many systems. In particular, long-lived kinetic traps can result in assembly times that vastly exceed experimentally accessible timescales. One proposed solution is to design non-equilibrium assembly protocols in which system parameters change over time to avoid such kinetic traps. Here, we develop a framework to combine Markov state model (MSM) analysis with optimal control theory to compute a time-dependent protocol that maximizes the yield of the target structure at a finite time. We present an adjoint-based gradient descent method that, in conjunction with MSMs for a system as a function of its control parameters, enables efficiently optimizing the assembly protocol. We also describe an interpolation approach to significantly reduce the number of simulations required to construct the MSMs. We demonstrate our approach with two examples; a simple semi-analytic model for the folding of a polymer of colloidal particles, and a more complex model for capsid assembly. Our results show that optimizing time-dependent protocols can achieve significant improvements in the yields of selected structures, including equilibrium free energy minima, long-lived metastable structures, and transient states.

    

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5. Supramolecular Energy Materials

   https://doi.org/10.1002/adma.201907247  

   Dumele, Oliver ; Chen, Jiahao ; Passarelli, James V. ; Stupp, Samuel I. ( March 2020 , Advanced Materials)

   Self‐assembly is a bioinspired strategy to craft materials for renewable and clean energy technologies. In plants, the alignment and assembly of the light‐harvesting protein machinery in the green leaf optimize the ability to efficiently convert light from the sun to form chemical bonds. In artificial systems, strategies based on self‐assembly using noncovalent interactions offer the possibility to mimic this functional correlation among molecules to optimize photocatalysis, photovoltaics, and energy storage. One of the long‐term objectives of the field described here as supramolecular energy materials is to learn how to design soft materials containing light‐harvesting assemblies and catalysts to generate fuels and useful chemicals. Supramolecular energy materials also hold great potential in the design of systems for photovoltaics in which intermolecular interactions in self‐assembled structures, for example, in electron donor and acceptor phases, maximize charge transport and avoid exciton recombination. Possible pathways to integrate organic and inorganic structures by templating strategies and electrodeposition to create materials relevant to energy challenges including photoconductors and supercapacitors are also described. The final topic discussed is the synthesis of hybrid perovskites in which organic molecules are used to modify both structure and functions, which may include chemical stability, photovoltaics, and light emission.

    

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