Interoperability for eLearning Service Management and Provision

Learning Objects offer flexibility and adaptability for users to request personalised information for learning. There are standards to guide the development of learning objects. However, individual developers may customise these standards for serving different purposes when defining, describing, managing and providing learning objects, which are normally stored in heterogeneous repositories. Barriers to interoperability hinder sharing of learning services and subsequently affect quality of instructional design as learners expect to be able to receive their personalised learning content. All these impose difficulties to the users in getting the right information from the right sources. This paper investigates the interoperability issues in eLearning services management and provision and presents an approach to resolve interoperability at three levels.

Analysis of Phytochemical Composition and Chemoprotective Capacity of Rocket (Eruca sativa and Diplotaxis tenuifolia) Leafy Salad Following Cultivation in Different Environments

Consumption of green leafy vegetables is associated with reduced risk of several types of cancer and cardiovascular disease. These beneficial effects are attributed to a range of phytochemicals including flavonoids and glucosinolates, both of which are found in high levels in Brassicaceous crops. Rocket is the general name attributed to cultivars of Eruca sativa and Diplotaxis tenufolia, known as salad rocket and wild rocket, respectively. We have shown that different light levels during the cultivation period of these crops have a significant impact on the levels of flavonoids present in the crop at harvest, with over 15-fold increase achieved in quercetin, isorhamnetin, and cyanidin in high light conditions. Postharvest storage further affects the levels of both flavonoids and glucosinolates, with cyanidin increasing during shelf life and some glucosinolates, such as glucoiberverin, being reduced over the same storage period. In vitro assays using human colon cell lines demonstrate that glucosinolate-rich extracts of Eruca sativa cv. Sky, but not Diplotaxis tenufolia cv. Voyager, confer significant resistance to oxidative stress on the cells, which is indicative of the chemoprotective properties of the leaves from this species. Our findings indicate that both pre and postharvest environment and genotypic selection, when developing new lines of Brassicaceous vegetables, are important considerations with the goal of improving human nutrition and health.

Alternatives to linear analysis of energy balance data from lactating dairy cows

The current energy requirements system used in the United Kingdom for lactating dairy cows utilizes key parameters such as metabolizable energy intake (MEI) at maintenance (MEm), the efficiency of utilization of MEI for 1) maintenance, 2) milk production (k(l)), 3) growth (k(g)), and the efficiency of utilization of body stores for milk production (k(t)). Traditionally, these have been determined using linear regression methods to analyze energy balance data from calorimetry experiments. Many studies have highlighted a number of concerns over current energy feeding systems particularly in relation to these key parameters, and the linear models used for analyzing. Therefore, a database containing 652 dairy cow observations was assembled from calorimetry studies in the United Kingdom. Five functions for analyzing energy balance data were considered: straight line, two diminishing returns functions, (the Mitscherlich and the rectangular hyperbola), and two sigmoidal functions (the logistic and the Gompertz). Meta-analysis of the data was conducted to estimate k(g) and k(t). Values of 0.83 to 0.86 and 0.66 to 0.69 were obtained for k(g) and k(t) using all the functions (with standard errors of 0.028 and 0.027), respectively, which were considerably different from previous reports of 0.60 to 0.75 for k(g) and 0.82 to 0.84 for k(t). Using the estimated values of k(g) and k(t), the data were corrected to allow for body tissue changes. Based on the definition of k(l) as the derivative of the ratio of milk energy derived from MEI to MEI directed towards milk production, MEm and k(l) were determined. Meta-analysis of the pooled data showed that the average k(l) ranged from 0.50 to 0.58 and MEm ranged between 0.34 and 0.64 MJ/kg of BW0.75 per day. Although the constrained Mitscherlich fitted the data as good as the straight line, more observations at high energy intakes (above 2.4 MJ/kg of BW0.75 per day) are required to determine conclusively whether milk energy is related to MEI linearly or not.

Inhibition of vascular smooth muscle cell proliferation by non-steroidal anti-inflammatory drugs

Abnormal vascular smooth muscle cell (VSMC) proliferation is known to play an important role in the pathogenesis of atherosclerosis, restenosis and instent stenosis. Recent studies suggest that salicylates, in addition to inhibiting cyclooxygenase activity, exert an antiproliferative effect on VSMC growth both in vitro and in vivo. However, whether all non-steroidal anti-inflammatory drugs (NSAID) exert similar antiproliferative effects on VSMCs, and do so via a common mechanism of action, remains unknown. In the present study, we demonstrated that the NSAIDs, aspirin, ibuprofen and sulindac induced a dose-dependent inhibition of proliferation in rat A10 VSMCs (IC50 = 1666 mumol/L, 937 mumol/L and 520 mumol/L, respectively). These drugs did not show significant cytotoxic effects as determined by LDH release assay, even at the highest concentrations tested (aspirin, 5000 mumol/L; ibuprofen, 2500 mumol/L; and sulindac, 1000 mumol/L). Flow cytometric analyses showed that a 48 h exposure of A10 VSMCs to ibuprofen (1000 mumol/L) and sulindac (750 mumol/L) led to a significant G1 arrest (from 68.7 +/- 2.0% of cells in G1 to 76.6 +/- 2.2% and 75.8 +/- 2.2%, respectively, p < 0.05). In contrast, aspirin (2500 mumol/L) failed to induce a significant G1 arrest (68.1 +/- 5.2%). Clearer evidence of a G1 block was obtained by treatment of cells with the mitotic inhibitor, nocodazole (40 ng/ml), for the final 24 h of the experiment. Under these conditions, aspirin still failed to induce a G1 arrest (from 25.9 +/- 10.9% of cells in G1 to 19.6 +/- 2.3%) whereas ibuprofen and sulindac led to a significant accumulation of cells in G1(51.8% +/- 17.2% and 54.1% +/- 10.6%, respectively, p < 0.05). These results indicate that ibuprofen and sulindac inhibit VSMC proliferation by arresting the cell cycle in the G1 phase whereas the effect of aspirin appears to be independent of any special phase of the cell cycle. Irrespective of mechanism, our results suggest that NSAIDs might be of benefit to the treatment of vascular proliferative disorders.

