A Novel Block Image Encryption Algorithm Based on DNA Dynamic Encoding and Chaotic System

A novel image encryption algorithm based on a hybrid model of deoxyribonucleic acid (DNA), cellular automata (CA) and chaotic system is proposed. CA due to its complex behavior has several applications such as generating random numbers and cryptography. DNA rules, DNA sequence XOR operator and CA rules are used simultaneously to encrypt the plain-image pixels. To determine rule number in DNA sequence and also CA, a 2-dimension logistic map is employed. Experimental results show that the proposed image encryption scheme has a lot of characteristics, including large key space, low correlation of adjacent cipher pixels, and high security, which can effectively protect the security of the encrypted image.

Technology is transforming rapidly. Security during data transmission is an increasingly critical and essential factor for the integrity and confidentiality of data in the financial domain, such as e-commerce transactions and bank transactions, etc. We cannot overestimate the importance of encryption/decryption of information in the digital economy. The need to strengthen and secure the digital economy is urgent. Cryptography maintains the security and integrity of data kept on computers and data communicated over the internet using encryption/decryption. A new concept in cryptography named DNA cryptography has attracted the interest of information security professionals. The DNA cryptography method hides data using a DNA sequence, with DNA encryption converting binary data into the DNA sequence. Deoxy Ribonucleic Acid (DNA) is a long polymer strand having nitrogen bases adenine (A), thymine (T), cytosine (C), and guanine (G), which play an important role in plain text encoding and decoding. DNA has high storage capacity, fast processing, and high computation capacity, and is more secure than other cryptography algorithms. DNA cryptography supports both symmetric and asymmetric cryptography. DNA cryptography can encrypt numeric values, English language and unicast. The main aim of this paper is to explain different aspects of DNA cryptography and how it works. We also compare different DNA algorithms/methods proposed in a previous paper, and implement DNA cryptography using one-time pad (OTP) and amino acid sequence using java language. OTP is used for symmetric key generation and the DNA sequence is converted to an amino acid sequence to create confusion.

A promoter is a brief stretch of DNA (100–1,000 bp) where RNA polymerase starts to transcribe a gene. A DNA (Deoxyribonucleic Acid) base pair is a fundamental unit of DNA structure and represents the pairing of two complementary nucleotide bases within the DNA double helix. The four DNA nucleotide bases are adenine (A), thymine (T), cytosine (C), and guanine (G). DNA base pairs are the building blocks of the DNA molecule, and their complementary pairing is central to the storage and transmission of genetic information in all living organisms. Normally, a promoter is found at the 5′ end of the transcription initiation site or immediately upstream. Numerous human disorders, particularly diabetes, cancer, and Huntington’s disease, have been shown to have DNA promoter as their root cause. The scientific community has long been interested in learning crucial information about protein-coding genes. Finding the promoters is therefore the first step in finding genes in DNA sequences. The scientific world has always been attracted by the effort to glean crucial knowledge about protein-coding genes. Consequently, identifying promoters has emerged as an intriguing challenge that has caught the interest of numerous researchers in the field of bioinformatics. We proposed Gaussian Decision Boundary Estimation in machine learning models to detect transcription start sites (promoters) in the DNA sequences of a common bacteria, Escherichia coli. The best features are identified through a score-based function to select relevant nucleotides that are directly responsible for promoter recognition, in order maximise the models’ performance. The Gaussian Decision Boundary Estimation based support-vector-machine model is trained with these features and finds the best hyperplane that separates the data into different classes. Throughout this study, promoter regions could be identified with high accuracy 99.9% which is better compared to other state of art algorithms. The comparison of machine learning classification models is another major emphasis of this paper in order to identify the model that most accurately predicts DNA sequence promoters. It provides analysis for further biological research as well as precision medicine.

