DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of data.To accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of DNA.Vector quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression efficiency.This article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage Systems.The proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data storage.Besides,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded form.In addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably minimized.In order to generate optimal codebook for LBG,the WSA is applied to it.The performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several measures.The comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

SOLVING MINIMUM SPANNING TREE PROBLEM WITH DNA COMPUTING

Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.

Job shop scheduling problem based on DNA computing

To solve job shop scheduling problem, a new approach-DNA computing is used in solving job shop scheduling problem. The approach using DNA computing to solve job shop scheduling is divided into three stands. Finally, optimum solutions are obtained by sequencing A small job shop scheduling problem is solved in DNA computing, and the ＂operations＂ of the computation were performed with standard protocols, as ligation, synthesis, electrophoresis etc. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.

Hamilton Graph Based on DNA Computing

DNA computing is a novel method for solving a class of intractable computational problem, in which the computing can grow exponentially with problem size. Up to now, many accomplishments have been achieved to improve its performance and increase its reliability. Hamilton Graph Problem has been solved by means of molecular biology techniques. A small graph was encoded in molecules of DNA, and the 'operations' of the computation were performed with standard protocols and enzymes. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.

A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph

The biggest bottleneck in DNA computing is exponential explosion, in which the DNA molecules used as data in information processing grow exponentially with an increase of problem size. To overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graph vertex coloring problem. The main points of the model are as follows： The exponential explosion prob- lem is solved by dividing subgraphs, reducing the vertex colors without losing the solutions, and ordering the vertices in subgraphs; and the bio-operation times are reduced considerably by a designed parallel polymerase chain reaction （PCR） technology that dramatically improves the processing speed. In this arti- cle, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNA computing model. The experiment showed that not only are all the solutions of the graph found, but also more than 99% of false solutions are deleted when the initial solution space is constructed. The powerful computational capability of the model was based on specific reactions among the large number of nanoscale oligonu- cleotide strands. All these tiny strands are operated by DNA self-assembly and parallel PCR. After thou- sands of accurate PCR operations, the solutions were found by recognizing, splicing, and assembling. We also prove that the searching capability of this model is up to 0（3^59）. By means of an exhaustive search, it would take more than 896 000 years for an electronic computer （5 x 10^14 s-1） to achieve this enormous task. This searching capability is the largest among both the electronic and non-electronic computers that have been developed since the DNA computing model was proposed by Adleman＇s research group in 2002 （with a searching capability of 0（2^20））.

Solving the independent set problem by sticker based DNA computers

In this paper, the sticker based DNA computing was used for solving the independent set problem. At first, solution space was constructed by using appropriate DNA memory complexes. We defined a new operation called “divide” and applied it in construction of solution space. Then, by application of a sticker based parallel algorithm using biological operations, independent set problem was resolved in polynomial time.

Applying Surface-Based DNA Computing for Solving the Dominating Set Problem

The surface-based DNA computing is one of the methods of DNA computing which uses DNA strands immobilized on a solid surface. In this paper, we applied surface-based DNA computing for solving the dominating set problem. At first step, surface-based DNA solution space was constructed by using appropriate DNA strands. Then, by application of a DNA parallel algorithm, dominating set problem was resolved in polynomial time.

Advances in microfluidic-based DNA methylation analysis

DNA methylation has been extensively investigated in recent years,not least because of its known relationship with various diseases.Progress in analytical methods can greatly increase the relevance of DNA methylation studies to both clinical medicine and scientific research.Microflu-idic chips are excellent carriers for molecular analysis,and their use can provide improvements from multiple aspects.On-chip molecular analysis has received extensive attention owing to its advantages of portability,high throughput,low cost,and high efficiency.In recent years,the use of novel microfluidic chips for DNA methylation analysis has been widely reported and has shown obvious superiority to conventional methods.In this review,wefirst focus on DNA methylation and its applications.Then,we discuss advanced microfluidic-based methods for DNA methylation analysis and describe the great progress that has been made in recent years.Finally,we summarize the advantages that microfluidic technology brings to DNA methylation analysis and describe several challenges and perspectives for on-chip DNA methylation analysis.This review should help researchers improve their understanding and make progress in developing microfluidic-based methods for DNA methylation analysis.

