"Multi-layer" encryption of medical data in DNA for highly-secure storage.

The exponential increasement and the attributes of medical data drive the requirement for secure medical data archiving. DNA data storage shows promise for storing sensitive and important data like medical records due to its high density and endurance. Nevertheless, current DNA data storage working scheme generally does not fully consider the data encryption, posing a risk of data corruption by routine DNA sequencing. Here, we designed a "multi-layer" encryption pipeline for medical data archiving. Initially, digital information was encrypted using Blowfish algorithm at information technology (IT) layer, followed by two-layer data encryption at the biotechnology (BT) layer. The first BT layer exploited the molecular weight of synthetic DNA or nucleoside to encrypt the key, while the second BT layer encrypted digital information within DNA sequences. Consequently, decryption involved layer-by-layer interpretation of data, including mass spectroscopy, sequencing, and Blowfish decryption, significantly enhancing data security. Utilizing mass spectroscopy to retrieve information allows for employment of both natural and unnatural nucleosides, as well as their synthetic oligonucleotides, for data storage, thereby considerably boosting scalability. Our work implies expanded flexibility of DNA-based data storage, highlighting the potential for leveraging various physical and chemical characteristics of DNA molecules to encode and access digital information.

Toward DNA-Based Recording of Biological Processes.

Exploiting the inherent compatibility of DNA-based data storage with living cells, various cellular recording approaches have been developed for recording and retrieving biologically relevant signals in otherwise inaccessible locations, such as inside the body. This review provides an overview of the current state of engineered cellular memory systems, highlighting their design principles, advantages, and limitations. We examine various technologies, including CRISPR-Cas systems, recombinases, retrons, and DNA methylation, that enable these recording systems. Additionally, we discuss potential strategies for improving recording accuracy, scalability, and durability to address current limitations in the field. This emerging modality of biological measurement will be key to gaining novel insights into diverse biological processes and fostering the development of various biotechnological applications, from environmental sensing to disease monitoring and beyond.

Strategy for Simple Control of High Performance PQ/PMMA Holographic Media.

Holographic data storage technology is a cost-effective solution for long-term archival data storage. However, the development of suitable holographic recording materials remains a challenge. Among these materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) stands out due to its low cost and controllable thickness. Nevertheless, its limited photosensitivity and diffraction efficiency hinder its widespread application. In order to solve these problems, we put forward a kind of convenient and simple, low cost strategy, by adding plasticizer N,N-dimethylformamide (DMF) for preparation of DMF-PQ/PMMA photopolymer, avoid the use of complex compounds. The addition of DMF not only influences the thermal polymerization stage but also forms weak interactions with PQ during the photoreaction process, thereby enhancing the holographic performance of DMF-PQ/PMMA. Consequently, we achieved a remarkable 9.1-fold increase in photosensitivity (from ∼0.35 to 3.18 cm J-1), improved diffraction efficiency by 20% (from 65% to 80%), and reduced volume shrinkage by a factor of 8 (from 0.4% to 0.05%). Furthermore, utilizing a collinear holographic storage system with multiplexing shift at a scale of 5 µm resulted in an impressively low minimum bit error rate (BER) of only 0.36% (with an average BER of 1.4%), highlighting the fast processing capability and potential for low BER applications in holographic information storage using DMF-PQ/PMMA.

Recent progress in DNA data storage based on high-throughput DNA synthesis.

DNA data storage has emerged as a solution for storing massive volumes of data by utilizing nucleic acids as a digital information medium. DNA offers exceptionally high storage density, long durability, and low maintenance costs compared to conventional storage media such as flash memory and hard disk drives. DNA data storage consists of the following steps: encoding, DNA synthesis (i.e., writing), preservation, retrieval, DNA sequencing (i.e., reading), and decoding. Out of these steps, DNA synthesis presents a bottleneck due to imperfect coupling efficiency, low throughput, and excessive use of organic solvents. Overcoming these challenges is essential to establish DNA as a viable data storage medium. In this review, we provide the overall process of DNA data storage, presenting the recent progress of each step. Next, we examine a detailed overview of DNA synthesis methods with an emphasis on their limitations. Lastly, we discuss the efforts to overcome the constraints of each method and their prospects.

Complex Sequence-Defined Heteropolymers Enable Controlled Film Growth in Layer-By-Layer Assembly.