The regulation of gene expression is controlled by multiple mechanisms. The primary level of control is embedded within the sequence itself, as the transcription factors bind specific deoxyribonucleic acid (DNA) sequences at the gene regulatory regions and either activate or silence the genes. A secondary level of control includes enhancer sequences, which may lie megabases away from the gene and are necessary for the induction of gene expression in the human genome 1. A third level of control is the three-dimensional structure of chromatin, which can bring enhancers and promoters into contact with one another and can be restricted by the CCCTC-binding factor (CTCF) that insulates chromatin segments into topologically associated domains. A number of novel methodological approaches have recently hastened characterisation of the regulatory elements in the genome 2. One commonly used approach is to couple the chromatin immunoprecipitation sequencing of the transcription factors with gene expression profiling to study the transcription factor fusion targets in leukaemia 3, 4. In comparison, the assay named global nuclear run-on sequencing allows simultaneous exploration of the dynamics of gene expression and regulatory element activity. In a cell model with inducible ETS variant 6-runt-related transcription factor 1 (ETV6-RUNX1) fusion, we were able to follow up the interplay of the enhancer elements and their host genes that occur downstream of this structural variation, which is the most common one found in childhood leukaemias 5. The ETV6-RUNX1 fusion bound the predominantly RUNX1 motif containing DNA sequences and caused repression of the enhancer activity as measured by the enhancer ribonucleic acid (RNA) levels. Interestingly, in most cases, the repression at the enhancers led to lower transcription at the nearby gene loci. In particular, the ETV6-RUNX1 aberration predominantly altered the expression of genes regulating B-cell adhesion and signalling. Furthermore, we noted a significantly higher repression of cluster of differentiation (CD)19/20 associated super-enhancers, in comparison with stem cell-linked CD34 super-enhancers 5. Super-enhancers are regions of the genome with clusterings of multiple enhancer sites and are deemed essential for lineage specification 6. They could provide a possible explanation for the disruption of B-cell differentiation, which is a hallmark of precursor leukaemias. A significant number of somatic mutations in leukaemia are located outside the coding regions of the genome. Mansour et al. 7 identified a small insertional mutation in a regulatory element at the T-cell acute lymphocytic leukaemia protein 1 (TAL1) gene locus in patients with T-cell acute lymphoblastic leukaemia. The mutation introduced a novel transcription factor binding site, for transcription factor myeloblastosis (MYB), thereby creating an aberrant super-enhancer that led to gross overexpression of the TAL1 gene. Such changes at the binding sites may also be inherited, as exemplified by the risk of neuroblastomas associated with a common single nucleotide polymorphism that alters a site where GATA transcription factors bind with DNA in a super-enhancer region of the LIM domain-only 1 (LMO1) gene 8. Moreover, the regulatory boundaries at the oncogene loci may be lost during disruption of the CTCF binding sites by mutations, as shown in T-cell acute lymphoblastic leukaemia 9, which leads to aberrant transcriptional activity. In addition to explaining the aberrant activation of oncogenes in leukaemic cells, highly active enhancers may create genetic instability. We explored the transcriptional features associated with structural variation sites in precursor B-cell leukaemias, which are represented by deletions, insertions, translocations and inversions. Structural variation sites tended to accumulate at certain loci in the leukaemia genomes and were more prevalent at actively transcribed regions. We discovered that simultaneous transcription from both DNA strands was often associated with these recurring structural variation sites 10. Enhancers that overlapped gene regions displayed this feature, which was accompanied by polymerase II stalling, access to DNA cleavage motifs and R-loop formation, a DNA:RNA hybrid formed during the transcription, which could lead to double-stranded DNA breaks that promote off-target cleavage by recombination-activating genes and activation-induced cytidine deaminase complexes. Stalled polymerases were found within the first intron of many key regulators of cell differentiation and tumour suppressor genes, including paired box protein 5 (PAX5) and B-cell translocation gene 1 (BTG1) protein 10, 11. The preleukaemic cells arrested at the pre-B stage are thereby vulnerable to secondary lesions at such genomic regions. Taken together, these observations highlighted that altered activity or mutations in both coding and noncoding regions can have a profound effect on the regulatory network that governs lymphoid cell differentiation. With the power of ever-developing sequencing techniques, these alterations, which may lead to childhood leukaemia, are now being elucidated at a genomewide scale. It is also tempting to speculate that the detection of changes in the regulatory elements of the genome might guide us to put patients in more specific stratifications as well as increase our understanding of the leukaemia as a disease.

