Classification of valence changes of trivalent rare earth ions in alkaline earth borates using artificial neural networks

The investigations of classification on the valence changes from RE3+ to RE2+ (RE = Eu, Sm, Yb, Tm) in host compounds of alkaline earth berate were performed using artificial neural networks (ANNs). For comparison, the common methods of pattern recognition, such as SIMCA, KNN, Fisher discriminant analysis and stepwise discriminant analysis were adopted. A learning set consisting of 24 host compounds and a test set consisting of 12 host compounds were characterized by eight crystal structure parameters. These parameters were reduced from 8 to 4 by leaps and bounds algorithm. The recognition rates from 87.5 to 95.8% and prediction capabilities from 75.0 to 91.7% were obtained. The results provided by ANN method were better than that achieved by the other four methods. (C) 1999 Elsevier Science B.V. All rights reserved.

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

PenBase, the shrimp antimicrobial peptide penaeidin database: Sequence-based classification and recommended nomenclature

Antimicrobial peptides play a major role in innate immunity. The penaeidins, initially characterized from the shrimp Litopenaeus vannamei, are a family of antimicrobial peptides that appear to be expressed in all penaeid shrimps. As of recent, a large number of penaeid nucleotide sequences have been identified from a variety of penaeid shrimp species and these sequences currently reside in several databases under unique identifiers with no nomenclatural continuity. To facilitate research in this field and avoid potential confusion due to a diverse number of nomenclatural designations, we have made a systematic effort to collect, analyse, and classify all the penaeidin sequences available in every database. We have identified a common penaeidin signature and subsequently established a classification based on amino acid sequences. In order to clarify the naming process, we have introduced a 'penaeidin nomenclature' that can be applied to all extant and future penaeidins. A specialized database, PenBase, which is freely available at http://www.penbase.immunaqua.com, has been developed for the penaeidin family of antimicrobial peptides, to provide comprehensive information about their properties, diversity and nomenclature. (c) 2005 Elsevier Ltd. All rights reserved.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Classification of jinjiang oysters Crassostrea rivularis (Gould, 1861) from China, based on morphology and phylogenetic analysis

The jinjiang oyster Crassostrea rivularis [Gould, 1861. Descriptions of Shells collected in the North Pacific Exploring Expedition under Captains Ringgold and Rodgers. Proc. Boston Soc. Nat. Hist. 8 (April) 33-40] is one of the most important and best-known oysters in China. Based on the color of its flesh, two forms of C rivularis are recognized and referred to as the "white meat" and 11 red meat" oysters. The classification of white and red forms of this species has been a subject of confusion and debate in China. To clarify the taxonomic status of the two forms of C. rivularis, we collected and analyzed oysters from five locations along China's coast using both morphological characters and DNA sequences from mitochondrial 16S rRNA and cytochrome oxidase 1, and the nuclear 28S rRNA genes. Oysters were classified as white or red forms according to their morphological characteristics and then subjected to DNA sequencing. Both morphological and DNA sequence data suggest that the red and white oysters are two separate species. Phylogenetic analysis of DNA sequences obtained in this study and existing sequences of reference species show that the red oyster is the same species as C. ariakensis Wakiya [1929. Japanese food oysters. Jpn. J. Zool. 2, 359-367.], albeit the red oysters from north and south China are genetically distinctive. The white oyster is the same species as a newly described species from Hong Kong, C. hongkongensis Lam and Morton [2003. Mitochondrial DNA and identification of a new species of Crassostrea (Bivalvia: Ostreidae) cultured for centuries in the Pearl River Delta, Hong Kong, China. Aqua. 228, 1-13]. Although the name C. rivularis has seniority over C. ariakensis and C. hongkongensis, the original description of Ostrea rivularis by Gould [1861] does not fit shell characteristics of either the red or the white oysters. We propose that the name of C. rivularis Gould [1861] should be suspended, the red oyster should take the name C. ariakensis, and the white oyster should take the name C. hongkongensis. (C) 2004 Elsevier B.V. All rights reserved.

Classification of common oysters from North China

Oysters are commonly found on rocky shores along China's northern coast, although there is considerable confusion as to what species they are. To determine the taxonomic status of these oysters, we collected specimens from nine locations north of the Yangtze River and conducted genetic identification using DNA sequences. Fragments from three genes, mitochondrial 165 rRNA, mitochondria! cytochrome oxidase I (COI), and nuclear 285 rRNA, were sequenced in six oysters from each of the nine sites. Phylogenetic analysis of all three gene fragments clearly demonstrated that the small oysters commonly found on intertidal rocks in north China are Crassostrea gigas (Thunberg, 1793), not C. plicatula (the zhe oyster) as widely assumed. Their small size and irregular shell characteristics are reflections of the stressful intertidal environment they live in and not reliable characters for classification. Our study confirms that the oysters from Weifang, referred to as Jinjiang oysters or C. rivularis (Gould, 1861), are C. ariakensis (Wakiya, 1929). We found no evidence for the existence of C. talienwhanensis (Crosse, 1862) and other Crassostrea species in north China. Our study highlights the need for reclassifying oysters of China with molecular data.