546,687 research outputs found
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
A compendium of Caenorhabditis elegans regulatory transcription factors: a resource for mapping transcription regulatory networks
Background
Transcription regulatory networks are composed of interactions between transcription factors and their target genes. Whereas unicellular networks have been studied extensively, metazoan transcription regulatory networks remain largely unexplored. Caenorhabditis elegans provides a powerful model to study such metazoan networks because its genome is completely sequenced and many functional genomic tools are available. While C. elegans gene predictions have undergone continuous refinement, this is not true for the annotation of functional transcription factors. The comprehensive identification of transcription factors is essential for the systematic mapping of transcription regulatory networks because it enables the creation of physical transcription factor resources that can be used in assays to map interactions between transcription factors and their target genes.
Results
By computational searches and extensive manual curation, we have identified a compendium of 934 transcription factor genes (referred to as wTF2.0). We find that manual curation drastically reduces the number of both false positive and false negative transcription factor predictions. We discuss how transcription factor splice variants and dimer formation may affect the total number of functional transcription factors. In contrast to mouse transcription factor genes, we find that C. elegans transcription factor genes do not undergo significantly more splicing than other genes. This difference may contribute to differences in organism complexity. We identify candidate redundant worm transcription factor genes and orthologous worm and human transcription factor pairs. Finally, we discuss how wTF2.0 can be used together with physical transcription factor clone resources to facilitate the systematic mapping of C. elegans transcription regulatory networks.
Conclusion
wTF2.0 provides a starting point to decipher the transcription regulatory networks that control metazoan development and function
Mapping road network communities for guiding disease surveillance and control strategies
Human mobility is increasing in its volume, speed and reach, leading to the
movement and introduction of pathogens through infected travelers. An
understanding of how areas are connected, the strength of these connections and
how this translates into disease spread is valuable for planning surveillance
and designing control and elimination strategies. While analyses have been
undertaken to identify and map connectivity in global air, shipping and
migration networks, such analyses have yet to be undertaken on the road
networks that carry the vast majority of travellers in low and middle income
settings. Here we present methods for identifying road connectivity
communities, as well as mapping bridge areas between communities and key
linkage routes. We apply these to Africa, and show how many highly-connected
communities straddle national borders and when integrating malaria prevalence
and population data as an example, the communities change, highlighting regions
most strongly connected to areas of high burden. The approaches and results
presented provide a flexible tool for supporting the design of disease
surveillance and control strategies through mapping areas of high connectivity
that form coherent units of intervention and key link routes between
communities for targeting surveillance.Comment: 11 pages, 5 figures, research pape
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