Recent advances in crosstalk simulation using integer-order memristive synapses have shown considerable progress. However, most existing models still employ a single-memristor structure, which constrains synaptic weight modulation and makes it difficult to represent both excitatory and inhibitory synaptic connections in a unified manner. These models also often fail to capture the memory effects and nonlocal dynamic properties inherent in biological neurons. To address these issues, this study introduces a fractional-order memristive bridge synapse model for crosstalk coupling. By combining Hindmarsh–Rose (HR) and FitzHugh–Nagumo (FN) neurons, we construct an 8D heterogeneous coupled neural network based on fractional calculus—designated as the Fractional-Order Memristive Bridge Crosstalk-Coupled Neural Network (FMBCCNN). A major innovation is the incorporation of a fractional-order memristive bridge structure that mimics synaptic connections in a bridge configuration. This design provides both historical memory characteristics and bidirectional synaptic weight regulation, overcoming limitations of traditional coupling forms.<br>Using dynamical analysis tools such as phase portraits, bifurcation diagrams, and Lyapunov exponents, we systematically investigate how synaptic and crosstalk strengths influence system behavior under conventional fractional-order conditions. The results reveal diverse dynamical behaviors, including attractor coexistence, forward and reverse period-doubling bifurcations, and chaotic crises. Further analysis under the more generalized condition of non-uniform fractional orders shows that, compared with the conventional case, the system maintains continuous periodic motion over broader parameter ranges and exhibits clear parameter hysteresis. Although local dynamic patterns remain similar, the corresponding parameter intervals are substantially widened. In addition, the system displays more concentrated and marked alternation between periodic and chaotic behaviors. We also simulate the effect of varying the fractional-order derivative, offering a more general mathematical characterization of neuronal firing activity.<br>Finally, the chaotic sequences generated by the system are applied to an image encryption algorithm incorporating bit-plane decomposition and DNA encoding. Security analysis confirms that the encrypted images have pixel correlation coefficients below 0.01 in horizontal, vertical, and diagonal directions, information entropy greater than 7.999, and a key space of 2<sup>2080</sup>. These results verify the excellent encryption performance and reliability of the proposed scheme and the generated sequences.

Cloneable contrast across all biological length scales.

Pneumocystis species are obligate fungal pathogens that cause severe pneumonia, particularly in immunocompromised individuals. The absence of robust genetic manipulation tools has impeded our mechanistic understanding of Pneumocystis biology and the development of novel therapeutic strategies. Herein, we describe a novel method for the stable transformation and CRISPR/Cas9-mediated genetic editing of Pneumocystis murina utilizing extracellular vesicles (EVs) as a delivery vehicle. Building upon our prior investigations demonstrating EV-mediated delivery of exogenous material to Pneumocystis, we engineered mouse lung EVs to deliver plasmid DNA encoding reporter genes and CRISPR/Cas9 components. Our initial findings demonstrated successful in vitro transformation and subsequent expression of mNeonGreen and DhpsARS in P. murina organisms. Subsequently, we established stable in vivo expression of mNeonGreen in mice infected with transformed P. murina for a duration of up to 5 weeks. Furthermore, we designed and validated a CRISPR/Cas9 system targeting the P. murina Dhps gene, confirming DNA cleavage efficiency in vitro. Ultimately, we achieved successful in vivo CRISPR/Cas9-mediated homologous recombination, precisely introducing a DhpsARS mutation into the P. murina genome, which was confirmed by Sanger sequencing across all tested animals. Here, we establish a foundational methodology for genetic manipulation in Pneumocystis, thereby opening avenues for functional genomics, drug target validation, and the generation of genetically modified strains for advanced research and potential therapeutic applications.IMPORTANCEPneumocystis species are obligate fungal pathogens and major causes of pneumonia in immunocompromised individuals. However, their strict dependence on the mammalian lung environment has precluded the development of genetic manipulation systems, limiting our ability to interrogate gene function, study antifungal resistance mechanisms, or validate therapeutic targets. Here, we report the first successful approach for stable transformation and CRISPR/Cas9-based genome editing of Pneumocystis murina, achieved through in vivo delivery of engineered extracellular vesicles containing plasmid DNA and encoding CRISPR/Cas9 components. We demonstrate sustained transgene expression and precise modification of the dhps locus via homology-directed repair. This modular, scalable platform overcomes a long-standing barrier in the field and establishes a foundation for functional genomics in Pneumocystis and other obligate, host-adapted microbes.

