Artificial Deoxyribonucleic Acid Research Articles

High-throughput DNA synthesis for data storage.

With the explosion of digital world, the dramatically increasing data volume is expected to reach 175 ZB (1 ZB = 1012 GB) in 2025. Storing such huge global data would consume tons of resources. Fortunately, it has been found that the deoxyribonucleic acid (DNA) molecule is the most compact and durable information storage medium in the world so far. Its high coding density and long-term preservation properties make itself one of the best data storage carriers for the future. High-throughput DNA synthesis is a key technology for "DNA data storage", which encodes binary data stream (0/1) into quaternary long DNA sequences consisting of four bases (A/G/C/T). In this review, the workflow of DNA data storage and the basic methods of artificial DNA synthesis technology are outlined first. Then, the technical characteristics of different synthesis methods and the state-of-the-art of representative commercial companies, with a primary focus on silicon chip microarray-based synthesis and novel enzymatic DNA synthesis are presented. Finally, the recent status of DNA storage and new opportunities for future development in the field of high-throughput, large-scale DNA synthesis technology are summarized.

An Artificial Intelligence Approach for Verifying Persons by Employing the Deoxyribonucleic Acid (DNA) Nucleotides

Deoxyribonucleic acid (DNA) can be considered as one of the most useful biometrics. It has effectively been used for recognizing persons. However, it seems that there is still a need to propose a new approach for verifying humans, especially after the recent big wars, where too many people lost and die. This approach should have the capability to provide high personal verification performance. In this paper, a personal recognition approach based on artificial intelligence is proposed. This approach is called the artificial DNA algorithm for recognition (ADAR). It utilizes a unique identity for each person acquired from DNA nucleotides, and it can verify individuals efficiently with high performance. The ADAR has been designed and applied to multiple datasets, namely, the DNA classification (DC), sample DNA sequence (SDS), human DNA sequences (HDS), and DNA sequences (DS). For all datasets, a low value of 0% is achieved for each of the false acceptance rate (FAR) and false rejection rate (FRR).

Secure Hash Algorithm-2 formed on DNA

We present a new version of the Secure Hash Algorithm-2 (SHA-2) formed on artificial sequences of deoxyribonucleic acid (DNA). This article is the first attempt to present the implementation of SHA-2 using DNA data processing. We called the new version DNSHA-2. We present new operations on an artificial DNA sequence, such as (1) bar {R}^{k}(alpha) and bar {L}^{k}(alpha) to mimic the right and left shift by k bits, respectively; (2) bar {S}^{k}(alpha) to mimic the right rotation by k bits; and (3) DNA-nucleotide addition (mod 264) to mimic word-wise addition (mod 264). We also show, in particular, how to carry out the different steps of SHA-512 on an artificial DNA sequence. At the same time, the proposed nucleotide operations can be used to mimic any hash algorithm of its bitwise operations similar to bitwise operations specified in SHA-2. The proposed hash has the following features: (1) it can be applied to all data, such as text, video, and image; (2) it has the same security level of SHA-2; and (3) it can be performed in a biological environment or on DNA computers.

Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA) technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM) method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD) between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods.

A Schema-Based DNA Algorithm Applicable on Graphics Processing Units

In this paper, an evolutionary algorithm inspired by biological deoxyribonucleic acid (DNA) computing is proposed to perform the task of optimization. As a novel branch of computational intelligence, DNA computing has the strong computing and proliferating capability to in DNA strands by DNA encoding and matching in the molecule layer. However, it is difficult to apply DNA computing to search processing in a nonlinear hyperspace, because traditional DNA computing often relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. To utilize the advantages and avoid the problems of biological DNA computing, this article proposes an artificial DNA computing approach, which accelerates the process by means of the operation of restriction enzymes based on constructed schemata. After DNA encoding, the typical DNA database is constructed and evolved by evolutionary algorithms and the schema theory of genetic algorithm (GA) integrated to enhance the accuracy and convergence rate of exploration in the search space. Meanwhile, to fully utilize parallelism characteristics of DNA computing, a novel algorithm, named parallel DNA computing algorithm, which is tailored to be implemented in graphics processing unit devices, is proposed and exanimated its performance on benchmark optimization problems. Experimental results demonstrate that the proposed method presents excellent expected processing time efficiency, compared with its counterparts without the designed accelerating processes, such as schemata, restriction enzyme, and migration. As well, the performance is also distinctly superior to two renowned GAs.

