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1,151 result(s) for "Rossi, Matteo"
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Multilayer social networks
\"This book covers the modeling, analysis and mining of systems made of multilayer social networks, from groups of individuals connected through different types of relationships to large systems of multiple online social network sites. A range of practical and theoretical topics are covered, from data collection, pre-processing, measuring and mining to theoretical models behind the evolution of interconnected social networks and dynamical processes like information spreading\"-- Provided by publisher.
Ultimate limits for quantum magnetometry via time-continuous measurements
We address the estimation of the magnetic field B acting on an ensemble of atoms with total spin J subjected to collective transverse noise. By preparing an initial spin coherent state, for any measurement performed after the evolution, the mean-square error of the estimate is known to scale as 1 J , i.e. no quantum enhancement is obtained. Here, we consider the possibility of continuously monitoring the atomic environment, and conclusively show that strategies based on time-continuous non-demolition measurements followed by a final strong measurement may achieve Heisenberg-limited scaling 1 J 2 and also a monitoring-enhanced scaling in terms of the interrogation time. We also find that time-continuous schemes are robust against detection losses, as we prove that the quantum enhancement can be recovered also for finite measurement efficiency. Finally, we analytically prove the optimality of our strategy.
Targeting Long Chain Acyl-CoA Synthetases for Cancer Therapy
The deregulation of cancer cell metabolic networks is now recognized as one of the hallmarks of cancer. Abnormal lipid synthesis and extracellular lipid uptake are advantageous modifications fueling the needs of uncontrolled cancer cell proliferation. Fatty acids are placed at the crossroads of anabolic and catabolic pathways, as they are implicated in the synthesis of phospholipids and triacylglycerols, or they can undergo β-oxidation. Key players to these decisions are the long-chain acyl-CoA synthetases, which are enzymes that catalyze the activation of long-chain fatty acids of 12–22 carbons. Importantly, the long-chain acyl-CoA synthetases are deregulated in many types of tumors, providing a rationale for anti-tumor therapeutic opportunities. The purpose of this review is to summarize the last up-to-date findings regarding their role in cancer, and to discuss the related emerging tumor targeting opportunities.
IBM Q Experience as a versatile experimental testbed for simulating open quantum systems
The advent of noisy intermediate-scale quantum (NISQ) technology is changing rapidly the landscape and modality of research in quantum physics. NISQ devices, such as the IBM Q Experience, have very recently proven their capability as experimental platforms accessible to everyone around the globe. Until now, IBM Q Experience processors have mostly been used for quantum computation and simulation of closed systems. Here, we show that these devices are also able to implement a great variety of paradigmatic open quantum systems models, hence providing a robust and flexible testbed for open quantum systems theory. During the last decade an increasing number of experiments have successfully tackled the task of simulating open quantum systems in different platforms, from linear optics to trapped ions, from nuclear magnetic resonance (NMR) to cavity quantum electrodynamics. Generally, each individual experiment demonstrates a specific open quantum system model, or at most a specific class. Our main result is to prove the great versatility of the IBM Q Experience processors. Indeed, we experimentally implement one and two-qubit open quantum systems, both unital and non-unital dynamics, Markovian and non-Markovian evolutions. Moreover, we realise proof-of-principle reservoir engineering for entangled state generation, demonstrate collisional models, and verify revivals of quantum channel capacity and extractable work, caused by memory effects. All these results are obtained using IBM Q Experience processors publicly available and remotely accessible online.
Engineering strategies to safely drive CAR T-cells into the future
Chimeric antigen receptor (CAR) T-cell therapy has proven a breakthrough in cancer treatment in the last decade, giving unprecedented results against hematological malignancies. All approved CAR T-cell products, as well as many being assessed in clinical trials, are generated using viral vectors to deploy the exogenous genetic material into T-cells. Viral vectors have a long-standing clinical history in gene delivery, and thus underwent iterations of optimization to improve their efficiency and safety. Nonetheless, their capacity to integrate semi-randomly into the host genome makes them potentially oncogenic via insertional mutagenesis and dysregulation of key cellular genes. Secondary cancers following CAR T-cell administration appear to be a rare adverse event. However several cases documented in the last few years put the spotlight on this issue, which might have been underestimated so far, given the relatively recent deployment of CAR T-cell therapies. Furthermore, the initial successes obtained in hematological malignancies have not yet been replicated in solid tumors. It is now clear that further enhancements are needed to allow CAR T-cells to increase long-term persistence, overcome exhaustion and cope with the immunosuppressive tumor microenvironment. To this aim, a variety of genomic engineering strategies are under evaluation, most relying on CRISPR/Cas9 or other gene editing technologies. These approaches are liable to introduce unintended, irreversible genomic alterations in the product cells. In the first part of this review, we will discuss the viral and non-viral approaches used for the generation of CAR T-cells, whereas in the second part we will focus on gene editing and non-gene editing T-cell engineering, with particular regard to advantages, limitations, and safety. Finally, we will critically analyze the different gene deployment and genomic engineering combinations, delineating strategies with a superior safety profile for the production of next-generation CAR T-cell.
