'Enclaves of exposure' : a conceptual viewpoint to explore cross-ideology exposure on social network sites

Previous studies indicate mixed results as to whether social media constitutes ideological echo chambers. This inconsistency may arise due to a lack of theoretical frames that acknowledge the fact that contextual and technological factors allow varying levels of cross-cutting exposure on social media. This study suggests an alternative theoretical lens, divergence of exposure – co-existence of user groups with varying degrees of cross-ideology exposure related to the same issue – as a notion that serves as an overarching perspective. We suggest that mediated spaces, such as social media groups, can serve as enclaves of exposure that offer affordances for formation of user groups irrespective of offline social distinctions. Yet social elements cause some of them to display more cross-ideology exchange than others. To establish this claim empirically, we examine two Facebook page user networks (‘Sri Lanka’s Killing Fields’ and ‘Sri Lankans Hate Channel 4’) that emerged in response to Sri Lanka’s Killing Fields, a controversial documentary broadcast by Channel 4 that accused Sri Lankan armed forces of human rights violation during the final stage of the separatist conflict in Sri Lanka. The results showed that the Facebook group network that supported the claims made by Channel 4 is more diverse in terms of ethnic composition, and is neither assortative nor disassortative across ethnicity, suggesting the presence of cross-ethnicity interaction. The pro-allegiant group was largely homogenous and less active, resembling a passive echo chamber. ‘Social mediation’ repurposes enclaves of exposure to represent polarized ideologies where some venues display cross-ideology exposure, while others resemble an ‘echo chamber’

Representational practices in VMT.

This chapter analyzes the interaction of three students working on mathematics problems over several days in a virtual math team. Our analysis traces out how successful collaboration in a later session was contingent upon the work of prior sessions, and shows how representational practices are important aspects of these participants’ mathematical problem solving. We trace the formation, transformation and refinement of one problem-solving practice—problem decomposition—and three representational practices—inscribe first solve second, modulate perspective and visualize decomposition. The analysis is of theoretical interest because it suggests that “situated cognition” is contingent upon not only the immediate situation but also the chronologically prior resources and associated practices; shows how inscriptions become representations for the group through an interactive process of interpretation; and sheds light on “group cognition” as an interactional process that is not identical to individual cognition yet that draws upon a dynamic interplay of individual contributions

Characterizing Communication Networks Associated with Political Hashtags.

Among the diverse forms of communication and information networks found in the Web 2.0 environment, “social” and “informational” communication networks have been characterized in terms of their network metrics. Although Twitter is partly based on relationships between actors, activity has been shown to reflect characteristics of information networks. This study examines activity in Twitter within spaces defined by hashtags on political topics. We gathered our own data on a hashtag associated with the 2012 Hawaii senatorial race and compared our results to those from other political hashtag networks, and to typical social and information networks as well as random graphs. Results show that hashtag-centered reply and retweet networks in this domain do not fall clearly into the social or informational categories. There appears to be a third kind of network associated with political debate. More generally, it may be productive to conceive of communication networks in terms of multidimensional characteristics rather than categories

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

<p>Abstract</p> <p>Background</p> <p>Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).</p> <p>Description</p> <p>MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.</p> <p>Conclusions</p> <p>The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).</p

\u3ci\u3eZea mays i\u3c/i\u3eRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species