In this paper, an optical image encryption using multimodal biometric keys under the framework of three-step phase-shifting digital holography is proposed. In the encryption process, first, the grayscale image is encrypted into the DNA compilation result by using DNA encoding with an iris chaotic mask; then, the DNA compilation result is encrypted into the three ciphertext holograms using phase-shifting digital holography and modified double random phase encoding (MDRPE) with the fingerprint and finger vein chaotic masks. In the decryption process, first, authentication is performed by decrypting and verifying three types of biometric features: iris, fingerprint, and finger vein; if the authentication is successful, the iris chaotic mask for subsequent decryption and the conjugate chaotic masks for both fingerprint and finger vein are generated by the decryption system. Finally, the final decryption image is obtained by utilizing digital holographic reconstruction technology and DNA decoding technology. To demonstrate the feasibility of the proposed method, a series of numerical simulations are performed, and the simulation results confirm that the proposed method shows high feasibility and robustness against various attacks, and the integration of multimodal biometric keys contributes to a high level of security, where each biometric key exhibits strong robustness against potential attacks.

Enhanced image encryption utilizing DNA encoding and hyperchaotic permutation for robust security

Privacy-preserving online medical image exchange via hyperchaotic memristive neural networks and DNA encoding

With the rapid advancement of hyperspectral remote sensing technology, the security of hyperspectral images (HSIs) has become a critical concern. However, traditional image encryption methods-designed primarily for grayscale or RGB images-fail to address the high dimensionality, large data volume, and spectral-domain characteristics inherent to HSIs. Existing chaotic encryption schemes often suffer from limited chaotic performance, narrow parameter ranges, and inadequate spectral protection, leaving HSIs vulnerable to spectral feature extraction and statistical attacks. To overcome these limitations, this paper proposes a novel hyperspectral image encryption algorithm based on a newly designed two-dimensional cross-coupled hyperchaotic map (2D-CSCM), which synergistically integrates Cubic, Sinusoidal, and Chebyshev maps. The 2D-CSCM exhibits superior hyperchaotic behavior, including a wider hyperchaotic parameter range, enhanced randomness, and higher complexity, as validated by Lyapunov exponents, sample entropy, and NIST tests. Building on this, a layered encryption framework is introduced: spectral-band scrambling to conceal spectral curves while preserving spatial structure, spatial pixel permutation to disrupt correlation, and a bit-level diffusion mechanism based on dynamic DNA encoding, specifically designed to secure high bit-depth digital number (DN) values (typically >8 bits). Experimental results on multiple HSI datasets demonstrate that the proposed algorithm achieves near-ideal information entropy (up to 15.8107 for 16-bit data), negligible adjacent-pixel correlation (below 0.01), and strong resistance to statistical, cropping, and differential attacks (NPCR ≈ 99.998%, UACI ≈ 33.30%). The algorithm not only ensures comprehensive encryption of both spectral and spatial information but also supports lossless decryption, offering a robust and practical solution for secure storage and transmission of hyperspectral remote sensing imagery.

The exponential growth of digital data transmission and storage demands robust encryption mechanisms capable of securing sensitive multimedia content across diverse platforms. This comprehensive review examines the convergence of chaotic systems, DNA encoding techniques, and big data analytics in developing next-generation image encryption algorithms. Through systematic analysis of recent advances in chaos-based cryptographic methods, this paper evaluates the effectiveness of hybrid approaches combining chaotic maps, DNA computing, and machine learning techniques for multimedia security. The study synthesizes findings from 24 peer-reviewed publications spanning 2022-2024, highlighting breakthrough methodologies in bit-level encryption, visual cryptography, and real-time security applications. Our analysis reveals that DNA-chaos hybrid systems achieve superior entropy rates (>7.99), correlation coefficients approaching zero, and processing speeds suitable for real-time applications. The integration of big data analytics enhances key generation mechanisms and provides adaptive security frameworks capable of responding to evolving cyber threats. These findings contribute significantly to the development of quantum-resistant encryption protocols and establish foundational principles for secure communication in the era of big data.

The exponential growth of digital data transmission and storage demands robust encryption mechanisms capable of securing sensitive multimedia content across diverse platforms. This comprehensive review examines the convergence of chaotic systems, DNA encoding techniques, and big data analytics in developing next-generation image encryption algorithms. Through systematic analysis of recent advances in chaos-based cryptographic methods, this paper evaluates the effectiveness of hybrid approaches combining chaotic maps, DNA computing, and machine learning techniques for multimedia security. The study synthesizes findings from 24 peer-reviewed publications spanning 2022-2024, highlighting breakthrough methodologies in bit-level encryption, visual cryptography, and real-time security applications. Our analysis reveals that DNA-chaos hybrid systems achieve superior entropy rates (>7.99), correlation coefficients approaching zero, and processing speeds suitable for real-time applications. The integration of big data analytics enhances key generation mechanisms and provides adaptive security frameworks capable of responding to evolving cyber threats. These findings contribute significantly to the development of quantum-resistant encryption protocols and establish foundational principles for secure communication in the era of big data.

