Screening and analysis of bioactive compounds with biofingerprinting chromatogram analysis of traditional Chinese medicines targeting DNA by microdialysis/HPLC

Biofingerprinting chromatogram, analysis, which is defined as the comparison of fingerprinting chromatograms of the extract of traditional Chinese medicines (TCMs) before and after the interaction with biological systems (DNA, protein. cell. etc.), was proposed for screening and analysis of the multiple bioactive compounds in TCMs. A method of microdialysis sampling combined with high performance liquid chromatography (HPLC) was applied to the study of DNA-binding property for the extracts of TCMs. Seven compounds were found to bind to calf thymus DNA (ct-DNA) from the TCMs of Coptis chinensis Franch (Coptis), but only three ones from Phellodendron amurense Rupr. (Phellodendron) and none from Sophoraflavescens Ait. (Sophora) to bind to ct-DNA. respectively. Three of them were identified as berberine, palmatine and jatrorrhizine and their association constants (K) to ct-DNA were determined by microdialysis/HPLC. Competitive binding behaviors of them to ct-DNA were also investigated. © 2005 Elsevier B.V. All rights reserved.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Action design research: a comparison with canonical action research and design science

This research in progress paper addresses the IS issue in relation to conducting relevant research while keeping academic rigor. In particular, it contributes to the ongoing academic conversation around the investigation on how to incor-porate action in design science research. In this document the philosophical underpinnings of the recently proposed methodology called Action Design Re-search [1] are derived, outlined and integrated into Burrel and Morgan’s Par-adigmatic Framework (1979)[6]. The results so far show how Action Design Research can be considered as a particular case of Design Science Research (rather than a methodology closely related to Action Research) although they can assume two different epistemological positions. From these philosophical perspectives, future works will involve the inclusion of actual research projects using the three different methodologies. The final goal is to outline and structure the divergences and similarities of Action Design Research with Design Science Research and Canonical Action Research.

Discoplana malagasensis sp. nov. a New Turbellarian (Platyhelminthes: Polycladida: Leptoplanidae) symbiotic in an ophiuroid (Echinodermata), with a cladistic analysis of the Discoplana/Euplana species

A new species of polyclad flatworm from Papua New Guinea is described. It is found symbiotic in the ophiuroid Ophiothrix purpurea von Martens, 1867 (Echinodermata: Ophiuroidea). Apparently it belongs to the taxon Discoplana Bock, 1913 and can be distinguished from the six previously described Discoplana species by its very short ejaculatory duct and a penial papilla covered with a penial sheath, but without any true sclerotised structures such as a stylet or spines. The cladistic analysis of the Discoplana/Euplana species, based on morphological features and including two outgroups, reveals that all species of Discoplana, except D. pacificola, form a monophyletic taxon, that is not a synonym of Euplana Girard, 1893. Therefore the name Discoplana is conserved and the new species will be described as Discoplana malagasensis sp. nov. A key for the Discoplana/Euplana group is provided. In this key the biogeographical distribution and possible synonyms are given.

Complement Component 3: an assessment of association with AMD and analysis of gene-gene and gene-environment interactions in a Northern Irish cohort

Purpose: A non-synonymous single nucleotide polymorphism ( SNP) in complement component 3 has been shown to increase the risk of age-related macular degeneration (AMD). We assess its effect on AMD risk in a Northern Irish sample, test for gene-gene and gene-environment interaction, and review a risk prediction model.

Impact of nitrogen seeding on confinement and power load control of a high-triangularity JET ELMy H-mode plasma with a metal wall

This paper reports the impact on confinement and power load of the high-shape 2.5 MA ELMy H-mode scenario at JET of a change from all carbon plasma-facing components to an all metal wall. In preparation to this change, systematic studies of power load reduction and impact on confinement as a result of fuelling in combination with nitrogen seeding were carried out in JET-C and are compared with their counterpart in JET with a metallic wall. An unexpected and significant change is reported on the decrease in the pedestal confinement but is partially recovered with the injection of nitrogen.

