Although structural DNA nanotechnology is a well-established field, computations performed using DNA algorithmic self-assembly is still in the primitive stages in terms of its adaptability of rule implementation and experimental complexity. Here, we discuss the feasibility of constructing an M-input/ N-output logic gate implemented into simple DNA building blocks. To date, no experimental demonstrations have been reported with M > 2 owing to the difficulty of tile design. To overcome this problem, we introduce a special tile referred to as an operator. We design appropriate binding domains in DNA tiles, and we demonstrate the growth of DNA algorithmic lattices generated by eight different rules from among 256 rules in a 3-input/1-output logic. The DNA lattices show simple, linelike, random, and mixed patterns, which we analyze to obtain errors and sorting factors. The errors vary from 0.8% to 12.8% depending upon the pattern complexity, and sorting factors obtained from the experiment are in good agreement with simulation results within a range of 1-18%.
Owing to its high information density, energy efficiency, and massive parallelism, DNA computing has undergone several advances and made significant contributions to nanotechnology. Notably, arithmetic calculations implemented by multiple logic gates such as adders and subtractors have received much attention because of their well-established logic algorithms and feasibility of experimental implementation. Although small molecules have been used to implement these computations, a DNA tile-based calculator has been rarely addressed owing to complexity of rule design and experimental challenges for direct verification. Here, we construct a DNA-based calculator with three types of building blocks (propagator, connector, and solution tiles) to perform addition and subtraction operations through algorithmic self-assembly. An atomic force microscope is used to verify the solutions. Our method provides a potential platform for the construction of various types of DNA algorithmic crystals (such as flip-flops, encoders, and multiplexers) by embedding multiple logic gate operations in the DNA base sequences.
Numerical simulation (e.g. Monte Carlo simulation) is an efficient computational algorithm establishing an integral part in science to understand complex physical and biological phenomena related with stochastic problems. Aside from the typical numerical simulation applications, studies calculating numerical constants in mathematics, and estimation of growth behavior via a non-conventional self-assembly in connection with DNA nanotechnology, open a novel perspective to DNA related to computational physics. Here, a method to calculate the numerical value of π, and way to evaluate possible paths of self-avoiding walk with the aid of Monte Carlo simulation, are addressed. Additionally, experimentally obtained variation of the π as functions of DNA concentration and the total number of trials, and the behaviour of self-avoiding random DNA lattice growth evaluated through number of growth steps, are discussed. From observing experimental calculations of π (πexp) obtained by double crossover DNA lattices and DNA rings, fluctuation of πexp tends to decrease as either DNA concentration or the number of trials increases. Based upon experimental data of self-avoiding random lattices grown by the three-point star DNA motifs, various lattice configurations are examined and analyzed. This new kind of study inculcates a novel perspective for DNA nanostructures related to computational physics and provides clues to solve analytically intractable problems.
A foldback intercoil (FBI) DNA nanostructure has important biological functions that are closely related to specific life phenomena, and it has a geometrically unique fourstranded DNA configuration that consists of two folded-back antiparallel DNA double helixes intertwining in the major groove by sharing the same helix axis. However, the geometrical complexity of its unusual FBI configuration has prohibited its in vitro formation from direct contact between B-form DNA duplexes. Although several efforts have been made to investigate its functionalities and configurations, the FBI structures have been rarely constructed (via structural DNA nanotechnology) and simulated (through computational biology) to determine their geometrical stability, experimental validity, and engineering feasibility. In this study, we designed an FBI configuration with a homologous DNA base sequence implemented on either a double-crossover DNA tile or a double-crossover DNA lattice which was observed using an atomic force microscope. In addition, we propose a 3-dimensional FBI structural model and perform a normal-mode analysis based on the mass-weighted chemical elastic network model. These results provide an implementation of a biological simulation in the design of unusual DNA nanostructures, a prediction of their corresponding biological functionality, and an assessment of the feasibility to construct naturally existing biological configurations through synthetic DNA molecules.
Existing firefighting robots are focused on simple storage or fire suppression outside buildings rather than detection or recognition. Utilizing a large number of robots using expensive equipment is challenging. This study aims to increase the efficiency of search and rescue operations and the safety of firefighters by detecting and identifying the disaster site by recognizing collapsed areas, obstacles, and rescuers on-site. A fusion algorithm combining a camera and threedimension light detection and ranging (3D LiDAR) is proposed to detect and localize the interiors of disaster sites. The algorithm detects obstacles by analyzing floor segmentation and edge patterns using a mask regional convolutional neural network (mask R-CNN) features model based on the visual data collected from a parallelly connected camera and 3D LiDAR. People as objects are detected using you only look once version 4 (YOLOv4) in the image data to localize persons requiring rescue. The point cloud data based on 3D LiDAR cluster the objects using the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and estimate the distance to the actual object using the center point of the clustering result. The proposed artificial intelligence (AI) algorithm was verified based on individual sensors using a sensor-mounted robot in an actual building to detect floor surfaces, atypical obstacles, and persons requiring rescue. Accordingly, the fused AI algorithm was comparatively verified.
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