Manuel Gomez Rodriguez

Inferring networks of diffusion and influence

Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

Apoptosis in myocardial infarction

Apoptosis, one of the major forms of cell death, has been implicated in different cardiovascular diseases. In this paper we review many of the different studies that have been performed to address the occurrence of apoptotic cell death associated with myocardial infarction. A definitive differentiation between apoptosis and other forms of cell death is still needed, mainly because of differences and limitations of the methods used for detection. In myocardial infarction apoptosis has been reported at acute stages of evolution in the ischemic area as well as in remote zones. In the ischemic area it might be a determinant of the final size of the infarct and it seems to depend on the presence of post-ischemic reperfusion. However, the incidence of apoptosis reported until now varies widely. In the myocardium remote from the ischemic area it might be associated with the progression towards heart failure. At present, the role and significance of apoptosis in myocardial infarction is rather inconclusive. Further studies are needed to solve methodological uncertainties and clarify the mechanisms involved in the process of cell death, which is particularly important as a basis for therapeutic interventions.

Uncovering the structure and temporal dynamics of information propagation

Time plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion— when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for ongoing news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

Remodeling of the vascular tunica media is essential for development of collateral vessels in the canine heart.

Previous studies have shown that neointima formation and adventitial remodeling play an important role in the enlargement of collateral vessels (CVs) during coronary arteriogenesis in the dog heart. In this study, we investigated the importance of remodeling of the tunica media in the same model. Basal membrane (BM), contractile and cytoskeletal components of smooth muscle cells (SMCs) were studied in growth of coronary CVs induced by chronic occlusion of the left circumflex (LCX) coronary artery by routine histology, electron microscopy (EM), and immunoconfocal microscopy using antibodies against α-smooth actin (α-SM actin), calponin, desmin, and laminin. In addition, matrix metalloproteinase-2 (MMP-2) and tissue inhibitor-1 of matrix metalloproteinase (TIMP-1) were investigated. The data showed that (1) in normal small arteries (NVs) laminin formed a network in which SMCs were encaged;α-SM actin, calponin and desmin were evenly expressed in SMCs; (2) in early (2 weeks) growing CVs the laminin network was disrupted, desmin was significantly reduced in SMCs, but α-SM actin and calponin still highly expressed; (3) in actively (6 weeks) growing CVs laminin was still weak in the tunica media (TM), but without network-like structure. Desmin was further reduced in SMCs of TM, whereas α-SM actin and calponin showed little changes, although they were significantly decreased in intimal SMCs; (4) in mature CVs, the network-like structure was re-formed, and α-SM actin, calponin, and desmin were all similar to that in normal vessels; (5) histology for BM confirmed laminin staining; (6) EM revealed that in NVs the SMCs contained abundant contractile filaments and were surrounded by a layer of BM whereas in growing CVs, BM structure was not observed, but the SMCs in the media still contained many myofilaments; (7) MMP-2 was highly expressed in the media of early growing vessels, but decreased in TM of actively growing vessels where TIMP-1 expression was high. In conclusion, our data revealed features of TM of growing CVs. Disruption and degradation of BM facilitate SMC proliferation, and together with reduction of desmin and fragmentation of the internal elastic lamina enable the vascular wall to expand and enlarge when blood pressure and shear stress increase. MMP2 may be an important player in regulating SMC phenotype, proliferation, migration and maintaining integrity of the vascular wall through governing proteolysis during arteriogenesis. (Mol Cell Biochem 264: 201–210, 2004)

DEVELOPMENT OF A MICRO-COMPUTED TOMOGRAPHY-BASED IMAGE-GUIDED CONFORMAL RADIOTHERAPY SYSTEM FOR SMALL ANIMALS

PURPOSE To report on the physical aspects of a system in which radiotherapy functionality was added to a micro-computed tomography (microCT) scanner, to evaluate the accuracy of this instrument, and to and demonstrate the application of this technology for irradiating tumors growing within the lungs of mice. METHODS AND MATERIALS A GE eXplore RS120 microCT scanner was modified by the addition of a two-dimensional subject translation stage and a variable aperture collimator. Quality assurance protocols for these devices, including measurement of translation stage positioning accuracy, collimator aperture accuracy, and collimator alignment with the X-ray beam, were devised. Use of this system for image-guided radiotherapy was assessed by irradiation of a solid water phantom as well as of two mice bearing spontaneous MYC-induced lung tumors. Radiation damage was assessed ex vivo by immunohistochemical detection of gammaH2AX foci. RESULTS The positioning error of the translation stage was found to be <0.05 mm, whereas after alignment of the collimator with the X-ray axis through adjustment of its displacement and rotation, the collimator aperture error was <0.1 mm measured at isocenter. Computed tomography image-guided treatment of a solid water phantom demonstrated target localization accuracy to within 0.1 mm. Gamma-H2AX foci were detected within irradiated lung tumors in mice, with contralateral lung tissue displaying background staining. CONCLUSIONS Addition of radiotherapy functionality to a microCT scanner is an effective means of introducing image-guided radiation treatments into the preclinical setting. This approach has been shown to facilitate small-animal conformal radiotherapy while leveraging existing technology.