Secure data communication is in growing demand in today's 6G era. Cryptographic schemes must be employed to protect the data over the air and facilitate secure communication. This paper proposes a novel quantum image encryption algorithm with Quantum True Random Number Generation (QTRNG) and an image-dependent 6D hyperchaotic system to enhance security. RX and RZ quantum gates generated Quantum True Random key sequences by introducing an exponential phase shift to the input. The encryption process integrates multistage DNA encoding and QTRNG to develop the initial encrypted image, which is represented by Novel Enhanced Quantum Representation (NEQR). The encrypted image is hashed using SHA-384 to generate 6D keys, followed by Quantum Fibonacci and bit-level scrambling to perform further quantum encryption. This process is repeated over three stages, each employing different hyperchaotic keys, DNA rules, and operations. The encryption metrics confirm the algorithm's reliability and robustness regarding security.

A color image encryption scheme hybridizing two finite rings and DNA encoding

DNA storage is considered to be a promising storage media in the current era of data explosion. DNA encoding is the beginning of the DNA storage process and lays the foundation for subsequent processes. However, many encoding methods suffer from low encoding rate, do not satisfy important constraints, or have insufficient sequence stability. To address these issues and improved sequences stability, this paper proposes a novel approach called the Repeating Substring Tree Encoding (RSTE) method. The method begins by applying the Longest Substring Backtracking Method (LSBM) to identify the longest repeated substrings within the binary file. These substrings are then encoded into compact DNA motifs using Huffman encoding. In contrast to the ideal coding density of 2 bits per nucleotide (2 bit/nt) targeted by previous studies, RSTE enhances the encoding rate by 13% through efficient utilization of repeated substrings. Furthermore, the DNA sequences generated by the RSTE method successfully meet three biological constraints: run-length limitation, GC content balance and end constraints. The experimental results of minimum free energy and melting temperature indicate that the stability of the sequences encoded by RSTE is also greatly improved. A series of experiments showed that the sequences encoded by RSTE have a higher coding rate, satisfy constraints, and are more stable.

Tamper detection, localization and self-recovery using slant transform with DNA encoding for medical images

As multimedia technologies evolve, digital images have become increasingly prevalent across various fields, highlighting an urgent demand for robust image privacy and security mechanisms. However, existing image encryption algorithms (IEAs) still face limitations in balancing strong security, real-time performance, and computational efficiency. Therefore, we proposes a new IEA that integrates an improved chaotic map (Tent map), an improved Zigzag transform, and dynamic DNA coding. Firstly, a pseudo-wavelet transform (PWT) is applied to plain images to produce four sub-images I1, I2, I3, and I4. Secondly, the improved Zigzag transform and its three variants are used to rearrange the sub-image I1, and then the scrambled sub-image is diffused using XOR operation. Thirdly, an inverse pseudo-wavelet transform (IPWT) is employed on the four sub-images to reconstruct the image, and then the reconstructed image is encoded into a DNA sequence utilizing dynamic DNA encoding. Finally, the DNA sequence is scrambled and diffused employing DNA-level index scrambling and dynamic DNA operations. The experimental results and performance evaluations, including chaotic performance evaluation and comprehensive security analysis, demonstrate that our IEA achieves high key sensitivity, low correlation, excellent entropy, and strong resistance to common attacks. This highlights its potential for deployment in real-time, high-security image cryptosystems, especially in fields such as medical image security and social media privacy.

Ex Vivo Electroporation of Chick Embryo Cerebellar Slices: A Method to Introduce Plasmid DNA Encoding Green Fluorescent Protein to Visualize Granular Cell Development

Digital images have been widely applied in fields such as mobile devices, the Internet of Things, and medical imaging. Although significant progress has been made in image encryption technology, it still faces many challenges, such as attackers using powerful computing resources and advanced algorithms to crack encryption systems. To address these challenges, this paper proposes a novel image encryption algorithm based on one-dimensional sawtooth wave chaotic system (1D-SAW) and the three-strand structure of DNA. Firstly, a new 1D-SAW chaotic system was designed. By introducing nonlinear terms and periodic disturbances, this system is capable of generating chaotic sequences with high randomness and initial value sensitivity. Secondly, a new diffusion rule based on the three-strand structure of DNA is proposed. Compared with the traditional DNA encoding and XOR operation, this rule further enhances the complexity and anti-attack ability of the encryption process. Finally, the security and randomness of the 1D-SAW and image encryption algorithms were verified through various tests. Results show that this method exhibits better performance in resisting statistical attacks and differential attacks.

