On-Chip Fluorescence Switching System for Constructing a Rewritable Random Access Data Storage Device

Efficient data reconstruction: The bottleneck of large-scale application of DNA storage

Over the past decade, the rapid development of DNA synthesis and sequencing technologies has enabled preliminary use of DNA molecules for digital data storage, overcoming the capacity and persistence bottlenecks of silicon-based storage media. DNA storage has now been fully accomplished in the laboratory through existing biotechnology, which again demonstrates the viability of carbon-based storage media. However, the high cost and latency of data reconstruction pose challenges that hinder the practical implementation of DNA storage beyond the laboratory. In this article, we review existing advanced DNA storage methods, analyze the characteristics and performance of biotechnological approaches at various stages of data writing and reading, and discuss potential factors influencing DNA storage from the perspective of data reconstruction.

Data Storage Using DNA

The exponential growth of global data has outpaced the storage capacities of current technologies, necessitating innovative storage strategies. DNA, as a natural medium for preserving genetic information, has emerged as a highly promising candidate for next-generation storage medium. Storing data in DNA offers several advantages, including ultrahigh physical density and exceptional durability. Facilitated by significant advancements in various technologies, such as DNA synthesis, DNA sequencing, and DNA nanotechnology, remarkable progress has been made in the field of DNA data storage over the past decade. However, several challenges still need to be addressed to realize practical applications of DNA data storage. In this review, the processes and strategies of in vitro DNA data storage are first introduced, highlighting recent advancements. Next, a brief overview of in vivo DNA data storage is provided, with a focus on the various writing strategies developed to date. At last, the challenges encountered in each step of DNA data storage are summarized and promising techniques are discussed that hold great promise in overcoming these obstacles.

Optimization of a Deep Learning Algorithm for Security Protection of Big Data from Video Images

With the rapid development of communication technology, digital technology has been widely used in all walks of life. Nevertheless, with the wide dissemination of digital information, there are many security problems. Aiming at preventing privacy disclosure and ensuring the safe storage and sharing of image and video data in the cloud platform, the present work proposes an encryption algorithm against neural cryptography based on deep learning. Primarily, the image saliency detection algorithm is used to identify the significant target of the video image. According to the significant target, the important region and nonimportant region are divided adaptively, and the encrypted two regions are reorganized to obtain the final encrypted image. Then, after demonstrating how attackers conduct attacks to the network under the ciphertext attack mode, an improved encryption algorithm based on selective ciphertext attack is proposed to improve the existing encryption algorithm of the neural network. Besides, a secure encryption algorithm is obtained through detailed analysis and comparison of the security ability of the algorithm. The experimental results show that Bob's decryption error rate will decrease over time. The average classification error rate of Eve increases over time, but when Bob and Alice learn a secure encryption network structure, Eve's classification accuracy is not superior to random prediction. Chosen ciphertext attack-advantageous neural cryptography (CCA-ANC) has an encryption time of 14s and an average speed of 69mb/s, which has obvious advantages over other encryption algorithms. The self-learning secure encryption algorithm proposed here significantly improves the security of the password and ensures data security in the video image.

Uncertainties in synthetic DNA-based data storage

Deoxyribonucleic acid (DNA) has evolved to be a naturally selected, robust biomacromolecule for gene information storage, and biological evolution and various diseases can find their origin in uncertainties in DNA-related processes (e.g. replication and expression). Recently, synthetic DNA has emerged as a compelling molecular media for digital data storage, and it is superior to the conventional electronic memory devices in theoretical retention time, power consumption, storage density, and so forth. However, uncertainties in the in vitro DNA synthesis and sequencing, along with its conjugation chemistry and preservation conditions can lead to severe errors and data loss, which limit its practical application. To maintain data integrity, complicated error correction algorithms and substantial data redundancy are usually required, which can significantly limit the efficiency and scale-up of the technology. Herein, we summarize the general procedures of the state-of-the-art DNA-based digital data storage methods (e.g. write, read, and preservation), highlighting the uncertainties involved in each step as well as potential approaches to correct them. We also discuss challenges yet to overcome and research trends in the promising field of DNA-based data storage.

Encoding Carbon Nanotubes with Tubular Nucleic Acids for Information Storage

DNA has evolved to be a type of unparalleled material for storing and transmitting genetic information. Much recent attention has been drawn to translate the natural specificity of DNA hybridization reactions for information storage in vitro. In this work, we developed a new type of tubular nucleic acid (TNA) by condensing DNA chains on the surface of one-dimensional carbon nanotubes (CNTs). We find that DNA interacts with CNTs in a sequence-specific manner, resulting in different conformations including helix, i-motif, and G-quadruplex. Atomic force microscopic (AFM) imaging revealed that TNAs exhibit distinct patterns with characteristic height and distance that can be exploited for two-dimensional encoding on CNTs. We further demonstrate the use of TNA-CNT for information storage with visual output. This noncanonical, DNA hybridization-free strategy provides a new route to DNA-based data storage.

