Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction

We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package "LICORS" (http://cran.r-project.org/web/packages/LICORS/).Comment: 11 pages; AISTATS 201

Hawkes process as a model of social interactions: a view on video dynamics

We study by computer simulation the "Hawkes process" that was proposed in a recent paper by Crane and Sornette (Proc. Nat. Acad. Sci. USA 105, 15649 (2008)) as a plausible model for the dynamics of YouTube video viewing numbers. We test the claims made there that robust identification is possible for classes of dynamic response following activity bursts. Our simulated timeseries for the Hawkes process indeed fall into the different categories predicted by Crane and Sornette. However the Hawkes process gives a much narrower spread of decay exponents than the YouTube data, suggesting limits to the universality of the Hawkes-based analysis.Comment: Added errors to parameter estimates and further description. IOP style, 13 pages, 5 figure

Power-law distributions in empirical data

Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.Comment: 43 pages, 11 figures, 7 tables, 4 appendices; code available at http://www.santafe.edu/~aaronc/powerlaws

Multistep, sequential control of the trafficking and function of the multiple sulfatase deficiency gene product, SUMF1 by PDI, ERGIC-53 and ERp44.

Sulfatase modifying factor 1 (SUMF1) encodes for the formylglicine generating enzyme, which activates sulfatases by modifying a key cysteine residue within their catalytic domains. SUMF1 is mutated in patients affected by multiple sulfatase deficiency, a rare recessive disorder in which all sulfatase activities are impaired. Despite the absence of canonical retention/retrieval signals, SUMF1 is largely retained in the endoplasmic reticulum (ER), where it exerts its enzymatic activity on nascent sulfatases. Part of SUMF1 is secreted and paracrinally taken up by distant cells. Here we show that SUMF1 interacts with protein disulfide isomerase (PDI) and ERp44, two thioredoxin family members residing in the early secretory pathway, and with ERGIC-53, a lectin that shuttles between the ER and the Golgi. Functional assays reveal that these interactions are crucial for controlling SUMF1 traffic and function. PDI couples SUMF1 retention and activation in the ER. ERGIC-53 and ERp44 act downstream, favoring SUMF1 export from and retrieval to the ER, respectively. Silencing ERGIC-53 causes proteasomal degradation of SUMF1, while down-regulating ERp44 promotes its secretion. When over-expressed, each of three interactors favors intracellular accumulation. Our results reveal a multistep control of SUMF1 trafficking, with sequential interactions dynamically determining ER localization, activity and secretion

PROCEE: a PROstate Cancer Evaluation and Education serious game for African Caribbean men

Purpose – Prostate cancer is the most common cancer diagnosed in men in the UK. Black men are in a higher prostate cancer risk group possibly due to inherent genetic factors. The purpose of this paper is to introduce PROstate Cancer Evaluation and Education (PROCEE), an innovative serious game aimed at providing prostate cancer information and risk evaluation to black African-Caribbean men. Design/methodology/approach – PROCEE has been carefully co-designed with prostate cancer experts, prostate cancer patients and members of the black African-Caribbean community in order to ensure that it meets the real needs and expectations of the target audience. Findings – During the co-design process, the users defined an easy to use and entertaining game which can effectively raise awareness, inform users about prostate cancer and their risk, and encourage symptomatic men to seek medical attention in a timely manner. Originality/value – During focus group evaluations, users embraced the game and emphasised that it can potentially have a positive impact on changing user behaviour among high risk men who are experiencing symptoms and who are reluctant to visit their doctor

Characterization of T-bet and eomes in peripheral human immune cells.

