297 research outputs found
Learning Bayesian Networks with the bnlearn R Package
No full textbnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010).
Characterization of HIV-1 genetic variability in newly HIV-1 diagnosed patients: role in influencing immunological and viral response, including reservoir burden
No full textMeasures of Variability for Graphical Models
No full textIn recent years, graphical models have been successfully applied in several different
disciplines, including medicine, biology and epidemiology. This has been made possible
by the rapid evolution of structure learning algorithms, from constraint-based ones to
score-based and hybrid ones. The main goal in the development of these algorithms
has been the reduction of the number of either independence tests or score comparisons
needed to learn the structure of the Bayesian network. In most cases the characteristics
of the learned networks have been studied using a small number of reference data sets
as benchmarks, and differences from the true structure heve been measured with purely
descriptive measures such as Hamming distance.
This approach to model validation is not possible for real world data sets, as the true
structure of their probability distribution is not known. An alternative is provided
by the use of either parametric or nonparametric bootstrap. By applying a learning
algorithm to a sufficiently large number of bootstrap samples it is possible to obtain the
empirical probability of any feature of the resulting network, such as the structure of
the Markov Blanket of a particular node. The fundamental limit in the interpretation
of the results is that the “reasonable” level of confidence for thresholding depends on
the data and the learning algorithm.
In this thesis we extend the aforementioned bootstrap-based approach for the in-
ference on the structure of a Bayesian or Markov network. The graph representing
the network structure and its underlying undirected graph (in the case of Bayesian
networks) are modelled using a multivariate extension of the Trinomial and Bernoulli
distributions; each component is associated with an arc. These assumptions allow the
derivation of exact and asymptotic measures of the variability of the network structure
or any of its parts. These measures are then applied to some common learning strate-
gies used in literature using the implementation provided by the bnlearn R package
implemented and maintained by the author.Negli ultimi anni i modelli grafici, ed in particolare i network Bayesiani, sono entrati
nella pratica corrente delle analisi statistiche in diversi settori scientifici, tra cui medi
cina e biostatistica. L’uso di questo tipo di modelli è stato reso possibile dalla rapida
evoluzione degli algoritmi per apprenderne la struttura, sia quelli basati su test statistici
che quelli basati su funzioni punteggio. L’obiettivo principale di questi nuovi algoritmi
è la riduzione del numero di modelli intermedi considerati nell’apprendimento; le loro
caratteristiche sono state usualmente valutate usando dei dati di riferimento (per i quali
la vera struttura del modello è nota da letteratura) e la distanza di Hamming.
Questo approccio tuttavia non può essere usato per dati sperimentali, poiché la loro
struttura probabilistica non è nota a priori. In questo caso una valida alternativa
è costituita dal bootstrap non parametrico: apprendendo un numero sufficientemente
grande di modelli da campioni bootstrap è infatti possibile ottenere una stima empirica
della probabilità di ogni caratteristica di interesse del network stesso.
In questa tesi viene affrontato il principale limite di questo secondo approccio: la
difficoltà di stabilire una soglia di significatività per le probabilità empiriche. Una
possibile soluzione è data dall’assunzione di una distribuzione Trinomiale multivariata
(nel caso di grafi orientati aciclici) o Bernoulliana multivariata (nel caso di grafi non
orientati), che permette di associare ogni arco del network ad una distribuzione mar
ginale. Questa assunzione permette di costruire dei test statistici, sia asintotici che
esatti, per la variabilità multivariata della struttura del network nel suo complesso o di
una sua parte. Tali misure di variabilità sono state poi applicate ad alcuni algoritmi di
apprendimento della struttura di network Bayesiani utilizzando il pacchetto R bnlearn,
implementato e mantenuto dall’autore
EPR and DSC study of the effects of propofol and nitrosopropofol on DMPC multilamellar liposomes
No full textThe mechanisms of reaction of propofol with nitrosoglutathione lead to the formation of an active species which was identified as 2,6-diisopropyl-4-nitrosophenol. In the present work, we discuss the interaction of propofol and 2,6- diisopropyl-4-nitrosophenol with dimyristoylphosphatidylcholine and egg yolk phosphatidylcholine multilamellar liposomes using differential scanning calorimetry and spin labelling techniques. The thermotropic profiles show that these molecules affect the temperature and the cooperativity of the gel to fluid state transition of the liposomes differently: the effects of 2,6-diisopropylphenol on the lipid organisation are quite similar to phenol and coherently interpretable in terms of the disorder produced in the membrane by a bulky group; 2,6-diisopropyl-4-nitrosophenol is a stronger perturbing agent, and ESR spectra suggest that this is due to a relative accumulation of the molecule into the interfacial region of the bilayer
