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Network scaling reveals consistent fractal pattern in hierarchical mammalian societies.

ABSTRACT: Recent studies have demonstrated that human societies are hierarchically structured with a consistent scaling ratio across successive layers of the social network; each layer of the network is between three and four times the size of the preceding (smaller) grouping level. Here we show that similar relationships hold for four mammalian taxa living in multi-level social systems. For elephant (Loxodonta africana), gelada (Theropithecus gelada) and hamadryas (Papio hamadryas hamadryas) baboon, successive layers of social organization have a scaling ratio of almost exactly 3, indicating that such branching ratios may be a consistent feature of all hierarchically structured societies. Interestingly, the scaling ratio for orca (Orcinus orca) was 3.8, which might mean that aquatic environments place different constraints on the organization of social hierarchies. However, circumstantial evidence from a range of other species suggests that scaling ratios close to 3 may apply widely, even in species where hierarchical social structures have not traditionally been identified. These results identify the origin of the hierarchical, fractal-like organization of mammalian social systems as a fundamental question.

SUBMITTER: Hill RA

PROVIDER: S-EPMC2614163 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Network scaling reveals consistent fractal pattern in hierarchical mammalian societies.

Hill Russell A RA Bentley R Alexander RA Dunbar Robin I M RI

Biology letters 20081201 6

Recent studies have demonstrated that human societies are hierarchically structured with a consistent scaling ratio across successive layers of the social network; each layer of the network is between three and four times the size of the preceding (smaller) grouping level. Here we show that similar relationships hold for four mammalian taxa living in multi-level social systems. For elephant (Loxodonta africana), gelada (Theropithecus gelada) and hamadryas (Papio hamadryas hamadryas) baboon, succ ...[more]

PMID: 18765349

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Project description:BackgroundGenetic regulatory networks of sequence specific transcription factors underlie pattern formation in multicellular organisms. Deciphering and representing the mammalian networks is a central problem in development, neurobiology, and regenerative medicine. Transcriptional networks specify intermingled embryonic cell populations during pattern formation in the vertebrate neural tube. Each embryonic population gives rise to a distinct type of adult neuron. The homeodomain transcription factor Lbx1 is expressed in five such populations and loss of Lbx1 leads to distinct respecifications in each of the five populations.Methodology/principal findingsWe have purified normal and respecified pools of these five populations from embryos bearing one or two copies of the null Lbx1(GFP) allele, respectively. Microarrays were used to show that expression levels of 8% of all transcription factor genes were altered in the respecified pool. These transcription factor genes constitute 20-30% of the active nodes of the transcriptional network that governs neural tube patterning. Half of the 141 regulated nodes were located in the top 150 clusters of ultraconserved non-coding regions. Generally, Lbx1 repressed genes that have expression patterns outside of the Lbx1-expressing domain and activated genes that have expression patterns inside the Lbx1-expressing domain.Conclusions/significanceConstraining epistasis analysis of Lbx1 to only those cells that normally express Lbx1 allowed unprecedented sensitivity in identifying Lbx1 network interactions and allowed the interactions to be assigned to a specific set of cell populations. We call this method ANCEA, or active node constrained epistasis analysis, and think that it will be generally useful in discovering and assigning network interactions to specific populations. We discuss how ANCEA, coupled with population partitioning analysis, can greatly facilitate the systematic dissection of transcriptional networks that underlie mammalian patterning.

Project description:BackgroundVoice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.MethodsThis paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.ResultsOn a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8 +/- 2.0%. The true positive classification performance is 95.4 +/- 3.2%, and the true negative performance is 91.5 +/- 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.ConclusionGiven the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.

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Network scaling reveals consistent fractal pattern in hierarchical mammalian societies.

Publications

Network scaling reveals consistent fractal pattern in hierarchical mammalian societies.

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