Recommending flavanols and procyanidins for cardiovascular health: current knowledge and future needs

Data on the potential health benefits of dietary flavanols and procyanidins, especially in the context of cardiovascular health, are considerable and continue to accumulate. Significant progress has been made in flavanol analytics and the creation of phytonutrient-content food databases, and novel data emanated from epidemiological investigations as well as dietary intervention studies. However, a comprehensive understanding of the pharmacological properties of flavanols and procyanidins, including their precise mechanisms of action in vivo, and a conclusive, consensus-based accreditation of a causal relationship between intake and health benefits in the context of primary and secondary cardiovascular disease prevention is still outstanding. Thus, the objective of this review is to identify and discuss key questions and gaps that will need to be addressed in order to conclusively demonstrate whether or not dietary flavanols and procyanidins have a role in preventing, delaying the onset of, or treating cardiovascular diseases, and thus improving human life expectancy and quality of life.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

The seventh colloquium on high resolution molecular spectroscopy

This conference, held 14-18 September 1981, addressed many aspects of high resolution molecular spectroscopy. Measurement techniques for remotely identifying trace gases in the atmosphere were discussed. Instrumentation for highly accurate and precise measurement of molecular emissions were described. The objective of the colloquium was to bring together molecular spectroscopists working in different regions of the electromagnetic spectrum from the ultraviolet to radio frequencies. These scientists shared a common interest in high resolution gas phase spectra and their analyses. The objective was met through the presentation of about 20 invited papers and many more contributed papers.

Lipids and the immune response: from molecular mechanisms to clinical applications

Purpose of review This review critically evaluates recent studies investigating the effects of fatty acids on immune and inflammatory responses in both healthy individuals and in patients with inflammatory diseases, with some reference to animal studies where relevant. It examines recent findings describing the cellular and molecular basis for the modulation of immune function by fatty acids. The newly emerging area of diet-genotype interactions will also be discussed, with specific reference to the anti-inflammatory effects of fish oil. Recent findings Fatty acids are participants in many intracellular signalling pathways. They act as ligands for nuclear receptors regulating a host of cell responses, they influence the stability of lipid rafts, and modulate eicosanoid metabolism in cells of the immune system. Recent findings suggest that some or all of these mechanisms may be involved in the modulation of immune function by fatty acids. Summary Human studies investigating the relationship between dietary fatty acids and some aspects of the immune response have been disappointingly inconsistent. This review presents the argument that most studies have not been adequately powered to take into account the influence of variation (genotypic or otherwise) on parameters of immune function. There is well-documented evidence that fatty acids modulate T lymphocyte activation, and recent findings describe a range of potential cellular and molecular mechanisms. However, there are still many questions remaining, particularly with respect to the roles of nuclear receptors, for which fatty acids act as ligands, and the modulation of eicosanoid synthesis, for which fatty acids act as precursors.

Analytical and compositional aspects of isoflavones in food and their biological effects

This paper provides an overview of analytical techniques used to determine isoflavones (IFs) in foods and biological fluids with main emphasis on sample preparation methods. Factors influencing the content of IFs in food including processing and natural variability are summarized and an insight into IF databases is given. Comparisons of dietary intake of IFs in Asian and Western populations, in special subgroups like vegetarians, vegans, and infants are made and our knowledge on their absorption, distribution, metabolism, and excretion by the human body is presented. The influences of the gut microflora, age, gender, background diet, food matrix, and the chemical nature of the IFs on the metabolism of IFs are described. Potential mechanisms by which IFs may exert their actions are reviewed, and genetic polymorphism as determinants of biological response to soy IFs is discussed. The effects of IFs on a range of health outcomes including atherosclerosis, breast, intestinal, and prostate cancers, menopausal symptoms, bone health, and cognition are reviewed on the basis of the available in vitro, in vivo animal and human data.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole-plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm-temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade-offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.

Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼20%. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.8.083690]

Application of complex extreme learning machine to multiclass classification problems with high dimensionality: a THz spectra classification problem

We extend extreme learning machine (ELM) classifiers to complex Reproducing Kernel Hilbert Spaces (RKHS) where the input/output variables as well as the optimization variables are complex-valued. A new family of classifiers, called complex-valued ELM (CELM) suitable for complex-valued multiple-input–multiple-output processing is introduced. In the proposed method, the associated Lagrangian is computed using induced RKHS kernels, adopting a Wirtinger calculus approach formulated as a constrained optimization problem similarly to the conventional ELM classifier formulation. When training the CELM, the Karush–Khun–Tuker (KKT) theorem is used to solve the dual optimization problem that consists of satisfying simultaneously smallest training error as well as smallest norm of output weights criteria. The proposed formulation also addresses aspects of quaternary classification within a Clifford algebra context. For 2D complex-valued inputs, user-defined complex-coupled hyper-planes divide the classifier input space into four partitions. For 3D complex-valued inputs, the formulation generates three pairs of complex-coupled hyper-planes through orthogonal projections. The six hyper-planes then divide the 3D space into eight partitions. It is shown that the CELM problem formulation is equivalent to solving six real-valued ELM tasks, which are induced by projecting the chosen complex kernel across the different user-defined coordinate planes. A classification example of powdered samples on the basis of their terahertz spectral signatures is used to demonstrate the advantages of the CELM classifiers compared to their SVM counterparts. The proposed classifiers retain the advantages of their ELM counterparts, in that they can perform multiclass classification with lower computational complexity than SVM classifiers. Furthermore, because of their ability to perform classification tasks fast, the proposed formulations are of interest to real-time applications.

Molecular and phenotypic characterization of the alternative seasonal growth habit and flowering time in barley (Hordeum vulgare ssp. vulgare L.)

Barley can be classified into three major agronomic types, based on its seasonal growth habit (SGH): spring, winter and alternative. Winter varieties require exposure to vernalization to promote subsequent flowering and are autumn-sown. Spring varieties proceed to flowering in the absence of vernalization and are sown in the spring. The ‘alternative’ (also known as ‘facultative’) SGH is only loosely defined and can be sown in autumn or spring. Here, we investigate the molecular genetic basis of alternative barley. Analysis of the major barley vernalization (VRN-H1, VRN-H2) and photoperiod (PPD-H1, PPD-H2) response genes in a collection of 386 varieties found alternative SGH to be characterized by specific allelic combinations. Spring varieties possessed spring loci at one or both of the vernalization response loci, combined with long-day non-responsive ppd-H1 alleles and wild-type alleles at the short-day photoperiod response locus, PPD-H2. Winter varieties possessed winter alleles at both vernalization loci, in combination with the mutant ppd-H2 allele conferring delayed flowering under short-day photoperiods. In contrast, all alternative varieties investigated possessed a single spring allele (either at VRN-H1 or at VRN-H2) combined with mutant ppd-H2 alleles. This allelic combination is found only in alternative types and is diagnostic for alternative SGH in the collection studied. Analysis of flowering time under controlled environment found alternative varieties flowered later than spring control lines, with the difference most pronounced under short-day photoperiods. This work provides genetic characterization of the alternative SGH phenotype, allowing precise manipulation of SGH and flowering time within breeding programmes, and provides the molecular tools for classification of all three SGH categories within national variety registration processes.