Systematic Uncertainties Associated with the Cosmological Analysis of the First PAN-STARRS1 Type Ia Supernova Sample

We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) sample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. describes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic uncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of observed Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II calibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the proper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We estimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of the full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including systematic uncertainties in our analysis of only SNe measurements, we find w = -1.120^+0.360_-0.206(Stat)^+0.269_-0.291(Sys). With additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and H₀ measurements, we find w = -1.166^+0.072_-0.069 and Ω_m = 0.280^+0.013_-0.012 (statistical and systematic errors added in quadrature). The significance of the inconsistency with w = -1 depends on whether we use Planck or Wilkinson Microwave Anisotropy Probe measurements of the CMB: w_{BAO+H0+SN+WMAP} = -1.124^+0.083_-0.065.

Problems of allometric scaling analysis: examples from mammalian reproductive biology.

Biological scaling analyses employing the widely used bivariate allometric model are beset by at least four interacting problems: (1) choice of an appropriate best-fit line with due attention to the influence of outliers; (2) objective recognition of divergent subsets in the data (allometric grades); (3) potential restrictions on statistical independence resulting from phylogenetic inertia; and (4) the need for extreme caution in inferring causation from correlation. A new non-parametric line-fitting technique has been developed that eliminates requirements for normality of distribution, greatly reduces the influence of outliers and permits objective recognition of grade shifts in substantial datasets. This technique is applied in scaling analyses of mammalian gestation periods and of neonatal body mass in primates. These analyses feed into a re-examination, conducted with partial correlation analysis, of the maternal energy hypothesis relating to mammalian brain evolution, which suggests links between body size and brain size in neonates and adults, gestation period and basal metabolic rate. Much has been made of the potential problem of phylogenetic inertia as a confounding factor in scaling analyses. However, this problem may be less severe than suspected earlier because nested analyses of variance conducted on residual variation (rather than on raw values) reveals that there is considerable variance at low taxonomic levels. In fact, limited divergence in body size between closely related species is one of the prime examples of phylogenetic inertia. One common approach to eliminating perceived problems of phylogenetic inertia in allometric analyses has been calculation of 'independent contrast values'. It is demonstrated that the reasoning behind this approach is flawed in several ways. Calculation of contrast values for closely related species of similar body size is, in fact, highly questionable, particularly when there are major deviations from the best-fit line for the scaling relationship under scrutiny.

The relationship of individual, team and organizational learning in Ontario hospital clinical labratories

I t is generally accepted among scholars that individual learning and team learning contribute to the concept we refer to as organizational learning. However, a small number of quantitative and qualitative studies that have investigated their relationship reported contradicting results. This thesis investigated the relationship between individual learning, team learning, and organizational learning. A survey instrument was used to collect information on individual learning, team learning, and organizational learning. The study sample comprised of supervisors from the clinical laboratories in teaching hospitals and community hospitals in Ontario. The analyses utilized a linear regression to investigate the relationship between individual and team learning. The relationship between individual and organizational learning, and team and organizational learning were simultaneously investigated with canonical correlation and set correlation. T-test and multivariate analysis of variance were used to compare the differences in learning scores of respondents employed by laboratories in teaching and those employed by community hospitals. The study validated its tests results with 1,000 bootstrap replications. Results from this study suggest that there are moderate correlations between individual learning and team learning. The correlation individual learning and organizational learning and team learning and organizational learning appeared to be weak. The scores of the three learning levels show statistically significant differences between respondents from laboratories in teaching hospitals and respondents from community hospitals.

Intelligent structuring and reducing of association rules with formal concept analysis

Association rules are used to investigate large databases. The analyst is usually confronted with large lists of such rules and has to find the most relevant ones for his purpose. Based on results about knowledge representation within the theoretical framework of Formal Concept Analysis, we present relatively small bases for association rules from which all rules can be deduced. We also provide algorithms for their calculation.

Conceptual knowledge processing with formal concept analysis and ontologies

Among many other knowledge representations formalisms, Ontologies and Formal Concept Analysis (FCA) aim at modeling ‘concepts’. We discuss how these two formalisms may complement another from an application point of view. In particular, we will see how FCA can be used to support Ontology Engineering, and how ontologies can be exploited in FCA applications. The interplay of FCA and ontologies is studied along the life cycle of an ontology: (i) FCA can support the building of the ontology as a learning technique. (ii) The established ontology can be analyzed and navigated by using techniques of FCA. (iii) Last but not least, the ontology may be used to improve an FCA application.