DNA technology for big data storage and error detection solutions: Hamming code vs Cyclic Redundancy Check (CRC)

With the informationization of social processes, the amount of related data has greatly increased, making traditional storage media unable to meet the current requirements for data storage. Due to its advantages of a high storage capacity and persistence, deoxyribonucleic acid (DNA) has been considered the most prospective storage media to solve the data storage problem. Synthesis is an important process for DNA storage, and low-quality DNA coding can increase errors during sequencing, which can affect the storage efficiency. To reduce errors caused by the poor stability of DNA sequences during storage, this paper proposes a method that uses the double-matching and error-pairing constraints to improve the quality of the DNA coding set. First, the double-matching and error-pairing constraints are defined to solve problems of sequences with self-complementary reactions in the solution that are prone to mismatch at the 3' end. In addition, two strategies are introduced in the arithmetic optimization algorithm, including a random perturbation of the elementary function and a double adaptive weighting strategy. An improved arithmetic optimization algorithm (IAOA) is proposed to construct DNA coding sets. The experimental results of the IAOA on 13 benchmark functions show a significant improvement in its exploration and development capabilities over the existing algorithms. Moreover, the IAOA is used in the DNA encoding design under both traditional and new constraints. The DNA coding sets are tested to estimate their quality regarding the number of hairpins and melting temperature. The DNA storage coding sets constructed in this study are improved by 77.7% at the lower boundary compared to existing algorithms. The DNA sequences in the storage sets show a reduction of 9.7-84.1% in the melting temperature variance, and the hairpin structure ratio is reduced by 2.1-80%. The results indicate that the stability of the DNA coding sets is improved under the two proposed constraints compared to traditional constraints.

Cyclic Redundancy Check (CRC) is a well-established coding method for error detection and is part of several industrial standards for data transmission and storage. If a second CRC is used on top of an existing standard CRC to provide additional reliability, we call this an embedded CRC. The performance of such CRC combinations has been considered only recently and is not yet well understood. In this paper we propose a sieving method, which can be used to find the best embedded CRC for a given standard CRC, and apply the method to common industry standards for field buses.

In computer communication and telecommunication applications, error detection and correction techniques must be employed to ensure a reliable data transmission from the source to the destination. Two of the most prevailing techniques, Cyclic Redundancy Check (CRC) detection and Hamming code correction, are implemented and analyzed. The CRC method picks up prominence because it joins three focal points: extraordinary blunder identification capacities, minimal overhead, and simplicity of usage. Moreover, both the CRC and Hamming code are binary linear codes. However, one significant difference is that Hamming code only works on data of some fixed size (depending on the Hamming code used), whereas CRC is a convolution code that works on data of any size. In this paper, the authors will show how CRC helps in removing errors by passing three distorted signals and using CRC to receive the signal error free in the MATLAB environment.

Cyclic redundancy check (CRC) bits that are conventionally used for error detection have recently found a new application in universal mobile telecommunications system standard for message length detection of variable-length message communications. It was anticipated that the CRC bits, when they are coworked with the inner convolutional code, can be used to detect the receiver-unaware of the message length-without much degradation in their error detection capability. This is unfortunately not true when the offset or difference between the wrong detected length and the true length is small. Two improvements, i.e., the DoCoMo's reverse CRC method and the flip CRC method, were accordingly proposed. In this paper, we revisited the flip CRC modification by considering the impact of joint decoding of the CRC code and the convolutional code. By generalizing the condition for the selection of the flip polynomials, we found that under error-free transmission, the range of the length offsets, at which the false length probability conditioning on the true message length can be made exactly zero (and hence, is minimized), can be extended from to , where and are, respectively, the number of the CRC bits and the memory order of the convolutional code. In addition, an upper bound and a lower bound for the overall false length probability with respect to a uniform pick of the true message length over a candidate message length set are derived. It is then confirmed numerically that the two bounds almost coincide for moderate value. Simulations show that the false length probability obtained analytically under error-free transmission assumption only mildly degrades for moderate-to-high SNRs. Interestingly, we also found that the system block error rate of the flip CRC method can be well approximated by the performance curve of the adopted convolutional code up to a certain SNR, and approach an error floor determined well by the previously derived false length probability bounds beyond this SNR, thereby facilitating the selection of the system parameters, such as the number of CRC bits and the memory order of the convolutional code.

