The Use of Artificial Neural Networks for Species Identification of Aegilops Based on Analysis of D Genomes

The possibilities of the use of artificial neural networks (ANNs) for identification of some polyploid species of genus Aegilopsbased on the idiograms of theirDgenomes were demonstrated.     

119份小麦种质资源醇溶蛋白遗传多样性分析

利用A-PAGE对119份小麦种质资源进行了醇溶蛋白遗传多样性分析。结果显示,共分离出蛋白带1 938条,迁移率不同的谱带类型116种,其中编号为10和14的2种谱带出现频率最高,分别为54.62%和96.44%,其余114种谱带类型具有较强的多态性;每个种质材料的醇溶蛋白谱带数为10~24条,大部分为13~19条;供试种质间遗传距离在0.24~0.83,平均为0.54;聚类分析将所测材料分为6大类,与种质资源所反映的系统关系类似,表明醇蛋白在一定程度上能反映种质间的亲缘关系。     

基于醇溶蛋白的20份小麦种质遗传完整性分析

采用醇溶蛋白电泳技术对同一品种不同繁殖年份的20份小麦种质进行遗传完整性分析。结果表明：供试种质中有10份具有一种醇溶蛋白谱带带型的同质性种质;另外10份具有2～4种醇溶蛋白谱带带型的异质性种质,其中6份为地方品种。表明地方品种具有较高的遗传多样性。在10份异质性种质中,两个繁殖年份种质之间的醇溶蛋白带型频率变化差异不显著的有5份,其第一繁殖年份的种质发芽率均高于75％,而另外5份存在显著差异的种质,第一年份的发芽率都低于66％。进一步分析表明,这10份异质性种质在两个繁殖年份之间,其发芽率差值与带型频率差值之间呈极显著正相关,相关系数为0．8665。上速结果表明,小麦更新时较高的发芽率是维持异质性种质遗传完整性的关键因素。     

人工神经网络在蝙蝠回声定位叫声识别方面的应用

近年来,人工神经网络被不断应用于野生动物的声学研究中,本文概括地介绍了人工神经网络的概念以及这项新技术的研究方法,并且重点介绍了它在蝙蝠回声定位叫声识别方面的应用。     

Simulation of Soybean Phenology with the Use of Artificial Neural Networks

Biophysics - The phenology of soybean explicitly indicates environmental changes and strongly depends on temperature and day length. We adapted an artificial neural network model to predict the...     

硬粒小麦-节节麦人工合成种HMW-GS组成及1.5+10亚基的遗传分析

对C IMM YT的99份硬粒小麦—节节麦人工合成种(简称合成种)的HMW-G S组成分析发现,G lu-B 1和G lu-D 1位点的变异类型比普通小麦丰富,分别有9种和12种亚基类型;筛选出含有比5 10亚基更优质的1.5 10和5 12亚基的合成种分别有8份和1份;含有优质亚基1.5 10的合成种与普通小麦杂交结实正常;对2个合成种与2个普通小麦品种的8个正反交组合F1种子电泳发现,优质亚基1.5 10在F1代能正常表达,双亲所有亚基在F1代都得到表达,表现共显性遗传.本研究为优质亚基1.5 10和5 12转育到普通小麦中奠定了基础.     

The Heuristic Work of the Brain and Artificial Neural Networks

Biophysics - This paper presents two new fundamental principles of the functioning of real neural networks of the brain. These principles have inspired the design of artificial neural networks (a...     

Use of Artificial Neural Networks To Accurately Identify Cryptosporidium Oocyst and Giardia Cyst Images

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400× (total magnification), processed, and converted into a binary numerical array. A variety of “negative” images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously “unseen” images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.     

The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.     

Identification and Discrimination of Oral Asaccharolytic Eubacterium spp. by Pyrolysis Mass Spectrometry and Artificial Neural Networks

Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNs) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C₂ and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.     

A Model for Improving the Learning Curves of Artificial Neural Networks

In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.     