ESG and corporate financial performance: the mediating role of green innovation: UK common law versus Germany civil law
PurposeThe purpose of this paper is to investigate the direct and indirect links between environmental, social and governance (ESG) practices and financial performance using the mediate role of green innovation.Design/methodology/approachTo test the current study hypotheses, the authors applied linear regressions with a panel data using the Thomson Reuters ASSET4 and Bloomberg database from a sample of 115 UK and 90 Germany companies selected from the ESG index over the period 2005–2019.FindingsThe results show that the strengths ESG increase the firm value and the weaknesses decrease it. In addition, the authors find that green innovation fully mediates the relationship between ESG practices and financial performance in UK and Germany.Practical implicationsThe findings provide interesting implications to academics practitioners and regulators who are interested in discovering ESG score, financial performance and green innovation. The results also provide insights to regulators and the board of directors on future growth opportunities for the company and the country.Originality/valueThis study is unique in examining the mediation effect of green innovation on the relationship between ESG practices and financial performance.
Stochastic collision model approach to transport phenomena in quantum networks
Noise-assisted transport phenomena highlight the nontrivial interplay between environmental effects and quantum coherence in achieving maximal efficiency. Due to the complexity of biochemical systems and their environments, effective open quantum system models capable of providing physical insights on the presence and role of quantum effects are highly needed. In this paper, we introduce a new approach that combines an effective quantum microscopic description with a classical stochastic one. Our stochastic collision model (SCM) describes both Markovian and non-Markovian dynamics without relying on the weak coupling assumption. We investigate the consequences of spatial and temporal heterogeneity of noise on transport efficiency in a fully connected graph and in the Fenna–Matthews–Olson (FMO) complex. Our approach shows how to meaningfully formulate questions, and provide answers, on important open issues such as the properties of optimal noise and the emergence of the network structure as a result of an evolutionary process.
Estimating Molecular Thermal Averages with the Quantum Equation of Motion and Informationally Complete Measurements
By leveraging the Variational Quantum Eigensolver (VQE), the “quantum equation of motion” (qEOM) method established itself as a promising tool for quantum chemistry on near-term quantum computers and has been used extensively to estimate molecular excited states. Here, we explore a novel application of this method, employing it to compute thermal averages of quantum systems, specifically molecules like ethylene and butadiene. A drawback of qEOM is that it requires measuring the expectation values of a large number of observables on the ground state of the system, and the number of necessary measurements can become a bottleneck of the method. In this work, we focus on measurements through informationally complete positive operator-valued measures (IC-POVMs) to achieve a reduction in the measurement overheads by estimating different observables of interest through the measurement of a single set of POVMs. We show with numerical simulations that the qEOM combined with IC-POVM measurements ensures satisfactory accuracy in the reconstruction of the thermal state with a reasonable number of shots.
Link Prediction with Continuous-Time Classical and Quantum Walks
Protein–protein interaction (PPI) networks consist of the physical and/or functional interactions between the proteins of an organism, and they form the basis for the field of network medicine. Since the biophysical and high-throughput methods used to form PPI networks are expensive, time-consuming, and often contain inaccuracies, the resulting networks are usually incomplete. In order to infer missing interactions in these networks, we propose a novel class of link prediction methods based on continuous-time classical and quantum walks. In the case of quantum walks, we examine the usage of both the network adjacency and Laplacian matrices for specifying the walk dynamics. We define a score function based on the corresponding transition probabilities and perform tests on six real-world PPI datasets. Our results show that continuous-time classical random walks and quantum walks using the network adjacency matrix can successfully predict missing protein–protein interactions, with performance rivalling the state-of-the-art.
Efficient quantum transport in a multi-site system combining classical noise and quantum baths
We study the population dynamics and quantum transport efficiency of a multi-site dissipative system driven by a random telegraph noise (RTN) by using a variational polaron master equation for both linear chain and ring configurations. By using two different environment descriptions-RTN only and a thermal bath+RTN-we show that the presence of the classical noise has a non-trivial role on quantum transport. We observe that there exist large areas of parameter space where the combined bath+RTN influence is clearly beneficial for populating the target state of the transport, and for average trapping time and transport efficiency when accounting for the presence of the reaction center via the use of the sink. This result holds for both of the considered intra-site coupling configurations including a chain and ring. In general, our formalism and achieved results provide a platform for engineering and characterizing efficient quantum transport in multi-site systems both for realistic environments and engineered systems.