The Critically Endangered black crested gibbon Nomascus concolor on Wuliang Mountain, Yunnan, China: the role of forest types in the species' conservation

The Critically Endangered black crested gibbon Nomascus concolor of China, Laos and Vietnam is threatened by deforestation and habitat destruction but there have been no studies of how it uses its forest habitat, probably because of the typically rugged t

CLASSIFICATION AND EVOLUTION OF ASIAN COLOBINES

In order to study the differentiation of Asian colobines, 14 variables measured on 123 skulls, including Rhinopithecus, Presbytis, Presbytiscus (Rhinopithecus avunculus), Pygathrix and Nasalis were analyzed by one-way, cluster and discriminant function analyses. Information on paleoenvironmental changes in China and southeast Asia since the late Tertiary was used to examine the influences of migratory routes and range of distribution in Asian colobines. A cladogram for 6 genera of Asian colobines was constructed from the results of various analyses. Some new points or revisions were suggested: (1) Following one of two migratory routes, ancient species of Asian colobines perhaps passed through Xizang (Tibet) along the northern bank of the Tethys sea and through the Heng Duan Shan regions of Yunnan into Vietnam. An ancient landmass linking Yunnan and Xizang was already present on the east bank of the Tethys sea. Accordingly, Asian colobines would have two centers of evolutionary origin: Sundaland and the Heng Duan Shan regions of China. (2) Pygathrix shares more cranial features with Presbytiscus than with Rhinopithecus. This differs somewhat from the conclusion reached by Groves. (3) Nasalis (karyotype: 2n = 48) may be the most primitive genus among Asian colobines. Certain features shared with Rhinopithecus, e.g. large body size, terrestrial activity and limb proportions, can be interpreted as symple-siomorphic characters. (4) Rhinopithecus, with respect to craniofacial features, is a special case among Asian colobines. It combines a high degree of evolutionary specialization with retention of some primitive features thought to have been present in the ancestral Asian colobine.

Discovery and characterization of two types of liver-expressed antimicrobial peptide 2 (LEAP-2) genes in rainbow trout

The sequences and gene organisation of two LEAP-2 molecules (LEAP-2A and LEAP-2B) from rainbow trout, Oncorhynchus mykiss are presented. Both genes consist of a 3 exon/2 intron structure, with exon sizes comparable to known mammalian genes. LEAP-2A notably differs from LEAP-2B in having larger introns and a larger 3'UTR. The predicted proteins contain a signal peptide and prodomain, followed by a mature peptide of 41 aa containing four conserved cysteines. The RXXR cleavage site to release the mature peptide was also conserved. Both genes were found to be constitutively expressed in the liver, with expression in the intestine, and to a lesser extent the skin, evident after bacterial challenge. (C) 2004 Elsevier B.V. All rights reserved.

Life history characteristics of three types of females in Brachionus calyciflorus Pallas (Rotifera) fed different algae

This study describes the life history characteristics of amictic, unfertilized mictic and fertilized mictic females of the rotifer Brachionus calyciflorus cultured individually on two different algae at 0.1 mg ml(-1) food concentration and 27 degreesC. The duration of the juvenile period of amictic females was significantly shorter on Chlorella pyrenoidosa Chick than on Scenedesmus obliquus Kutz or both algae together. The duration of the juvenile period of unfertilized mictic females was significantly longer, and the number of eggs produced by amictic females was significantly larger on Chlorella pyrenoidosa than on S. obliquus. When fed the same type of alga, the duration of the juvenile period of the fertilized mictic females was the longest among the three types of females, and the durations of the reproductive period of the amictic females and the post-reproductive period of the fertilized mictic females were longer than, or equal to those of the other two types of females, respectively. The number of eggs produced by an unfertilized mictic female was the largest among the three types of females, and that of amictic females was larger than or equal to that of fertilized mictic females, depending on the type of diet.

The effect of food concentration on the life history of three types of Brachionus calyciflorus females

The effect of food concentration on the life history of three types of Brachionus calciflorus females (amictic, unfertilized mictic and fertilized mictic female) was studied with replicated individual cultures at 25 degrees and at four food concentrations (1.5, 3.0, 6.0 and 9.0 x 10(6) cells mL(-1)) of Scenedesmus obliquus. There were highly significant effects of both food concentration and female type, independently and in interaction on the duration of juvenile period of the rotifer, but neither a;ere the effects on the duration of post-reproductive period and mean life-span. The duration of juvenile period of unfertilized mictic female at the food concentration of 9.0 x 10(6) cells mL(-1) was the longest among all the food concentration-female type combinations. Both food concentration and female type influenced significantly the duration of reproductive period and the number of eggs produced by each type of female per life cycle, respectively. There was, however, no significant interaction between food level and female type. Among the three types of females, the number of eggs produced by an unfertilized mictic female was the largest. and that of a fertilized mictic female was the smallest.

STUDY ON THE MODE OF PRIMARY CONTROL IN THE EGG OF GYNOGENETIC CRUCIAN CARP FOR INHIBITING THE DEVELOPMENT OF 2 TYPES OF SPERM NUCLEI

By comparing the different developmental characteristics of two types of sperm nuclei which were from gynogenetic fish (crucian carp) and amphimictic fishes (red carp, red goldfish and sex-reversal red carp) respectively in the eggs of gynogenetic crucian carp, it was preliminarily revealed that there existed selective inhibiting actions of the primary control in the eggs of crucian carp for inhibiting the development of the two types of sperm nuclei. To homologous sperms, the primary control showed weak effect, thus leading to the decondensation of homologous sperm nuclei at different degrees in the eggs of crucian carp. But to heterologous sperms, the primary control showed strong effects, resulting in the total inhibition of the development of heterologous sperm nuclei. Moreover, our experimental results also showed that the different developmental behavior of the two types of sperm nuclei might have a great relationship to the changes of the sex ratio in the population of gynogenetic crucian carp. The infiltration of "the genetic materials in sperm nuclei" into the female nucleus at random might play an important role in male emergence in the naturally gynogenetic population of crucian carp.

A Cancer Recognition Method Based on DNA Microarray

The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on Biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Method for improving classification performance of neural network based on fuzzy input and network inversion

In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.

Viruses' complete genomes classification based on geometrical learning

In this paper, we proposed a method of classification for viruses' complete genomes based on graph geometrical theory in order to viruses classification. Firstly, a model of triangular geometrical graph was put forward, and then constructed feature-space-samples-graphs for classes of viruses' complete genomes in feature space after feature extraction and normalization. Finally, we studied an algorithm for classification of viruses' complete genomes based on feature-space-samples-graphs. Compared with the BLAST algorithm, experiments prove its efficiency.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Cancer classification based on direction-basis-function neural networks

Automatic molecular classification of cancer based on DNA microarray has many advantages over conventional classification based on morphological appearance of the tumor. Using artificial neural networks is a general approach for automatic classification. In this paper, Direction-Basis-Function neuron and Priority-Ordered algorithm are applied to neural networks. And the leukemia gene expression dataset is used as an example to testify the classifier. The result of our method is compared to that of SVM. It shows that our method makes a better performance than SVM.