Digitally-encoded poly(phosphodiesters) (d-PPDE) with highly complex primary structures are evaluated for layer-by-layer (LbL) assembly. To be easily decoded by mass spectrometry (MS), these digital polymers contain many different monomers: 2 coding units allowing binary encryption, 1 cleavable spacer allowing controlled MS fragmentation, and 3 mass tags allowing fragment identification. These complex heteropolymers are therefore composed of 6 different motifs. Despite this strong sequence heterogeneity, it is found that they enable a highly controlled LbL film formation. For instance, a regular growth is observed when alternating the deposition of negatively-charged d-PPDE and positively-charged poly(allyl amine hydrochloride) (PAH). Yet, in this approach, the interdistance between consecutive coded d-PPDE layers remains relatively small, which may be an issue for data storage applications, especially for the selective decoding of the stored information. Using poly(sodium 4-styrene sulfonate) (PSS) as an intermediate non-coded polyanion, it is shown that a controlled interdistance between d-PPDE layers can be easily achieved, while still maintaining a regular LbL growth. Last but not least, it is found in this work that d-PPDE of relatively small molecular weight (i.e., significantly smaller than those of PAH and PSS) still enables a controlled LbL assembly.

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner.

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

Advanced Synthesis, Structural Characterization, and Functional Applications of Precision Polymers.

In the realm of biological macromolecules, entities such as nucleic acids and proteins are distinguished by their homochirality, consistently defined chain lengths, and integral sequence-dependent functionalities. Historically, these refined attributes have eluded traditional synthetic polymers, which often exhibit wide variabilities in chain lengths, limited batch-to-batch reproducibility, and stochastic monomer arrangements. Bridging this divide represents a pivotal challenge within the domain of polymer science-a challenge that the burgeoning discipline of precision polymer chemistry is poised to address. Recent advancements have yielded precision polymers that boast prescribed monomer sequences and narrow molecular weight distributions, heralding a new era for developing model systems to decipher structure-property correlations within functional polymers, analogous to those within biological matrices. This review discusses the innovative liquid-phase and solid-phase synthesis techniques for creating precision polymers and the advanced characterization tools essential for dissecting their structure and properties. We highlight potential applications in self-assembly, catalysis, data storage, imaging, and therapy, and provide insights into the future challenges and directions of precision polymers.

Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function.

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2[Formula: see text]3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field.

7D High-Dynamic Spin-Multiplexing.

Multiplexing technology creates several orthogonal data channels and dimensions for high-density information encoding and is irreplaceable in large-capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin-multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self-developed laser-induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high-dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra-high-capacity storage and real-time encryption.

The Telehealth chain: a framework for secure and transparent telemedicine transactions on the blockchain.

BACKGROUND: Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. AIM: A theoretical framework for creating a blockchain-based digital system created to facilitate telemedicine system. RESULTS: This paper proposes a theoretical framework based on Hyperledger fabric for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to interact remotely with patient transactions. CONCLUSION: The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger fabric platform to build a secure blockchain-assisted telemedicine platform.

Storage-D: A user-friendly platform that enables practical and personalized DNA data storage.

Deoxyribonucleic acid (DNA) has been suggested as a very promising medium for data storage in recent years. Although numerous studies have advocated for DNA data storage, its practical application remains obscure and there is a lack of a user-oriented platform. Here, we developed a DNA data storage platform, named Storage-D, which allows users to convert their data into DNA sequences of any length and vice versa by selecting algorithms, error-correction, random-access, and codec pin strategies in terms of their own choice. It incorporates a newly designed "Wukong" algorithm, which provides over 20 trillion codec pins for data privacy use. This algorithm can also control GC content to the selected standard, as well as adjust the homopolymer run length to a defined level, while maintaining a high coding potential of ~1.98 bis/nt, allowing it to outperform previous algorithms. By connecting to a commercial DNA synthesis and sequencing platform with "Storage-D," we successfully stored "Diagnosis and treatment protocol for COVID-19 patients" into 200 nt oligo pools in vitro, and 500 bp genes in vivo which replicated in both normal and extreme bacteria. Together, this platform allows for practical and personalized DNA data storage, potentially with a wide range of applications.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data.

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule-based quaternary transcoding method that satisfies bio-constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio-constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval.

Information Density Enhancement Using Lossy Compression in DNA Data Storage.