Infrared ultraspectral signature classification based on a restricted Boltzmann machine with sparse and prior constraints

The state-of-the-art ultraspectral technology brings a new hope for the high precision applications due to its high spectral resolution. However, it comes with new challenges brought by the improvement of spectral resolution such as the Hughes phenomenon and over-fitting issue, and our work is aimed at addressing these problems. As new Markov random field (MRF) models, the restricted Boltzmann machines (RBMs) have been used as generative models for many different pattern recognition and artificial intelligence applications showing promising and outstanding performance. In this article, we propose a new method for infrared ultraspectral signature classification based on the RBMs, which adopt the regularization-based techniques to improve the classification accuracy and robustness to noise compared to traditional RBMs. First, we add an arctan-like term to the objective function as a sparse constraint to improve the classification accuracy. Second, we utilize a Gaussian prior to avoid the over-fitting problem. Third, to further improve the classification performance, a multi-layer RBM model, a deep belief network (DBN), is adopted for infrared ultraspectral signature classification. Experiments using different spectral libraries provided by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Environmental Protection Agency (EPA) were performed to evaluate the performance of the proposed method by comparing it with other traditional methods, including spectral coding-based classifiers (binary coding (BC), spectral feature-based binary coding (SFBC), and spectral derivative feature coding (SDFC) matching methods), a novel feature extraction method termed crosscut feature extraction matching (CF), and three machine learning methods (artificial deoxyribonucleic acid (DNA)-based spectral matching (ADSM), DBN, and sparse deep belief network (SparseDBN)). Experimental results demonstrate that the proposed method is superior to the other methods with which it was compared and can simultaneously improve the accuracy and robustness of classification.

N- and p-Type doping phenomenon by artificial DNA and M-DNA on two-dimensional transition metal dichalcogenides.

Deoxyribonucleic acid (DNA) and two-dimensional (2D) transition metal dichalcogenide (TMD) nanotechnology holds great potential for the development of extremely small devices with increasingly complex functionality. However, most current research related to DNA is limited to crystal growth and synthesis. In addition, since controllable doping methods like ion implantation can cause fatal crystal damage to 2D TMD materials, it is very hard to achieve a low-level doping concentration (nondegenerate regime) on TMD in the present state of technology. Here, we report a nondegenerate doping phenomenon for TMD materials (MoS2 and WSe2, which represent n- and p-channel materials, respectively) using DNA and slightly modified DNA by metal ions (Zn(2+), Ni(2+), Co(2+), and Cu(2+)), named as M-DNA. This study is an example of interdisciplinary convergence research between DNA nanotechnology and TMD-based 2D device technology. The phosphate backbone (PO4(-)) in DNA attracts and holds hole carriers in the TMD region, n-doping the TMD films. Conversely, M-DNA nanostructures, which are functionalized by intercalating metal ions, have positive dipole moments and consequently reduce the electron carrier density of TMD materials, resulting in p-doping phenomenon. N-doping by DNA occurs at ∼6.4 × 10(10) cm(-2) on MoS2 and ∼7.3 × 10(9) cm(-2) on WSe2, which is uniform across the TMD area. p-Doping which is uniformly achieved by M-DNA occurs between 2.3 × 10(10) and 5.5 × 10(10) cm(-2) on MoS2 and between 2.4 × 10(10) and 5.0 × 10(10) cm(-2) on WSe2. These doping levels are in the nondegenerate regime, allowing for the proper design of performance parameters of TMD-based electronic and optoelectronic devices (VTH, on-/off-currents, field-effect mobility, photoresponsivity, and detectivity). In addition, by controlling the metal ions used, the p-doping level of TMD materials, which also influences their performance parameters, can be controlled. This interdisciplinary convergence research will allow for the successful integration of future layered semiconductor devices requiring extremely small and very complicated structures.

Nanoscale topographical replication of graphene architecture by artificial DNA nanostructures

Despite many studies on how geometry can be used to control the electronic properties of graphene, certain limitations to fabrication of designed graphene nanostructures exist. Here, we demonstrate controlled topographical replication of graphene by artificial deoxyribonucleic acid (DNA) nanostructures. Owing to the high degree of geometrical freedom of DNA nanostructures, we controlled the nanoscale topography of graphene. The topography of graphene replicated from DNA nanostructures showed enhanced thermal stability and revealed an interesting negative temperature coefficient of sheet resistivity when underlying DNA nanostructures were denatured at high temperatures.

