Variation in carotenoid content of kale and other vegetables:a review of pre- and post-harvest effects

Lutein and zeaxanthin are carotenoids that are selectively taken up into the macula of the eye, where they are thought to protect against the development of age-related macular degeneration. They are obtained from dietary sources, with the highest concentrations found in dark green leafy vegetables, such as kale and spinach. In this Review, compositional variations due to variety/cultivar, stage of maturity, climate or season, farming practice, storage, and processing effects are highlighted. Only data from studies which report on lutein and zeaxanthin content in foods are reported. The main focus is kale; however, other predominantly xanthophyll containing vegetables such as spinach and broccoli are included. A small amount of data about exotic fruits is also referenced for comparison. The qualitative and quantitative composition of carotenoids in fruits and vegetables is known to vary with multiple factors. In kale, lutein and zeaxanthin levels are affected by pre-harvest effects such as maturity, climate, and farming practice. Further research is needed to determine the post-harvest processing and storage effects of lutein and zeaxanthin in kale; this will enable precise suggestions for increasing retinal levels of these nutrients.

Dietary habits are major determinants of the plasma antioxidant status in healthy elderly subjects

Previous studies indicate that regular consumption of a diet rich in fruits and vegetables is associated with a lower risk for age-related diseases. The aim of the present study was to evaluate whether the often-reported age-related decrease of plasma antioxidants in man depends on differences in dietary intake or on other age- and gender-related factors. In this observational case-control study, thirty-nine community-dwelling healthy subjects aged 65 years and older consuming high intakes of fruits and vegetables daily (HI) and forty-eight healthy subjects aged 65 and older consuming low intakes of fruit and vegetables daily (LI) were enrolled. Plasma levels of retinol, tocopherols, carotenoids and malondialdehyde (MDA) as well as content of protein carbonyls in Ig G were measured. Plasma levels of retinol, tocopherols and carotenoids were significantly higher in group HI than in group LI subjects independent of age and gender. MDA levels were inversely correlated with vitamin A and α-carotene. Protein carbonyls were inversely correlated with γ-tocopherol. In the elderly, a higher daily intake of fruits and vegetables is associated with an improved antioxidant status in comparison to subjects consuming diets poor in fruits and vegetables. Modification of nutritional habits among other lifestyle changes should be encouraged to lower prevalence of disease risk factors in later life. © The Authors 2005.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

A unified classification of manufacturing strategies and design processes

This article categorises manufacturing strategy design processes and presents the characteristics of resulting strategies. This work will therefore assist practitioners to appreciate the implications of planning activities. The article presents a framework for classifying manufacturing strategy processes and the resulting strategies. Each process and respective strategy is then considered in detail. In this consideration the preferred approach is presented for formulating a world class manufacturing strategy. Finally, conclusions and recommendations for further work are given.

The role of working memory in compulsive checking and OCD:a systematic classification of 58 experimental findings

Despite the large body of research regarding the role of memory in OCD, the results are described as mixed at best (Hermans et al., 2008). For example, inconsistent findings have been reported with respect to basic capacity, intact verbal, and generally affected visuospatial memory. We suggest that this is due to the traditional pursuit of OCD memory impairment as one of the general capacity and/or domain specificity (visuospatial vs. verbal). In contrast, we conclude from our experiments (i.e., Harkin & Kessler, 2009, 2011; Harkin, Rutherford, & Kessler, 2011) and recent literature (e.g., Greisberg & McKay, 2003) that OCD memory impairment is secondary to executive dysfunction, and more specifically we identify three common factors (EBL: Executive-functioning efficiency, Binding complexity, and memory Load) that we generalize to 58 experimental findings from 46 OCD memory studies. As a result we explain otherwise inconsistent research – e.g., intact vs. deficient verbal memory – that are difficult to reconcile within a capacity or domain specific perspective. We conclude by discussing the relationship between our account and others', which in most cases is complementary rather than contradictory.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.