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To ensure the confidentiality and integrity of image data and prevent unauthorized data tampering and privacy leaks. This study proposes a new color image encryption scheme based on the Mackey–Glass time-delay chaotic system and quantum random walk. This approach fully leverages the unpredictability of quantum random walks to generate random values. It combines the differences in Hamming distance between the three RGB channels of color images to create a highly complex and random key. The overall image and the three independent RGB channels are arranged in ascending order using Logistic-tent chaotic mapping and the Mackey–Glass time-delay chaotic system to obfuscate the image data. The deformed fractional-order Lorenz chaotic system is introduced, integrated with DNA encoding and decoding technology, and XOR operations are performed to achieve encryption at the spatial and pixel levels, thereby increasing the complexity of decryption. Through extensive experimental research, this solution has demonstrated excellent results in tests such as adjacent pixel correlation, information entropy, and key sensitivity. It has an excellent ability to protect the privacy of images and provides a reliable guarantee for the security of image data.

In the paper, a novel image encryption algorithm based on DNA sequence operations and chaotic systems is proposed. The encryption architecture of permutation and diffusion is adopted. Firstly, 256-bit hash value of the plain image is gotten to calculate the initial values and system parameters of the 2D Logistic-adjusted-Sine map (2D-LASM) and a new 1D chaotic system; thus, the encryption scheme highly depends on the original image. Next, the chaotic sequences from 2D-LASM are used to produce the DNA encoding/decoding rule matrix, and the plain image is encoded into a DNA matrix according to it. Thirdly, DNA level row permutation and column permutation are performed on the DNA matrix of the original image, inter-DNA-plane permutation and intra-DNA-plane permutation can be attained simultaneously, and then, DNA XOR operation is performed on the permutated DNA matrix using a DNA key matrix, and the key matrix is produced by the combination of two 1D chaotic systems. Finally, after decoding the confused DNA matrix, the cipher image is obtained. Experimental results and security analyses demonstrate that the proposed scheme not only has good encryption effect, but also is secure enough to resist against the known attacks.

In this paper, we proposed a novel and effective image encryption algorithm based on Chaos and DNA encoding rules. Piecewise Linear Chaotic Map (PWLCM) and Logistic Map are applied to generate all parameters the presented algorithm needs and DNA encoding technology functions as an auxiliary tool. The proposed algorithm consists of these parts: firstly, use PWLCM to produce a key image, whose pixels are generated by Chaos; Secondly, encode the plain image and the key image with DNA rules by rows respectively and different rows are encoded according to various rules decided by logistic map; After that, employ encoded key image to conduct DNA operations with the encoded plain image row by row to obtain an intermediate image and the specific operation executed every row is chosen by logistic map; Then, decode the intermediate image as the plain image of next step. Finally, repeat steps above by columns again to get the ultimate cipher image. The experiment results and analysis indicate that the proposed algorithm is capable of withstanding typical attacks and has good character of security.

