scholarly journals Macro-molecular data storage with petabyte/cm³ density, highly parallel read/write operations, and genuine 3D storage capability

2008 ◽  
pp. 63-77
Author(s):  
Masud Mansuripur ◽  
Pramod Khulbe
Keyword(s):  
Data Storage ◽  
Molecular Data

scholarly journals Reading mixtures of uniform sequence-defined macromolecules to increase data storage capacity

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Maximiliane Frölich ◽  
Dennis Hofheinz ◽  
Michael A. R. Meier
Keyword(s):  
Data Storage ◽  
Storage Capacity ◽  
Data Interpretation ◽  
Molecular Data ◽  
Research Topic ◽  
Side Chains ◽  
Esi Ms ◽  
Python Script ◽  
High Data ◽  
Synthetic Macromolecules

AbstractIn recent years, the field of molecular data storage has emerged from a niche to a vibrant research topic. Herein, we describe a simultaneous and automated read-out of data stored in mixtures of sequence-defined oligomers. Therefore, twelve different sequence-defined tetramers and three hexamers with different mass markers and side chains are successfully synthesised via iterative Passerini three-component reactions and subsequent deprotection steps. By programming a straightforward python script for ESI-MS/MS analysis, it is possible to automatically sequence and thus read-out the information stored in these oligomers within one second. Most importantly, we demonstrate that the use of mass-markers as starting compounds eases MS/MS data interpretation and furthermore allows the unambiguous reading of sequences of mixtures of sequence-defined oligomers. Thus, high data storage capacity considering the field of synthetic macromolecules (up to 64.5 bit in our examples) can be obtained without the need of synthesizing long sequences, but by mixing and simultaneously analysing shorter sequence-defined oligomers.

A Hadoop-based Massive Molecular Data Storage Solution for Virtual Screening

2012 ◽  
Author(s):  
Yan Zhang ◽  
Ruisheng Zhang ◽  
Qiuqiang Chen ◽  
Xiaopan Gao ◽  
Rongjing Hu ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Data Storage ◽  
Molecular Data ◽  
Storage Solution

Plasmonic Silver Nanowire Structures for Two-Dimensional Multiple-Digit Molecular Data Storage Application

ACS Photonics ◽  
2014 ◽  
Vol 1 (7) ◽  
pp. 631-637 ◽  
Author(s):  
Yan Cui ◽  
In Yee Phang ◽  
Ravi S. Hegde ◽  
Yih Hong Lee ◽  
Xing Yi Ling
Keyword(s):  
Data Storage ◽  
Molecular Data ◽  
Silver Nanowire ◽  
Two Dimensional

Healthcare Fusion: The Future is Here Now (Preprint)

2020 ◽  
Author(s):  
Kevin Zhai ◽  
Nasseer A Masoodi ◽  
Mohammad S Yousef ◽  
M. Walid Qoronfleh
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Healthcare Delivery ◽  
Disease Outbreaks ◽  
Molecular Data ◽  
Healthcare Management ◽  
Patient Centered ◽  
Public Health Agencies ◽  
Healthcare Data ◽  
Novel Coronavirus

UNSTRUCTURED Shakespeare famously wrote, “What is in a name?” What is healthcare fusion? Why is it important? Perhaps the ultimate goal for healthcare management is the delivery of effective patient-centered care in an equitable and timely manner. One essential tool to achieve this goal is an integrated and comprehensive pathway for healthcare data storage, analysis, and utilization. The potential exists for a real-time, cloud-based system that links physicians, hospitals, public health agencies, insurance and pharmaceutical companies, and most importantly, patients. The envisioned system provides a way to improve clinical quality management and deliver consistent and effective treatments. Indeed, massive integration of personalized health and large-scale epidemiological and molecular data, compounded with the use of artificial intelligence and machine learning, is underway. Here, we propose the healthcare fusion framework, which unifies the data and business sectors involved in healthcare delivery. This fusion aims to achieve culturally and demographically relevant outcomes in precision medicine and population health, in ways that appeal to stakeholders and investors. The proposed framework may prove highly relevant in informing governmental and private sector responses to infectious disease outbreaks, such as the novel coronavirus (COVID-19).

Modulation of electron transmission for molecular data storage

2003 ◽  
Vol 14 (2) ◽  
pp. 138-142 ◽  
Author(s):  
R Stadler ◽  
M Forshaw ◽  
C Joachim
Keyword(s):  
Data Storage ◽  
Molecular Data ◽  
Electron Transmission

scholarly journals Rewritable Two-Dimensional DNA-Based Data Storage with Machine Learning Reconstruction

2021 ◽  
Author(s):  
Chao Pan ◽  
S. Kasra Tabatabaei ◽  
SM Hossein Tabatabaei Yazdi ◽  
Alvaro G. Hernandez ◽  
Charles M. Schroeder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Storage ◽  
Storage System ◽  
Image Inpainting ◽  
Molecular Data ◽  
Two Dimensional ◽  
Sequencing Errors ◽  
Synthetic Dna ◽  
Copyright Information ◽  
Image Metadata

DNA-based data storage platforms traditionally encode information only in the nucleotide sequence of the molecule. Here, we report on a two-dimensional molecular data storage system that records information in both the sequence and the backbone structure of DNA. Our “2DDNA” method efficiently stores high-density images in synthetic DNA and embeds metadata as nicks in the DNA backbone. To avoid costly redundancy used to combat sequencing errors and missing information content that typically requires additional synthesis, specialized machine learning methods are developed for automatic discoloration detection and image inpainting. The 2DDNA platform is experimentally tested on a library of images that show undetectable visual degradation after processing, while the image metadata is erased and rewritten to modify copyright information. Our results show that DNA can serve both as a write-once and rewritable memory for heterogenous data. Moreover, the storage density of the molecules can be increased by using different encoding dimensions and avoiding error-correction redundancy.

Application of an image management system in microscopy

1992 ◽  
Vol 50 (2) ◽  
pp. 1052-1053
Author(s):  
Richard S. Chemock
Keyword(s):  
Data Storage ◽  
Cost Savings ◽  
Image Data ◽  
Analytical Techniques ◽  
Operational Mode ◽  
Fine Grained ◽  
Image Recording ◽  
Primary Output ◽  
Capacity Data ◽  
Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

Creating a standard method of controlling digital microscopes: the microscope access protocol (map)

1995 ◽  
Vol 53 ◽  
pp. 16-17
Author(s):  
T. A. Dodson ◽  
E. Völkl ◽  
L. F. Allard ◽  
T. A. Nolan
Keyword(s):  
Data Storage ◽  
Storage Systems ◽  
Digital Systems ◽  
Data Networks ◽  
Digital Microscopy ◽  
Command Language ◽  
Access Protocol ◽  
Analog To Digital ◽  
Basic Physics ◽  
Scanning Electron

The process of moving to a fully digital microscopy laboratory requires changes in instrumentation, computing hardware, computing software, data storage systems, and data networks, as well as in the operating procedures of each facility. Moving from analog to digital systems in the microscopy laboratory is similar to the instrumentation projects being undertaken in many scientific labs. A central problem of any of these projects is to create the best combination of hardware and software to effectively control the parameters of data collection and then to actually acquire data from the instrument. This problem is particularly acute for the microscopist who wishes to "digitize" the operation of a transmission or scanning electron microscope. Although the basic physics of each type of instrument and the type of data (images & spectra) generated by each are very similar, each manufacturer approaches automation differently. The communications interfaces vary as well as the command language used to control the instrument.

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