Cyclic Redundancy Check (CRC) is a widely used error detection code in digital communication systems to detect errors in data transmitted between transmitter and receiver. With the development of Internet-of-Things (IoT) technology, where many devices communicate wirelessly, the application of error detection codes such as CRC is essential to obtain reliable communication. The computation of CRC can be implemented in hardware using a linear feedback shift register (LFSR) or in software with shift and bitwise exclusive OR (XOR) operations for polynomial division. Due to the frequent use of CRC in wireless communication for IoT, research for the design and implementation of CRC continues to this day. This study aims to design and implement the encoder and decoder of CRC-8 with generator polynomial 0x07 on IoT devices. Two algorithms for computing the CRC, namely bitwise computation using LFSR and another one using lookup tables, are implemented on the Arduino Uno R3 board. Two Arduino boards connected in serial were used to verify the encoder and decoder implementation in the data transmission and error detection process. The experiment results showed that the encoder could correctly calculate the CRC value from the input data. Furthermore, the computation using lookup tables takes about four times faster than the bitwise method but requires more memory. In contrast, the bitwise computation method requires less memory but slower computation time. When testing using input data of 128 characters, the bitwise-based encoder requires a computation time of 2.37 milliseconds, while the lookup table-based encoder utilizing a lookup table requires a computation time of 0.5 milliseconds. In the error detection test, the results demonstrated that the receiver could detect errors in the transmitted data with a percentage of 100%.

Error detection and correction options for data services in B-ISDN (broadband integrated services digital network) are examined. B-ISDN uses the asynchronous transfer mode (ATM) technique for transporting voice, data, and video services. Currently, the ATM cell header size, functions, and adaption layer fields for data services are being standardized in CCITT. Error protection for data payloads will be provided by a per-cell CRC (cyclic redundancy check) field. Different data services may need different degrees of error protection. The authors discuss and analyze different options for error detection and correction by considering various schemes ranging from no error protection to only error detection. Based on the analysis and results presented, a per-cell CRC has been adopted in the adaptation layer for all data services. The per-cell CRC substantially improves cell misdelivery for all data services. For error-insensitive data services, single-bit correction over MID and segment-type fields improves cell loss substantially. At the same time, single-bit correction keeps cell misdelivery sufficiently low (1 in 10/sup 13/ in the worst case). >

Cloud computing is the latest technology. Provides various on-demand services and online for network services, platform services, data storage, etc. Many organizations are not thrilled with using cloud services due to data security concerns, as the data resides on the cloud service provider's servers. To address this problem, various researchers around the world have applied various approaches to strengthen the security of data stored in cloud computing. The latest development in the field of cryptography is DNA encryption. It arose after the disclosure of the computational ability of deoxyribonucleic acid (DNA). DNA encryption uses DNA as a computational tool along with various molecular techniques to manipulate it. Due to the large storage capacity of DNA, this field is becoming very promising. This paper used a layered DNA encryption method for the data encryption and decryption process. Using the four DNA bases (A, C, G, T), we generate dynamic DNA tables to replace the message characters with a dynamic DNA sequence. The implementation of the proposed approach is performed in Python and the experimental results are verified. The resulting encrypted text contains information that will provide greater security against intruder attacks.

The development of the polymerase chain reaction (PCR) in the mid-1980s by K. B. Mullis, an American biochemist, stimulated the rapid growth of a new avenue of anthropological inquiry. Using this process, miniscule amounts of genetic material from ancient materials can be studied. Targeted segments of preserved deoxyribonucleic acid (DNA) are copied and re-copied until there is enough present for further analysis. The area selected for amplification depends upon the specific research question. The majority of anthropological applications involving DNA analysis fall under the realm of physical anthropology. Both modern and ancient human DNA are used to determine the status of genetic traits with restricted distributions in order to address questions of kinship, population movement, the occurrence of disease, gender distributions, and evolutionary lineage. Reviews focusing primarily on the analysis of ancient DNA (or aDNA) from human remains recovered from archaeological contexts appear in Kaestle and Horsburgh (2002) and Jones (2003); Mulligan (2006) provides a critical review of aDNA extracted from the remains of humans, other animals and plants, as well as aDNA preserved in archaeological residues. Descriptions of the procedures and issues surrounding the analysis of aDNA are provided in Brown (2001), Cano (2000), Kaestle and Horsburgh (2002), Mulligan (2006), and O’Rourke et al. (2000). Further information about the amplification and analysis of DNA using PCR, DNA cloning, and DNA sequencing can be found in biochemistry textbooks such as Lehninger et al. (1993) and Nelson and Cox (2008).