Determination of the Early Time of Death by Computerized Image Analysis of DNA Degradation: Which Is the Best Quantitative Indicator of DNA Degradation?

This study evaluated the correlation between DNA degradation of the splenic lymphocytes and the early time of death, examined the early time of death by computerized image analysis technique （CIAT） and identified the best parameter that quantitatively reflects the DNA degradation. The spleen tissues from 34 SD rats were collected, subjected to cell smearing every 2 h within the first 36 h after death, stained by Feulgen-Van＇s staining, three indices reflecting DNA content in splenic lymphocytes, including integral optical density （IOD）, average optical density （AOD）, average gray scale （AG） were measured by the image analysis. Our results showed that IOD and AOD decreased and AG increased over time within the first 36 h. A stepwise linear regression analysis showed that only AG was fitted. A correlation between the postmortem interval （PMI） and AG was identified and the corresponding regression equation was obtained. Our study suggests that CIAT is a useful and promising tool for the estimation of early PMI with good objectivity and reproducibility, and AG is a more effective and better quantitative indicator for the estimation of PMI within the first 36 h after death in rats.

DNA BIO SOFT COMPUTING AND ITS APPLICATIONS TO INTELLIGENT SYSTEMS

Recently, the possibility of using DNA as a computing tool arouses wide interests of many researchers. In this paper, we first explored the mechanism of DNA computing and its biological mathematics based on the mechanism of biological DNA. Then we integrated DNA computing with evolutionary computation, fuzzy systems, neural networks and chaotic systems in soft computing technologies. Finally, we made some prospects on the further work of DNA bio soft computing.

DNA labelled graphs with DNA computing

Let k ? 2, 1 ? i ? k and α ? 1 be three integers. For any multiset which consists of some k-long oligonucleotides, a DNA labelled graph is defined as follows: each oligonucleotide from the multiset becomes a point; two points are connected by an arc from the first point to the second one if the i rightmost nucleotides of the first point overlap with the i leftmost nucleotides of the second one. We say that a directed graph D can be (k, i; α)-labelled if it is possible to assign a label (l 1(x), ..., l k (x)) to each point x of D such that l j (x) ? {0, ..., α ? 1} for any j ? {1, ..., k} and (x, y) ? E(D) if and only if (l k?i+1(x), ..., l k (x)) = (l 1(y), ..., l i (y)). By the biological background, a directed graph is a DNA labelled graph if there exist two integers k, i such that it is (k, i; 4)-labelled. In this paper, a detailed discussion of DNA labelled graphs is given. Firstly, we study the relationship between DNA labelled graphs and some existing directed graph classes. Secondly, it is shown that for any DNA labelled graph, there exists a positive integer i such that it is (2i, i; 4)-labelled. Furthermore, the smallest i is determined, and a polynomial-time algorithm is introduced to give a (2i, i; 4)-labelling for a given DNA labelled graph. Finally, a DNA algorithm is given to find all paths from one given point to another in a (2i, i; 4)-labelled directed graph.

基于DNA宏条形码技术的斑海豹食性分析

野生动物食性研究是掌握动物生境需求的核心内容,对野生动物的保护和管理具有重要意义。在我国辽东湾斑海豹(Phoca largha)传统繁殖地和盘锦栖息地海域采集其粪便,选用12S rRNA作为分子标记进行粪便DNA扩增,利用高通量测序鉴定其食物组成。结果发现:在斑海豹粪便中共鉴定出鱼类16种,隶属5目8科13属。食物组成的相对丰度显示:梭鱼(Liza haematocheilus)为绝对优势的饵料食物(40.72%),其次为鰕虎科(Gobiidae)种类(23.18%)。属水平不同采样群体的相对丰度显示:2021年、2023年辽东湾北部冰区和2023年盘锦辽河口栖息地排在前3位的种类分别是梭属(Liza)31.91%、矛尾鰕虎属(Chaeturichthys)14.06%和缟鰕虎属(Tri⁃dentiger)8.39%,梭属42.37%、复鰕虎属(Acanthogobius)14.06%和矛尾鰕虎属11.17%,及梭属47.93%、矛尾鰕虎属13.93%和绵鳚属(Zoarces)12.18%,分别合计占斑海豹3个群体食物组成相对丰度的54.36%、67.60%和74.04%。研究结果与辽东湾北部的渔业资源优势物种一致,表明斑海豹为广食性物种,其食物组成主要取决于栖息海域、季节及主要猎物种类的丰度。