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.

Commissioning of a novel microCT/RT system for small animal conformal radiotherapy

The purpose of this work was to commission a 120 kVp photon beam produced by a micro-computed tomography (microCT) scanner for use in irradiating mice to therapeutic doses. A variable-aperture collimator has been integrated with a microCT scanner to allow the delivery of beams with pseudocircular profiles of arbitrary width between 0.1 and 6.0 cm. The dose rate at the isocenter of the system was measured using ion chamber and gafchromic EBT film as 1.56-2.13 Gy min(-1) at the water surface for field diameters between 0.2 and 6.0 cm. The dose rate decreases approximately 10% per every 5 mm depth in water for field diameters between 0.5 and 1.0 cm. The flatness, symmetry and penumbra of the beam are 3.6%, 1.0% and 0.5 mm, respectively. These parameters are sufficient to accurately conform the radiation dose delivered to target organs on mice. The irradiated field size is affected principally by the divergence of the beam. In general, the beam has appropriate dosimetric characteristics to accurately deliver the dose to organs inside the mices bodies. Using multiple beams delivered from a variety of angular directions, targets as small as 2 mm may be irradiated while sparing surrounding tissue. This microCT/RT system is a feasible tool to irradiate mice using treatment planning and delivery methods analogous to those applied to humans.

Bridging offline and online social graph dynamics

The online and offline worlds are converging. Location-based services, ubiquitous mobile devices and on-the-go social network accessibility are blurring the distinction between in-person activities and their virtual counterpart. An important effect of this convergence is the rapid and powerful impact of offline events (meetings, conferences) on the evolution and temporal dynamics of the online connectivity between members of social and professional networks. However, these effects have been largely unexplored. We study these effects by using data from LinkedIn, a popular professional social networking site. We find that offline events may induce connectivity changes in the online network -- there is a dramatic increase in the number of connections between event attendees shortly after the date of the event. Building on these insights, we describe a non-supervised method that exploits connectivity changes temporally correlated to real world events to successfully infer more than 40% of specific event attendees. Finally, we revisit the link prediction problem by including user contributed information about off-line events to achieve higher link prediction performance.

Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods

In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and granularity of large-scale diffusion data, which, in principle, allows for understanding and modeling not only macroscopic diffusion but also microscopic (node-level) diffusion. To this aim, a bottom-up approach has been typically considered, which starts by considering how particular ideas, pieces of information, products, or, more generally, contagions spread locally from node to node apparently at random to later produce global, macroscopic patterns at a network level.However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges which require leveraging methods from machine learning, probabilistic modeling, temporal point processes and graph theory, as well as the nascent field of network science. In this tutorial, we will present several diffusion models designed for fine-grained large-scale diffusion and social event data, present some canonical research problem in the context of diffusion, and introduce state-of-the-art algorithms to solve some of these problems, in particular, network estimation, influence estimation and control, and rumor source identification.

On the Causal Effect of Badges

A wide variety of online platforms use digital badges to encourage users to take certain types of desirable actions. However, despite their growing popularity, their causal effect on users» behavior is not well understood. This is partly due to the lack of counterfactual data and the myriad of complex factors that influence users» behavior over time. As a consequence, their design and deployment lacks general principles. In this paper, we focus on first-time badges, which are awarded after a user takes a particular type of action for the first time, and study their causal effect by harnessing the delayed introduction of several badges in a popular Q&A website. In doing so, we introduce a novel causal inference framework for first-time badges whose main technical innovations are a robust survival-based hypothesis testing procedure, which controls for the heterogeneity in the benefit users obtain from taking an action, and a bootstrap difference-in-differences method, which controls for the random fluctuations in users» behavior over time. Our results suggest that first-time badges steer users» behavior if the initial benefit a user obtains from taking the corresponding action is sufficiently low, otherwise, we do not find significant effects. Moreover, for badges that successfully steered user behavior, we perform a counterfactual analysis and show that they significantly improved the functioning of the site at a community level.