Inhibition of lipid peroxidation by S-nitrosoglutathione and copper
No full textThe antioxidant properties of S-nitrosoglutathione, a nitric oxide-derived product were studied in different experimental systems. By using the crocin bleaching test, S-nitrosoglutathione, in the presence of copper ions, shows an antioxidant capacity about six times higher than that of Trolox c and referable to the interception of peroxyl radicals by nitric oxide. Copper alone shows a modest inhibitory action, which is about seven times lower than that of Trolox c. S-nitrosoglutathione prevents lipid peroxidation induced by the well-known Fe2+/ascorbate system (IC50 = 450 microM) and the inhibitory effect is strongly reinforced by the presence of copper ions (IC50 = 6.5 microM). In addition, cumene hydroperoxide-induced lipid peroxidation is markedly decreased by S-nitrosoglutathione, provided that copper ions, maintained reduced by ascorbate, are present. Decomposition of S-nitrosoglutathione through metal catalysis and/or the presence of reducing agents and the consequent release of nitric oxide are of crucial importance for eliciting the antioxidant power. In this way, copper ions and/or reducing species with low antioxidant potency are able to promote the formation of an extremely strong antioxidant species such as nitric oxide
Oxidation of adrenaline and its derivatives by S-nitrosoglutathione
No full textAn oxidizing effect of S-nitrosoglutathione toward adrenaline and its cyclic derivatives (adrenochrome and adrenolutin) is reported. The oxidation was monitored either spectrophotometrically or as oxygen uptake. Adrenaline was first oxidized to adrenochrome that, after isomerization to adrenolutin, was further oxidized to products monitored as fluorescence decrease. To occur to a significant extent, this oxidation requires copper ions that, in addition to a direct effect on the oxidation of the ortho-diphenol moiety, are also able to decompose nitrosothiols, giving rise to nitric oxide. The latter, after interaction with oxygen and superoxide, produces nitrogen oxides and peroxynitrite, respectively, that are important contributors to the oxidative process. In this context, catecholamines might act as regulatory factors toward nitric oxide and its derivative
Different effects of propofol and nitrosopropofol on DMPC multilamellar liposomes
No full textThe mechanisms of reaction of propofol with nitrosoglutathione lead to the formation of an active species which was identified, and then synthesised, as 2,6-diisopropyl-4-nitrosophenol. In the present work, we demonstrate the in vitro formation of 2,6-diisopropyl-4-nitrosophenol, then we discuss the interaction of propofol and 2,6-diisopropyl-4-nitrosophenol with dimyristoylphosphatidylcholine and egg yolk phosphatidylcholine multilamellar liposomes using differential scanning calorimetry and spin labelling techniques. It was demonstrated that both molecules are highly lipophylic and absorb almost entirely in the lipid phase. The thermotropic profiles showed that these molecules affect the temperature and the co-operativity of the gel-to-fluid state transition of the liposomes differently: the effects of 2,6-diisopropylphenol on the lipid organisation are quite similar to phenol and coherently interpretable in terms of the disorder produced in the membrane by a bulky group; 2,6-diisopropyl-4-nitrosophenol is a stronger perturbing agent, and ESR spectra suggest that this is due to a relative accumulation of the molecule into the interfacial region of the bilayer
Induction of mitochondrial permeability transition by auranofin, a gold(I)-phosphine derivative
No full text1 Gold(I)-thiolate drugs are compounds that specifically interact with thiol and/or selenol groups and are essentially utilized in the treatment of rheumatoid arthritis. 2 Considering the importance of thiol groups in regulating mitochondrial membrane permeability, the effects of auranofin (S-triethylphosphinegold(I)-2,3,4,6-tetra-O-acetyl-1-thio-beta-D-glucopyranoside), a second-generation gold drug, were studied on mitochondria isolated from rat liver. 3 Auranofin, at submicromolar concentrations, was able to induce the mitochondrial membrane permeability transition observed as swelling and loss of membrane potential. Both events are completely inhibited by cyclosporin A, the specific inhibitor of mitochondrial permeability transition. Calcium ions and energization by succinate are required for the occurrence of permeability transition. 4 By interacting with the active site selenol group, auranofin results as an extremely potent inhibitor of mitochondrial thioredoxin reductase, both isolated and in its mitochondrial environment. 5 It is concluded that auranofin, in the presence of calcium ions, is a highly efficient inducer of mitochondrial membrane permeability transition, potentially referable to its inhibition of mitochondrial thioredoxin reductase
Learning Bayesian Networks with the bnlearn R Package
No full textbnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010)
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