PAS1-modified optical SIS sensor for highly sensitive and specific detection of toluene

We report on a novel solution immersed silicon (SIS) sensor modified with bio-receptor to detect toluene. To perform this approach, bio-receptor PAS1 which specifically interacts with toluene was chosen as a capture agent for SIS ellipsometric sensing. We constructed wild PAS1 and mutant PAS1 (F46A and F79Y) which are toluene binding-defective. Especially, we utilized an easily accessible capturing approach based on silica binding peptide (SBP) for direct immobilization of PAS1 on the SiO₂ surfaces. After the immobilization of SBP-tagged PAS1 to the sensing layers, PAS1-based SIS sensor was evaluated for its ability to recognize toluene. As a result, a significant up-shift in Psi (Ψ) was clearly observed with a low limit of detection (LOD) of 0.1 μM, when treated with toluene on wild PAS1-surface, but not on mutant PAS1-sensing layers, indicating the selective interactions between PAS1 and toluene molecule. The PAS1-SIS sensor showed no changes in Psi (Ψ), if any, negligible, when exposed to benzene, phenol, xylene and 4-nitrophenol as negative controls, thereby demonstrating the specificity of interaction between PAS1 and toluene. Taken together, our results strongly indicate that PAS1-modified ellipsometry sensor can provide a high fidelity system for the accurate and selective detection of toluene.

Effect of substitution on the excited state photophysical and spectral properties of boron difluoride curcumin complex dye and their derivatives: A time dependent-DFT study

The optical, charge transport and electronic properties of boron difluoride curcumin (BFC) complex have been explored using the DFT (Density Functional Theory) method and B3LYP functional with the combination of 6-31 + G(d,p) as a basis set. The influence of substitution with various electron releasing and withdrawing groups on the above properties is analyzed and discussed in this work. The results reveal that the BFC complex on additional electron releasing substitution experiences redshifts in the optical transitions, and this is correlated with the dipole moment, NBO charges, HOMO-LUMO energy gap. Further, the absorption (λ_abs) and emission (λ_ems) spectra of substituted and unsubstituted BFCs are calculated using Time-Dependent Density Functional Theory (TD-DFT). The results show that the electron releasing groups strongly influence the absorption and emission spectra of BFC. Electron releasing groups in BFC derivatives generate the wavelength shift (Bathochromic), but the electron-withdrawing groups in BFC don't affect the λ_abs and λ_ems when compare to its original (parent) compound. The output of the research work strongly recommends that the amino, phenyl and N, N'-dimethylamino derivatives are potential candidates to act as fluorescent materials due to enhance the emission behavior of BFC and also can be used as an electron/charge transport material for organic light-emitting diodes (OLEDs).

Nucleic Acid Databases and Molecular-Scale Computing

DNA outperforms most conventional storage media in terms of information retention time, physical density, and volumetric coding capacity. Advances in synthesis and sequencing technologies have enabled implementations of large synthetic DNA databases with impressive storage capacity and reliable data recovery. Several robust DNA storage architectures featuring random access, error correction, and content rewritability have been constructed with the potential for scalability and cost reduction. We survey these recent achievements and discuss alternative routes for overcoming the hurdles of engineering practical DNA storage systems. We also review recent exciting work on in vivo DNA memory including intracellular recorders constructed by programmable genome editing tools. Besides information storage, DNA could serve as a versatile molecular computing substrate. We highlight several state-of-the-art DNA computing techniques such as strand displacement, localized hybridization chain reactions, and enzymatic reaction networks. We summarize how these simple primitives have facilitated rational designs and implementations of in vitro DNA reaction networks that emulate digital/analog circuits, artificial neural networks, or nonlinear dynamic systems. We envision these modular primitives could be strategically adapted for sophisticated database operations and massively parallel computations on DNA databases. We also highlight in vivo DNA computing modules such as CRISPR logic gates for building scalable genetic circuits in living cells. To conclude, we discuss various implications and challenges of DNA-based storage and computing, and we particularly encourage innovative work on bridging these two areas of research to further explore molecular parallelism and near-data processing. Such integrated molecular systems could lead to far-reaching applications in biocomputing, security, and medicine.

Immobilized Enzymes in Biosensor Applications

Enzyme-based biosensing devices have been extensively developed over the last few decades, and have proven to be innovative techniques in the qualitative and quantitative analysis of a variety of target substrates over a wide range of applications. Distinct advantages that enzyme-based biosensors provide, such as high sensitivity and specificity, portability, cost-effectiveness, and the possibilities for miniaturization and point-of-care diagnostic testing make them more and more attractive for research focused on clinical analysis, food safety control, or disease monitoring purposes. Therefore, this review article investigates the operating principle of enzymatic biosensors utilizing electrochemical, optical, thermistor, and piezoelectric measurement techniques and their applications in the literature, as well as approaches in improving the use of enzymes for biosensors.