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Enzyme mimics (EMs) with intrinsic catalysis activity have attracted enormous interest in biomedicine. However, there is a lack of environmentally adaptive EMs for sensitive diagnosis and specific catalytic therapeutics in simultaneous manners. Herein, the coordination modulation strategy is designed to synthesize silicon-based phosphorescence enzyme-mimics (SiPEMs). Specifically, the atomic-level engineered Co–N4structure in SiPEMs enables the environment-adaptive peroxidase, oxidase, and catalase-like activities. More intriguingly, the internal Si–O networks are able to stabilize the triplet state, exhibiting long-lived phosphorescence with lifetime of 124.5 ms, suitable for millisecond-range time-resolved imaging of tumor cells and tissue in mice (with high signal-to-background ratio values of ∼60.2 for in vitroand ∼611 for in vivo). Meanwhile, the SiPEMs act as an oxidative stress amplifier, allowing the production of ·OH via cascade reactions triggered by the tumor microenvironment (∼136-fold enhancement in peroxidase catalytic efficiency); while the enzyme-mimics can scavenge the accumulation of reactive oxygen species to alleviate the oxidative damage in normal cells, they are therefore suitable for environment-adaptive catalytic treatment of cancer in specific manners. We innovate a systematic strategy to develop high-performance enzymemics, constructing a promising breakthrough for replacing traditional enzymes in cancer treatment applications.

Developmental self-assembly of DNA nanostructures provides an ideal platform for studying the power and programmability of kinetically controlled structural growth in engineered molecular systems. Triggered initiation and designated sequencing of assembly and disassembly steps have been demonstrated in structures with branches and loops. Here we introduce a new strategy for selectively activating distinct subroutines in a developmental self-assembly program, allowing structures with distinct properties to be created in response to various molecular signals. We demonstrate this strategy in triggered self-assembly of a DNA ring, the size and growth direction of which are responsive to a key molecule. We articulate that reversible assembly steps with slow kinetics at appropriate locations in a reaction pathway could enable multiple populations of structures with stimulus-responsive properties to be simultaneously created in one developmental program. These results open up a broad design space for the self-assembly of molecules with adaptive behaviors toward advanced control in synthetic materials and molecular motors.

As a common tracer in the atmosphere, airglow can be used as an important means to study the interaction between the lower atmosphere, near space, and ionosphere. In the near-Earth space, the high-altitude balloon can realize long time flight, which makes the airglow detection realize both high range resolution and time resolution. In this paper, a balloon-based multi-band airglow imager is designed, which can observe OI (557.7 nm), Na (589.3 nm), OI (630.0 nm), and OH (720–910 nm) with annular field of view (30° inner ring and 80° outer ring), and its resolution is 500 m at 250 km. The multi-band airglow imager designed in this paper is equipped in the payload cabin and raised to above 30 km for flat flying for more than 6 h. The experimental results show that the imager worked normally, and airglow images were photographed and stored; the optical system can stand the harsh environment in the near space. The multi-band airglow imager designed in this paper will take part in other near-space exploration tasks in the future and obtain corresponding results.

Herein, we report a DNA origami plasmonic nanoantenna for the programmable surface-enhanced Raman scattering (SERS) detection of cytokine release syndrome (CRS)-associated cytokines (e.g., tumor necrosis factor-α (TNF-α) and interferon-γ (IFN-γ)) in cancer immunotherapy. Typically, the nanoantenna was made of self-assembled DNA origami nanotubes (diameter: ∼19 nm; length: ∼90 nm) attached to a silver nanoparticle-modified silicon wafer (AgNP/Si). Each DNA origami nanotube contains one miniature gold nanorod (AuNR) inside (e.g., length: ∼35 nm; width: ∼7 nm). Intriguingly, TNF-α and IFN-γ logically regulate the opening of the nanotubes and the dissociation of the AuNRs from the origami structure upon binding to their corresponding aptamers. On this basis, we constructed a complete set of Boolean logic gates that read cytokine molecules as inputs and return changes in Raman signals as outputs. Significantly, we demonstrated that the presented system enables the quantification of TNF-α and IFN-γ in the serum of tumor-bearing mice receiving different types of immunotherapies (e.g., PD1/PD-L1 complex inhibitors and STING agonists). The sensing results are consistent with those of the ELISA. This strategy fills a gap in the use of DNA origami for the detection of multiple cytokines in real systems.

