Kuznetsov independence for interval-valued expectations and sets of probability distributions: Properties and algorithms

Kuznetsov independence of variables X and Y means that, for any pair of bounded functions f(X) and g(Y), E[f(X)g(Y)]=E[f(X)] *times* E[g(Y)], where E[.] denotes interval-valued expectation and *times* denotes interval multiplication. We present properties of Kuznetsov independence for several variables, and connect it with other concepts of independence in the literature; in particular we show that strong extensions are always included in sets of probability distributions whose lower and upper expectations satisfy Kuznetsov independence. We introduce an algorithm that computes lower expectations subject to judgments of Kuznetsov independence by mixing column generation techniques with nonlinear programming. Finally, we define a concept of conditional Kuznetsov independence, and study its graphoid properties.

Computing lower and upper expectations under epistemic independence

This paper investigates the computation of lower/upper expectations that must cohere with a collection of probabilistic assessments and a collection of judgements of epistemic independence. New algorithms, based on multilinear programming, are presented, both for independence among events and among random variables. Separation properties of graphical models are also investigated.

Metabolic signatures of human Alzheimer's disease (AD): H-1 NMR analysis of the polar metabolome of post-mortem brain tissue

Brain tissue from so-called Alzheimer's disease (AD) mouse models has previously been examined using H-1 NMR-metabolomics, but comparable information concerning human AD is negligible. Since no animal model recapitulates all the features of human AD we undertook the first H-1 NMR-metabolomics investigation of human AD brain tissue. Human post-mortem tissue from 15 AD subjects and 15 age-matched controls was prepared for analysis through a series of lyophilised, milling, extraction and randomisation steps and samples were analysed using H-1 NMR. Using partial least squares discriminant analysis, a model was built using data obtained from brain extracts. Analysis of brain extracts led to the elucidation of 24 metabolites. Significant elevations in brain alanine (15.4 %) and taurine (18.9 %) were observed in AD patients (p ≤ 0.05). Pathway topology analysis implicated either dysregulation of taurine and hypotaurine metabolism or alanine, aspartate and glutamate metabolism. Furthermore, screening of metabolites for AD biomarkers demonstrated that individual metabolites weakly discriminated cases of AD [receiver operating characteristic (ROC) AUC <0.67; p < 0.05]. However, paired metabolites ratios (e.g. alanine/carnitine) were more powerful discriminating tools (ROC AUC = 0.76; p < 0.01). This study further demonstrates the potential of metabolomics for elucidating the underlying biochemistry and to help identify AD in patients attending the memory clinic

Photo-dynamic biocidal action of methylene blue and hydrogen peroxide on the cyanobacterium Synechococcus leopoliensis under visible light irradiation

Biofilm growth on stone surfaces is a significant contributing factor to stone biodeterioration. Current market based biocides are hazardous to the environment and to public health. We have investigated the photo-dynamic effect of methylene blue (MB) in the presence of hydrogen peroxide (H₂O₂) on the destruction of the cyanobacterium Synechococcus leopoliensis (S. leopoliensis) under irradiation with visible light. Data presented in this paper illustrate that illumination of S. leopoliensis in the presence of a photosensitiser (MB) and H₂O₂ results in the decomposition of both the cyanobacterium and the photosensitiser. The presence of MB and H₂O₂ affects the viability of the photosensitiser and the cyanobacterium with the fluorescence of both decreasing by 80% over the irradiation time investigated. The photo-dynamic effect was observed under aerobic and anaerobic conditions indicating that oxygen was not necessary for the process. This novel combination could be effective for the remediation of biofilm colonised stone surfaces

Replication of the association of GLT6D1 with aggressive periodontitis in a Sudanese population

BACKGROUND: Susceptibility to aggressive periodontitis (AgP) is influenced by genetic as well as environmental factors. Studies linking gene variants to AgP have been mainly centred in developed countries with limited data from Africa.
AIM: To investigate whether previously reported candidate gene associations with AgP could be replicated in a population from Sudan.


METHODS: The investigation was a case-control design. Cases with AgP (n = 132) and controls (n = 136) were identified from patients attending the Periodontal Department in Khartoum Dental Hospital. Genotyping was performed using the Sequenom MassARRAY iPLEX platform. Analysis focused on gene variants with a minor allele frequency (MAF) > 25% in the Sudanese subjects that had previously been reported to be associated with AgP.


RESULTS: One candidate gene rs1537415 (GLT6D1) was significantly associated with AgP, OR = 1.50 (95% CI 1.04-2.17), p = 0.0295 (increasing to p = 0.09 after correction for multiple testing). The association strengthened to OR = 1.56 (95% CI 1.15-2.16), p = 0.0042 when the controls were supplemented with data from the Hap map for the Yoruba in Ibadan (n = 147) and remained significant (p = 0.013) after correction for multiple testing.


CONCLUSION: The study independently replicated the finding that rs1537415, a variant in glycosyl transferase gene GLT6D1, is associated with AgP and provided the first report of genetic associations with AgP in a Sudanese population.