Effects of oxidised low density lipoprotein on dendritic cells: a possible immunoregulatory component of the atherogenic micro-environment?

Objective: The objective of this study was to explore the relationship between low density lipoprotein (LDL) and dendritic cell (DC) activation, based upon the hypothesis that reactive oxygen species (ROS)-mediated modification of proteins that may be present in local DC microenvironments could be important as mediators of this activation. Although LDL are known to be oxidised in vivo, and taken up by macrophages during atherogenesis; their effect on DC has not been explored previously. Methods: Human DCs were prepared from peripheral blood monocytes using GM-CSF and IL-4. Plasma LDLs were isolated by sequential gradient centrifugation, oxidised in CuSO4, and oxidation arrested to yield mild, moderate and highly oxidised LDL forms. DCs exposed to these LDLs were investigated using combined phenotypic, functional (autologous T cell activation), morphological and viability assays. Results: Highly-oxidised LDL increased DC HLA-DR, CD40 and CD86 expression, corroborated by increased DC-induced T cell proliferation. Both native and oxidised LDL induced prominent DC clustering. However, high concentrations of highly-oxidised LDL inhibited DC function, due to increased DC apoptosis. Conclusions: This study supports the hypothesis that oxidised LDL are capable of triggering the transition from sentinel to messenger DC. Furthermore, the DC clustering–activation–apoptosis sequence in the presence of different LDL forms is consistent with a regulatory DC role in immunopathogenesis of atheroma. A sequence of initial accumulation of DC, increasing LDL oxidation, and DC-induced T cell activation, may explain why local breach of tolerance can occur. Above a threshold level, however, supervening DC apoptosis limits this, contributing instead to the central plaque core.

Obesity and body fat classification in the metabolic syndrome: impact on cardiometabolic risk metabotype

Obesity is a key factor in the development of the metabolic syndrome (MetS), which is associated with increased cardiometabolic risk. We investigated whether obesity classification by body mass index (BMI) and body fat percentage (BF%) influences cardiometabolic profile and dietary responsiveness in 486 MetS subjects (LIPGENE dietary intervention study). Anthropometric measures, markers of inflammation and glucose metabolism, lipid profiles, adhesion molecules and haemostatic factors were determined at baseline and after 12 weeks of 4 dietary interventions (high saturated fat (SFA), high monounsaturated fat (MUFA) and 2 low fat high complex carbohydrate (LFHCC) diets, 1 supplemented with long chain n-3 polyunsaturated fatty acids (LC n-3 PUFAs)). 39% and 87% of subjects classified as normal and overweight by BMI were obese according to their BF%. Individuals classified as obese by BMI (± 30 kg/m2) and BF% (± 25% (men) and ± 35% (women)) (OO, n = 284) had larger waist and hip measurements, higher BMI and were heavier (P < 0.001) than those classified as non-obese by BMI but obese by BF% (NOO, n = 92). OO individuals displayed a more pro-inflammatory (higher C reactive protein (CRP) and leptin), pro-thrombotic (higher plasminogen activator inhibitor-1 (PAI-1)), pro-atherogenic (higher leptin/adiponectin ratio) and more insulin resistant (higher HOMA-IR) metabolic profile relative to the NOO group (P < 0.001). Interestingly, tumour necrosis factor alpha (TNF-α) concentrations were lower post-intervention in NOO individuals compared to OO subjects (P < 0.001). In conclusion, assessing BF% and BMI as part of a metabotype may help identify individuals at greater cardiometabolic risk than BMI alone.

Genetic variability in partitioning to the yield component of cacao (Theobroma cacao L.)

Biomass partitioning of cacao (Theobroma cacao L.) was studied in seven clones and five hybrids in a replicated experiment in Bahia, Brazil. Over an eighteen month period, a seven- fold difference in dry bean yield was demonstrated between genotypes, ranging from the equivalent of 200 to 1389 kg.ha-1. During the same interval, the increase in trunk cross-sectional area ranged from 11.1 cm2 for clone EEG-29 to 27.6 cm2 for hybrid PA-150 * MA-15. Yield efficiency increment (the ratio of cumulative yield to the increase in trunk circumference), which indicated partitioning between the vegetative and reproductive components, ranged from 0.008 kg.cm-2 for clone CP-82 to 0.08 kg.cm-2 for clone EEG-29. An examination of biomass partitioning within the pod of the seven clones revealed that the beans accounted for between 32.0% (CP-82) and 44.5% (ICS-9) of the pod biomass. The study demonstrated the potential for yield improvement in cacao by selectively breeding for more efficient partitioning to the yield component.