DNA origami tilings provide methods for creating complex molecular patterns and shapes using flat DNA origami structures as building blocks. Square tiles have been developed to construct micrometer-scale arrays and to generate patterns using stochastic or deterministic strategies. Here we show triangular tiles as a complementary approach for enriching the design space of DNA tilings and for extending the shape of the self-assembled arrays from 2D to 3D. We introduce a computational approach for maximizing binding specificity in a fully symmetric tile design, with which we construct a 20-tile structure resembling a rhombic triacontahedron. We demonstrate controlled transition between 3D and 2D structures using simple methods including tile concentration, magnesium, and fold symmetry in tile edge design. Using these approaches, we construct 2D arrays with unbounded and designed sizes. The programmability of the edge design and the flexibility of the structure make the triangular DNA origami tile an ideal building block for complex self-assembly and reconfiguration in artificial molecular machines and fabricated nanodevices.

From bacteria following simple chemical gradients1to the brain distinguishing complex odour information2, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks3, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation4has been suggested5,6as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits7and Hopfield networks8used previously3, winner-take-all circuits are computationally more powerful4, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement9,10reactions. We use a previously developed seesaw DNA gate motif3,11,12, extended to include a simple and robust component that facilitates the cooperative hybridization13that is involved in the process of selecting a ‘winner’. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits ‘1’ to ‘9’. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns ‘remembered’ during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory. DNA-strand-displacement reactions are used to implement a neural network that can distinguish complex and noisy molecular patterns from a set of nine possibilities—an improvement on previous demonstrations that distinguished only four simple patterns.

Self-assembled DNA nanostructures enable nanometre-precise patterning that can be used to create programmable molecular machines and arrays of functional materials. DNA origami is particularly versatile in this context because each DNA strand in the origami nanostructure occupies a unique position and can serve as a uniquely addressable pixel. However, the scale of such structures has been limited to about 0.05 square micrometres, hindering applications that demand a larger layout and integration with more conventional patterning methods. Hierarchical multistage assembly of simple sets of tiles can in principle overcome this limitation, but so far has not been sufficiently robust to enable successful implementation of larger structures using DNA origami tiles. Here we show that by using simple local assembly rules that are modified and applied recursively throughout a hierarchical, multistage assembly process, a small and constant set of unique DNA strands can be used to create DNA origami arrays of increasing size and with arbitrary patterns. We illustrate this method, which we term ‘fractal assembly’, by producing DNA origami arrays with sizes of up to 0.5 square micrometres and with up to 8,704 pixels, allowing us to render images such as the Mona Lisa and a rooster. We find that self-assembly of the tiles into arrays is unaffected by changes in surface patterns on the tiles, and that the yield of the fractal assembly process corresponds to about 0.95m − 1for arrays containing m tiles. When used in conjunction with a software tool that we developed that converts an arbitrary pattern into DNA sequences and experimental protocols, our assembly method is readily accessible and will facilitate the construction of sophisticated materials and devices with sizes similar to that of a bacterium using DNA nanostructures.

Scaling up the complexity and diversity of synthetic molecular structures will require strategies that exploit the inherent stochasticity of molecular systems in a controlled fashion. Here we demonstrate a framework for programming random DNA tilings and show how to control the properties of global patterns through simple, local rules. We constructed three general forms of planar network—random loops, mazes and trees—on the surface of self-assembled DNA origami arrays on the micrometre scale with nanometre resolution. Using simple molecular building blocks and robust experimental conditions, we demonstrate control of a wide range of properties of the random networks, including the branching rules, the growth directions, the proximity between adjacent networks and the size distribution. Much as combinatorial approaches for generating random one-dimensional chains of polymers have been used to revolutionize chemical synthesis and the selection of functional nucleic acids, our strategy extends these principles to random two-dimensional networks of molecules and creates new opportunities for fabricating more complex molecular devices that are organized by DNA nanostructures.