The T-box transcription factors T-bet and Eomesodermin (Eomes) have been well defined as key drivers of immune cell development and cytolytic function. While the majority of studies have defined the roles of these factors in the context of murine T-cells, recent results have revealed that T-bet, and possibly Eomes, are expressed in other immune cell subsets. To date, the expression patterns of these factors in subsets of human peripheral blood mononuclear cells beyond T-cells remain relatively uncharacterized. In this study, we used multiparametric flow cytometry to characterize T-bet and Eomes expression in major human blood cell subsets, including total CD4(+) and CD8(+) T-cells, γδ T-cells, invariant NKT cells, natural killer cells, B-cells, and dendritic cells. Our studies identified novel cell subsets that express T-bet and Eomes and raise implications for their possible functions in the context of other human immune cell subsets besides their well-known roles in T-cells. The corrigendum regards data and text for the final figure of the manuscript, Figure 7: Subsequent analysis of T-bet levels in human lymphocytes comparing different permeabilization procedures (eBioscience FoxP3 transcription factor kit, BD Pharmingen Cytofix/Cytoperm) has revealed variable findings in the level of T-bet expression detected within certain lymphocyte populations. While this does not change our conclusions for the majority of the populations assessed in this study, B cells in particular show differences under these conditions. Specifically, permeabilization via the eBioscience FoxP3 transcription factor staining buffer set indicates that subpopulations of memory B cells express significantly higher levels of T-bet (MFI) compared to plasmablasts, and that plasmablasts express T-bet only at low levels. Subsequent RNA transcript analysis confirms that plasmablasts express T-bet RNA at a level comparable to naïve B cells. Together, in combination with fluorescence-minus-one and isotype control studies, these new findings suggest that subsets memory B cells, not plasmablasts, express the highest levels of T-bet in the B cell compartment and plasmablasts express T-bet at a lower frequency than is reported in Figure 7. Figure 7 Legend should read: (C) Histograms depicting T-bet expression levels in B-cells and NK cells from a representative donor. Histograms represent the following subsets: naïve B-cells (thick black line), memory B-cells (shaded gray), plasmablasts (thin black line), CD56bright NK cells (gray line), and CD56dim NK cells (shaded black). B-cell results section should be titled T-bet is predominantly expressed in mature memory B-cells and should read: While Eomes was undetectable in B-cells (data not shown), we found T-bet in ~10% of B-cells (Figure 7B). This T-bet expression was largely relegated to memory B-cells, with significantly lower amounts observed in transitional/immature B-cells, naïve B-cells, and plasmablasts (Figure 7B). Greater than 15% of memory B-cells expressed T-bet, a significantly higher frequency than that of all other B-cell populations, suggesting that T-bet may play a particularly important role in memory B-cell function. The discussion related to T-bet expression in plasmablasts should be reconsidered as follows: We found that T-bet is not significantly expressed in transitional/immature B-cells, naïve B-cells, and plasmablasts, but is highly expressed in subsets of memory-B cells. Reduced frequencies of T-bet expression in plasmablasts indicate a specific role for T-bet at the memory B-cell stage of development, which may no longer be necessary after further differentiation to the plasmablast stage. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest

Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems

Most current methods for identifying coherent structures in spatially-extended systems rely on prior information about the form which those structures take. Here we present two new approaches to automatically filter the changing configurations of spatial dynamical systems and extract coherent structures. One, local sensitivity filtering, is a modification of the local Lyapunov exponent approach suitable to cellular automata and other discrete spatial systems. The other, local statistical complexity filtering, calculates the amount of information needed for optimal prediction of the system's behavior in the vicinity of a given point. By examining the changing spatiotemporal distributions of these quantities, we can find the coherent structures in a variety of pattern-forming cellular automata, without needing to guess or postulate the form of that structure. We apply both filters to elementary and cyclical cellular automata (ECA and CCA) and find that they readily identify particles, domains and other more complicated structures. We compare the results from ECA with earlier ones based upon the theory of formal languages, and the results from CCA with a more traditional approach based on an order parameter and free energy. While sensitivity and statistical complexity are equally adept at uncovering structure, they are based on different system properties (dynamical and probabilistic, respectively), and provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv requirements; write first author for higher-resolution version

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on their behavior or other measurable responses. We show that, generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular we demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects, and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these results.Comment: 27 pages, 9 figures. V2: Revised in response to referees. V3: Ditt