Sequence Analysis of Mitochondrial DNA D-loop Region in Xinjiang Goose

[Objective] The sequences of mitochondrial DNA D-loop region of Xinjiang Goose with three different colors of plumage were analyzed in order to study the genetic diversity of Xinjiang Goose, as well as the phylogeny and evolution. [Method] Ten geese were selected randomly from the core populations of grey-, mosaic- and white-plumaged Xinjiang Goose respectively with a total number of thirty as experi- mental materials, of which the blood samples were collected from the largest vein under the wing （brachial vein） for DNA extraction. Sequences of mitochondrial DNA D-loop regions were determined using DNA sequencing technology to analyze the polymorphism. In addition, the genetic distances among different populations were estimated through the comparison with the reference sequences. [Resull] The con- tents of A, G, C and T nucleotides in the D-loop region of Xinjiang Goose were 28.85%, 17.05%, 25.38% and 28.72%, respectively. The average haplotype diversity and nucleotide diversity of Xinjiang Goose were 0.583 and 0.056. Xinjiang Goose and Greylag Goose were clustered into the same group. [Conclusion] The results showed that Xinjiang Geese with three different colors of plumage all descend from Greylag Goose （Anser anser）.

不同病情乙型肝炎患者HBV-DNA定量、两对半检测结果差异

目的分析乙型肝炎患者乙型肝炎病毒(HBV)-DNA定量检测与两对半检测结果的相关性,并观察不同病情HBV-DNA阳性率和两对半检测结果差异。方法选取江苏省泰兴市人民医院2021年4月至2023年4月收治的375例乙型肝炎患者病例为研究资料。比较不同乙肝两对半检测[乙肝表面抗原(HBsAg)、乙肝表面抗体(HBsAb)、乙肝e抗原(HBeAg)、乙肝e抗体(HBeAb)、乙肝核心抗体(HBcAb)]结果患者HBV-DNA定量检测结果差异,将患者根据HBV-DNA定量检测结果分为阳性组和阴性组,比较乙肝两对半结果差异,使用Pearson相关性分析;将患者根据疾病类型分为肝炎组、肝硬化组和肝癌组,比较三组患者乙肝两对半和HBV-DNA定量检测结果差异。结果不同乙肝两对半类型患者HBV-DNA阳性率比较,差异有统计学意义(P<0.05);HBV-DNA阳性组患者HBsAg、HBeAg水平高于HBV-DNA阴性组,HBsAb、HBeAb、HBcAb水平低于HBV-DNA阴性组,差异有统计学意义(P<0.05);HBsAg、HBeAg水平与HBV-DNA呈正相关(P<0.05),HBsAb、HBeAb、HBcAb水平与HBV-DNA呈负相关(P<0.05);不同疾病类型患者乙肝两对半类型HBV-DNA阳性率比较,差异有统计学意义(P<0.05),不同疾病类型患者HBV-DNA定量检测结果比较,差异有统计学意义(P<0.05)。结论乙型肝炎患者HBV-DNA定量检测与两对半检测结果有相关性,不同乙肝两对半类型间及不同疾病类型间HBV-DNA均有差异,HBV-DNA定量检测联合两对半检测可较好地反映病毒复制和疾病进展情况,为临床治疗提供指导。

Development of DNA computing and information processing based on DNA-strand displacement

DNA computing, currently a hot research field in information processing, has the advantages of parallelism, low energy consumption, and high storability, therefore, it has been applied to a variety of complicated computational problems. The emerging field of DNA nanotechnology has also developed quickly; within it, the method of DNA strand displacement has drawn great attention because it is self-induced, sensitive, accurate, and operationally simple. This article summarizes five aspects of the recent developments of DNA-strand displacement in DNA computing:(1) cascading circuits;(2) catalyzed reaction;(3) logic computation;(4) DNA computing on surfaces; and(5) logic computing based on nanoparticles guided by strand displacement. The applications and mechanisms of strand displacement in DNA computing are discussed and possible future developments are presented.

