Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Survey on Medical Data Storage Systems

Nowadays digital data storage and digital communication are widely used in the healthcare sector. Since data in the digital form significantly easier to store, retrieve, manipulate, analyses, and manage. Also, digital data eliminate the threat of data loss considerably. These advantages pushing many hospitals to store their data digitally. But, as the patients reveal their private and important information to the doctor, it is very crucial to maintain the privacy, security, and reliability of the healthcare data. In this process of handling the data securely, several technologies are being used like cloud storage, data warehousing, blockchain, etc. The main aim of this survey is to study the different models and technologies in the healthcare sector and analyses them on different parameters like security, privacy, performance, etc. This study will help the new developing healthcare systems to choose appropriate technology and approach to build a more efficient, robust, secure, and reliable system.

Data storage using peptide sequences

From the beginning of civilization, the media for storing data have been continuously evolving from such as stone tablets, animal bones and bamboo tablets to paper, with improvements on data density over time. Since the invention of electronics in the last century, the percentage of data stored in digital form has been increasing rapidly to almost 100% recently. Moreover, the amount of data generated has been increasing exponentially, from several ZB in 2008 to an expected 74 ZB in 2021, causing a much increased demand for data storage correspondingly. Most of the digital data are stored in physical media such as hard drives. In addition, many of the data are rarely accessed and are archived on reels of magnetic tapes. However, the physical thickness of the tapes and the size of magnetic domains limit the maximum data density, which is expected to reach a plateau soon. Furthermore, data in old tapes need to be copied onto new tapes regularly, as the magnetic tapes can normally last for ten to twenty years only. This process is time-consuming and expensive. Hence, next-generation media that can store digital data with a much higher data density and durability are needed.Here we report the use of peptide sequences for digital data storage, a method that has not been reported before. The data-bearing peptides are commercially synthesized, and the data retrieval process is described here. As an example, we stored one dataset consists of (i) 848 bits of ASCII formatted text in 40 peptides, and (ii) another dataset consists of 13752 bits of the “silent night” music in MIDI format together with its title in ASCII format in 511 peptides. These files are available in Supplementary Files section.

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.

Aerolysin nanopores decode digital information stored in tailored macromolecular analytes

Digital data storage is a growing need for our society and finding alternative solutions than those based on silicon or magnetic tapes is a challenge in the era of “big data.” The recent development of polymers that can store information at the molecular level has opened up new opportunities for ultrahigh density data storage, long-term archival, anticounterfeiting systems, and molecular cryptography. However, synthetic informational polymers are so far only deciphered by tandem mass spectrometry. In comparison, nanopore technology can be faster, cheaper, nondestructive and provide detection at the single-molecule level; moreover, it can be massively parallelized and miniaturized in portable devices. Here, we demonstrate the ability of engineered aerolysin nanopores to accurately read, with single-bit resolution, the digital information encoded in tailored informational polymers alone and in mixed samples, without compromising information density. These findings open promising possibilities to develop writing-reading technologies to process digital data using a biological-inspired platform.