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To provide valuable references for the greening plant species selection, planting design and location in city the present study was undertaken. Leaf dust capturing capacities of 14 common urban greening plants and their relationships with surface micromorphology has been investigated. Among the selected plant species, leaf dust capturing capacities ranged from 0.23 g/m2 (Trifolium repens) to 4.51 g/m2 (Pittosporum tobira), and dust accumulation of plant leaves might be reach maximum values in five days after rainfall. The particles mainly deposited on adaxial surfaces, only very few deposited in the stomatal regions on abaxial surfaces. Six plant species (Pittosporum tobira, Pinus tabulaeformis, Ligustrum lucidum, Viburnum odoratissimum, Cedrus deodara and Platanus acerifolia) showed higher dust capturing capacities (3.87∼4.51 g/m2), which surface structures of their foliage were benefit for capturing particles. The lower dust capturing capacity of Trifolium repens may relate to wax tubules covered on leaf surface which creates self-cleaning properties. From the findings it can be concluded that plants with higher dust capturing capacities and these leaf micromorphological properties such as epidermal cell, wax and sunken stomas can be recommended for introducing as urban greening plant species.

The Soil water availability (SWA) reflects the degree of difficulty that plant absorbs and utilizes soil moisture. However, these SWA indexes including static criterions and dynamic indexes are inconsistent and difficult to use practically. In this paper, we thought SWA actually reflected consumption energy value of uptake water by plant, and the consumption energy value could be determined a definite integral from permanent wilting point to field capacity about soil water retention curve. The energy integral of soil water retention curve per soil volume water content change is used and tested on different soil types in the Loess Plateau of China. The results indicated that the method provides clearly theory-based water-uptake energy consumption by plants, and achieved the same results as those by productivity index and root water-uptake rate.

A new fluorescent nanoprobe, 4-amino-2,2,6,6-tetramethylpiperidine oxide (AT)-functionalized CdTe quantum dots (QDs–AT), was synthesized, for selective detection of nonprotein thiols based on electron transfer (ET). In the presence of nonprotein thiols, the nitroxide radicals in QDs–AT were converted to hydroxylamines, resulting in the fluorescence recovery of the quenched QDs. The detection mechanism of the probe was investigated using Rh-Se-2 probe. The nanoprobe has high sensitivity toward glutathione (GSH) with a detection limit of 7.1 × 10−8 M. The fluorescent imaging of living cells showed that QDs–AT could distinguish the concentration differences of GSH in HL-7702 and HepG2 cells.

Information Management System realization including establishment and maintenance of the database and front-end application development. This paper describes the system functional and architecture design, and emphasizes the system's functionality, database design and functional modules, etc. Fully functional, flexible and convenient application and friendly interface provide a good guarantee for student information management.

Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach under standard conditions, and the proposed estimator reduces to the classical one if there is no crossing. The performance of the constrained estimator has shown significant improvement by adding smoothing and stability across the quantile levels. Copyright 2010, Oxford University Press.

This thesis introduced the idea and techniques of web2.0, studied the application of the idea and techniques of web2.0 in Personal Knowledge Management System, finally designed a Web-2.0 Based Personal Knowledge Management System to make the personal knowledge management system plays its maximum effectiveness under the platform of web2.0, to enhance personal core competitiveness, also to achieve the comprehensive development of the individuals in the era of knowledge economy.

This thesis introduces the basic function of web 2.0-based teaching management system and proposes the entire structure design of the system combined with the current situation of teaching management system. The thesis emphatically elaborated the functional module design of this system. The whole system have consummate function, it is very convenient and flexible for application , the contact surface is friendly, all of which provide perfect guarantee for the teaching management. Based on all of these, it foresees its application perspective providing a new way for the teaching management system in the Web 2.0 era.

The thesis took the fine courses in colleges and universities as the starting point, proposed the architecture and functional module design of Web2.0-based learning Support Service System based on analysis of the characteristics of Web2.0 and concept of learning support services system, explored the achievement process of partial functional modules, provided a new paradigm for the e-learning of Web2.0 era.

E-learning behavior analysis is an important issue to the instruction based on Internet. This paper proposed a new method to analyze the E-learning behavior.It classified E-learning behaviors into several clusters by fuzzy clustering algorithm. Behaviors in the same cluster have the most common in characters, while behaviors between clusters have the least common. Experiments fully demonstrated that the proposed method can achieve good performance of analyzing E-learning behavior. It shows that by using cluster analysis, teachers can understand the students better in interest, personality and other informations. It also helps to develop effective educational resource and carry out the personalized instruction.