DNA N6-methyladenine (6 mA) is one of the most vital epigenetic modifications and involved in controlling the various gene expression levels. With the avalanche of DNA sequences generated in numerous databases, the accurate identification of 6 mA plays an essential role for understanding molecular mechanisms. Because the experimental approaches are time-consuming and costly, it is desirable to develop a computation model for rapidly and accurately identifying 6 mA. To the best of our knowledge, we first proposed a computational model named i6mA-Fuse to predict 6 mA sites from the Rosaceae genomes, especially in Rosa chinensis and Fragaria vesca. We implemented the five encoding schemes, i.e., mononucleotide binary, dinucleotide binary, k-space spectral nucleotide, k-mer, and electron–ion interaction pseudo potential compositions, to build the five, single-encoding random forest (RF) models. The i6mA-Fuse uses a linear regression model to combine the predicted probability scores of the five, single encoding-based RF models. The resultant species-specific i6mA-Fuse achieved remarkably high performances with AUCs of 0.982 and 0.978 and with MCCs of 0.869 and 0.858 on the independent datasets of Rosa chinensis and Fragaria vesca, respectively. In the F. vesca-specific i6mA-Fuse, the MBE and EIIP contributed to 75% and 25% of the total prediction; in the R. chinensis-specific i6mA-Fuse, Kmer, MBE, and EIIP contribute to 15%, 65%, and 20% of the total prediction. To assist high-throughput prediction for DNA 6 mA identification, the i6mA-Fuse is publicly accessible at https://kurata14.bio.kyutech.ac.jp/i6mA-Fuse/.Key messageThe existing prediction models are not suitable to identify 6mA in the Rosaceae genome because the existing algorithms are species-specific. Thus, a novel predictor is desired to be established to identify 6mA sites in the Rosaceae genome. To the best of our knowledge, we first propose a computation model named i6mA-Fuse (Identification of N6-MethylAdenine sites by Fusing multiple feature representation) to predict 6mA sites from the Rosaceae genomes, especially in Rosa chinensis and Fragaria vesca.

Background Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhancer sequences among a myriad of DNA sequences using computational advances. Although recent studies have come up with improved performance, shortfalls in these learning models still remain. To overcome limitations of existing learning models, we introduce iEnhancer-ECNN, an efficient prediction framework using one-hot encoding and k -mers for data transformation and ensembles of convolutional neural networks for model construction, to identify enhancers and classify their strength. The benchmark dataset from Liu et al.’s study was used to develop and evaluate the ensemble models. A comparative analysis between iEnhancer-ECNN and existing state-of-the-art methods was done to fairly assess the model performance. Results Our experimental results demonstrates that iEnhancer-ECNN has better performance compared to other state-of-the-art methods using the same dataset. The accuracy of the ensemble model for enhancer identification (layer 1) and enhancer classification (layer 2) are 0.769 and 0.678, respectively. Compared to other related studies, improvements in the Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, and Matthews’s correlation coefficient (MCC) of our models are remarkable, especially for the model of layer 2 with about 11.0%, 46.5%, and 65.0%, respectively. Conclusions iEnhancer-ECNN outperforms other previously proposed methods with significant improvement in most of the evaluation metrics. Strong growths in the MCC of both layers are highly meaningful in assuring the stability of our models.

INTRODUCTION Whole genome methylation sequencing (WGMS) in blood identifies differential DNA methylation in persons with late‐onset dementia due to Alzheimer's disease (AD) but has not been tested in persons with mild cognitive impairment (MCI). METHODS We used WGMS to compare DNA methylation levels at 25,244,219 CpG loci in 382 blood samples from 99 persons with MCI, 109 with AD, and 174 who are cognitively unimpaired (CU). RESULTS WGMS identified 9756 differentially methylated positions (DMPs) in persons with MCI, including 1743 differentially methylated genes encoding proteins in biological pathways related to synapse organization, dendrite development, and ion transport. A total of 447 DMPs exhibit progressively increasing or decreasing DNA methylation levels among CU, MCI, and AD that correspond to cognitive status. DISCUSSION WGMS identifies DMPs in known and newly detected genes in blood from persons with MCI and AD that support blood DNA methylation levels can distinguish cognitive status. Highlights Whole genome methylation levels in blood from 99 persons with mild cognitive impairment (MCI), 109 with Alzheimer's disease, and 174 who are cognitively unimpaired were analyzed. Nine thousand seven hundred fifty‐six differentially methylated positions (DMPs) were identified in MCI. One thousand seven hundred forty‐three genes comprise one or more DMPs in persons with MCI. Fifty‐eight DMPs and 392 differentially methylated genes are shared among the three pairwise comparisons. Four hundred forty‐seven DMPs exhibit progressive changes that correspond to cognitive status.

