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Significance Most shape-morphing materials rely on nonrenewable fossil resources or finely extracted biomaterials, which need strict reaction control, elaborate processing equipment, or prefabricated templates to achieve controllable transformation. To circumvent these challenges, we developed an eco-friendly and scalable strategy for programmable shape evolution that integrates easy-to-process pollen biomass with cost-effective digital printing. Using this approach, specific geometrical features and architectures were customized to build complex materials with user-defined, shape-morphing abilities. These fabrication efforts were complemented by computational simulations to build quantitative and mechanistic insights into the biomaterial’s characteristics for creating complex shapes with methods that are suitable for scalable manufacturing., We demonstrate how programmable shape evolution and deformation can be induced in plant-based natural materials through standard digital printing technologies. With nonallergenic pollen paper as the substrate material, we show how specific geometrical features and architectures can be custom designed through digital printing of patterns to modulate hygrophobicity, geometry, and complex shapes. These autonomously hygromorphing configurations can be “frozen” by postprocessing coatings to meet the needs of a wide spectrum of uses and applications. Through computational simulations involving the finite element method and accompanying experiments, we develop quantitative insights and a general framework for creating complex shapes in eco-friendly natural materials with potential sustainable applications for scalable manufacturing.

We are amidst an ongoing flood of sequence data arising from the application of high-throughput technologies, and a concomitant fundamental revision in our understanding of how genomes evolve individually and within the biosphere. Workflows for phylogenomic inference must accommodate data that are not only much larger than before, but often more error prone and perhaps misassembled, or not assembled in the first place. Moreover, genomes of microbes, viruses and plasmids evolve not only by tree-like descent with modification but also by incorporating stretches of exogenous DNA. Thus, next-generation phylogenomics must address computational scalability while rethinking the nature of orthogroups, the alignment of multiple sequences and the inference and comparison of trees. New phylogenomic workflows have begun to take shape based on so-called alignment-free (AF) approaches. Here, we review the conceptual foundations of AF phylogenetics for the hierarchical (vertical) and reticulate (lateral) components of genome evolution, focusing on methods based on k-mers. We reflect on what seems to be successful, and on where further development is needed.

International audience; The size of available graphs has drastically increased in recent years. The real-time visualization of graphs with millions of edges is a challenge but is necessary to grasp information hidden in huge datasets. This article presents an end-to-end technique to visualize huge graphs using an established Big Data ecosystem and a lightweight client running in a Web browser. For that purpose, levels of abstraction and graph tiles are generated by a batch layer and the interactive visualization is provided using a serving layer and client-side real-time computation of edge bundling and graph splatting. A major challenge is to create techniques that work without moving data to an ad hoc system and that take advantage of the horizontal scalability of these infrastructures. We introduce two novel scalable algorithms that enable to generate a canopy clustering and to aggregate graph edges. These two algorithms are both used to produce levels of abstraction and graph tiles. We prove that our technique guarantee a quality of visualization by controlling both the necessary bandwidth required for data transfer and the quality of the produced visualization. Furthermore, we demonstrate the usability of our technique by providing a complete prototype. We present benchmarks on graphs with millions of elements and we compare our results to those obtained by state of the art techniques. Our results show that new Big Data technologies can be incorporated into visualization pipeline to push out the size limits of graphs one can visually analyze.

The analysis of the dynamic behaviour of genome-scale models of metabolism (GEMs) currently presents considerable challenges because of the difficulties of simulating such large and complex networks. Bacterial GEMs can comprise about 5000 reactions and metabolites, and encode a huge variety of growth conditions; such models cannot be used without sophisticated tool support. This article is intended to aid modellers, both specialist and non-specialist in computerized methods, to identify and apply a suitable combination of tools for the dynamic behaviour analysis of large-scale metabolic designs. We describe a methodology and related workflow based on publicly available tools to profile and analyse whole-genome-scale biochemical models. We use an efficient approximative stochastic simulation method to overcome problems associated with the dynamic simulation of GEMs. In addition, we apply simulative model checking using temporal logic property libraries, clustering and data analysis, over time series of reaction rates and metabolite concentrations. We extend this to consider the evolution of reaction-oriented properties of subnets over time, including dead subnets and functional subsystems. This enables the generation of abstract views of the behaviour of these models, which can be large—up to whole genome in size—and therefore impractical to analyse informally by eye. We demonstrate our methodology by applying it to a reduced model of the whole-genome metabolism of Escherichia coli K-12 under different growth conditions. The overall context of our work is in the area of model-based design methods for metabolic engineering and synthetic biology.

Paper is a ubiquitous material that has various applications in day to day life. A sheet of paper is produced by pressing moist wood cellulose fibers together. Paper offers unique properties: paper allows passive liquid transport, it is compatible with many chemical and biochemical moieties, it exhibits piezoelectricity, and it is biodegradable. Hence, paper is an attractive low-cost functional material for sensing devices. In recent years, researchers in the field of science and engineering have witnessed an exponential growth in the number of research contributions that focus on the development of cost-effective and scalable fabrication methods and new applications of paper-based devices. In this review article, we highlight recent advances in the development of paper-based sensing devices in the areas of electronics, energy storage, strain sensing, microfluidic devices, and biosensing, including piezoelectric paper. Additionally, this review includes current limitations of paper-based sensing devices and points out issues that have limited the commercialization of some of the paper-based sensing devices.

Inspired by the morphology characteristics of the earthworms and the excellent deformability of origami structures, this research creates a novel earthworm-like locomotion robot through exploiting the origami techniques. In this innovation, appropriate actuation mechanisms are incorporated with origami ball structures into the earthworm-like robot 'body', and the earthworm's locomotion mechanism is mimicked to develop a gait generator as the robot 'centralized controller'. The origami ball, which is a periodic repetition of waterbomb units, could output significant bidirectional (axial and radial) deformations in an antagonistic way similar to the earthworm's body segment. Such bidirectional deformability can be strategically programmed by designing the number of constituent units. Experiments also indicate that the origami ball possesses two outstanding mechanical properties that are beneficial to robot development: one is the structural multistability in the axil direction that could contribute to the robot control implementation; and the other is the structural compliance in the radial direction that would increase the robot robustness and applicability. To validate the origami-based innovation, this research designs and constructs three robot segments based on different axial actuators: DC-motor, shape-memory-alloy springs, and pneumatic balloon. Performance evaluations reveal their merits and limitations, and to prove the concept, the DC-motor actuation is selected for building a six-segment robot prototype. Learning from earthworms' fundamental locomotion mechanism-retrograde peristalsis wave, seven gaits are automatically generated; controlled by which, the robot could achieve effective locomotion with qualitatively different modes and a wide range of average speeds. The outcomes of this research could lead to the development of origami locomotion robots with low fabrication costs, high customizability, light weight, good scalability, and excellent re-configurability.