Identification of Bacillus anthracis by Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Artificial Neural Networks

This report demonstrates the applicability of a combination of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and chemometrics for rapid and reliable identification of vegetative cells of the causative agent of anthrax, Bacillus anthracis. Bacillus cultures were prepared under standardized conditions and inactivated according to a recently developed MS-compatible inactivation protocol for highly pathogenic microorganisms. MALDI-TOF MS was then employed to collect spectra from the microbial samples and to build up a database of bacterial reference spectra. This database comprised mass peak profiles of 374 strains from Bacillus and related genera, among them 102 strains of B. anthracis and 121 strains of B. cereus. The information contained in the database was investigated by means of visual inspection of gel view representations, univariate t tests for biomarker identification, unsupervised hierarchical clustering, and artificial neural networks (ANNs). Analysis of gel views and independent t tests suggested B. anthracis- and B. cereus group-specific signals. For example, mass spectra of B. anthracis exhibited discriminating biomarkers at 4,606, 5,413, and 6,679 Da. A systematic search in proteomic databases allowed tentative assignment of some of the biomarkers to ribosomal protein or small acid-soluble proteins. Multivariate pattern analysis by unsupervised hierarchical cluster analysis further revealed a subproteome-based taxonomy of the genus Bacillus. Superior classification accuracy was achieved when supervised ANNs were employed. For the identification of B. anthracis, independent validation of optimized ANN models yielded a diagnostic sensitivity of 100% and a specificity of 100%.Members of the genus Bacillus are rod-shaped bacteria that exhibit catalase activity and can be characterized as endospore-forming obligate or facultative aerobes. The genus Bacillus contains two important groups of bacteria named after B. subtilis and B. cereus. The best-characterized member of the former group is B. subtilis, a renowned model organism for genetic research. Other group members, like B. pumilis, B. licheniformis, B. atrophaeus, and B. amyloliquefaciens, exhibit a high degree of phenotypic similarity and are thus not easily distinguishable (15).The B. cereus group comprises a number of closely related bacteria, some of which interfere with human health. Bacteria classified as B. cereus are occasionally associated with food poisoning (16, 28), while B. thuringiensis is primarily an insect pathogen because of its ability to produce toxins that have been widely used for the biocontrol of insect pests (28, 30). A third member of the B. cereus group, B. anthracis, is the causative agent of anthrax and is highly relevant to human and animal health. Other members of the B. cereus group are B. mycoides, B. pseudomycoides, and B. weihenstephanensis (4, 15).B. anthracis is a possible agent in biological warfare and bioterrorism. Its applicability as a biological warfare agent was made apparent by an accidental release from a Soviet military facility in Sverdlovsk (1, 10). Also, the well-publicized mailing of B. anthracis spores in the United States, which caused 18 confirmed cases of cutaneous and inhalational anthrax and an additional 4 suspected cases of cutaneous anthrax (3, 22), demonstrated that B. anthracis may become a threat from terrorist groups (10).Rapid detection of B. anthracis may be challenging because of its great genetic similarity to other species of the B. cereus group (10) and the difficulties of phenotypic differentiation of B. cereus group members (15). There is some controversy in the literature regarding the taxonomy of the B. cereus group. Indeed, some authors state that B. anthracis, B. cereus, and B. thuringiensis are one species with various virulence plasmids for the toxin pXO1 and the capsule pXO2 of B. anthracis and the insecticidal toxin of B. thuringiensis (10, 19). Other authors do not support this opinion and suggest the presence of even more species within the group (21).Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) intact-cell mass spectrometry (ICMS) has been suggested as a rapid, objective, and reliable technique for bacterial identification (8, 13, 23, 25, 38). As a proteomic technique, ICMS of whole bacterial cells, or cell lysates, relies on the reproducible detection of microbial protein patterns and thus delivers information complementary to genotypic or phenotypic test methods. With the pattern-matching approach, microbial identification is achieved by comparing experimental mass spectra with a collection of mass spectra of known organisms. This requires the compilation of large databases of bacterial reference spectra but has the advantage that an extensive knowledge of biomarker identities is not required. Another advantage of the pattern-matching approach is that genus- and species-specific procedures or consumables are not required, i.e., the same methodology can in principle be applied to all kinds of microorganisms (multiplex advantage).It is thus believed that ICMS offers the possibility to systematically investigate the diversity of bacterial subproteomes, complementing existing methodologies of bacterial characterization. This potential and the need for a rapid, objective, and reliable microbial identification technique that does not rely on nucleic acid detection and the availability of an MS-compatible inactivation protocol for highly pathogenic biosafety level 3 microorganisms and bacterial endospores (26) prompted us to systematically study the MALDI-TOF MS profiles of Bacillus strains and to establish a database of bacterial mass spectra. In the present work, we describe strategies of spectral analysis that allow the identification and validation of group- and species-specific sets of biomarkers. Using unsupervised hierarchical cluster analysis (UHCA) and supervised artificial neural network (ANN) analysis, we also demonstrate how microbial spectra can be employed to establish an MS-based methodology for rapid, objective, and reliable identification of the target species, B. anthracis.     