Solid nanopore-based deoxyribonucleic acid (DNA) sequencing has led to low-cost, fast, reliable, controlled, and amplified or label-free and high-resolution recognition and identification of DNA nucleotides. Solid-state materials and biological nanopores have a low signal-to-noise ratio (SNR) and generally are too thick to read at single-nucleotide resolution. The issue with solid-state nanopores is that the DNA strands stick to the nanopore sides and on the surface during the translocation process. The coexistence of DNA nucleotides on the surface and the nanopore sides will complicate the ionic current signals, making nucleotide detection difficult. Therefore, different sized nanogaps can be promising to overcome some of these issues. Using all-atom molecular dynamics (MD) simulations, we have studied the translocation of single-stranded (ss) DNA through solid-state nanogaps embedded in a graphene membrane device. A nucleotide-specific DNA sequencing technique is proposed based on unique differences in the ionic current responses for all the four ssDNA nucleotides (dAMP16, dGMP16, dTMP16, and dCMP16). As the individual homogeneous ssDNA translocate through the nanogaps, characteristic changes are observed in the ionic current. Our results show that ssDNA nucleotides can translocate through the proposed graphene nanogap devices by applying an external electric field. In addition, the sticking issue can be resolved using graphene nanogaps during the ssDNA translocation processes. Therefore, the significant difference in ionic current sensitivity and the translocation event/time yielded by the graphene nanogap-based devices reveal possibilities for utilizing it for ultrafast nanogap-based DNA sequencing.

Deoxyribonucleic acid (DNA) profiling uses various molecular biological techniques to identify, classify, and study the ecology and evolution of organisms based on differences in DNA sequence. These differences in DNA sequence are known as polymorphisms, which may be as simple as a single nucleotide at a given nucleotide position within the genome or as complex as variant numbers of tandem repeat sequences or the presence of an interspersed repetitive element. Individuals, populations or species‐specific DNA polymorphisms are used as targets to generate fingerprint patterns suitable for the differentiation of individuals and populations. Data generated by DNA profiling methods are used to provide quantitative or qualitative information for population genetics, human diseases, diversity, phylogeny, epidemiological, forensics and conservation management purposes. Key Concepts: DNA profiling methodologies allow for the classification of organisms based on polymorphisms in DNA sequences at a given loci. DNA profiling methods that can be used to detect single nucleotide and repetitive element polymorphisms include: amplified and restriction fragment length polymorphisms (AFLP/RFLP), multilocus fingerprinting using hybridisation, random amplified polymorphic DNA and denaturing gradient and pulse field gel electrophoresis (DGGE/PFGE). DNA profiling patterns can be used to identify individuals, determine kinship and genealogy, examine inbreeding, and bottlenecks and differentiate populations.

Deoxyribonucleic acid (DNA) has become one of the most examined molecules on the planet. Scientist around the world have been trying to unravel its secrets for many purposes. For example, genetic information is currently used to raise better plants and animals, create enhanced pharmaceuticals for humans, and for gene therapy in medicine. Science as a whole has benefited from the study of genetics because of the increased understanding of biological process that all organisms share. In recent decades, a significant amount of research has been directed towards sequencing and understanding the entire human genome through the Human Genome Project (HGP) launched in 1986. The goal of the HGP was to find the location of the approximately 1×105 human genes, and read all the sequence of human genome (about 3×109 base pairs, bp). An exponential grow rate of that research has resulted in reaching the goal by 2003. Similarly, the speed of finding genes and their locations is also increasing rapidly. On the other hand, the traditional methods of finding genes and their location at chromatosomes through testing their biological function have been inherently slow. Although numerous faster techniques have been developed, there is still a need to augment them with new approaches. Therefore, robust computational solutions to the gene-finding problem could provide a valuable resource for the HGP and for the molecular-biology community. Most of the current research in the deciphering the meaning of DNA sequences is approached from the lowest base-pair level. Its main objective is to search for patterns or correlations existing in the DNA sequence related to codons, amino acids, and proteins. A number of gene-finding systems have been developed in recent decades. These systems use a variety of sophisticated computational data-miming techniques, including neural networks, dynamic programming, rule-based methods, decision trees, probability reasoning, hidden Markov chains, genetic programming, and support vector machines. Most of these approaches are based on local measures only. In addition, many of the techniques rely on the statistical qualities of exons in the gene, thus using only the known gene pool as a training set for their classification. Although the techniques have demonstrated limited success, better techniques should be developed. An approach to finding such improved techniques is to consider long-range relations (in addition to short-range relations) in the DNA sequence, spanning 104 nucleotides. If we had a good technique to measure such long-range relations, we would be able to estimate any existing self-affinity (fractality) in the DNA sequence, without any a priori assumptions about its structure. This would be a data-driven approach, rather than the common modeldriven approach. Along those lines, preliminary results have already been reported in the literature on a local self-similarity with a 180 bp periodicity in mammalian nuclear DNA sequence. Other publications have provided evidence that the long-range fractal correlations appear in DNA sequences with different values in different regions of the sequence. This paper describes such a multiscale approach, together with an algorithm based on a multifractal analysis, and demonstrates that multifractal estimates can be used to characterize DNA sequences [1], [2], [3]. This multifractal approach appears to be new, and may provide a key to cognitive analysis of DNA sequences. It should be clear that the DNA sequencing and gene finding techniques constitute a subset of bioinformatics, the science of using information to understand biology, with its numerous tools. In turn, bioinformatics is a subset of computational biology which is the application of quantitative analytical techniques in modelling biological systems. Very often, for structural biologists, DNA is not just a sequence of symbols, but implies 3D structures, molecular shapes and conformational changes, active sites, chemical reactions, and intermolecular interactions, each with a hierarchy of importance. What might be missing from this web of various approaches to solving life-related problems is cognitive informatics with its new approaches [4].

