4 resultados para detection methods
em Aston University Research Archive
Resumo:
This thesis includes analysis of disordered spin ensembles corresponding to Exact Cover, a multi-access channel problem, and composite models combining sparse and dense interactions. The satisfiability problem in Exact Cover is addressed using a statistical analysis of a simple branch and bound algorithm. The algorithm can be formulated in the large system limit as a branching process, for which critical properties can be analysed. Far from the critical point a set of differential equations may be used to model the process, and these are solved by numerical integration and exact bounding methods. The multi-access channel problem is formulated as an equilibrium statistical physics problem for the case of bit transmission on a channel with power control and synchronisation. A sparse code division multiple access method is considered and the optimal detection properties are examined in typical case by use of the replica method, and compared to detection performance achieved by interactive decoding methods. These codes are found to have phenomena closely resembling the well-understood dense codes. The composite model is introduced as an abstraction of canonical sparse and dense disordered spin models. The model includes couplings due to both dense and sparse topologies simultaneously. The new type of codes are shown to outperform sparse and dense codes in some regimes both in optimal performance, and in performance achieved by iterative detection methods in finite systems.
Resumo:
Protein lipoxidation refers to the modification by electrophilic lipid oxidation products to form covalent adducts, which for many years has been considered as a deleterious consequence of oxidative stress. Oxidized lipids or phospholipids containing carbonyl moieties react readily with lysine to form Schiff bases; alternatively, oxidation products containing α,β-unsaturated moieties are susceptible to nucleophilic attack by cysteine, histidine or lysine residues to yield Michael adducts, overall corresponding to a large number of possible protein adducts. The most common detection methods for lipoxidized proteins take advantage of the presence of reactive carbonyl groups to add labels, or use antibodies. These methods have limitations in terms of specificity and identification of the modification site. The latter question is satisfactorily addressed by mass spectrometry, which enables the characterization of the adduct structure. This has allowed the identification of lipoxidized proteins in physiological and pathological situations. While in many cases lipoxidation interferes with protein function, causing inhibition of enzymatic activity and increased immunogenicity, there are a small number of cases where lipoxidation results in gain of function or activity. For certain proteins lipoxidation may represent a form of redox signaling, although more work is required to confirm the physiological relevance and mechanisms of such processes. This article is part of a Special Issue entitled: Posttranslational Protein modifications in biology and Medicine. © 2013 Elsevier B.V.
Resumo:
We examine contemporaneous jumps (cojumps) among individual stocks and a proxy for the market portfolio. We show, through a Monte Carlo study, that using intraday jump tests and a coexceedance criterion to detect cojumps has a power similar to the cojump test proposed by Bollerslev et al. (2008). However, we also show that we should not expect to detect all common jumps comprising a cojump when using such coexceedance based detection methods. Empirically, we provide evidence of an association between jumps in the market portfolio and cojumps in the underlying stocks. Consistent with our Monte Carlo evidence, moderate numbers of stocks are often detected to be involved in these (systematic) cojumps. Importantly, the results suggest that market-level news is able to generate simultaneous large jumps in individual stocks. We also find evidence of an association between systematic cojumps and Federal Funds Target Rate announcements. © 2013 Elsevier B.V.