Synthesis of chiral 3,4,5,6-tetrahydro-1,4-thiazin-2-ones (thiamorpholin-2-ones)-novel heterocycles possessing enhanced carbonyl electrophilicity over their oxygen counterparts

We report herein the first synthesis of chiral derivatives possessing the 1,4-thiazinone core. As predicted, the thiolactone is more susceptible to nucleophilic attack than the equivalent lactone system.

Morpholinone mediated oxazolone-free C-terminus amide coupling permitting a convergent strategy for peptide synthesis

3-Substituted-5-phenylmorpholinones have been demonstrated to act as N-protected C-terminus activated alpha-amino acids capable of undergoing solution phase N-terminus peptide extension following standard coupling procedures. The N-acylated morpholinones do not undergo epimerisation of the stereocentre of the C-terminus amino acid residue as oxazolone formation is sterically prevented, although C-terminus peptide coupling is still possible. This convergent approach to peptide synthesis is exemplified by the preparation of L-ala-L-ala-L-ala and L-ala-D-ala-L-ala. Copyright (c) 2008 European Peptide Society and John Wiley & Sons, Ltd.

A combined Bayesian Markovian approach for behaviour recognition

Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.

Cross validation and segment support for stereo belief propagation

Typically, algorithms for generating stereo disparity maps have been developed to minimise the energy equation of a single image. This paper proposes a method for implementing cross validation in a belief propagation optimisation. When tested using the Middlebury online stereo evaluation, the cross validation improves upon the results of standard belief propagation. Furthermore, it has been shown that regions of homogeneous colour within the images can be used for enforcing the so-called "Segment Constraint". Developing from this, Segment Support is introduced to boost belief between pixels of the same image region and improve propagation into textureless regions.

Investigation of support vector machine for the detection of architectural distortion in mammographic images

This paper investigates detection of architectural distortion in mammographic images using support vector machine. Hausdorff dimension is used to characterise the texture feature of mammographic images. Support vector machine, a learning machine based on statistical learning theory, is trained through supervised learning to detect architectural distortion. Compared to the Radial Basis Function neural networks, SVM produced more accurate classification results in distinguishing architectural distortion abnormality from normal breast parenchyma.

Unsupervised segmentation using Gabor wavelets and statistical features in LIDAR data analysis

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

Energy and protein interactions and their effect on nitrogen excretion in dairy cows

The principal driver of nitrogen (N) losses from the body including excretion and secretion in milk is N intake. However, other covariates may also play a role in modifying the partitioning of N. This study tests the hypothesis that N partitioning in dairy cows is affected by energy and protein interactions. A database containing 470 dairy cow observations was collated from calorimetry experiments. The data include N and energy parameters of the diet and N utilization by the animal. Univariate and multivariate meta-analyses that considered both within and between study effects were conducted to generate prediction equations based on N intake alone or with an energy component. The univariate models showed that there was a strong positive linear relationships between N intake and N excretion in faeces, urine and milk. The slopes were 0.28 faeces N, 0.38 urine N and 0.20 milk N. Multivariate model analysis did not improve the fit. Metabolizable energy intake had a significant positive effect on the amount of milk N in proportion to faeces and urine N, which is also supported by other studies. Another measure of energy considered as a covariate to N intake was diet quality or metabolizability (the concentration of metabolizable energy relative to gross energy of the diet). Diet quality also had a positive linear relationship with the proportion of milk N relative to N excreted in faeces and urine. Metabolizability had the largest effect on faeces N due to lower protein digestibility of low quality diets. Urine N was also affected by diet quality and the magnitude of the effect was higher than for milk N. This research shows that including a measure of diet quality as a covariate with N intake in a model of N execration can enhance our understanding of the effects of diet composition on N losses from dairy cows. The new prediction equations developed in this study could be used to monitor N losses from dairy systems.

The anions [(ZrOH(CO3)3)2]6− and [(ZrOH(C2O4)3)2]6−: single crystal X-ray diffraction studies of the complexes guanidinium zirconium carbonate [(C(NH2)3)3ZrOH(CO3)3·H2O]2 and sodium zirconium oxalate Na6(ZrOH(C2O4)3)2·7H2O

The complex [(C(NH2)3)3ZrOH(CO3)3·H2O]2 (A) has been shown by means of a single crystal X-ray diffraction study to contain [C(NH2)3]+ cations and dimeric anions of formulation [(ZrOH(CO3)3)2]6−. The anion is centrosymmetric with each metal being bonded to two bridging OH groups and three chelating CO2−3 ions. The Zr atoms are thus eight coordinate with a dodecahedral environments. The ZrO distances formed by the bridgng OH groups are shorter than those formed through zirconiu carbonate interactions. The non-bonded Zr…Zr distance is 3.47(2) Å. An infrared spectroscopic investigation of A provides data which support the findings of the crystallographic study. Likewise the complex Na6(ZrOH(CO2O4)3)2·7H2O (B) contains the anion [(ZrOH(C2O4)3)2]6−. This anion is structurally related to the anion in A as each Zr atom has an eight-coordinate dodecahedral environment being bonded to two bridging OH groups and three chelating oxalate ligands, but has no imposed crysallographic symmetry. The Zr…Zr non-bonded distance is 3.50(1) Å. The OZrO bridge angles are 69.7(4)° and A and 67.4(3)° in B.