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Journal articleVasiliauskaite V, Evans TS, 2019,
Social success of perfumes
, PLoS ONE, Vol: 14, ISSN: 1932-6203We study data on perfumes and their odour descriptors-notes-to understand how note compositions, called accords, influence successful fragrance formulas. We obtain accords which tend to be present in perfumes that receive significantly more customer ratings. Our findings show that the most popular notes and the most over-represented accords are different to those that have the strongest effect to the perfume ratings. We also used network centrality to understand which notes have the highest potential to enhance note compositions. We find that large degree notes, such as musk and vanilla as well as generically-named notes, e.g. floral notes, are amongst the notes that enhance accords the most. This work presents a framework which would be a timely tool for perfumers to explore a multidimensional space of scent compositions.
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Journal articlePatel VM, Panzarasa P, Ashrafian H, et al., 2019,
Collaborative patterns, authorship practices and scientific success in biomedical research: a network analysis.
, Journal of the Royal Society of Medicine, Vol: 112, Pages: 245-257, ISSN: 1758-1095OBJECTIVE: To investigate the relationship between biomedical researchers' collaborative and authorship practices and scientific success. DESIGN: Longitudinal quantitative analysis of individual researchers' careers over a nine-year period. SETTING: A leading biomedical research institution in the United Kingdom. PARTICIPANTS: Five hundred and twenty-five biomedical researchers who were in employment on 31 December 2009. MAIN OUTCOME MEASURES: We constructed the co-authorship network in which nodes are the researchers, and links are established between any two researchers if they co-authored one or more articles. For each researcher, we recorded the position held in the co-authorship network and in the bylines of all articles published in each three-year interval and calculated the number of citations these articles accrued until January 2013. We estimated maximum likelihood negative binomial panel regression models. RESULTS: Our analysis suggests that collaboration sustained success, yet excessive co-authorship did not. Last positions in non-alphabetised bylines were beneficial for higher academic ranks but not for junior ones. A professor could witness a 20.57% increase in the expected citation count if last-listed non-alphabetically in one additional publication; yet, a lecturer suffered from a 13.04% reduction. First positions in alphabetised bylines were positively associated with performance for junior academics only. A lecturer could experience a 8.78% increase in the expected citation count if first-listed alphabetically in one additional publication. While junior researchers amplified success when brokering among otherwise disconnected collaborators, senior researchers prospered from socially cohesive networks, rich in third-party relationships. CONCLUSIONS: These results help biomedical scientists shape successful careers and research institutions develop effective assessment and recruitment policies that will ultimately sustain the quality of biomedical r
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Journal articleRassouli B, Rosas FE, Gunduz D, 2019,
Data disclosure under perfect sample privacy
Perfect data privacy seems to be in fundamental opposition to the economicaland scientific opportunities associated with extensive data exchange. Defyingthis intuition, this paper develops a framework that allows the disclosure ofcollective properties of datasets without compromising the privacy ofindividual data samples. We present an algorithm to build an optimal disclosurestrategy/mapping, and discuss it fundamental limits on finite andasymptotically large datasets. Furthermore, we present explicit expressions tothe asymptotic performance of this scheme in some scenarios, and study caseswhere our approach attains maximal efficiency. We finally discuss suboptimalschemes to provide sample privacy guarantees to large datasets with a reducedcomputational cost.
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Journal articleSahasranaman A, Jensen H, 2019,
Rapid migrations and dynamics of citizen response
, Royal Society Open Science, Vol: 6, Pages: 1-13, ISSN: 2054-5703One of the pressing social concerns of our timeis the need for meaningful responses to migrantsand refugees fleeing conflict and environmentalcatastrophe. We develop a computational model tomodel the influx of migrants into a city, varyingthe rates of entry, and find a non-linear inverserelationship between the fraction of resident populationwhose tolerance levels are breached due to migrantentry and the average time to such tolerancebreach. Essentially, beyond a certain rate of migrantentry, there is a rapid rise in the fraction ofresidents whose tolerances are breached, even as theaverage time to breach decreases. We also modelan analytical approximation of the computationalmodel and find qualitative correspondence in theobserved phenomenology, with caveats. The sharpincrease in the fraction of residents with tolerancebreach could potentially underpin the intensity ofresident responses to bursts of migrant entry intotheir cities. Given this non-linear relationship, it isperhaps essential that responses to refugee situationsare multi-country or global efforts so that sharpspikes of refugee migrations are equitably distributedamong nations, potentially enabling all participatingcountries to avoid impacting resident tolerancesbeyond limits that are socially sustainable.
