Correcting Errors in Image Encryption Based on DNA Coding

Medical Image Protection Algorithm Based on Deoxyribonucleic Acid Chain of Dynamic Length

Current image encryption algorithms have various deficiencies in effectively protecting medical images with large storage capacity and high pixel correlation. This article proposed a new image protection algorithm based on the deoxyribonucleic acid chain of dynamic length, which achieved image encryption by DNA dynamic coding, generation of DNA dynamic chain, and dynamic operation of row chain and column chain. First, the original image is encoded dynamically according to the binary bit from a pixel, and the DNA sequence matrix is scrambled. Second, DNA sequence matrices are dynamically segmented into DNA chains of different lengths. After that, row and column deletion operation and transposition operation of DNA dynamic chain are carried out, respectively, which made DNA chain matrix double shuffle. Finally, the encrypted image is got after recombining DNA chains of different lengths. The proposed algorithm was tested on a list of medical images. Results showed that the proposed algorithm showed excellent security performance, and it is immune to noise attack, occlusion attack, and all common cryptographic attacks.

Implementing a Symmetric Lightweight Cryptosystem in Highly Constrained IoT Devices by Using a Chaotic S-Box

In the Internet of Things (IoT), a lot of constrained devices are interconnected. The data collected from those devices can be the target of cyberattacks. In this paper, a lightweight cryptosystem that can be efficiently implemented in highly constrained IOT devices is proposed. The algorithm is mainly based on Advanced Encryption Standard (AES) and a new chaotic S-box. Since its adoption by the IEEE 802.15.4 protocol, AES in embedded platforms have been increasingly used. The main cryptographic properties of the generated S-box have been validated. The randomness of the generated S-box has been confirmed by the NIST tests. Experimental results and security analysis demonstrated that the cryptosystem can, on the one hand, reach good encryption results and respects the limitation of the sensor’s resources, on the other hand. So the proposed solution could be reliably applied in image encryption and secure communication between networked smart objects.

An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO

The high density, large capacity, and long-term stability of DNA molecules make them an emerging storage medium that is especially suitable for the long-term storage of large datasets. The DNA sequences used in storage need to consider relevant constraints to avoid nonspecific hybridization reactions, such as the No-runlength constraint, GC-content, and the Hamming distance. In this work, a new nonlinear control parameter strategy and a random opposition-based learning strategy were used to improve the Harris hawks optimization algorithm (for the improved algorithm NOL-HHO) in order to prevent it from falling into local optima. Experimental testing was performed on 23 widely used benchmark functions, and the proposed algorithm was used to obtain better coding lower bounds for DNA storage. The results show that our algorithm can better maintain a smooth transition between exploration and exploitation and has stronger global exploration capabilities as compared with other algorithms. At the same time, the improvement of the lower bound directly affects the storage capacity and code rate, which promotes the further development of DNA storage technology.

Constraining DNA Sequences With a Triplet-Bases Unpaired

DNA computing, the combination of computer science and molecular biology, is a burgeoning research field that holds promise for many applications. The accuracy of DNA computing is determined by reliable DNA sequences, the quality of which affects the accuracy of hybridization reactions. Evaluating the sequences obtained from the previous combination constraints in NUPACK for simulation experiments, we find that the concentration of the sequences after entering solution was significantly lower than that before entering solution, which should affect the accuracy of DNA hybridization reactions. Therefore, in this study, we propose a new constraint, a triplet-bases unpaired constraint, which can be combined with other constraints to form a new combination constraint. In addition, we combine the Harmony Search algorithm with the Whale Optimization Algorithm (WOA) to present a new algorithm, termed HSWOA, which we used to design DNA sequences that meet the new combination constraint. Finally, compared with previous findings, our result shows that our algorithm not only improves the efficiency of hybridization reactions but also yields a better fitness value.

Constructing Controllable Logic Circuits Based on DNAzyme Activity

Recently, DNA molecules have been widely used to construct advanced logic devices due to their unique properties, such as a simple structure and predictable behavior. In fact, there are still many challenges in the process of building logic circuits. Among them, the scalability of the logic circuit and the elimination of the crosstalk of the cascade circuit have become the focus of research. Inspired by biological allosteric regulation, we developed a controllable molecular logic circuit strategy based on the activity of DNAzyme. The E6 DNAzyme sequence was temporarily blocked by hairpin DNA and activated under appropriate input trigger conditions. Using a substrate with ribonucleobase (rA) modification as the detection strand, a series of binary basic logic gates (YES, AND, and INHIBIT) were implemented on the computational component platform. At the same time, we demonstrate a parallel demultiplexer and two multi-level cascade circuits (YES-YES and YES-Three input AND (YES-TAND)). In addition, the leakage of the cascade process was reduced by exploring factors such as concentration and DNA structure. The proposed DNAzyme activity regulation strategy provides great potential for the expansion of logic circuits in the future.

Molecular Computing and Bioinformatics

Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

Neo4j graph database realizes efficient storage performance of oilfield ontology

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.