The high-capacity and long-lasting storage features of DNA have enabled researchers to demonstrate its data storage capabilities.Current challenges becoming apparent are high storage costs and slow retrieval of the data.Enzymatic read-in and read-out hold promise for lower prices for DNA synthesis and data retrieval.New fast read-out options include direct optic readout and sequence-mediated signals.Graphene for fast sequencing along with DNA wires and nanocellulose as chassis boost application. The storage of digital data is becoming a worldwide problem. DNA has been recognized as a biological solution due to its ability to store genetic information without alteration over long periods. The first demonstrations of high-capacity long-lasting DNA digital data storage have been shown. However, high storage costs and slow retrieval of the data must be overcome to make DNA data storage more applicable and marketable. Herein, we discuss the issues and recent advances in DNA data storage methods and highlight pathways to make this technology more applicable to real-world digital data storage. We envision that a combination of molecular biology, nanotechnology, novel polymers, electronics, and automation with systematic development will allow DNA data storage sufficient for everyday use. The storage of digital data is becoming a worldwide problem. DNA has been recognized as a biological solution due to its ability to store genetic information without alteration over long periods. The first demonstrations of high-capacity long-lasting DNA digital data storage have been shown. However, high storage costs and slow retrieval of the data must be overcome to make DNA data storage more applicable and marketable. Herein, we discuss the issues and recent advances in DNA data storage methods and highlight pathways to make this technology more applicable to real-world digital data storage. We envision that a combination of molecular biology, nanotechnology, novel polymers, electronics, and automation with systematic development will allow DNA data storage sufficient for everyday use.

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications.

Under the Internet platform, the digital images are widely applied in many fields at present. To protect the content of multiple images, a new multiple-image encryption (MIE) algorithm based on Deoxyribonucleic acid (DNA) encoding and chaotic system is proposed in this paper. Different from the traditional image encryption algorithms, the permutation and diffusion of the new algorithm is manipulated on the three-dimensional DNA matrix. Firstly, k plain images are combined into a big image which is then encoded with the DNA codes; secondly, the big image is scrambled by a chaotic sequence; thirdly, the big image is segmented into k images with the same size of the plain images, and they are diffused by a chaotic image encoded with the DNA codes; finally, k encrypted images are obtained after the DNA decoding. SHA-256 hash value of the plain images is employed to calculate the initial values and control parameters of the chaotic systems. Experimental results and algorithm analyses show that the new encryption algorithm has excellent encryption effect and high security.

Cyanobacteria are thought to be the main N(2)-fixing organisms (diazotrophs) in marine pelagic waters, but recent molecular analyses indicate that non-cyanobacterial diazotrophs are also present and active. Existing data are, however, restricted geographically and by limited sequencing depths. Our analysis of 79,090 nitrogenase (nifH) PCR amplicons encoding 7,468 unique proteins from surface samples (ten DNA samples and two RNA samples) collected at ten marine locations world-wide provides the first in-depth survey of a functional bacterial gene and yield insights into the composition and diversity of the nifH gene pool in marine waters. Great divergence in nifH composition was observed between sites. Cyanobacteria-like genes were most frequent among amplicons from the warmest waters, but overall the data set was dominated by nifH sequences most closely related to non-cyanobacteria. Clusters related to Alpha-, Beta-, Gamma-, and Delta-Proteobacteria were most common and showed distinct geographic distributions. Sequences related to anaerobic bacteria (nifH Cluster III) were generally rare, but preponderant in cold waters, especially in the Arctic. Although the two transcript samples were dominated by unicellular cyanobacteria, 42% of the identified non-cyanobacterial nifH clusters from the corresponding DNA samples were also detected in cDNA. The study indicates that non-cyanobacteria account for a substantial part of the nifH gene pool in marine surface waters and that these genes are at least occasionally expressed. The contribution of non-cyanobacterial diazotrophs to the global N(2) fixation budget cannot be inferred from sequence data alone, but the prevalence of non-cyanobacterial nifH genes and transcripts suggest that these bacteria are ecologically significant.