Agents, Bookmarks and Clicks: A topical model of Web traffic

Analysis of aggregate and individual Web traffic has shown that PageRank is a poor model of how people navigate the Web. Using the empirical traffic patterns generated by a thousand users, we characterize several properties of Web traffic that cannot be reproduced by Markovian models. We examine both aggregate statistics capturing collective behavior, such as page and link traffic, and individual statistics, such as entropy and session size. No model currently explains all of these empirical observations simultaneously. We show that all of these traffic patterns can be explained by an agent-based model that takes into account several realistic browsing behaviors. First, agents maintain individual lists of bookmarks (a non-Markovian memory mechanism) that are used as teleportation targets. Second, agents can retreat along visited links, a branching mechanism that also allows us to reproduce behaviors such as the use of a back button and tabbed browsing. Finally, agents are sustained by visiting novel pages of topical interest, with adjacent pages being more topically related to each other than distant ones. This modulates the probability that an agent continues to browse or starts a new session, allowing us to recreate heterogeneous session lengths. The resulting model is capable of reproducing the collective and individual behaviors we observe in the empirical data, reconciling the narrowly focused browsing patterns of individual users with the extreme heterogeneity of aggregate traffic measurements. This result allows us to identify a few salient features that are necessary and sufficient to interpret the browsing patterns observed in our data. In addition to the descriptive and explanatory power of such a model, our results may lead the way to more sophisticated, realistic, and effective ranking and crawling algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in Proceedings of the 21th ACM conference on Hypertext and Hypermedi

Emerging targets in human lymphoma: targeting the MYD88 mutation

B cell neoplasms co-opt the molecular machinery of normal B cells for their survival. Technological advances in cancer genomics has significantly contributed to uncovering the root cause of aggressive lymphomas, revealing a previously unknown link between TLR signaling and B cell neoplasm. Recurrent oncogenic mutations in MYD88 have been found in 39% of the activated B cell-like subtype of diffuse large B cell lymphoma (ABC DLBCL). Interestingly, 29% of ABC DLBCL have a single amino acid substitution of proline for the leucine at position 265 (L265P), and the exact same variant has also been identified in a number of lymphoid malignancies. The MYD88 L265P variant was recently identified in 90% of Wadenstrom's macroglobulinemia patients. These recent developments warrant the need for novel diagnostic tools as well as targeted therapeutics. In this review, we discuss the physiological functions of MYD88 and focus on its role in B cell lymphomas, evaluating the potential for targeting oncogenic MYD88 in lymphoma

Is now the time for molecular driven therapy for diffuse large B-cell lymphoma?

INTRODUCTION: Recent genetic and molecular discoveries regarding alterations in diffuse large B-cell lymphoma (DLBCL) deeply changed the approach to this lymphoproliferative disorder. Novel additional predictors of outcomes and new therapeutic strategies are being introduced to improve outcomes. Areas covered: This review aims to analyse the recent molecular discoveries in DLBCL, the rationale of novel molecular driven treatments and their impact on DLBCL prognosis, especially in ABC-DLBCL and High Grade B Cell Lymphoma. Pre-clinical and clinical evidences are reviewed to critically evaluate the novel DLBCL management strategies. Expert commentary: New insights in DLBCL molecular characteristics should guide the therapeutic approach; the results of the current studies which are investigating safety and efficacy of novel 'X-RCHOP' will probably lead, in future, to a cell of origin (COO) based upfront therapy. Moreover, it is necessary to identify early patients with DLBCL who carried MYC, BCL2 and/or BCL6 rearrangements double hit lymphomas (DHL) because they should not receive standard R-CHOP but high intensity treatment as reported in many retrospective studies. New prospective trials are needed to investigate the more appropriate treatment of DHL

A Phase I/II first-line study of R-CHOP plus B-cell receptor/NF-κB-double-targeting to molecularly assess therapy response

The ImbruVeRCHOP trial is an investigator-initiated, multicenter, single-arm, open label Phase I/II study for patients 61-80 years of age with newly diagnosed CD20+ diffuse large B-cell lymphoma and a higher risk profile (International Prognostic Index ≥2). Patients receive standard chemotherapy (CHOP) plus immunotherapy (Rituximab), a biological agent (the proteasome inhibitor Bortezomib) and a signaling inhibitor (the Bruton's Tyrosine Kinase-targeting therapeutic Ibrutinib). Using an all-comers approach, but subjecting patients to another lymphoma biopsy acutely under first-cycle immune-chemo drug exposure, ImbruVeRCHOP seeks to identify an unbiased molecular responder signature that marks diffuse large B-cell lymphoma patients at risk and likely to benefit from this regimen as a double, proximal and distal B-cell receptor/NF-κB-co-targeting extension of the current R-CHOP standard of care. EudraCT-Number: 2015-003429-32; ClinicalTrials.gov identifier: NCT03129828

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation, and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.Comment: Submitted to 'Systems Medicine' as a book chapte