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.

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.

Carbon source/sink function of a subtropical, eutrophic lake determined from an overall mass balance and a gas exchange and carbon burial balance

Although studies on carbon burial in lake sediments have shown that lakes are disproportionately important carbon sinks, many studies on gaseous carbon exchange across the water-air interface have demonstrated that lakes are supersaturated with CO2 and CH4 causing a net release of CO2 and CH4 to the atmosphere. In order to more accurately estimate the net carbon source/sink function of lake ecosystems, a more comprehensive carbon budget is needed, especially for gaseous carbon exchange across the water-air interface. Using two methods, overall mass balance and gas exchange and carbon burial balance, we assessed the carbon source/sink function of Lake Donghu, a subtropical, eutrophic take, from April 2003 to March 2004. With the overall mass balance calculations, total carbon input was 14 905 t, total carbon output was 4950 1, and net carbon budget was +9955 t, suggesting that Lake Donghu was a great carbon sink. For the gas exchange and carbon burial balance, gaseous carbon (CO2 and CH4) emission across the water-air interface totaled 752 t while carbon burial in the lake sediment was 9477 t. The ratio of carbon emission into the atmosphere to carbon burial into the sediment was only 0.08. This low ratio indicates that Lake Donghu is a great carbon sink. Results showed good agreement between the two methods with both showing Lake Donghu to be a great carbon sink. This results from the high primary production of Lake Donghu, substantive allochthonous carbon inputs and intensive anthropogenic activity. Gaseous carbon emission accounted for about 15% of the total carbon output, indicating that the total output would be underestimated without including gaseous carbon exchange. (C) 2007 Elsevier Ltd. All rights reserved.

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.

草地生态系统土壤呼吸及其影响因素研究进展

土壤呼吸在全球碳收支中占有重要的地位,笔者对草地生态系统土壤呼吸在陆地生态系统碳平衡中的作用、土壤呼吸的分类及其影响因素等方面进行了综述。结果表明,草地生态系统土壤呼吸在不同时间空间各组分所占比例不同,生物、非生物及人为活动等因素对草地土壤呼吸影响各异,主要从土壤温度、气候变暖、土壤湿度、降水、干旱化、土壤C/N等非生物因素,叶面积指数、植物光合作用、植被凋落物等生物因素以及人类干扰活动等方面具体阐述这些因素变化对土壤呼吸产生的影响,并对草地土壤呼吸的Q10值及各影响因素间的交互作用进行归纳总结。提出草地生态系统土壤呼吸研究存在的问题和今后重点发展方向,并对未来草地生态系统土壤呼吸的研究工作做了进一步的展望。

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.