A New Slant on Old Ideas Do genes define your destiny?The field of epigenetics examines how genes and the environment interact to form the basis of heredity and comes up with some surprising findings.We often think of deoxyribonucleic acid (DNA) as the sole physical basis for heredity, the genetic code that determines everything from eye and hair color to specific personality traits.The growing field of epigenetics suggests that this traditional paradigm is an oversimplification.Epigenetics is the study of factors that are heritable, but which occur by mechanisms other than changes in the DNA code itself.C.H. Waddington first used the term epigenetics in the early 1940s to describe ''the interactions of genes with their environment, which bring the phenotype into being.''Epigenetic marks are susceptible to environmental influencesboth chemical and nonchemical-and can be inherited in ways that may seem counterintuitive (see sidebar, Expanding Ideas About Biological Inheritance).Effects of early life experiences, such as parental care or nutrition, can show up later in life and even be passed on to future generations.This emerging area of research has interesting and important implications for the field of ecotoxicology-from basic science to international policy.In classic genetics, genetic material is in the form of DNA and encodes all of the information necessary for life.

Bacterial deoxyribonucleic acid (DNA) polymerase I is a family of enzymes involved in bacterial DNA synthesis and lesion repair. These enzymes have a multidomain structure, consisting of a single polypeptide chain that encompasses a distinct polymerase domain, a proofreading 3′–5′ exonuclease and/or a 5′–3′ exonuclease activity. Members of the polymerase I family have been investigated extensively, both because of their vital role in replicating and maintaining bacterial chromosomes, and because of their importance as tools in molecular biology, in particular for the polymerase chain reaction (PCR) and DNA sequencing. Studies on the Escherichia coli DNA polymerase I in particular have yielded significant insights into the mechanism of DNA polymerisation which is shared by almost all other nucleotide polymerases. Key Concepts: Bacterial DNA polymerase I enzymes are multidomain proteins with separate polymerisation, proofreading and 5′–3′ exonuclease functions. They perform a vital function in bacteria by carrying out DNA repair and some aspects of chromosomal replication. Polymerases incorporate one nucleotide at a time to the 3′ end of the primer DNA strand in a template‐directed manner. Members of the DNA polymerase I family are able to replicate DNA with far greater accuracy than base pairing alone can account for. The polymerisation reaction is a multistep process during which the enzyme switches between substrate binding and nucleotide incorporation conformations. The 5′–3′ exonuclease allows the polymerase to degrade RNA primers and to assist with DNA repair. Thermostable family I DNA polymerases are used extensively in polymerase chain reaction to amplify DNA and for DNA sequencing.

Deoxyribonucleic acid (DNA) sequencing provides the opportunity to advance prosperity by helping individuals or developing precision medications. The potential application of a zigzag (10, 0) AlN nanotube (AlNNT) as a DNA sequencer is studied herein by using B3LYP-gCP-D3 density functional model chemistry. The energy released after adsorption of DNA nucleobases by the AlNNT lies in the following order: guanine (G, 1.22 eV) > cytosine (C, 1.12) ≈ thymine (T, 1.09) ≫ adenine (A, 0.71). Energy decomposition analysis reveals that the nucleobases that include an oxygen atom (G, C, and T) interact covalently with the AlNNT while the A base adsorbs via a π–π stacking interaction. The adsorption of DNA nucleobases on the AlNNT clearly increases its electrical conductivity, and the sensing response of the AlNNT toward G, C, T, or A is predicted to be 1339, 608, 523, or 248. Thus, an AlNNT could be applied to selectively sense G, C, T, and A, which is conducive to determining the nucleobase sequence of DNA. Short recovery times are also obtained for desorption of the DNA nucleobases.

