When is it Biased? Assessing the Representativeness of Twitter's Streaming API

Twitter has captured the interest of the scientific community not only for its massive user base and content, but also for its openness in sharing its data. Twitter shares a free 1% sample of its tweets through the "Streaming API", a service that returns a sample of tweets according to a set of parameters set by the researcher. Recently, research has pointed to evidence of bias in the data returned through the Streaming API, raising concern in the integrity of this data service for use in research scenarios. While these results are important, the methodologies proposed in previous work rely on the restrictive and expensive Firehose to find the bias in the Streaming API data. In this work we tackle the problem of finding sample bias without the need for "gold standard" Firehose data. Namely, we focus on finding time periods in the Streaming API data where the trend of a hashtag is significantly different from its trend in the true activity on Twitter. We propose a solution that focuses on using an open data source to find bias in the Streaming API. Finally, we assess the utility of the data source in sparse data situations and for users issuing the same query from different regions

Finding Eyewitness Tweets During Crises

Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time

Conformational perturbation, allosteric modulation of cellular signaling pathways, and disease in P23H rhodopsin

In this investigation we use THz spectroscopy and MD simulation to study the functional dynamics and conformational stability of P23H rhodopsin. The P23H mutation of rod opsin is the most common cause of human binding autosomal dominant retinitis pigmentosa (ADRP), but the precise mechanism by which this mutation leads to photoreceptor cell degeneration has not yet been elucidated. Our measurements confirm conformational instability in the global modes of the receptor and an activestate that uncouples the torsional dynamics of the retinal with protein functional modes, indicating inefficient signaling in P23H and a drastically altered mechanism of activation when contrasted with the wild-type receptor. Further, our MD simulations indicate that P23H rhodopsin is not functional as a monomer but rather, due to the instability of the mutant receptor, preferentially adopts a specific homodimerization motif. The preferred homodimer configuration induces structural changes in the receptor tertiary structure that reduces the affinity of the receptor for the retinal and significantly modifies the interactions of the Meta-II signaling state. We conjecture that the formation of the specific dimerization motif of P23H rhodopsin represents a cellular-wide signaling perturbation that is directly tied with the mechanism of P23H disease pathogenesis. Our results also support a direct role for rhodopsin P23H dimerization in photoreceptor rod death

Multiagentensimulation sozialer Phänomene: eine praktische Einführung

'Simulationen sind Nachbildungen von Abläufen in realen und meist komplexen Systemen. Multiagentensimulationen beschreiben das Verhalten der einzelnen Akteure dieser Systeme. In den Sozialwissenschaften finden Multiagentensimulationen zunehmend Verbreitung (Gilbert & Troitzsch 2005, Gilbert 2007, Miller et al. 2007). Dies ist vor allem durch die fortschreitenden Entwicklungen im Bereich der Homecomputer ermöglicht, da Multiagentensimulationen ohne moderne EDV-Infrastruktur undenkbar wäre. Dieser Artikel gibt im ersten Teil eine Einführung in die Theorie der Modellbildung und Simulation sowie einen Abschnitt zur Simulation als virtuelles Experiment. Der zweite Teil dient der praktischen Einführung in die Simulationsumgebung StarLogo. Simulationsumgebungen werden dabei als inhaltsneutrale, computerbasierte Tools verstanden, in denen komplexe, reale Systeme nachgebildet und simuliert werden können. Ziel des vorliegenden Artikels ist es, Sozialwissenschaftlerinnen und Sozialwissenschaftler ohne Erfahrungen aus dem Bereich der Programmierung in die Programmerstellung von Multiagentensimulationen einzuführen und zur Umsetzung von eigenen Projekten anzuregen und zu motivieren.' (Autorenreferat

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals

This Sample seems to be good enough! Assessing Coverage and Temporal Reliability of Twitter's Academic API

Because of its willingness to share data with academia and industry, Twitter has been the primary social media platform for scientific research as well as for the consulting of businesses and governments in the last decade. In recent years, a series of publications have studied and criticized Twitter's APIs and Twitter has partially adapted its existing data streams. The newest Twitter API for Academic Research allows to "access Twitter's real-time and historical public data with additional features and functionality that support collecting more precise, complete, and unbiased datasets." The main new feature of this API is the possibility of accessing the full archive of all historic Tweets. In this article, we will take a closer look at the Academic API and will try to answer two questions. First, are the datasets collected with the Academic API complete? Secondly, since Twitter's Academic API delivers historic Tweets as represented on Twitter at the time of data collection, we need to understand how much data is lost over time due to Tweet and account removal from the platform. Our work shows evidence that Twitter's Academic API can indeed create (almost) complete samples of Twitter data based on a wide variety of search terms. We also provide evidence that Twitter's data endpoint v2 delivers better samples than the previously used endpoint v1.1. Furthermore, collecting Tweets with the Academic API at the time of studying a phenomenon rather than creating local archives of stored Tweets, allows for a straightforward way of following Twitter's developer agreement. Finally, we will also discuss technical artifacts and implications of the Academic API. We hope that our work can add another layer of understanding of Twitter data collections leading to more reliable studies of human behavior via social media data

Turing instabilities in a mathematical model for signaling networks

GTPase molecules are important regulators in cells that continuously run through an activation/deactivation and membrane-attachment/membrane-detachment cycle. Activated GTPase is able to localize in parts of the membranes and to induce cell polarity. As feedback loops contribute to the GTPase cycle and as the coupling between membrane-bound and cytoplasmic processes introduces different diffusion coefficients a Turing mechanism is a natural candidate for this symmetry breaking. We formulate a mathematical model that couples a reaction-diffusion system in the inner volume to a reaction-diffusion system on the membrane via a flux condition and an attachment/detachment law at the membrane. We present a reduction to a simpler non-local reaction-diffusion model and perform a stability analysis and numerical simulations for this reduction. Our model in principle does support Turing instabilities but only if the lateral diffusion of inactivated GTPase is much faster than the diffusion of activated GTPase.Comment: 23 pages, 5 figures; The final publication is available at http://www.springerlink.com http://dx.doi.org/10.1007/s00285-011-0495-