This paper investigates the design of optimal vector quantization given channel and error statistics by inclusion of cyclic redundancy checks (CRC) into the consideration. Given a broadcast system with multiresolution vector quantization (MRVQ) and error detection availability for each resolution, a closed-form formula for the weighted end-to-end distortion (EED) is first derived under a random index assignment. Based on the closed-form formula, an iterative algorithm is then proposed for designing optimal MRVQ to minimize the EED with CRC. Experiments show that for a wide range of channel error probability, the inclusion of CRC indeed reduces the EED. Finally, the best tradeoff between the number of bits for quantization and those for CRC is also investigated by experiments.

Block cyclic redundancy check (CRC) codes represent a popular and powerful class of error detection techniques used almost exclusively in modern data communication systems. Though efficient, CRCs can detect errors only after an entire block of data has been received and processed. In this work, we exploit the nature of error detection that results from using arithmetic codes for error detection, which provides a novel tradeoff between the amount of added redundancy and the amount of time needed to detect an error once it occurs. We demonstrate how this continuous error detection framework improves the overall performance of communication systems, and show how considerable performance gains can be attained. We focus on several important scenarios: 1) automatic repeat request (ARQ) based transmission; 2) forward error correction (FEC frameworks based on (serially) concatenated coding systems involving an inner error-correction code and an outer error-detection code; and 3) reduced state sequence estimation (RSSE) for channels with memory. We demonstrate that the proposed CED framework improves the throughput of ARQ systems by up to 15% and reduces the computational/storage complexity of FEC and RSSE by a factor of two in the comparisons that we make against state-of-the-art systems.

Error Control CodesComputing has always had to live with errors, especially in data transmission and data recording.Sometimes these errors are only a nuisance and a simple retry can obtain satisfactory, accurate, data.But sometimes an error can be serious, and perhaps even disastrous if an accurate original copy is inaccessible.Two related, but somewhat parallel disciplines, have developed to deal with the handling of erroneous data, both part of the general theme of "Coding Theory" and collected under the generic title of "Error Control".Error Detection Error Detection extends the ideas of parity to provide powerful and reliable detection of errors, usually by appending a "checksum" of 8, 16 or 32 bits to the data.The checksum is carefully designed to be sensitive to the probable errors: a checksum for manual data entry must be sensitive to digit transposition and repetition, while one for data transmission must detect long bursts of errors.A detected error invariably leads to an alarm of some sort and request for data re-entry or retransmission.

Extraction of non-degraded and contaminant-free DNA from field specimen requires collection under liquid nitrogen which is not readily available in resource constrained laboratories in low and middle income countries (LMICs). A method of extracting DNA from silica gel-preserved common bean (Proteus vulgaris L.) leaves is presented. The method, which does not involve the use of phenol, chloroform or isoamyl alcohol also obviates the need for low temperature incubation during the DNA extraction steps and the grinding of desiccated leaf tissue in liquid nitrogen. It relies on inactivating proteins using SDS and proteinase K along with precipitation of polysaccharides using a high salt solution (0.8 M NaCl). DNA is further purified by exploiting its insolubility in aqueous media. High quality pure DNA (mean concentration 2.84 ± 0.013 µg/g of dry leaf tissue) with mean DNA purity values of 2.1 ± 0.1 was extracted. The DNA was also found to be free of protein and polysaccharide contamination. This method enables DNA amplification using molecular markers routinely used in molecular biology laboratories like random amplified polymorphic (RAPD) markers, inter simple sequence repeat (ISSR) markers, sequence-characterized amplified region (SCAR) markers and simple sequence repeat (SSR) markers. The findings of this study show that it is possible to obtain high quality DNA from leaf tissue preserved in silica gel. The method used in this research will be invaluable to resource constrained laboratories in low and middle income countries (LMICs) that cannot afford to buy or access liquid nitrogen in order to extract high quality DNA and for research groups undertaking field surveys that require several days or weeks off station without laboratory freezers to maintain the integrity of the tissues which is crucial for obtaining high quality DNA. Key words: Random amplified polymorphic (RAPD), inter simple sequence repeat (ISSR), simple sequence repeat (SSR), sequence-characterized amplified region (SCAR), deoxyribonucleic acid (DNA), low and middle income countries (LMICs).