There are continuous threats to network technologies due to its rapidly-changing nature, which raises the demand for data-safe transmission. As a result, the need to come up with new techniques for securing data and accommodating the growing quantities of information is crucial. From nature to science, the idea that genes themselves are made of information stimulated the research in molecular deoxyribonucleic acid (DNA). DNA is capable of storing huge amounts of data, which leads to its promising effect in steganography. DNA steganography is the art of using DNA as an information carrier which achieves high data storage capacity as well as high security level. Currently, DNA steganography techniques utilize the properties of only one DNA strand, since the other strand is completely dependent on the first one. This paper presents a DNA-based steganography technique that hides data into both DNA strands with respect to the dependency between the two strands. In the proposed technique, a key of the same length of the reference DNA sequence is generated after using the second DNA strand. The sender sends both the encrypted DNA message and its reference DNA sequence together into a microdot. If the recipient receives this microdot uncontaminated, the sender can safely send the generated key afterwards. The proposed technique doubles the amount of data stored and guarantees a secure transmission process as well, for even if the attacker suspects the first-sent DNA sequence, they will never receive the key, and hence full data extraction is nearly impossible. The conducted experimental study confirms the effectiveness of the proposed.

SUMMARY The changes in the level of deoxyribonucleic acid (DNA) and in the activities of alkaline and acid phosphatases were followed in the mammary glands of rats and mice killed at intervals of 3 days during pregnancy and lactation or pregnancy, lactation and involution, respectively. Polynomial regression equations were fitted to the data after they had been transformed to a logarithmic scale. During pregnancy and lactation, the general form of the curves described by the equations were similar for DNA and alkaline phosphatase, however expressed, and for acid phosphatase, other than when expressed as a concentration per mg. of DNA. All equations had a large linear term with a smaller significant negative and/or cubic term. During most of pregnancy and in early lactation the curves showed a steep, almost linear rise. During early pregnancy the slope was somewhat lower, while in the latter part of lactation the rate of increase was small or became negative. In the mouse, when the values for animals killed during involution were included in the analyses, the equations tended to be dominated by the quadratic term, a reflexion of the steep fall in the levels of all constituents after weaning. If the assumption was accepted that the average DNA content of cells did not change during the lactational cycle, the following conclusions could be drawn. In the rat, the marked similarity of the equation for total content of DNA and for concentration of DNA (per unit weight of tissue corrected for 'milk' content) indicated that the increase, and subsequent decrease, in cell numbers was mirrored almost exactly by inverse changes in average cell mass. The equations describing changes in total content of DNA (expressed in logarithms) implied that, for both species, the mitotic rate in the mammary gland was constant, or nearly constant, for most of pregnancy and the first part of lactation. In the rat and in the mouse, the increase in total alkaline phosphatase activity during pregnancy and throughout most of lactation was in part due to an increase in the enzyme activity per cell. On the other hand, the changes in total activity of acid phosphatase seemed to be due entirely to changes in the number of cells.

The detection of mycobacterial DNA sequences in uncultured clinical specimens with cloned Mycobacterium tuberculosis DNA as probes

Two of the adenovirus capsid proteins, the fiber and the hexon, complexed with either KB cell or type 5 adenovirus deoxyribonucleic acid (DNA). Maximal binding occurred at 0.01 m NaCl; increasing the ionic strength of the reaction mixture to 0.2 m NaCl resulted in a decrease in the association of either antigen to DNA. Variations of pH between 6.3 and 8.4 did not affect the binding of fiber antigen to DNA. Below pH 7.5, however, there was a small decrease in the ability of the hexon to bind nucleic acid. The association between the adenovirus structural proteins and DNA was reversible and was independent of whether the DNA was native or denatured. The fiber or hexon protein inhibited the DNA-dependent ribonucleic acid (RNA) polymerase and the DNA polymerase from KB cells. On a weight basis, the fiber protein inhibited enzymatic activity to a greater extent than the hexon. Increasing the template DNA concentration decreased this inhibition. The inhibition of the DNA-dependent RNA polymerase activity by either antigen could be reversed by increasing the ionic strength of the reaction mixture. After infection of KB cells with type 5 adenovirus, the levels of DNA and RNA polymerases remained unchanged for 15 to 20 hr. Thereafter, the specific activity of both enzymes decreased. By 30 hr postinfection, the polymerase activities were only about 30% of the enzyme activities in uninfected cells.

A novel cryptosystem using DNA sequencing and contextual array splicing system for Medical Internet of Things