Qiaoqi Luo

Sulfitobacter pseudonitzschiae sp. nov., isolated from the toxic marine diatom Pseudo-nitzschia multiseries.

A taxonomic study was carried out on bacterial strain H3(T), which was isolated from the toxic marine diatom Pseudo-nitzschia multiseries. Cells of strain H3(T) were Gram-stain-negative, rod-shaped, non-motile and capable of reducing nitrate to nitrite, but not denitrification. Growth was observed at NaCl concentrations of 1-9%, pH 6-12 and 10-37 °C. It was unable to degrade aesculin or gelatin. The dominant fatty acids (>10 %) were C18:1ω7c/ω6c (summed feature 8) and C16:0. The respiratory ubiquinone was Q10. The major lipids were phosphatidylethanolamine, phosphatidylglycerol, an aminolipid and one unknown lipid, and the minor lipids were two phospholipids and three unknown lipids. The G+C content of the chromosomal DNA was 61.7 mol%. 16S rRNA gene sequence comparison showed that strain H3(T) was related most closely to Sulfitobacter donghicola DSW-25(T) (97.3% similarity) and levels of similarity with other species of the genus Sulfitobacter were 95.1-96.9%. The mean (± sd) DNA-DNA hybridization value between strain H3(T) and Sulfitobacter donghicola DSW-25(T) was 18.0 ± 2.25%. The average nucleotide identity between strain H3(T) and Sulfitobacter donghicola DSW-25(T) was 70.45%. Phylogenetic analyses based on 16S rRNA gene sequences showed that strain H3(T) formed a separate clade close to the genus Sulfitobacter and was distinguishable from phylogenetically related species by differences in several phenotypic properties. On the basis of the phenotypic and phylogenetic data, strain H3(T) represents a novel species of the genus Sulfitobacter, for which the name Sulfitobacter pseudonitzschiae is proposed (type strain H3(T) =DSM 26824(T) =MCCC 1A00686(T)).

Marine Phytoplankton Recognition Using Hybrid Classification Methods

Marine phytoplanktons are unicellular algae with a variety of shapes and ornamentation, and they are widely used as indicators of marine ecosystem changes. A dual layer and hybrid classifier is presented in this study for phytoplankton recognition. The method is based on k-NN, SVM mechanisms and uses shape and texture information such as moments, geometric features and gray level co-occurrence matrix features. Each individual classifier has its own specific input feature and decision mechanism. The marine phytoplankton recognition experiment shows that the proposed classification method outperforms two well-known stand-alone classifiers, k-NN and SVM.

Variations in the Total Lipid Content and Biological Characteristics of Diatom Species for Potential Biodiesel Production

The selection of suitable and indigenous species is a fundamental requirement in developing oil-rich microalgae for biodiesel production. So it is necessary to evaluate the growth characteristics and lipid content of the interested microalgae species and obtain a basic knowledge about their potential towards biodiesel production. In this study, the total lipid content and biomass concentration under normal culture conditions of thirty- five diatom species (with twenty-six species being studied for the first time) belonging to nineteen genera obtained from different locations were examined. The results showed that the total lipid content is species-dependent, ranging from 4.86% to 48.61% dry weight. Twenty-four strains (64.9%) had total lipid contents higher than 20% dry weight, and nine strains (24.3%) had total lipid contents greater than 30% dry weight. Achnanthes amoena and Proschkinia sp. had total lipid contents up to 48.61% and 41.42% dry weight, respectively. Compared with the centric diatoms, most species with high lipid content were pennate. In addition, various biological characteristics were documented, including cell size and the ability to survive in various environments. The results revealed that some diatom species here such as Bacillaria paradoxa, Navicula molli, Navicula halophila and Phaeodactylum tricornutum could be regarded as potential sources for biodiesel production.

Automatic Identification of Round Diatom

In this work, a method for automatic identification of round diatoms based on image texture features is presented. This method combines segmentation adjustment by curve fitting and texture measures based on the spectrum features. The classification accuracy was tested using leave on out methods. With classification carried out using a BP neural network we attained 96.2% accuracy from a set of image containing six species of round diatom. The result is an effective attempt of round diatom identification based on texture character.

A Svm-Based Algorithm for Automatic Species Classification of a Marine Diatom Genus Coscinodiscus Ehrenberg

Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. These properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However，it proves to be complicated task because of the high spatial variability of ornamentation properties. Researchers have shown a Teach-program and a number of library functions operating on sample image lists (SILs) and operating on classifiers (CFs) to solve the problem. In this paper, we present Coscinodiscus Ehrenberg ornamentation classifier algorithm called support vector machines (SVMs) to derive a new set of SILs and CFs. The principal purpose of SVMs is Coscinodiscus Ehrenberg images pattern recognition approach. A pattern is in this context always the SILs contained in a sub-rectangle of some given (possibly larger) image. For the same classifier this sub-rectangle must always have the same dimensions, while the query image to be searched may be arbitrarily large. The training is done by preparing SILs for the pattern taxa in question and feeding them to CFs created. Our classifier generation with preprocessing code optimization achieves a AAAAA preprocessing code, a 0.981 learning success, a 100% computational complexity. Train with SILs achieves 212 samples, 17 taxa, a 0.472% error rate and Test with Query image searching achieves 253 samples，17 taxa，a 15.81% error rate. The experiments demonstrate that the proposed method is very robust to the threshold segmentation and ornamentation feature extraction of Coscinodiscus Ehrenberg images, and is effective and useful for species classification of Coscinodiscus Ehrenberg.