50份无花果种质资源遗传多样性分析及DNA指纹图谱构建

为探究国内外无花果遗传多样性并对种质资源进行鉴定,利用15对SSR分子标记引物对50份无花果种质资源从DNA水平上进行遗传多样性分析,并构建DNA指纹图谱。结果表明,15对SSR引物在50份无花果种质资源中共扩增出67个等位基因,等位基因数范围为2~8个,观测杂合度(Ho)和期望杂合度(He)平均值分别为0.5947和0.5703,Shannon’s多态性信息指数(I)平均值为1.0452,多态性信息含量指数(PIC)变化范围为0.2411~0.7353,平均为0.5101,表明无花果种质资源具有丰富的遗传多样性。50份无花果种质资源之间的遗传距离为0~0.68,平均为0.39,与其他种质资源遗传距离最大的为哈代。基于SSR分子标记结果,利用UPGMA方法对50份无花果种质资源进行聚类分析,根据遗传距离可将50份无花果种质资源分为四大类,第Ⅰ类为哈代,第Ⅱ类为斯特拉,第Ⅲ类为梦幻甜蜜,其余无花果种质资源划归为第Ⅳ类。构建了26份无花果种质资源的SSR指纹图谱,可用于品种鉴定,以期为无花果分子辅助育种提供科学依据。

基于核心KASP标记的江苏粳稻品种DNA指纹图谱构建

为了建立江苏粳稻品种DNA指纹图谱数据库,本研究利用水稻10K液相芯片对314份来源广泛的水稻种质资源进行SNP基因分型,筛选出一系列高多态性的SNP位点并开发出122个KASP标记。利用122个KASP标记对38份江苏粳稻品种进行检测,以多态性信息量(PIC)>0.3,最小等位基因频率(MAF)>0.2,检出率>0.9为筛选标准,筛选出56个具有高效鉴别效率的核心KASP标记。遗传距离相关性分析结果表明,基于56个核心KASP标记的江苏粳稻品种的遗传距离与基于122个高多态性KASP标记的的江苏粳稻品种的遗传距离相关系数为89.1%,呈极显著正相关(P<0.01),表明56个核心标记可以有效代替122个高多态性标记进行品种鉴定。进一步利用56个核心KASP标记对102份江苏粳稻品种的遗传多样性进行分析,并构建了102份品种的DNA指纹图谱和分子身份证二维码,本研究结果为江苏地区粳稻品种鉴定、选育和种质资源高效利用提供了参考。

基于粪便DNA宏条形码的人工草地放牧绵羊采食组分研究

准确获取食草动物食性数据有助于了解草地放牧家畜的营养状况,同时也对掌握草地植物群落的动态变化具有重要意义。本研究通过从滩羊粪便样本中提取DNA,利用ITS2条形码和粪便DNA宏条形码技术(fDNA metabarcoding),基于出现频率(Frequency of occurrence,FO)和相对序列丰度(Relative read abundance,RRA)两个指标快速分析获得粪便中的物种组成。分析获得的滩羊食物组成包含28个植物类群,主要有藜属(Chenopodium sp.)、苜蓿属(Medicago sp.)、蒿属(Artemisia sp.)等隶属于13科23属,其中27个鉴定到种水平,物种鉴定分辨率高达96%。藜属FO和RRA均最高,分别为100.0%和31.5%,其次为紫花苜蓿(Medicago sativa),分别为100.0%和26.9%。基于两种算法分析得到的食物比例具有差异性和一致性,其中紫花苜蓿是放牧滩羊的主要采食来源,但摄入比例需要校正。研究证明了基于ITS2片段的粪便DNA宏条形码技术在食草家畜快速准确食性分析中的可行性,为此类研究提供了有力借鉴。建议采用该技术时应完善潜在摄食植物DNA条形码数据库,准确分析目标家畜在特定区域内的采食种类。