Block cyclic redundancy check (CRC) codes are typically used to perform error detection in automatic repeat request (ARQ) protocols for data communications. Although efficient, CRCs can detect errors only after an entire block of data has been received and processed. We propose a new continuous error detection scheme using arithmetic coding that provides a novel tradeoff between the amount of added redundancy and the amount of time needed to detect an error once it occurs. This method of error detection, first introduced by Bell, Witten, and Cleary (1990), is achieved through the use of an arithmetic codec, and has the attractive feature that it can be combined physically with arithmetic source coding, which is widely used in state of-the-art image coders. We analytically optimize the tradeoff between added redundancy and error-detection time, achieving significant gains in bit rate throughput over conventional ARQ schemes for binary symmetric channel models for all probabilities of error.

Objectives Early identification of fetal gender is important in management of X-linked and other metabolic disorders. Since ultrasound may not predict gender accurately during the first trimester, noninvasive fetal gender determination using deoxyribonucleic acid (DNA) amplification has been proposed. The aim of this study is to evaluate the feasibility of noninvasive prenatal gender determination by examining cell-free fetal DNA (cffDNA) from maternal plasma. Materials and methods Blood samples were collected from 49 pregnant women of gestational ages ranging from 12 to 41 weeks. Deoxyribonucleic acid was extracted from maternal plasma using a QIAamp DNA Blood Mini Kit. Real-time quantitative polymerase chain reaction (PCR) was performed to amplify the male specific DNA marker sex-determining region Y (SRY). Results From a total of 49 subjects, fetal gender was correctly determined in 13 out of 14 male fetuses and 32 out of 35 female fetuses, giving an overall accuracy of 92%. The sensitivity and specificity of the test to detect male fetuses was 93 and 91% respectively. There were three false-positive cases and one false-negative case. Conclusion Identification of fetal gender from maternal plasma using real-time PCR technique is feasible in a developing country, like Pakistan, and appears to be a promising tool for noninvasive prenatal diagnosis. How to cite this article Mohammed N, Nuruddin R, Hoodbhoy Z. Gender Determination by Isolation of Cell-free Fetal DNA from the Maternal Circulation. J South Asian Feder Obst Gynae 2016;8(1):29-32.

Over the past few years, data has been growing significantly due to the advent of new connected devices, availability of bandwidth, and the emergence of new applications which utilize cloud computing infrastructure in the data centers. This increased amount of data faces many problems in terms of storage, transmission, management, and processing, etc. Therefore, the term big data has gained significant attention from researchers in recent years. The rapidly growing quantity, velocity, and variety of data require more probable and logical tools for its storage. For this purpose, the industry is highly emphasizing the development of more viable tools for the storage of big data. The traditional big data storage tools are unsuccessful in storing an enormous amount of data. Hence, the structural modifications of management mechanisms of conventional storage systems such as SQL databases to NoSQL databases technology are necessary to cope up with drastically increasing requirements of big data storage. The primary objective of this paper is to concentrate exclusively on designing a road map for NoSQL big data storage technologies, evaluate current evidence, research progresses in NoSQL data storage systems and their applications in various domains. We conducted a systematic literature review (SLR) of various studies published in recent years. We propose a framework to classify selected articles on the basis of various factors such as motivations behind big data storage, NoSQL techniques used for storing big data, and significant applications of big data in different domains. Furthermore, we also discuss research issues and define an outline for future research in the big data storage domain for NoSQL databases.