Genetic diversity within and among six subpopulations of Larix decidua Mill. from two altitudinal transects of Swiss Alps was investigated using 6 enzyme systems coding for 8 loci. Globally, the mean proportion of pol...Genetic diversity within and among six subpopulations of Larix decidua Mill. from two altitudinal transects of Swiss Alps was investigated using 6 enzyme systems coding for 8 loci. Globally, the mean proportion of polymorphic loci was 22.9%, the average number of alleles per locus was 1.3, and the mean expected heterozygosity was 0.095. Only 5.8% of the genetic variation resided among populations. The mean genetic distance was 0.006. Several significant differences of gene frequencies were found between different age classes. Positive values of the species mean fixation index observed in this study suggested a considerable deficit of heterozygotes in the populations of L. decidua of Swiss Alps. At one of the sites (Arpette), the highest subpopulation in elevation gave the lowest level of genetic diversity (as evidenced by the lowest proportion of polymorphic loci and the lowest mean expected heterozygosity) and the largest value of genetic distance when compared to other subpopulations. The genetic differences between the highest subpopulation and the other ones suggest that the founder effect may be an important factor influencing genetic differentiation of L. decidua populations at Arpette transect.展开更多
The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjia...The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.展开更多
We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchic...We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.展开更多
AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with d...AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS： The overall classification error was 14.9% （accuracy 85.1%）. FibroTest＇s cases with true scores of FO and F4 were classified with very high accuracy （18/20 for FO, 9/9 for FO-1 and 92/96 for F4） and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION： The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression,展开更多
This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from tra...This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.展开更多
There is a major defect when using the traditional topic-opinion model for post opinion classifications in an online forum discussion.The accuracy of the classification based on the topic-opinion model highly depends ...There is a major defect when using the traditional topic-opinion model for post opinion classifications in an online forum discussion.The accuracy of the classification based on the topic-opinion model highly depends on the observable topic-opinion features aiming at the subject,while a large number of posts do not have such features in a forum.Therefore,for the most part,the accuracy is less than 78%.To solve this problem,we propose a new method to identify post opinions based on the Tree Conditional Random Fields(T-CRFs)model.First,we select the topic-opinion features of the posts and associated opinion features between posts to construct the T-CRFs model,and then we use the T-CRFs model to label the opinions of the tree-structured posts under the same topic iteratively to reach a maximum joint probability.To reduce the training cost,we design a simplified tree diagram module and some feature templates.Experimental results suggest the proposed method costs less training time and improves the accuracy by 11%.展开更多
Information on forest structure is important for forest management decisions. This is inadequate in many situations, especially where timber is not of primary interest. We analyzed the structure of two forest types in...Information on forest structure is important for forest management decisions. This is inadequate in many situations, especially where timber is not of primary interest. We analyzed the structure of two forest types in the Oban Division of Cross River National Park, Nigeria. Systematic sampling technique was used to establish two transects measuring 2,000 x 2 m, at 600 m interval in the two forest types in four locations. Four 50 m x 50 m plots were located alternately at 500 m intervals along each transect, constituting 32 plots per forest type and 64 plots in all, Diameters at breast height （DBH）, base; middle and top; crown diameter; total height and crown length were measured on all trees with DBH 〉_ 10 cm. There were 159 stems/ha in the close-canopy forest and 132 stems/ha in the secondary forest. The mean DBH were 34.5 cm and 33.62 cm respectively. The mean heights were 24.79 m and 23.97 m, respectively. Basal area/ha were 41.59 m2 ha~ and 27.38 m2 hal for the two forest types. Majority of the trees encountered in the two forest types belonged to the middle stratum which has implication for small mammals＇ populations. Emergent trees which are otherwise scarce in other parts of the country were recorded, which also has implications for density thinning and seed supplies.展开更多
文摘Genetic diversity within and among six subpopulations of Larix decidua Mill. from two altitudinal transects of Swiss Alps was investigated using 6 enzyme systems coding for 8 loci. Globally, the mean proportion of polymorphic loci was 22.9%, the average number of alleles per locus was 1.3, and the mean expected heterozygosity was 0.095. Only 5.8% of the genetic variation resided among populations. The mean genetic distance was 0.006. Several significant differences of gene frequencies were found between different age classes. Positive values of the species mean fixation index observed in this study suggested a considerable deficit of heterozygotes in the populations of L. decidua of Swiss Alps. At one of the sites (Arpette), the highest subpopulation in elevation gave the lowest level of genetic diversity (as evidenced by the lowest proportion of polymorphic loci and the lowest mean expected heterozygosity) and the largest value of genetic distance when compared to other subpopulations. The genetic differences between the highest subpopulation and the other ones suggest that the founder effect may be an important factor influencing genetic differentiation of L. decidua populations at Arpette transect.