The Preparation and Characterization of an Aggregated Gliadin Fraction from Wheat

An aggregated gliadin fraction was prepared by gel filtrationon Sephacryl S300 in a solvent containing 6.0 M guanidine hydrochloride.This was reduced, alkylated, and separated by ion exchange chromatography.SDS-PAGE of the resulting fraction showed a number of polypeptides,mostly with apparent M¹s of around 44 000. The amino acid compositionwas similar to those reported previously for monomeric -, ß-and -gliadins. Automated amino acid sequencing from the N-terminusalso showed the presence of sequence types characteristic of-, ß-, and -gliadins, but the major sequence typewas not related to any described previously. This sequence wasNH₂-Ser-His-Ile-Pro-Gly-Leu-Glu-Arg-Pro-Ser-Gln-Gln-Gln-Gln-Leu-. Key words: Wheat, Gluten, Gliadin, Seed     

Identification of Gluten Protein Subunits and Their PCR Amplified Products Related to Sedimentation Volume in Durum Wheat

Protein content and gluten strength are the major parameters, which decide the firmness, stickiness and cooking loss of pasta. In the present study, a population of 140 F_2:7 recombinant inbred lines derived from a durum wheat cross was analyzed for the association of protein and PCR based markers with gluten strength as estimated by sodium dodecyl sulfate-sedimentation (SDSS) volume. The regression analysis showed strong association of Glu-B3a, Glu-B2a and one ω-gliadin band coded by Gli-B3 locus with the SDSS volume. The association between gluten strength and Glu-B2/Gli-B3 loci is new observation. Glu-B1b was also found to be associated with gluten strength, while no association of Glu-A3 locus was observed. All these markers collectively explained 51.9% to 59.1% variation in SDSS volume at three diverse environments.     

Molecular Characterization and Comparative Analysis of Six Durum Wheat Accessions Including Graziella Ra

Genetic identity and relatedness of the durum wheat Graziella Ra, four Italian commercial durum cultivars (Cappelli, Grazia, Flaminio and Svevo) and Kamut were evaluated using amplified fragment length polymorphisms (AFLPs), simple sequence repeats (SSRs) and α-gliadin gene sequence analysis. Our primary objective was to study molecular genetic diversity in such a set of wheats including three modern (Grazia, Flaminio and Svevo) and three older (Cappelli, Kamut and Graziella Ra) durum accessions. Specifically, we aimed at determining the relationship between the historic accession Graziella Ra and Kamut, which is considered an ancient relative of the durum subspecies. Obtained results revealed that (1) both AFLP and SSR molecular markers detected highly congruent patterns of genetic diversity among the accessions showing nearly similar efficiency; (2) for AFLPs, percentage of polymorphic loci within accession ranged from 6.57% to 19.71% (mean, 12.77%) and for SSRs, from 0% to 57.14% (mean, 28.57%); (3) principal component analysis of genetic distance among accessions showed the first two axes accounting for 58.03% (for AFLPs) and 61.60% (for SSRs) of the total variability; (4) for AFLPs, molecular variance was partitioned into 80% (variance among accessions) and 20% (within accession) and for SSRs, into 73% (variance among accessions) and 27% (within accession); (5) cluster analysis of AFLPs and SSRs datasets displayed Graziella Ra and Kamut constantly grouped into the same cluster; and (6) molecular comparison of α-gliadin gene sequences showed Graziella Ra and Kamut in separate clusters. All these findings support the hypothesis that Graziella Ra and Kamut, although very similar, at least in the little part of the genome investigated by molecular markers employed in this study, might be considered as distinct accessions.     