Approaches to label-free flexible DNA biosensors using low-temperature solution-processed InZnO thin-film transistors

Low-temperature solution-processed In–Zn–O (IZO) thin-film transistors (TFTs) exhibiting a favorable microenvironment for electron transfer by adsorbed artificial deoxyribonucleic acid (DNA) have extraordinary potential for emerging flexible biosensor applications. Superb sensing ability to differentiate even 0.5μL of 50nM DNA target solution was achieved through using IZO TFTs fabricated at 280°C. Our IZO TFT had a turn-on voltage (Von) of −0.8V, on/off ratio of 6.94×105, and on-current (Ion) value of 2.32×10−6A in pristine condition. A dry-wet method was applied to immobilize two dimensional double crossover tile based DNA nanostructures on the IZO surface, after which we observed a negative shift of the transfer curve accompanied by a significant increase in the Ion and degradation of the Von and on/off ratio. As the concentration of DNA target solution increased, variances in these parameters became increasingly apparent. The sensing mechanism based on the current evolution was attributed to the oxidation of DNA, in which the guanine nucleobase plays a key role. The sensing behavior obtained from flexible biosensors on a polymeric substrate fabricated under the identical conditions was exactly analogous. These results compare favorably with the conventional field-effect transistor based DNA sensors by demonstrating remarkable sensitivity and feasibility of flexible devices that arose from a different sensing mechanism and a low-temperature process, respectively.

Electrical Responses of Artificial DNA Nanostructures on Solution-Processed In-Ga-Zn-O Thin-Film Transistors with Multistacked Active Layers

We propose solution-processed In-Ga-Zn-O (IGZO) thin-film transistors (TFTs) with multistacked active layers for detecting artificial deoxyribonucleic acid (DNA). Enhanced sensing ability and stable electrical performance of TFTs were achieved through use of multistacked active layers. Our IGZO TFT had a turn-on voltage (V(on)) of -0.8 V and a subthreshold swing (SS) value of 0.48 V/decade. A dry-wet method was adopted to immobilize double-crossover DNA on the IGZO surface, after which an anomalous hump effect accompanying a significant decrease in V(on) (-13.6 V) and degradation of SS (1.29 V/decade) was observed. This sensing behavior was attributed to the middle interfaces of the multistacked active layers and the negatively charged phosphate groups on the DNA backbone, which generated a parasitic path in the TFT device. These results compared favorably with those reported for conventional field-effect transistor-based DNA sensors with remarkable sensitivity and stability.

Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data

In this paper, a spectral encoding and matching algorithm inspired by biological deoxyribonucleic acid (DNA) computing is proposed to perform the task of spectral signature classification for hyperspectral remote sensing data. As a novel branch of computational intelligence, DNA computing has the strong computing and matching capability to discriminate the tiny differences in DNA strands by DNA encoding and matching in the molecule layer. Similar to DNA discrimination, a hyperspectral remote sensing data matching approach is used to recognize the land cover material from a spectral library or image, according to the rich spectral information. However, it is difficult to apply DNA computing to hyperspectral remote sensing data processing because traditional DNA computing often relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. To utilize the advantages and avoid the problems of biological DNA computing, an artificial DNA computing approach is proposed for spectral encoding and matching for hyperspectral remote sensing data. A DNA computing-based spectral matching approach is used to first transform spectral signatures into DNA codewords by capturing the key spectral features with a spectral feature encoding operation. After DNA encoding, the typical DNA database for interesting classes is constructed and saved by DNA evolutionary operating mechanisms such as crossover, mutation, and structured mutation. During the course of spectral matching, each pixel of the hyperspectral image, or each signature measured in the field, is input to the constructed DNA database. By computing the distance between an unclassified spectrum and the typical DNA codewords from the database, the class property of each pixel is set as the minimum distance class. Experiments using different hyperspectral data sets were performed to evaluate the performance of the proposed artificial DNA computing-based spectral matching algorithm by comparing it with other traditional hyperspectral classifiers, including spectral matching classifiers (binary coding, spectral angle mapper and spectral derivative feature coding (SDFC) matching methods) and a novel statistical method of machine learning termed support vector machine (SVM). Experimental results demonstrate that the proposed algorithm is distinctly superior to the three traditional hyperspectral data classification algorithms. It presents excellent processing efficiency, compared to SVM, with high-dimensional data captured by the Hyperspectral Digital Imagery Collection Experiment sensor, and hence provides an effective option for spectral matching classification of hyperspectral remote sensing data.

