PCR-RFLP analysis of mitochondrial DNA ND5/6 region among 3 subspecies of common carp (Cyprinus carpio L.) and its application to genetic discrimination of subspecies

Mitochondrial DNA ND5/6 region was studied by PCR-RFLP analysis among ten representative strains belonging to three subspecies (Cyprinus carpio carpio, Cyprinus carpio haematopterus and Cyprinus carpio rubrofuscus) of common carp (Cyprinus carpio L.). A total of 2.4 kb fragment was amplified and subjected to restriction endonuclease analysis with nine restriction endonucleases subsequently. The results indicated that each subspecies owned one hyplotype and four restriction enzymes (Dde I, HaeIII, Taq I and Mbo I) produced diagnostic restriction sites which could be used for discriminating the three subspecies and as molecular genetic markers for assistant selective breeding of common carp.

Fast Discrimination of Commerical Corn Varieties Based on Near Infrared Spectra

The existing methods for the discrimination of varieties of commodity corn seed are unable to process batch data and speed up identification, and very time consuming and costly. The present paper developed a new approach to the fast discrimination of varieties of commodity corn by means of near infrared spectral data. Firstly, the experiment obtained spectral data of 37 varieties of commodity corn seed with the Fourier transform near infrared spectrometer in the wavenurnber range from 4 000 to 12 000 cm (1). Secondly, the original data were pretreated using statistics method of normalization in order to eliminate noise and improve the efficiency of models. Thirdly, a new way based on sample standard deviation was used to select the characteristic spectral regions, and it can search very different wavenumbers among all wavenumbers and reduce the amount of data in part. Fourthly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first ten components were more than 99.98%. Finally, according to the first ten components, recognition models were established based on BPR. For every 25 samples in each variety, 15 samples were randomly selected as the training set. The remaining 10 samples of the same variety were used as the first testing set, and all the 900 samples of the other varieties were used as the second testing set. Calculation results showed that the average correctness recognition rate of the 37 varieties of corn seed was 94.3%. Testing results indicate that the discrimination method had higher precision than the discrimination of various kinds of commodity corn seed. In short, it is feasible to discriminate various varieties of commodity corn seed based on near infrared spectroscopy and BPR.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

Evaluation of the thermal/optical reflectance method for discrimination between char- and soot-EC

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Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields, including work in cardiovascular research and drug toxicology. In this study, metabonomics method was employed to the diagnosis of Type 2 diabetes mellitus (DM2) based on serum lipid metabolites. The results suggested that serum fatty acid profiles determined by capillary gas chromatography combined with pattern recognition analysis of the data might provide an effective approach to the discrimination of Type 2 diabetic patients from healthy controls. And the applications of pattern recognition methods have improved the sensitivity and specificity greatly. (C) 2004 Elsevier B.V. All rights reserved.

Roundness parameters in barrier-lagoon systems in northeastern Shandong Peninsula, China: limitations to environmental discrimination

Twenty-five samples from six subenvironments in the barrier-lagoon systems in northeastern Shandong province, China, are examined. A statistical method is used to study the roundness variation of grains of different sizes. Roundness of very fine pebble and very coarse sand varies significantly in different subenvironments. It is possible to discriminate among aqueous depositional environments using the roundness of grains of these sizes. Roundness of grains finer than 0.84 φ is not distinguishable in different subenvironments.

Footwear for Gender Recognition

Conventionally, biometrics resources, such as face, gait silhouette, footprint, and pressure, have been utilized in gender recognition systems. However, the acquisition and processing time of these biometrics data makes the analysis difficult. This letter demonstrates for the first time how effective the footwear appearance is for gender recognition as a biometrics resource. A footwear database is also established with reprehensive shoes (footwears). Preliminary experimental results suggest that footwear appearance is a promising resource for gender recognition. Moreover, it also has the potential to be used jointly with other developed biometrics resources to boost performance.

Discrimination of herbal medicines by molecular spectroscopy and chemical pattern recognition

The molecular spectroscopy (including near infrared diffuse reflection spectroscopy, Raman spectroscopy and infrared spectroscopy) with OPUS/Ident software was applied to clustering ginsengs according to species and processing methods. The results demonstrate that molecular spectroscopic analysis could provide a rapid, nondestructive and reliable method for identification of Chinese traditional medicine. It's found that the result of Raman spectroscopic analysis was the best one among these three methods. Comparing with traditional methods, which are laborious and time consuming, the molecular spectroscopic analysis is more effective.

A NEWLY PROPOSED MOLECULAR TOPOLOGICAL INDEX FOR THE DISCRIMINATION OF CIS/TRANS ISOMERS AND FOR THE STUDIES OF QSAR/QSPR

A new topological index, the general a(N)-index (GAI), on quantum chemistry, is described in this paper. The GAI can be applied to molecules that contain heteroatoms and multiple bonds, and performs well in distinguishing cis/trans isomers. The relationships between the GAIs and physicochemical properties of olefins and neutral phosphorus compounds were observed with satisfactory results.

A feminist trade union agenda? The continued significance of class, gender and race.

During the 1990s attempts to identify a feminist trade union agenda have focused on both the content and process of such a potential agenda. In a period in which trade unions have changed significantly, the general national agenda appears to be changing, acknowledging issues of importance to women. UNISON, Britain's largest trade union, has enshrined proportionality and fair representation in its constitution, developing national initiatives aimed at improving opportunities in work and in the union for women, black workers, manual workers, disabled workers, etc. who traditionally have been less well represented. Many issues affecting women generally have moved to centre stage, yet issues affecting women ancillary workers seem as excluded as ever. Through a study of cleaners in the National Health Service this article argues that workplace interests reflect wider social divisions, but in a variety of patterns depending on the social organization of work. Despite thewidening trade union agenda, particular interests — more specifically the workplace interests of working-class women and black women — continue to be neglected.

Gender and health: a social movement’s agenda for Big Pharma.

Social movements have an important new campaigning and organizing competence in new information communication technologies. These technologies also enable the members of social movements to readily research the accuracy of information: knowledge becomes globalized and readily accessible. In relation to Big Pharma, women’s social movements and social movements of the medicated intersect, and there is now a substantial challenge to Big Pharma both within developed and developing countries from the terrain of gender and health. This paper documents those challenges and looks towards their consequences in the future both in respect of Big Pharma but also in terms of 'academic' research

Application of metabolomics to plant genotype discrimination using statistics and machine learning

Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248).