Search for: All records

Methods based on upward canopy gap fractions are widely employed to measure in-situ effective LAI (Le) as an alternative to destructive sampling. However, these measurements are limited to point-level and are not practical for scaling up to larger areas. To address the point-to-landscape gap, this study introduces an innovative approach, named NeRF-LAI, for corn and soybean Le estimation that combines gap-fraction theory with the neural radiance field (NeRF) technology, an emerging neural network-based method for implicitly representing 3D scenes using multi-angle 2D images. The trained NeRF-LAI can render downward photorealistic hemispherical depth images from an arbitrary viewpoint in the 3D scene, and then calculate gap fractions to estimate Le. To investigate the intrinsic difference between upward and downward gaps estimations, initial tests on virtual corn fields demonstrated that the downward Le matches well with the upward Le, and the viewpoint height is insensitive to Le estimation for a homogeneous field. Furthermore, we conducted intensive real-world experiments at controlled plots and farmer-managed fields to test the effectiveness and transferability of NeRF-LAI in real-world scenarios, where multi-angle UAV oblique images from different phenological stages were collected for corn and soybeans. Results showed the NeRF-LAI is able to render photorealistic synthetic images with an average peak signal-to-noise ratio (PSNR) of 18.94 for the controlled corn plots and 19.10 for the controlled soybean plots. We further explored three methods to estimate Le from calculated gap fractions: the 57.5° method, the five-ring-based method, and the cell-based method. Among these, the cell-based method achieved the best performance, with the r2 ranging from 0.674 to 0.780 and RRMSE ranging from 1.95 % to 5.58 %. The Le estimates are sensitive to viewpoint height in heterogeneous fields due to the difference in the observable foliage volume, but they exhibit less sensitivity to relatively homogeneous fields. Additionally, the cross-site testing for pixel-level LAI mapping showed the NeRF-LAI significantly outperforms the VI-based models, with a small variation of RMSE (0.71 to 0.95 m2/m2) for spatial resolution from 0.5 m to 2.0 m. This study extends the application of gap fraction-based Le estimation from a discrete point scale to a continuous field scale by leveraging implicit 3D neural representations learned by NeRF. The NeRF-LAI method can map Le from raw multi-angle 2D images without prior information, offering a potential alternative to the traditional in-situ plant canopy analyzer with a more flexible and efficient solution. 

Abstract DNA tiles serve as the fundamental building blocks for DNA self-assembled nanostructures such as DNA arrays, origami, and designer crystals. Introducing additional binding arms to DNA crossover tiles holds the promise of unlocking diverse nano-assemblies and potential applications. Here, we present one-, two-, and three-layer T-shaped crossover tiles, by integrating T junction with antiparallel crossover tiles. These tiles carry over the orthogonal binding directions from T junction and retain the rigidity from antiparallel crossover tiles, enabling the assembly of various 2D tessellations. To demonstrate the versatility of the design rules, we create 2-state reconfigurable nanorings from both single-stranded tiles and single-unit assemblies. Moreover, four sets of 4-state reconfiguration systems are constructed, showing effective transformations between ladders and/or rings with pore sizes spanning ~20 nm to ~168 nm. These DNA tiles enrich the design tools in nucleic acid nanotechnology, offering exciting opportunities for the creation of artificial dynamic DNA nanopores. 

We created 29 parallel double-crossover DNA motifs varying in hybridization pathways, domain lengths, and crossover locations, producing diverse assemblies. 

DNA nanotechnology has been proven to be a powerful platform to assist the development of imaging probes for biomedical research. The attractive features of DNA nanostructures, such as nanometer precision, controllable size, programmable functions, and biocompatibility, have enabled researchers to design and customize DNA nanoprobes for bioimaging applications. However, DNA probes with low molecular weights (e.g., 10–100 nt) generally suffer from low stability in physiological buffer environments. To improve the stability of DNA nanoprobes in such environments, DNA nanostructures can be designed with relatively larger sizes and defined shapes. In addition, the established modification methods for DNA nanostructures are also essential in enhancing their properties and performances in a physiological environment. In this review, we begin with a brief recap of the development of DNA nanostructures including DNA tiles, DNA origami, and multifunctional DNA nanostructures with modifications. Then we highlight the recent advances of DNA nanostructures for bioimaging, emphasizing the latest developments in probe modifications and DNA-PAINT imaging. Multiple imaging modules for intracellular biomolecular imaging and cell membrane biomarkers recognition are also summarized. In the end, we discuss the advantages and challenges of applying DNA nanostructures in bioimaging research and speculate on its future developments.