This study develops two deoxyribonucleic acid (DNA) lossy compression models, Models A and B, to encode grayscale images into DNA sequences, enhance information density, and enable high-fidelity image recovery. These models, distinguished by their handling of pixel domains and interpolation methods, offer a novel approach to data storage for DNA. Model A processes pixels in overlapped domains using linear interpolation (LI), whereas Model B uses non-overlapped domains with nearest-neighbor interpolation (NNI). Through a comparative analysis with Joint Photographic Experts Group (JPEG) compression, the DNA lossy compression models demonstrate competitive advantages in terms of information density and image quality restoration. The application of these models to the Modified National Institute of Standards and Technology (MNIST) dataset reveals their efficiency and the recognizability of decompressed images, which is validated by convolutional neural network (CNN) performance. In particular, Model B2, a version of Model B, emerges as an effective method for balancing high information density (surpassing over 20 times the typical densities of two bits per nucleotide) with reasonably good image quality. These findings highlight the potential of DNA-based data storage systems for high-density and efficient compression, indicating a promising future for biological data storage solutions.

A Novel DNA Synthesis Platform Design with High-Throughput Paralleled Addressability and High-Density Static Droplet Confinement.

Using DNA as the next-generation medium for data storage offers unparalleled advantages in terms of data density, storage duration, and power consumption as compared to existing data storage technologies. To meet the high-speed data writing requirements in DNA data storage, this paper proposes a novel design for an ultra-high-density and high-throughput DNA synthesis platform. The presented design mainly leverages two functional modules: a dynamic random-access memory (DRAM)-like integrated circuit (IC) responsible for electrode addressing and voltage supply, and the static droplet array (SDA)-based microfluidic structure to eliminate any reaction species diffusion concern in electrochemical DNA synthesis. Through theoretical analysis and simulation studies, we validate the effective addressing of 10 million electrodes and stable, adjustable voltage supply by the integrated circuit. We also demonstrate a reaction unit size down to 3.16 × 3.16 µm2, equivalent to 10 million/cm2, that can rapidly and stably generate static droplets at each site, effectively constraining proton diffusion. Finally, we conducted a synthesis cycle experiment by incorporating fluorescent beacons on a microfabricated electrode array to examine the feasibility of our design.

Coding, Decoding and Retrieving a Message Using DNA: An Experience from a Brazilian Center Research on DNA Data Storage.

DNA data storage based on synthetic oligonucleotides is a major attraction due to the possibility of storage over long periods. Nowadays, the quantity of data generated has been growing exponentially, and the storage capacity needs to keep pace with the growth caused by new technologies and globalization. Since DNA can hold a large amount of information with a high density and remains stable for hundreds of years, this technology offers a solution for current long-term data centers by reducing energy consumption and physical storage space. Currently, research institutes, technology companies, and universities are making significant efforts to meet the growing need for data storage. DNA data storage is a promising field, especially with the advancement of sequencing techniques and equipment, which now make it possible to read genomes (i.e., to retrieve the information) and process this data easily. To overcome the challenges associated with developing new technologies for DNA data storage, a message encoding and decoding exercise was conducted at a Brazilian research center. The exercise performed consisted of synthesizing oligonucleotides by the phosphoramidite route. An encoded message, using a coding scheme that adheres to DNA sequence constraints, was synthesized. After synthesis, the oligonucleotide was sequenced and decoded, and the information was fully recovered.

A scalable blockchain based framework for efficient IoT data management using lightweight consensus.

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required.

DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage.

DNA storage is one of the most promising ways for future information storage due to its high data storage density, durable storage time and low maintenance cost. However, errors are inevitable during synthesizing, storing and sequencing. Currently, many error correction algorithms have been developed to ensure accurate information retrieval, but they will decrease storage density or increase computing complexity. Here, we apply the Bloom Filter, a space-efficient probabilistic data structure, to DNA storage to achieve the anti-error, or anti-contamination function. This method only needs the original correct DNA sequences (referred to as target sequences) to produce a corresponding data structure, which will filter out almost all the incorrect sequences (referred to as non-target sequences) during sequencing data analysis. Experimental results demonstrate the universal and efficient filtering capabilities of our method. Furthermore, we employ the Counting Bloom Filter to achieve the file version control function, which significantly reduces synthesis costs when modifying DNA-form files. To achieve cost-efficient file version control function, a modified system based on yin-yang codec is developed.