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Journal articleDuarte D, Amarteifio S, Ang H, et al., 2019,
Defining the in vivo characteristics of acute myeloid leukemia cells behavior by intravital imaging
, Immunology and Cell Biology, Vol: 97, Pages: 229-235, ISSN: 0818-9641The majority of acute myeloid leukemia (AML) patients have a poor response to conventional chemotherapy. The survival of chemoresistant cells is thought to depend on leukemia-bone marrow (BM) microenvironment interactions, which are not well understood. The CXCL12/CXCR4 axis has been proposed to support AML growth but was not studied at the single AML cell level. We recently showed that T-cell acute lymphoblastic leukemia (T-ALL) cells are highly motile in the BM; however, the characteristics of AML cell migration within the BM remain undefined. Here, we characterize the in vivo migratory behavior of AML cells and their response to chemotherapy and CXCR4 antagonism, using high-resolution 2-photon and confocal intravital microscopy of mouse calvarium BM and the well-established MLL-AF9-driven AML mouse model. We used the Notch1-driven T-ALL model as a benchmark comparison and AMD3100 for CXCR4 antagonism experiments. We show that AML cells are migratory, and in contrast with T-ALL, chemoresistant AML cells become less motile. Moreover, and in contrast with T-ALL, the in vivo exploratory behavior of expanding and chemoresistant AML cells is unaffected by AMD3100. These results expand our understanding of AML cells-BM microenvironment interactions, highlighting unique traits of leukemia of different lineages.
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Conference paperRassouli B, Rosas F, Gunduz D, 2019,
Latent Feature Disclosure under Perfect Sample Privacy
, 10th IEEE International Workshop on Information Forensics and Security (WIFS), Publisher: IEEE, ISSN: 2157-4766 -
Journal articleVazquez P, del Rio JA, Cedano KG, et al., 2018,
Network characterization of the Entangled Model for sustainability indicators. Analysis of the network properties for scenarios
, PLoS ONE, Vol: 13, ISSN: 1932-6203Policy-makers require strategies to select a set of sustainability indicators that are useful for monitoring sustainability. For this reason, we have developed a model where sustainability indicators compete for the attention of society. This model has shown to have steady situations where a set of sustainability indicators are stable. To understand the role of the network configuration, in this paper we analyze the network properties of the Entangled Sustainability model. We have used the degree distribution, the clustering coefficient, and the interaction strength distribution as main measures. We also analyze the network properties for scenarios compared against randomly generated scenarios. We found that the stable situations show different characteristics from the unstable transitions present in the model. We also found that the complex emergent feature of sustainability shown in the model is an attribute of the scenarios, however, the randomly generated scenarios do not present the same network properties.
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Journal articleJensen H, 2018,
Tangled nature: A model of emergent structure and temporal mode among co-evolving agents
, European Journal of Physics, Vol: 40, ISSN: 0143-0807Understanding systems level behaviour of many interacting agents is challenging in various ways. In this review we will focus on how the interaction between components can lead to hierarchical structures with different types of dynamics, or causations, at different levels. We use the Tangled Nature model to discuss the co-evolutionary aspects of the connection between the microscopic level of the individual and the macroscopic systems level. At the microscopic level the individual agent may undergo evolutionary changes due to 'mutations of strategies'. The micro-dynamics always run at a constant rate. Nevertheless, the systems level dynamics exhibit a completely different type of intermittent abrupt dynamics where major upheavals keep throwing the system between metastable configurations. These dramatic transitions are described by a log-Poisson time statistics. The long time effect is a collective adaptation of the ecological networks. We discuss the ecological and macro-evolutionary consequences of the adaptive dynamics and briefly describe work using the Tangled Nature framework to analyse problems in economics, sociology, innovation and sustainability.
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Journal articleGarcia Millan R, Pausch J, Walter B, et al., 2018,
Field-theoretic approach to the universality of branching processes
, Physical Review E, Vol: 98, ISSN: 1539-3755Branching processes are widely used to model phenomena from networks to neuronal avalanching. In a large class of continuous-time branching processes, we study the temporal scaling of the moments of the instant population size, the survival probability, expected avalanche duration, the so-called avalanche shape, the n-point correlation function, and the probability density function of the total avalanche size. Previous studies have shown universality in certain observables of branching processes using probabilistic arguments; however, a comprehensive description is lacking. We derive the field theory that describes the process and demonstrate how to use it to calculate the relevant observables and their scaling to leading order in time, revealing the universality of the moments of the population size. Our results explain why the first and second moment of the offspring distribution are sufficient to fully characterize the process in the vicinity of criticality, regardless of the underlying offspring distribution. This finding implies that branching processes are universal. We illustrate our analytical results with computer simulations.
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Journal articleRosas De Andraca F, Chen K-C, Gunduz D, 2018,
Social learning for resilient data fusion against data falsification attacks
, Computational Social Networks, Vol: 5, ISSN: 2197-4314BackgroundInternet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers.MethodsTo tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.ResultsWe analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.ConclusionsOur results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.
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