In this study, the aged refuse excavated from a 10-year old closed landfill compartment at Shanghai Laogang Refuse Landfill was used as the substrate in a sequencing aged refuse bioreactor process for treatment of fresh landfill leachate which sampled from the balance tank in the same landfill. Typically, screened aged refuse with a diameter less than 8 mm was used as bio-filtration material in the two round shaped bioreactor with an inner diameter of 40 cm and a height of 130 cm. Influent fresh leachate with initial CODCr, TN and NH3-N concentrations of 11000?15000, 1340?1770 and 11200?1650 mg/L, respectively, was introduced into the system. The corresponding concentrations in the effluent were reduced to below 300, about 250, and below 15mg/L, respectively. The sequencing aged refuse process is effective in the fresh leachate treatment, the removal efficiency of COD and TN removal is about 90% and 80% respectively, and the ammonia nitrogen transformation efficiency is about 99.5%. The treatment mechanism of nitrogen removal was studied. In the process, the nitrogen removal is mainly due to the simultaneous nitrification and denitrification in both bioreactors. Recirculated nitrate nitrogen denitrification exists in bioreactor 1, but is not the main nitrogen removal pathway, which is different from the nitrogen removal mechanism in aged leachate treatment.

SOA is a software architecture. It is a real solution to integrate education resources and enhance resource utilizes. This essay introduced SOA's principle, structure and reference model of application. Based on this, it designed a SOA-based education resource service system. It used Web Service, WSDL, UDDI, SOAP and CELTS to implement the system. Through the system, we can use and develop the education resource much more easier and quicker.

It is a fascinating yet challenging problem to accurately and efficiently localize regionally distinct features between face groups in multi-dimensional signal processing and analysis. Given a data with unknown distribution and small sample size, we propose a new statistical analysis framework using hybrid randomization (i.e., permutation) tests to improve the system's efficiency in identifying distinct features. The proposed method fits the nonparametric distribution of the test statistic with Pearson distribution series. We bypass the tedious online randomization via calculating the first four moments of the permutation distribution. This can reduce the computational complexity from O(n!) to O(n2) over traditional methods for the modified Hotelling's T2 test statistics. Experiments on simulated data and 3D face analysis demonstrate the efficiency, accuracy and robustness of the proposed approach.

DNA abundance provides important information about cell physiology and proliferation activity. In a typical in vitro cellular assay, the distribution of the DNA content within a sample is comprised of cell debris, G0/G1-, S-, and G2/M-phase cells. In some circumstances, there may be a collection of cells that contain more than two copies of DNA. The primary focus of DNA content analysis is to deconvolute the overlapping mixtures of the cellular components, and subsequently to investigate whether a given treatment has perturbed the mixing proportions of the sample components. We propose a restricted mixture model that is parameterized to incorporate the available biological information. A likelihood ratio (LR) test is developed to test for changes in the mixing proportions between two cell populations. The proposed mixture model is applied to both simulated and real experimental data. The model fitting is compared with unrestricted models; the statistical inference on proportion change is compared between the proposed LR test and the Kolmogorov–Smirnov test, which is frequently used to test for differences in DNA content distribution. The proposed mixture model outperforms the existing approaches in the estimation of the mixing proportions and gives biologically interpretable results; the proposed LR test demonstrates improved sensitivity and specificity for detecting changes in the mixing proportions. © 2007 International Society for Analytical Cytology

As gene expression profile data from DNA microarrays accumulate rapidly, there is a natural need to compare data across labs and platforms. Comparisons of microarray data can be quite challenging due to data complexity and variability. Different labs may adopt different technology platforms. One may ask about the degree of agreement we can expect from different labs and different platforms. To address this question, we conducted a study of inter-lab and inter-platform agreement of microarray data across three platforms and three labs. The statistical measures of consistency and agreement used in this paper are the Pearson correlation, intraclass correlation, kappa coefficients, and a measure of intra-transcript correlation. The three platforms used in the present paper were Affymetrix GeneChip, custom cDNA arrays, and custom oligo arrays. Using the within-platform variability as a benchmark, we found that these technology platforms exhibited an acceptable level of agreement, but the agreement between two technologies within the same lab was greater than that between two labs using the same technology. The consistency of replicates in each experiment varies from lab to lab. When there is high consistency among replicates, different technologies show good agreement within and across labs using the same RNA samples. On the other hand, the lab effect, especially when confounded with the RNA sample effect, plays a bigger role than the platform effect on data agreement.

Background NCI60 cell lines are derived from cancers of 9 tissue origins and have been invaluable in vitro models for cancer research and anti-cancer drug screen. Although extensive studies have been carried out to assess the molecular features of NCI60 cell lines related to cancer and their sensitivities to more than 100,000 chemical compounds, it remains unclear if and how well these cell lines represent or model their tumor tissues of origin. Identification and confirmation of correct origins of NCI60 cell lines are critical to their usage as model systems and to translate in vitro studies into clinical potentials. Here we report a direct comparison between NCI60 cell lines and primary tumors by analyzing global gene expression profiles. Results Comparative analysis suggested that 51 of 59 cell lines we analyzed represent their presumed tumors of origin. Taking advantage of available clinical information of primary tumor samples used to generate gene expression profiling data, we further classified those cell lines with the correct origins into different subtypes of cancer or different stages in cancer development. For example, 6 of 7 non-small cell lung cancer cell lines were classified as lung adenocarcinomas and all of them were classified into late stages in tumor progression. Conclusion Taken together, we developed and applied a novel approach for systematic comparative analysis and integrative classification of NCI60 cell lines and primary tumors. Our results could provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screenings. Moreover, this gene expression profile based approach can be generally applied to evaluate experimental model systems such as cell lines and animal models for human diseases.