Slide-block: End-to-end amplified security to improve DevOps resilience through pattern-based authentication.

DevOps represents the fusion of cultural philosophies, tools, and practices that rapidly enhance an organization's capacity to deploy services and applications. Cloud-based tools, a subset of DevOps services, facilitate collaboration between development and operations teams within an organization. However, persistent challenges such as inadequate security management, substantial leakage of sensitive data, and system/service unavailability pose significant threats to sustainability. We propose an end-to-end enhanced security framework to fortify DevOps resilience by implementing authentication and vulnerability management through the Slide-Block methodology. Our approach comprises four sequential processes: pattern-based authentication, tri-level access control, privacy-focused data storage, and vulnerability management and correction. Initially, we establish candidate legitimacy through pattern-based authentication using the Magnificent Chacha-Poly 1305 algorithm. Subsequently, we devise effective access policies using the Enhanced Deep Deterministic Policy Gradient (EDDPG) algorithm, employing tri-level access control based on trust value, attributes, and roles for optimal user and developer selection via the African Vulture Optimization Algorithm (AVOA). Moreover, we encrypt data in transit and at rest using Mcha-Poly 1305, considering sensitivity, and store it in a blockchain to enhance data privacy. Our approach incorporates a sliding window blockchain for secure data transmission and storage. Finally, we identify and address attack and application-based issues using the Tweak Naive Bayes (Tweak-NB) algorithm and Intruder Vulnerability Scanner (IVS). Our Slide-Block framework demonstrates superior performance in detection rate, authentication time, packet loss, security strengthening, communication overhead, and latency compared to existing models.

Enzymatic Preparation of DNA with an Expanded Genetic Alphabet Using Terminal Deoxynucleotidyl Transferase and Its Applications.

Efficient preparation of DNA oligonucleotides containing unnatural nucleobases (UBs) that can pair with their cognates to form unnatural base pairs (UBPs) is an essential prerequisite for the application of UBPs in vitro and in vivo. Traditional preparation of oligonucleotides containing unnatural nucleobases largely relies on solid-phase synthesis, which needs to use unstable nucleoside phosphoramidites and a DNA synthesizer, and is environmentally unfriendly and limited in product length. To overcome these limitations of solid-phase synthesis, we developed enzymatic methods for daily laboratory preparation of DNA oligonucleotides containing unnatural nucleobase dNaM, dTPT3, or one of the functionalized dTPT3 derivatives, which can be used for orthogonal DNA labeling or the preparation of DNAs containing UBP dNaM-dTPT3, one of the most successful UBPs to date, based on the template-independent polymerase terminal deoxynucleotidyl transferase (TdT). Here, we first provide a detailed procedure for the TdT-based preparation of DNA oligonucleotides containing 3'-nucleotides of dNaM, dTPT3, or one of dTPT3 derivatives. We then present the procedures for enzyme-linked oligonucleotide assay (ELONA) and imaging of bacterial cells using DNA oligonucleotides containing 3'-nucleotides of dTPT3 derivatives with different functional groups. The procedure for enzymatic synthesis of DNAs containing an internal UBP dNaM-dTPT3 is also described. Hopefully, these methods will greatly facilitate the application of UBPs and the construction of semi-synthetic organisms with an expanded genetic alphabet.

Sub-Diffraction Readout Method of High-Capacity Optical Data Storage Based on Polarization Modulation.

The big data era demands an efficient and permanent data storage technology with the capacity of PB to EB scale. Optical data storage (ODS) offers a good candidate for long-lifetime storage, as the developing far-field super-resolution nanoscale writing technology improves its capacity to the PB scale. However, methods to efficiently read out this intensive ODS data are still lacking. In this paper, we demonstrate a sub-diffraction readout method based on polarization modulation, which experimentally achieves the sub-diffraction readout on Disperse Red 13 thin film with a resolution of 500 nm, exceeding the diffraction limit by 1.2 times (NA = 0.5). Differing from conventional binary encoding, we propose a specific polarization encoding method that enhances the capacity of ODS by 1.5 times. In the simulation, our method provides an optical data storage readout resolution of 150 nm, potentially to 70 nm, equivalent to 1.1 PB in a DVD-sized disk. This sub-diffraction readout method has great potential as a powerful readout tool for next-generation optical data storage.