1,037 research outputs found

    Quantum complex networks

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    This Thesis focuses on networks of interacting quantum harmonic oscillators and in particular, on them as environments for an open quantum system, their probing via the open system, their transport properties, and their experimental implementation. Exact Gaussian dynamics of such networks is considered throughout the Thesis. Networks of interacting quantum systems have been used to model structured environments before, but most studies have considered either small or non-complex networks. Here this problem is addressed by investigating what kind of environments complex networks of quantum systems are, with specific attention paid on the presence or absence of memory effects (non-Markovianity) of the reduced open system dynamics. The probing of complex networks is considered in two different scenarios: when the probe can be coupled to any system in the network, and when it can be coupled to just one. It is shown that for identical oscillators and uniform interaction strengths between them, much can be said about the network also in the latter case. The problem of discriminating between two networks is also discussed. While state transfer between two sites in a (typically non-complex) network is a well-known problem, this Thesis considers a more general setting where multiple parties send and receive quantum information simultaneously through a quantum network. It is discussed what properties would make a network suited for efficient routing, and what is needed for a systematic search and ranking of such networks. Finding such networks complex enough to be resilient to random node or link failures would be ideal. The merit and applicability of the work described so far depends crucially on the ability to implement networks of both reasonable size and complex structure, which is something the previous proposals lack. The ability to implement several different networks with a fixed experimental setup is also highly desirable. In this Thesis the problem is solved with a proposal of a fully reconfigurable experimental realization, based on mapping the network dynamics to a multimode optical platform

    Polymer Networks Produced by Marine Diatoms in the Northern Adriatic Sea

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    Using high resolution molecular technique of atomic force microscopy, we address the extracellular polymer production of Adriatic diatom Cylindrotheca closterium analyzed at the single cell level and the supramolecular organization of gel phase isolated from the Northern Adriatic macroaggregates. Our results revealed that extracellular polysaccharides freshly produced by marine diatoms can self-assemble directly to form gel network characteristics of the macroscopic gel phase in the natural aquatorium. Based on the experiments performed with isolated polysaccharide fractions of C. closterium and of macroaggregates gel phase, we demonstrated that the polysaccharide self-assembly into gel network can proceed independent of any bacterial mediation or interaction with inorganic particles

    Bipartite network models to design combination therapies in acute myeloid leukaemia

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    Combination therapy is preferred over single-targeted monotherapies for cancer treatment due to its efficiency and safety. However, identifying effective drug combinations costs time and resources. We propose a method for identifying potential drug combinations by bipartite network modelling of patient-related drug response data, specifically the Beat AML dataset. The median of cell viability is used as a drug potency measurement to reconstruct a weighted bipartite network, model drug-biological sample interactions, and find the clusters of nodes inside two projected networks. Then, the clustering results are leveraged to discover effective multi-targeted drug combinations, which are also supported by more evidence using GDSC and ALMANAC databases. The potency and synergy levels of selective drug combinations are corroborated against monotherapy in three cell lines for acute myeloid leukaemia in vitro. In this study, we introduce a nominal data mining approach to improving acute myeloid leukaemia treatment through combinatorial therapy.Peer reviewe

    Rheology of Supramolecular Polymers

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    Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure

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    © 2013 IEEE. Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients
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