基于神经网络和遗传算法的木糖醇发酵培养基优化研究

发酵过程机理复杂、影响因素众多。菌种的生理生化特性及发酵的工艺确定之后 ,适宜的培养基配方成了发酵水平、原料成本高低的决定因素。为了优化培养基配方 ,采用遗传算法是一种行之有效的方法。遗传算法 (ＧＡ)是基于达尔文进化论和孟德尔遗传学说来实现随机、自适应、并行性全局搜索的一种无须数学模型的优化算法。与其它搜索方法相比 ,ＧＡ的优越性主要有 :(1)在搜索过程中ＧＡ不易陷入局部最优 ,即使所定义的目标函数非连续、不规则或伴有噪声 ,它也能以很大的概率找到全局最优解 ;(2 )由于ＧＡ固有的并行性 ,使得它非常适合于大规模并…     

人工海水胁迫下小麦种质资源的耐盐性筛选与鉴定

利用人工配制的海水筛选耐盐性较好的小麦品种,为沿海滩涂地区的小麦耐盐育种提供重要信息。本研究利用人工海水处理的方法,对363份小麦种质资源进行了芽期耐盐性初步鉴定,筛选出芽期耐盐性为1级的小麦种质28份。进一步对芽期耐盐性较好的48份小麦种质进行了苗期耐盐性鉴定,并对其耐盐指标进行隶属值模糊评价分析,从中鉴定出了2个苗期耐盐性较强的小麦种质,分别为淮麦31和红壳洋麦。依据来源的不同,发现小麦种质资源的芽期耐盐性大小依次为地方品种>育成品种>国外引进品种。小麦芽期与苗期的耐盐性相关分析表明,二者相关性极低(r=-0.0051)。     

FT-IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

A rapid spectroscopic approach for whole‐organism fingerprinting of Fourier transform infrared (FT‐IR) spectroscopy was used to analyse 16 isolates from five closely related species of Fusarium: F. graminearum, F. moniliforme, F. nivale, F. semitectum and F. oxysporum. Principal components analysis and hierarchical cluster analysis were used to study the clusters in the data. On visual inspection of the clusters from both methods, the spectra were not differentiated into five separate clusters corresponding to species and these unsupervised methods failed to identify these fungal strains. When the data were trained by back propagation algorithm of artificial neural networks (ANNs) with principal components scores of spectra used as input modes, the strains were accurately predicted and recognized. The results in this study show that FT‐IR spectroscopy in combination with principal component artificial neural networks (PC‐ANNs) is well suited for identifying Fusarium spp. It would be advantageous to establish a comprehensive database of taxonomically well‐defined Fusarium species to aid the identification of unknown strains.     

Identification of Enterobacter sakazakii from closely related species: The use of Artificial Neural Networks in the analysis of biochemical and 16S rDNA data

Background  

Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process.     

Identification and Preliminary Analysis of Several Centromere-associated Bacterial Artificial Chromosome Clones from a Diploid Wheat Library

Although the centromeres of some plants have been investlgated prevlously, our knowledge of the wheat centromere Is still very llmlted. To understand the structure and functlon of the wheat centromere, we used two centromeric repeats （RCS1 and CCS1-5ab） to obtain some centromere-assoclated bacterial artificial chromosome （BAC） clones in 32 RCS1-related BAC clones that had been screened out from a diploid wheat （Triticum boeoticum Boiss.; 2n=2x=14） BAC library. Southern hybridization results indicated that, of the 32 candidates, there were 28 RCS1-positive clones. Based on gel blot patterns, the frequency of RCS1 was approximately one copy every 69.4 kb in these 28 RCS1-positive BAC clones. More bands were detected when the same filter was probed with CCS1-5ab. Furthermore, the CCS1 bands covered all the bands detected by RCS1, which suggests that some CCS1 repeats were distributed together with RCS1. The frequency of CCS1 families was once every 35.8 kb, nearly twice that of RCS1. Fluorescence in situ hybridization （FISH） analysis Indicated that the five BAC clones containing RCS1 and CCS1 sequences all detected signals at the centromerlc regions in hexaplold wheat, but the signal intensities on the A-genome chromosomes were stronger than those on the B- and/or Dgenome chromosomes. The FISH analysis among nine Triticeae cereals indicated that there were A-genomespecific （or rich） sequences dispersing on chromosome arms in the BAC clone TbBACS. In addition, at the interphase cells, the centromeres of diploid species usually clustered at one pole and formed a ring-like allocation In the period before metaphase.