To meet the growing demand for secure and reliable image protection in digital communication, this paper proposes a novel image encryption framework that addresses the challenges of high plaintext sensitivity, resistance to statistical attacks, and key security. The method combines a two-dimensional dynamically coupled map lattice (2D DCML) with elementary cellular automata (ECA) to construct a heterogeneous chaotic system with strong spatiotemporal complexity. To further enhance nonlinearity and diffusion, a multi-source perturbation mechanism and adaptive DNA encoding strategy are introduced. These components work together to obscure the image structure, pixel correlations, and histogram characteristics. By embedding spatial and temporal symmetry into the coupled lattice evolution and perturbation processes, the proposed method ensures a more uniform and balanced transformation of image data. Meanwhile, the method enhances the confusion and diffusion effects by utilizing the principle of symmetric perturbation, thereby improving the overall security of the system. Experimental evaluations on standard images demonstrate that the proposed scheme achieves high encryption quality in terms of histogram uniformity, information entropy, NPCR, UACI, and key sensitivity tests. It also shows strong resistance to chosen plaintext attacks, confirming its robustness for secure image transmission.

This study proposes a novel methodology for encrypting color digital images and embedding them within a cover image to enhance secure data transmission. The first stage focuses on encrypting the secret image using an integrated set of algorithms, including a six-dimensional hyper-chaotic system for generating complex encryption keys, alongside encoding inspired by the complementary DNA base pairs to strengthen data obfuscation. In the second stage, the encrypted image is embedded into the cover image employing multiple fusion techniques such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD), ensuring effective and robust data hiding with high resistance to detection attacks. The encryption and embedding quality were evaluated using standard metrics including Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Entropy, reflecting the efficiency and security of the proposed approach. Keywords: Discrete Wavelet Transformation (DWT), Discrete Cosine Transformation (DCT), Singular Value Decomposition (SVD), 6-dimensional hyper chaotic system and DNA encoding

Pneumocystis species are obligate fungal pathogens that cause severe pneumonia, particularly in immunocompromised individuals. The absence of robust genetic manipulation tools has impeded our mechanistic understanding of Pneumocystis biology and the development of novel therapeutic strategies. Herein, we describe a novel method for the stable transformation and CRISPR/Cas9-mediated genetic editing of Pneumocystis murina utilizing extracellular vesicles (EVs) as a delivery vehicle. Building upon our prior investigations demonstrating EV-mediated delivery of exogenous material to Pneumocystis, we engineered mouse lung EVs to deliver plasmid DNA encoding reporter genes and CRISPR/Cas9 components. Our initial findings demonstrated successful in vitro transformation and subsequent expression of mNeonGreen and DhpsARS in P. murina organisms. Subsequently, we established stable in vivo expression of mNeonGreen in mice infected with transformed P. murina for a duration of up to 5 weeks. Furthermore, we designed and validated a CRISPR/Cas9 system targeting the P. murina Dhps gene, confirming its in vitro cleavage efficiency. Ultimately, we achieved successful in vivo CRISPR/Cas9-mediated homologous recombination, precisely introducing a DhpsARS mutation into the P. murina genome, which was confirmed by Sanger sequencing across all tested animals. Here, we establish a foundational methodology for genetic manipulation in Pneumocystis, thereby opening avenues for functional genomics, drug target validation, and the generation of genetically modified strains for advanced research and potential therapeutic applications.

Health services and telemedicine have proven to be an important area for information protection in research, especially with medical services and smart health care applications. In these systems, medical imaging protection are important not only for clinical diagnosis, but also to protect the very sensitive and confidential patient data. With progress in imaging technologies and biomedical processing algorithms, the amount of image data increases rapidly. However, securing this information while transferring through insecure channel is still a constant challenge. Existing encryption techniques often face limitations such as high computational complexity, insufficient security against advanced cryptographic attacks, poor reversal and pixel correlation. To overcome these challenges, the proposed approach provides an innovative hybrid encryption technique that integrates DNA cryptography with Elliptical Curve Cryptography (ECC). The DNA-based coding shows high randomness and equality while the ECC provides strong security and confidentiality. The DNA encoding and secure key generation are employed in the proposed technique to obtain the encrypted medical image. The combination of these techniques addresses the main boundaries of existing disadvantage by increasing both security and calculation efficiency, making it well suited for real time medical applications. The experimental analysis was carried out with various parameters like histogram analysis, correlation coefficient, Chi square, MSE, PSNR, entropy etc. The result analysis states that the proposed methodology outperforms the state-of-the-art existing methods with enhanced performance such as entropy of 7.9981, Correlation coefficient of 0.0019 and PSNR of 53.97. Also, the proposed methodology is tested for runtime analysis, memory analysis and security analysis.