败酱草及其近缘种叶绿体全基因组分析和DNA条形码构建

目的:基于败酱类药材基原植物的叶绿体基因组序列分析开发特定的DNA条形码,准确鉴别败酱草及其近缘种。方法:采用Illumina高通量测序技术对败酱科败酱属植物白花败酱、黄花败酱和异叶败酱的叶绿体基因组进行测序;采用生物信息学软件对基因组进行组装注释、特征分析、序列比较和系统发育分析;基于叶绿体基因组高突变区构建特异性DNA条形码进行验证。结果:3种败酱属植物叶绿体基因组呈四分体结构,全长分别为159585、158919、158919 bp,编码的基因数分别为130、132、132,包含8个rRNA基因和37个tRNA基因,蛋白质编码基因数分别为85、87、87。ccsA、ndhG、rpl23、ndhF序列作为特异性DNA条形码成功扩增16份样品,黄花败酱、白花败酱和少蕊败酱聚类在同一支,异叶败酱和糙叶败酱聚类在另一支。通过ccsA、ndhG、ndhF序列可进一步鉴别糙叶败酱和异叶败酱。结论:ccsA、ndhG、rpl23、ndhF序列可作为补充特异性DNA条形码区分败酱草及其近缘种。

外周血循环肿瘤DNA预测晚期非小细胞肺癌免疫治疗疗效及预后价值

目的探讨外周血循环肿瘤DNA预测晚期非小细胞肺癌(NSCLC)免疫治疗疗效及预后的价值。方法对2021年1-12月收治于河北北方学院附属第一医院呼吸与危重症医学科住院的78例晚期驱动基因阴性使用替雷利珠单抗治疗的NSCLC患者进行前瞻性研究,免疫治疗2周期后按照实体瘤疗效评价标准(RECIST1.1)评价疗效,包括完全缓解(CR)、部分缓解(PR)、疾病稳定及疾病进展,将CR和PR患者定义为观察组(n=48),其他患者被定义为对照组(n=30),测定两组患者治疗前后外周血中ctDNA水平,采用ROC曲线分析外周血ctDNA水平对于免疫治疗后达客观缓解的预测价值。对所有患者进行随访,统计其无进展生存期,采用单因素及多因素回归分析免疫治疗后患者预后的影响因素,采用Spearman相关系数对ctDNA水平与PFS进行相关性分析,采用Kalplan-Meier生存曲线进行生存分析。结果观察组治疗前后外周血ctDNA水平分别为(4.47±1.21)、(2.65±1.14)ng/μL(t=7.559,P<0.001),对照组治疗前后外周血ctDNA水平为(4.54±1.15)、(4.29±1.57)ng/μL(t=0.699,P=0.487),两组患者在治疗前外周血ctDNA水平差异无统计学意义(t=-0.25,P=0.801),观察组治疗后外周血ctDNA水平较对照组下降(t=-5.35,P<0.001)。ROC曲线分析外周血ctDNA水平预测免疫治疗后达客观缓解的曲线下面积为0.819,预测的敏感性度81.3%,特异度为80%,外周血ctDNA水平与患者无进展生存期(PFS)呈负相关(r=-0.784,P<0.001),采用单因素Cox回归对入组患者的临床病理特征及ctDNA水平进行分析,结果显示肿瘤最大径>5 cm(HR=0.501,95%CI:0.282~0.890)、肿瘤Ⅳ期(HR=0.392,95%CI:0.227~0.677)、治疗方式(HR=15.473,95%CI:6.731~35.567)及ctDNA水平(HR=4.567,95%CI:3.182~6.555)均为晚期NSCLC患者免疫治疗后PFS的影响因素,再将有统计学差异的上述指标进行多因素分析,结果显示:治疗方式(HR=2.981,95%CI:1.019~8.722)及外周血ctDNA水平(HR=3.918,95%CI:2.619~5.861)是晚期NSCLC患者PFS的独立影响因素,采用Kalplan-Meier生存曲线进行分析,结果显示观察组的中位PFS为8.4个月,对照组的中位PFS为5.4个月,差异有统计学意义(χ^(2)=46.828,P=0.000)。结论免疫联合化疗可增强杀伤肿瘤细胞的能力,外周血ctDNA水平可评估免疫治疗的疗效及预后,可用于指导晚期NSCLC患者的免疫治疗。