The prospects of programming molecular systems to perform complex autonomous tasks have motivated research into the design of synthetic biochemical circuits. Of particular interest to us are cell-free nucleic acid systems that exploit non-covalent hybridization and strand displacement reactions to create cascades that implement digital and analogue circuits. To date, circuits involving at most tens of gates have been demonstrated experimentally. Here, we propose a simple DNA gate architecture that appears suitable for practical synthesis of large-scale circuits involving possibly thousands of gates.

Molecular circuits capable of processing temporal information are essential for complex decision making in response to both the presence and history of a molecular environment. A particular type of temporal information that has been recognized to be important is the relative timing of signals. Here we demonstrate the strategy of temporal memory combined with logic computation in DNA strand-displacement circuits capable of making decisions based on specific combinations of inputs as well as their relative timing. The circuit encodes the timing information on inputs in a set of memory strands, which allows for the construction of logic gates that act on current and historical signals. We show that mismatches can be employed to reduce the complexity of circuit design and that shortening specific toeholds can be useful for improving the robustness of circuit behavior. We also show that a detailed model can provide critical insights for guiding certain aspects of experimental investigations that an abstract model cannot. We envision that the design principles explored in this study can be generalized to more complex temporal logic circuits and incorporated into other types of circuit architectures, including DNA-based neural networks, enabling the implementation of timing-dependent learning rules and opening up new opportunities for embedding intelligent behaviors into artificial molecular machines.

DNA catalysts are fundamental building blocks for diverse molecular information-processing circuits. Allosteric control of DNA catalysts has been developed to activate desired catalytic pathways at desired times. Here we introduce a new type of DNA catalyst that we call a cooperative catalyst: a pair of reversible reactions are employed to drive a catalytic cycle in which two signal species, which can be interpreted as an activator and an input, both exhibit catalytic behavior for output production. We demonstrate the role of a dissociation toehold in controlling the kinetics of the reaction pathway and the significance of a wobble base pair in promoting the robustness of the activator. We show near-complete output production with input and activator concentrations that are 0.1 times the gate concentration. The system involves just a double-stranded gate species and a single-stranded fuel species, as simple as the seesaw DNA catalyst, which has no allosteric control. The simplicity and modularity of the design make the cooperative DNA catalyst an exciting addition to strand-displacement motifs for general-purpose computation and dynamics.

Models of well-mixed chemical reaction networks (CRNs) have provided a solid foundation for the study of programmable molecular systems, but the importance of spatial organization in such systems has increasingly been recognized. In this paper, we explore an alternative chemical computing model introduced by Qian & Winfree in 2014, the surface CRN, which uses molecules attached to a surface such that each molecule only interacts with its immediate neighbours. Expanding on the constructions in that work, we first demonstrate that surface CRNs can emulate asynchronous and synchronous deterministic cellular automata and implement continuously active Boolean logic circuits. We introduce three new techniques for enforcing synchronization within local regions, each with a different trade-off in spatial and chemical complexity. We also demonstrate that surface CRNs can manufacture complex spatial patterns from simple initial conditions and implement interesting swarm robotic behaviours using simple local rules. Throughout all example constructions of surface CRNs, we highlight the trade-off between the ability to precisely place molecules and the ability to precisely control molecular interactions. Finally, we provide a Python simulator for surface CRNs with an easy-to-use web interface, so that readers may follow along with our examples or create their own surface CRN designs.

This paper presents an approach to enhance the resolution of refocused images by super resolution methods. In plenoptic imaging, we demonstrate that the raw sensor image can be divided to a number of low-resolution angular images with sub-pixel shifts between each other. The sub-pixel shift, which defines the super-resolving ability, is mathematically derived by considering the plenoptic camera as equivalent camera arrays. We implement simulation to demonstrate the imaging process of a plenoptic camera. A high-resolution image is then reconstructed using maximum a posteriori (MAP) super resolution algorithms. Without other degradation effects in simulation, the super resolved image achieves a resolution as high as predicted by the proposed model. We also build an experimental setup to acquire light fields. With traditional refocusing methods, the image is rendered at a rather low resolution. In contrast, we implement the super-resolved refocusing methods and recover an image with more spatial details. To evaluate the performance of the proposed method, we finally compare the reconstructed images using image quality metrics like peak signal to noise ratio (PSNR).