以儒家視角探討生命的本質——以人工合成生命為例

LANGUAGE NOTE | Document text in Chinese; abstract also in English.面對科學技術給我們帶來的越來越多的倫理學難題的時候，我們不妨從孔子的教導中尋找答案。美國科學家克雷格．文特爾目前宣佈，他們利用了一段人工合成的DNA 創造了人類有史以來的第一個人造生命，並取名其為“辛西婭”。這也使得人們開始重新思考生命的定義，生命的本質等本源問題。本文將從儒家的哲學視角分析辛西婭的生物學地位，並從儒家的“天命觀”的角度分析合成生物技術。希望能對新技術得出儒家獨到的見解。On May 20, 2010, the Craig Venter Institute, a U.S. private research institution, announced that they had successfully synthesized bacterial deoxyribonucleic acid (DNA) and implanted it in another bacterium. After several attempts, the implanted artificial DNA regained life and began to reproduce in a lab dish. This “artificial life” was named “Synthia” (meaning “man-made children”). The result of this research has attracted broad attention. Even U.S. President Barack Obama expressed concern, and has asked the White House Council on Bioethics to provide a detailed report on synthetic biology within six months, to determine the appropriate ethical boundaries and minimize the potential harm. Indeed, this latest research in synthetic biology requires careful philosophical, ethical and cultural considerations on the essence of life.This essay attempts to analyze the biological status of Synthia and explores the essence of life from the perspective of Confucian philosophy. In particular, it attempts to draw on the Confucian idea of Heaven (tian) or God (shangdi) to disclose unique Confucian insights into new technology in general and artificial life in particular. Indeed, as advanced and pioneering science and technology have brought about more and more ethical difficulties and dilemmas, the Chinese people need to draw on the wisdom of Confucius to work out suitable solutions and guide their actions. Researchers at the Craig Venter Institute recently announced that they could take advantage of man-made DNA to create the first artificial human life. How should Confucians reflect on such actions in terms of their view of the essence and meaning of life?This essay assumes that following the Mandate of Heaven (tianming) is central to Confucian teaching on the essence of life. The Mandate of Heaven is reflected in the natural development and transformation of the myriad things in the universe. However, things like Synthia are outside of this natural process of development and transformation, and cannot be taken as being consistent with the Mandate of Heaven. Just as Confucianism cannot support the creation of human cloning, it cannot support artificial human life such as Synthia, because both violate a fundamental understanding of the essence of life in the Confucian faith. Confucian scholars cannot hold a utilitarian view of maximizing human interests, no matter what those interests are taken to be. Instead, Confucianism must insist that human interests be consistent with the Mandate of Heaven to promote the virtuous essence of human life.DOWNLOAD HISTORY | This article has been downloaded 174 times in Digital Commons before migrating into this platform.

Using multiple artificial DNA tracers in hydrology

AbstractTracer testing is an efficient and versatile method to characterize hydrological processes both in surface waters and in the subsurface, and with synthetic DNA (deoxyribonucleic acid) experiments can be performed with a theoretically unlimited number of tracers. We explored the use of six synthetic DNA tracers in injection experiments in the Strijbeekse Beek and the Merkske, two small brooks in the Netherlands. We wanted to test the applicability of synthetic DNA markers in combination with the quantitative polymerase chain reaction (qPCR), an analytical technique used to determine DNA concentrations. The most important results from the two experiments were that DNA concentrations during breakthrough ranged from 0·1 to 100 fM (femtomolar = 10−15 mol/l), and that DNA mass transport in the stream was not retarded compared to transport of an injected NaCl mass. However, DNA mass was substantially reduced, most likely as a result of adsorption, attachment, decay and/or biological uptake processes in the stream itself. Although more insight in the various mass balance components of DNA mass in the stream is required, we concluded that the use of synthetic DNA markers in combination with qPCR has potential for spatially distributed surface water‐groundwater interaction tracer experiments. Copyright © 2011 John Wiley & Sons, Ltd.

Engineering of the Extracellular Matrix: Working toward Neural Stem Cell Programming and Neurorestoration— Concept and Progress Report

In the concept of neurorestoration, cellular and structural elements that have been lost are replaced, and their function is restored. Central to this therapeutic strategy is the transplantation of neural progenitor cells such as clonogenically expanded stem cells. Stem cells make decisions regarding fate and patterning in response to external environmental signals. The therapeutic effectiveness of neural stem cells may be facilitated by the ability to manipulate these signals in a temporal and spatially appropriate fashion. Artificial deoxyribonucleic acid and artificial protein technology combines elements of protein engineering, molecular biology, and recombinant deoxyribonucleic acid technology to produce proteins with functional domains derived from naturally occurring proteins and represents a potentially powerful tool to modulate stem cell behavior. To this end, we have developed three artificial extracellular matrix proteins that incorporate the active domain of hJagged1 and hDelta1 into an elastin backbone. hJagged1 and hDelta1 are members of the DSL family of ligands to the Notch receptor, a signaling system that is very important in development and is the strongest known signal to instruct neural progenitor cells to choose glial fates over neuronal fates. The successful cloning of the artificial genes was confirmed by test digestions with appropriate restriction enzymes as well as direct deoxyribonucleic acid sequencing. In addition, we have demonstrated that all three artificial extracellular matrix proteins express at a high level in a prokaryotic host. This report describes the concept and progress in an entirely novel and previously unreported approach to modulate neural stem cell behavior. Its future application could include in vitro processing of stem cells before transplantation, supporting and programming the cells after transplantation, as well as the development of bioactive biomaterials.