Speakers of English often understand ethnic and racial differences in terms of food imagery. It is quite common in this language to encounter metaphors presenting different groups of people in terms of beans, rice, bread, cheese, apples or chocolate. Given the cognitive and social force of metaphor in our understanding of the world and of ourselves as well as the important role language plays as a channel through which ideas and beliefs are transmitted and perpetuated, such food images may offer a window on the (de)construction of ethnic identi-ties and, ultimately, hide racist views against others who are different because of their skin color, physical features, languages and, obviously, diets.
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).
Quantitative and qualitative data on active-ingredient drug composition are essential information for characterizing near-field exposure of consumers to product-related chemicals, among other things. Equally as important is the characterization of the relationship between one or many active ingredients in terms of the diseases they are prescribed for. Such evaluations, however, require quantitative information at different anatomical levels. To complement the available sources of information on active substances and diseases, we have designed a database with enough versatility to potentially be used in a variety of analyzes. By using information provided by a well-established online pharmacological dictionary, we present a database with 11 tables which are easy to access and manipulate. Specifically, we present datasets containing the details of 12,827 marketed drug products, 40,164 diseases, 6231 active pharmaceutical ingredients and 4093 side effects. We exemplify the usefulness of our database with three simple visualizations, which confirm the importance of the data for quantifying the complexity in the associations among active substances, diseases and side effects. Although there are databases with detailed information on active substances and diseases, none of them can be found in Spanish. Our work presents an option that contributes substantially to obtaining well classified information in order to evaluate the roles of active pharmaceutical ingredients, diseases and side effects. These datasets also provide information about clinical and pharmacological groupings which may be useful for clinical and academic researchers. The database will be regularly updated and extended with the newly available Virtual Medicinal Products.
The study of natural language using a network approach has made it possible to characterize novel properties ranging from the level of individual words to phrases or sentences. A natural way to quantitatively evaluate similarities and differences between spoken and written language is by means of a multiplex network defined in terms of a similarity distance between words. Here, we use a multiplex representation of words based on orthographic or phonological similarity to evaluate their structure. We report that from the analysis of topological properties of networks, there are different levels of local and global similarity when comparing written vs. spoken structure across 12 natural languages from 4 language families. In particular, it is found that differences between the phonetic and written layers is markedly higher for French and English, while for the other languages analyzed, this separation is relatively smaller. We conclude that the multiplex approach allows us to explore additional properties of the interaction between spoken and written language.
The complexity of natural language can be explored by means of multiplex analyses at different scales, from single words to groups of words or sentence levels. Here, we plan to investigate a multiplex word-level network, which comprises an orthographic and a phonological network defined in terms of distance similarity. We systematically compare basic structural network properties to determine similarities and differences between them, as well as their combination in a multiplex configuration. As a natural extension of our work, we plan to evaluate the preservation of the structural network properties and information-based quantities from the following perspectives: (i) presence of similarities across 12 natural languages from 4 linguistic families (Romance, Germanic, Slavic and Uralic), (ii) increase of the size of the number of words (corpus) from 104 to 50 × 103, and (iii) robustness of the networks. Our preliminary findings reinforce the idea of common organizational properties among natural languages. Once concluded, will contribute to the characterization of similarities and differences in the orthographic and phonological perspectives of language networks at a word-level.
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