基金Under the auspices of National Natural Science Foundation of China (No. 40871188) National Key Technologies R&D Program of China (No. 2006BAD23B03)
文摘The main objective of this research is to determine the capacity of land cover classification combining spec- tral and textural features of Landsat TM imagery with ancillary geographical data in wetlands of the Sanjiang Plain, Heilongjiang Province, China. Semi-variograms and Z-test value were calculated to assess the separability of grey-level co-occurrence texture measures to maximize the difference between land cover types. The degree of spatial autocorrelation showed that window sizes of 3×3 pixels and 11×11 pixels were most appropriate for Landsat TM im- age texture calculations. The texture analysis showed that co-occurrence entropy, dissimilarity, and variance texture measures, derived from the Landsat TM spectrum bands and vegetation indices provided the most significant statistical differentiation between land cover types. Subsequently, a Classification and Regression Tree (CART) algorithm was applied to three different combinations of predictors: 1) TM imagery alone (TM-only); 2) TM imagery plus image texture (TM+TXT model); and 3) all predictors including TM imagery, image texture and additional ancillary GIS in- formation (TM+TXT+GIS model). Compared with traditional Maximum Likelihood Classification (MLC) supervised classification, three classification trees predictive models reduced the overall error rate significantly. Image texture measures and ancillary geographical variables depressed the speckle noise effectively and reduced classification error rate of marsh obviously. For classification trees model making use of all available predictors, omission error rate was 12.90% and commission error rate was 10.99% for marsh. The developed method is portable, relatively easy to im- plement and should be applicable in other settings and over larger extents.
基金supported by the National High Technology Research and Development Program of China under Grant No.2012AA011104the Fundamental Research Funds for the Center Universities
文摘We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.
基金Supported by A grant of the Universidad Nacional Autonoma de Mexico SDI.PTID.05.6
文摘AIM： To assess the usefulness of FibroTest to forecast scores by constructing decision trees in patients with chronic hepatitis C.METHODS： We used the C4.5 classification algorithm to construct decision trees with data from 261 patients with chronic hepatitis C without a liver biopsy. The FibroTest attributes of age, gender, bilirubin, apolipoprotein, haptoglobin, α2 macroglobulin, and γ-glutamyl transpeptidase were used as predictors, and the FibroTest score as the target. For testing, a 10-fold cross validation was used.RESULTS： The overall classification error was 14.9% （accuracy 85.1%）. FibroTest＇s cases with true scores of FO and F4 were classified with very high accuracy （18/20 for FO, 9/9 for FO-1 and 92/96 for F4） and the largest confusion centered on F3. The algorithm produced a set of compound rules out of the ten classification trees and was used to classify the 261 patients. The rules for the classification of patients in FO and F4 were effective in more than 75% of the cases in which they were tested.CONCLUSION： The recognition of clinical subgroups should help to enhance our ability to assess differences in fibrosis scores in clinical studies and improve our understanding of fibrosis progression,
基金Project supported by the National Natural Science Foundation ofChina (No. 40101014) and by the Science and technology Committee of Zhejiang Province (No. 001110445) China
文摘This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.
基金supported by the National Natural Science Foundation of China under Grant No. 60873246China Information Technology Security Evaluation Centre
文摘There is a major defect when using the traditional topic-opinion model for post opinion classifications in an online forum discussion.The accuracy of the classification based on the topic-opinion model highly depends on the observable topic-opinion features aiming at the subject,while a large number of posts do not have such features in a forum.Therefore,for the most part,the accuracy is less than 78%.To solve this problem,we propose a new method to identify post opinions based on the Tree Conditional Random Fields(T-CRFs)model.First,we select the topic-opinion features of the posts and associated opinion features between posts to construct the T-CRFs model,and then we use the T-CRFs model to label the opinions of the tree-structured posts under the same topic iteratively to reach a maximum joint probability.To reduce the training cost,we design a simplified tree diagram module and some feature templates.Experimental results suggest the proposed method costs less training time and improves the accuracy by 11%.
文摘Information on forest structure is important for forest management decisions. This is inadequate in many situations, especially where timber is not of primary interest. We analyzed the structure of two forest types in the Oban Division of Cross River National Park, Nigeria. Systematic sampling technique was used to establish two transects measuring 2,000 x 2 m, at 600 m interval in the two forest types in four locations. Four 50 m x 50 m plots were located alternately at 500 m intervals along each transect, constituting 32 plots per forest type and 64 plots in all, Diameters at breast height （DBH）, base; middle and top; crown diameter; total height and crown length were measured on all trees with DBH 〉_ 10 cm. There were 159 stems/ha in the close-canopy forest and 132 stems/ha in the secondary forest. The mean DBH were 34.5 cm and 33.62 cm respectively. The mean heights were 24.79 m and 23.97 m, respectively. Basal area/ha were 41.59 m2 ha~ and 27.38 m2 hal for the two forest types. Majority of the trees encountered in the two forest types belonged to the middle stratum which has implication for small mammals＇ populations. Emergent trees which are otherwise scarce in other parts of the country were recorded, which also has implications for density thinning and seed supplies.
