Julián Candia

Uncovering individual and collective human dynamics from mobile phone records

Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.

Understanding health and disease with multidimensional single-cell methods.

Current efforts in the biomedical sciences and related interdisciplinary fields are focused on gaining a molecular understanding of health and disease, which is a problem of daunting complexity that spans many orders of magnitude in characteristic length scales, from small molecules that regulate cell function to cell ensembles that form tissues and organs working together as an organism. In order to uncover the molecular nature of the emergent properties of a cell, it is essential to measure multiple-cell components simultaneously in the same cell. In turn, cell heterogeneity requires multiple-cells to be measured in order to understand health and disease in the organism. This review summarizes current efforts towards a data-driven framework that leverages single-cell technologies to build robust signatures of healthy and diseased phenotypes. While some approaches focus on multicolor flow cytometry data and other methods are designed to analyze high-content image-based screens, we emphasize the so-called Supercell/SVM paradigm (recently developed by the authors of this review and collaborators) as a unified framework that captures mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific contributions to basic and translational biomedical research, these efforts illustrate, from a larger perspective, the powerful synergy that might be achieved from bringing together methods and ideas from statistical physics, data mining, and mathematics to solve the most pressing problems currently facing the life sciences.

Diffusion and drift of cosmic rays in highly turbulent magnetic fields

We determine numerically the parallel, perpendicular and antisymmetric diffusion coefficients for charged particles propagating in highly turbulent magnetic fields, by means of extensive Monte Carlo simulations. We propose simple expressions, given in terms of a small set of fitting parameters, to account for the diffusion coefficients as functions of magnetic rigidity and turbulence level, and corresponding to different kinds of turbulence spectra. The results obtained satisfy scaling relations, which make them useful for describing the cosmic ray origin and transport in a variety of different astrophysical environments.

EFFECTS OF MASS MEDIA AND CULTURAL DRIFT IN A MODEL FOR SOCIAL INFLUENCE

In the context of an extension of Axelrods model for social influence, we study the interplay and competition between the cultural drift, represented as random perturbations, and mass media, introduced by means of an external homogeneous field. Unlike previous studies [J. C. Gonzalez-Avella et al., Phys. Rev. E72, 065102(R) (2005)], the mass media coupling proposed here is capable of affecting the cultural traits of any individual in the society, including those who do not share any features with the external message. A noise-driven transition is found: for large noise rates, both the ordered (culturally polarized) phase and the disordered (culturally fragmented) phase are observed, while, for lower noise rates, the ordered phase prevails. In the former case, the external field is found to induce cultural ordering, a behavior opposite to that reported in previous studies using a different prescription for the mass media interaction. We compare the predictions of this model to statistical data measuring the impact of a mass media vasectomy promotion campaign in Brazil.

From Cellular Characteristics to Disease Diagnosis: Uncovering Phenotypes with Supercells

Cell heterogeneity and the inherent complexity due to the interplay of multiple molecular processes within the cell pose difficult challenges for current single-cell biology. We introduce an approach that identifies a disease phenotype from multiparameter single-cell measurements, which is based on the concept of “supercell statistics”, a single-cell-based averaging procedure followed by a machine learning classification scheme. We are able to assess the optimal tradeoff between the number of single cells averaged and the number of measurements needed to capture phenotypic differences between healthy and diseased patients, as well as between different diseases that are difficult to diagnose otherwise. We apply our approach to two kinds of single-cell datasets, addressing the diagnosis of a premature aging disorder using images of cell nuclei, as well as the phenotypes of two non-infectious uveitides (the ocular manifestations of Behçets disease and sarcoidosis) based on multicolor flow cytometry. In the former case, one nuclear shape measurement taken over a group of 30 cells is sufficient to classify samples as healthy or diseased, in agreement with usual laboratory practice. In the latter, our method is able to identify a minimal set of 5 markers that accurately predict Behçets disease and sarcoidosis. This is the first time that a quantitative phenotypic distinction between these two diseases has been achieved. To obtain this clear phenotypic signature, about one hundred CD8+ T cells need to be measured. Although the molecular markers identified have been reported to be important players in autoimmune disorders, this is the first report pointing out that CD8+ T cells can be used to distinguish two systemic inflammatory diseases. Beyond these specific cases, the approach proposed here is applicable to datasets generated by other kinds of state-of-the-art and forthcoming single-cell technologies, such as multidimensional mass cytometry, single-cell gene expression, and single-cell full genome sequencing techniques.

Mass media influence spreading in social networks with community structure

We study an extension of Axelrods model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

Cosmic ray photodisintegration and the knee of the spectrum

Abstract We explore in some detail the scenario proposed to explain the observed knee of the cosmic ray (CR) spectrum as due to the effects of photodisintegration of the CR nuclei by interactions with optical and soft UV photons in the source region. We show that the photon column densities needed to explain the experimental data are significantly lower than those obtained in previous estimations which neglected multinucleon emission in the photodisintegration process. We also treat more accurately the photodisintegration thresholds, we discuss the effects of photopion production processes and the neutron escape mechanism, identifying the physical processes responsible for the qualitative features of the results. This scenario would require the CR nuclei to traverse column densities of ∼5×10 27 –2×10 28 eV/cm 2 after being accelerated in order to reproduce the observed knee, and predicts that the CR composition should become lighter above ∼10 16 eV.

Criticality and the onset of ordering in the standard Vicsek model

Experimental observations of animal collective behaviour have shown stunning evidence for the emergence of large-scale cooperative phenomena resembling phase transitions in physical systems. Indeed, quantitative studies have found scale-free correlations and critical behaviour consistent with the occurrence of continuous, second-order phase transitions. The standard Vicsek model (SVM), a minimal model of self-propelled particles in which their tendency to align with each other competes with perturbations controlled by a noise term, appears to capture the essential ingredients of critical flocking phenomena. In this paper, we review recent finite-size scaling and dynamical studies of the SVM, which present a full characterization of the continuous phase transition through dynamical and critical exponents. We also present a complex network analysis of SVM flocks and discuss the onset of ordering in connection with XY-like spin models.

Turbulent diffusion and drift in galactic magnetic fields and the explanation of the knee in the cosmic ray spectrum

We reconsider the scenario in which the knee in the cosmic ray spectrum is explained as due to a change in the escape mechanism of cosmic rays from the galaxy from one dominated by transverse diffusion to one dominated by drifts. We solve the diffusion equations adopting realistic galactic field models and using diffusion coefficients appropriate for strong turbulence (with a Kolmogorov spectrum of fluctuations) and consistent with the assumed magnetic fields. We show that properly taking into account these effects leads to a natural explanation of the knee in the spectrum, and a transition towards a heavier composition above the knee is predicted.

Irreversible opinion spreading on scale-free networks.

We study the dynamical and critical behavior of a model for irreversible opinion spreading on Barabási-Albert (BA) scale-free networks by performing extensive Monte Carlo simulations. The opinion spreading within an inhomogeneous society is investigated by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. The deposition dynamics, which is studied as a function of the degree of the occupied sites, shows evidence for the leading role played by hubs in the growth process. Systems of finite size grow either ordered or disordered, depending on the temperature. By means of standard finite-size scaling procedures, the effective order-disorder phase transitions are found to persist in the thermodynamic limit. This critical behavior, however, is absent in related equilibrium spin systems such as the Ising model on BA scale-free networks, which in the thermodynamic limit only displays a ferromagnetic phase. The dependence of these results on the degree exponent is also discussed for the case of uncorrelated scale-free networks.