Human Milk Oligosaccharides in Colostrum and Mature Milk of Chinese Mothers: Lewis Positive Secretor Subgroups.

To study the variability in human milk oligosaccharide (HMO) composition of Chinese human milk over a 20-wk lactation period, HMO profiles of 30 mothers were analyzed using CE-LIF. This study showed that total HMO concentrations in Chinese human milk decreased significantly over a 20-wk lactation period, independent of the mother's SeLe status, although with individual variations. In addition, total acidic and neutral HMO concentrations in Chinese human milk decreased over lactation, and levels are driven by their mother's SeLe status. Analysis showed that total neutral fucosylated HMO concentrations in Chinese human milk were higher in the two secretor groups as compared to the nonsecretor group. On the basis of the total neutral fucosylated HMO concentrations in Chinese human milk, HMO profiles within the Se+Le+ group can be divided into two subgroups. HMOs that differed in level between Se+Le+ subgroups were 2'FL, DF-L, LNFP I, and F-LNO. HMO profiles in Dutch human milk also showed Se+Le+ subgroup division, with 2'FL, LNT, and F-LNO as the driving force.

Multi-platform metabolomics analyses of a broad collection of fragrant and non-fragrant rice varieties reveals the high complexity of grain quality characteristics.

The quality of rice in terms not only of its nutritional value but also in terms of its aroma and flavour is becoming increasingly important in modern rice breeding where global targets are focused on both yield stability and grain quality. In the present paper we have exploited advanced, multi-platform metabolomics approaches to determine the biochemical differences in 31 rice varieties from a diverse range of genetic backgrounds and origin. All were grown under the specific local conditions for which they have been bred and all aspects of varietal identification and sample purity have been guaranteed by local experts from each country. Metabolomics analyses using 6 platforms have revealed the extent of biochemical differences (and similarities) between the chosen rice genotypes. Comparison of fragrant rice varieties showed a difference in the metabolic profiles of jasmine and basmati varieties. However with no consistent separation of the germplasm class. Storage of grains had a significant effect on the metabolome of both basmati and jasmine rice varieties but changes were different for the two rice types. This shows how metabolic changes may help prove a causal relationship with developing good quality in basmati rice or incurring quality loss in jasmine rice in aged grains. Such metabolomics approaches are leading to hypotheses on the potential links between grain quality attributes, biochemical composition and genotype in the context of breeding for improvement. With this knowledge we shall establish a stronger, evidence-based foundation upon which to build targeted strategies to support breeders in their quest for improved rice varieties.

On the increase of predictive performance with high-level data fusion.

The combination of the different data sources for classification purposes, also called data fusion, can be done at different levels: low-level, i.e. concatenating data matrices, medium-level, i.e. concatenating data matrices after feature selection and high-level, i.e. combining model outputs. In this paper the predictive performance of high-level data fusion is investigated. Partial least squares is used on each of the data sets and dummy variables representing the classes are used as response variables. Based on the estimated responses y(j) for data set j and class k, a Gaussian distribution p(g(k)|y(j)) is fitted. A simulation study is performed that shows the theoretical performance of high-level data fusion for two classes and two data sets. Within group correlations of the predicted responses of the two models and differences between the predictive ability of each of the separate models and the fused models are studied. Results show that the error rate is always less than or equal to the best performing subset and can theoretically approach zero. Negative within group correlations always improve the predictive performance. However, if the data sets have a joint basis, as with metabolomics data, this is not likely to happen. For equally performing individual classifiers the best results are expected for small within group correlations. Fusion of a non-predictive classifier with a classifier that exhibits discriminative ability lead to increased predictive performance if the within group correlations are strong. An example with real life data shows the applicability of the simulation results.

Powder pattern indexing using the weighted crosscorrelation and genetic algorithms.

X-ray diffraction is a powerful technique for investigating the structure of crystals and crystalline powders. Unfortunately, for powders, the first step in the structure elucidation process, retrieving the unit cell parameters (indexing), is still very critical. In the present article, an improved approach to powder pattern indexing is presented. The proposed method matches peak positions from experimental X-ray powder patterns with peak positions from trial cells using a recently published method for pattern comparison (weighted crosscorrelation). Trial cells are optimized with Genetic Algorithms. Patterns are not pretreated to remove any existing zero point shift, as this is determined during optimization. Another improvement is the peak assignment procedure. This assignment is needed for determining the similarity between lines from trial cells and experiment. It no longer allows calculated peaks to be assigned twice to different experimental peaks, which is beneficial for the indexing process. The procedure proves to be robust with respect to false peaks and accidental or systematic absensences of reflections, and is successfully applied to powder patterns originating from orthorhombic, monoclinic, and triclinic compounds measured with synchrotron as well as with conventional laboratory X-ray diffractometers.

Synthesis of novel 3,7-substituted-2-(3',4'-dihydroxyphenyl)flavones with improved antioxidant activity.

A series of 3,7-disubstituted-2-(3',4'-dihydroxyphenyl)flavones was synthesized as potential cardioprotective agents in doxorubicin antitumor therapy. The influence of substituents on the 3 and 7 positions of the flavone nucleus on radical scavenging and antioxidant properties was explored to improve the antioxidant activity of our lead compound monoHER. In the TEAC assay most compounds had a similar potency (3.5-5 times as potent as trolox), but in the LPO assay IC(50) values ranged from 0.2 to 37 microM. In general, the 3-substituted flavones (9a-j) were the most potent compounds in the LPO assay. The number of hydroxyl groups is not the only prerequisite for antioxidant activity. Substitution in ring A of the flavonoid is not necessary for high activity, but the presence of a 7-OH group significantly modifies the antioxidant activity. The compounds are good antioxidants, which makes it interesting to evaluate them as cardioprotective agents.