Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Control of cocklebur seed germination by nitrogenous compounds : Nitrite, nitrate, hydroxylamine, thiourea, azide and cyanide

Germination of non-dormant upper cocklebur (Xanthium pinsylvanicum Wallr.) seeds was stimulated by not only CS(NH2)2 but also NH2OH, KCN and NaN3. This stimulation was not via the enhancement of aerobic C2H4 production. NH2OH, KCN and NaN3 in certain concentrations promoted the initial growth of axial and/or cotyledonary parts, but the degree of growth promotion by NH2OH, NaN3 and KCN was slight compared with that by CS(NH2)2. As in the case of CS(NH2)2, however, the germinationstimulating effect of NH2OH disappeared rapidly as the preceding imbibition period was prolonged. In contrast, KCN and NaN3 were still effective in stimulating the germination of aged seeds maintained on a water substratum, as previously seen with anaerobiosis. Anaerobic induction was enhanced not only by NaN3 and KCN but also by NH2OH, KNO3, KNO2 CO(NH2)2 and CS(NH2)2 applied during the anaerobic treatment, but without causing an increase in anaerobic production of C2H4. Furthermore, KCN and NaN3, given prior to the anaerobic treatment acted additively with anaerobic induction. The germination-stimulating actions of nitrogenous compounds are discussed in comparison with those of C2H4 and anaerobiosis.

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.

Fruit anatomy,seed germination and seedling development in the Japanese seagrass phyllospadix

Phyllospadix iwatensis Makino and phyllospadix japonicus Makino have similar frunt morphology and anatomy.The rhomboid fruit of Japanese phyllospadix is dark brown in colour and is characterized by two arms bearing stiff inflected bristles which can act as an anchoring system. The fruit covering consists of a thin cuticular seed coat and pericarp remains mainly fibrous endocarp. In the groove region of the fruit.the cuticular seed coat and endocarp are replaced by nucellus cells with wall in growths and crushed pigment strands with lignified walls.these tissues appera to control the transfer of nutrients to developing seed.the seed is oval with a small embryo and a large hypocotyl. the embryo is straight and simple,with the plumule containing three leaf primordia and a pair of root primordia surrounded by a cotyledon.the hypocotyl has large vontral lobe containing central provascular tissue and two small dorsal lobes.the hypocotyl contains starch.lipid and protein.and acts as a nutrient store.the seed of P.iwatensis has a dormancy period of 2-6 weeks and germination eventually reaches-65%.but is not synchronized.during germination the leaves emerge first.and then after at least three young leaves have formed and abseised.the roots emerge,usually?6 months after the commencement of germination.Utilizaton of the nutrient reserves is initially from the perihpery of the hypocotyl and then progressively towards its centre.

Seed germination and seedling morphology of the seagrass, Halophila engelmannii (Hydrocharitaceae)

Seeds of Halophila engelmannii Aschers., that were collected in Redfish Bay, Texas, at weekly intervals from mid-May to mid-June 1986, began to germinate 3–4 weeks after collection. Most of the collections subsequently showed an increase in the rate of germination under increased light intensity and all had a stoppage of germination after transfer to darkness, indicating a light requirement to break endogenous seed dormancy. During the 5 weeks after seeds germinated, seedlings in soil culture produced a rosette of six leaves before the appearance of a rhizome bud in the axil of the third leaf. The first node of the rhizome produced a root and an upright shoot with a pseudowhorl of three to five leaves.

Seed-bank development, germination and early seedling survival of two seagrass species from The Netherlands: Zostera marina L. and Zostera noltii hornem

Flowering and seed-bank development of annual Zostera marina L. and perennial Z. noltii hornem. were studied in the Zandkreek (S.W. Netherlands). Flowering of Z. noltii started at the end of June and continued until the end of September. A maximum of ca. 1000 flowering shoots (11% of the total amount of shoots per square metre) occurred in early August. Flowering of Z. marina started at the end of July and continued throughout October. Seed banks of both species appeared to be annual. Actual seed densities of Z. noltii were much lower than predicted on the basis of the amount of inflorescences.Germination was studied in the laboratory in relation to temperature (10, 20 and 30°C), salinity (1.0, 10.0, 20.0, 30.0 and 40.0‰) and stratification (at 4°C). Both species showed a maximal germination at 30°C and 1.0‰ salinity, decreasing with higher salinities and lower temperatures. Stratification stimulated germination only at salinities 20.0‰. Desiccation and anaerobia were lethal to Z. marina seeds. Seedlings of Z. marina survived best at 10°C and 10.0–20.0‰ salinity and those of Z. noltii survived best at 10°C and 1.0‰ salinity. Overall, seedlings of Z. marina survived better than those of Z. noltii.

Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample. This paper proposes an effective approach of semisupervised dimensionality reduction through label propagation and label regression. Different from previous efforts, the new approach propagates the label information from labeled to unlabeled data with a well-designed mechanism of random walks, in which outliers are effectively detected and the obtained virtual labels of unlabeled data can be well encoded in a weighted regression model. These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction. By this means, when the manifold or the clustering assumption of data is satisfied, the labels of labeled data can be correctly propagated to the unlabeled data; and thus, the proposed approach utilizes the labeled and the unlabeled data more effectively than previous work. Experimental results are carried out upon several databases, and the advantage of the new approach is well demonstrated.