The monitoring of ecological quality and the classification of standing waters in temperate regions: a review and proposal based on a worked scheme for British waters

This paper reviews the ways that quality can be assessed in standing waters, a subject that has hitherto attracted little attention but which is now a legal requirement in Europe. It describes a scheme for the assessment and monitoring of water and ecological quality in standing waters greater than about I ha in area in England & Wales although it is generally relevant to North-west Europe. Thirteen hydrological, chemical and biological variables are used to characterise the standing water body in any current sampling. These are lake volume, maximum depth, onductivity, Secchi disc transparency, pH, total alkalinity, calcium ion concentration, total N concentration,winter total oxidised inorganic nitrogen (effectively nitrate) concentration, total P concentration, potential maximum chlorophyll a concentration, a score based on the nature of the submerged and emergent plant community, and the presence or absence of a fish community. Inter alia these variables are key indicators of the state of eutrophication, acidification, salinisation and infilling of a water body.

Role of the two-component regulator CpxAR in the virulence of Salmonella entetica serotype typhimurium

The CpxAR (Cpx) two-component regulator controls the expression of genes in response to a variety of environmental cues. The Cpx regulator has been implicated in the virulence of several gram-negative pathogens, although a role for Cpx in vivo has not been demonstrated directly. Here we investigate whether positive or negative control of gene expression by Cpx is important for the pathogenesis of Salmonella enterica serotype Typhimurium. The Cpx signal pathway in serotype Typhimurium was disrupted by insertional inactivation of the cpxA and cpxR genes. We also constitutively activated the Cpx pathway by making an internal in-frame deletion in cpxA (a cpxA* mutation). Activation of the Cpx pathway inhibited induction of the envelope stress response pathway controlled by the alternative sigma factor sigma(E) (encoded by rpoE). Conversely, the Cpx pathway was highly up-regulated (>40-fold) in a serotype Typhimurium rpoE mutant. The cpxA* mutation, but not the cpxA or the cpxR mutation, significantly reduced the capacity of serotype Typhimurium to adhere to and invade eucaryotic cells, although intracellular replication was not affected. The cpxA and cpxA* mutations significantly impaired the ability of serotype Typhimurium to grow in vivo in mice. To our knowledge, this is the first demonstration that the Cpx system is important for a bacterial pathogen in vivo.

PMCRI: a parallel modular classification rule induction framework

In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction of Decision Trees (TDIDT) algorithm is a very widely used technology to predict the classification of newly recorded data. However alternative technologies have been derived that often produce better rules but do not scale well on large datasets. Such an alternative to TDIDT is the PrismTCS algorithm. PrismTCS performs particularly well on noisy data but does not scale well on large datasets. In this paper we introduce Prism and investigate its scaling behaviour. We describe how we improved the scalability of the serial version of Prism and investigate its limitations. We then describe our work to overcome these limitations by developing a framework to parallelise algorithms of the Prism family and similar algorithms. We also present the scale up results of a first prototype implementation.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

P-Prism: a computationally efficient approach to scaling up classification rule induction

Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism.

Towards a computationally efficient approach to modular classification rule induction

Induction of classification rules is one of the most important technologies in data mining. Most of the work in this field has concentrated on the Top Down Induction of Decision Trees (TDIDT) approach. However, alternative approaches have been developed such as the Prism algorithm for inducing modular rules. Prism often produces qualitatively better rules than TDIDT but suffers from higher computational requirements. We investigate approaches that have been developed to minimize the computational requirements of TDIDT, in order to find analogous approaches that could reduce the computational requirements of Prism.

J-PMCRI: a methodology for inducing pre-pruned modular classification rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.