126,362 research outputs found
Evolutionary Dynamics and Optimization: Neutral Networks as Model-Landscapes for RNA Secondary-Structure Folding-Landscapes
We view the folding of RNA-sequences as a map that assigns a pattern of base
pairings to each sequence, known as secondary structure. These preimages can be
constructed as random graphs (i.e. the neutral networks associated to the
structure ). By interpreting the secondary structure as biological
information we can formulate the so called Error Threshold of Shapes as an
extension of Eigen's et al. concept of an error threshold in the single peak
landscape. Analogue to the approach of Derrida & Peliti for a of the population
on the neutral network. On the one hand this model of a single shape landscape
allows the derivation of analytical results, on the other hand the concept
gives rise to study various scenarios by means of simulations, e.g. the
interaction of two different networks. It turns out that the intersection of
two sets of compatible sequences (with respect to the pair of secondary
structures) plays a key role in the search for ''fitter'' secondary structures.Comment: 20 pages, uuencoded compressed postscript-file, Proc. of ECAL '95
conference, to appear., email: chris @ imb-jena.d
Chance and Necessity in Evolution: Lessons from RNA
The relationship between sequences and secondary structures or shapes in RNA
exhibits robust statistical properties summarized by three notions: (1) the
notion of a typical shape (that among all sequences of fixed length certain
shapes are realized much more frequently than others), (2) the notion of shape
space covering (that all typical shapes are realized in a small neighborhood of
any random sequence), and (3) the notion of a neutral network (that sequences
folding into the same typical shape form networks that percolate through
sequence space). Neutral networks loosen the requirements on the mutation rate
for selection to remain effective. The original (genotypic) error threshold has
to be reformulated in terms of a phenotypic error threshold. With regard to
adaptation, neutrality has two seemingly contradictory effects: It acts as a
buffer against mutations ensuring that a phenotype is preserved. Yet it is
deeply enabling, because it permits evolutionary change to occur by allowing
the sequence context to vary silently until a single point mutation can become
phenotypically consequential. Neutrality also influences predictability of
adaptive trajectories in seemingly contradictory ways. On the one hand it
increases the uncertainty of their genotypic trace. At the same time neutrality
structures the access from one shape to another, thereby inducing a topology
among RNA shapes which permits a distinction between continuous and
discontinuous shape transformations. To the extent that adaptive trajectories
must undergo such transformations, their phenotypic trace becomes more
predictable.Comment: 37 pages, 14 figures; 1998 CNLS conference; high quality figures at
http://www.santafe.edu/~walte
A mechanistic model of connector hubs, modularity, and cognition
The human brain network is modular--comprised of communities of tightly
interconnected nodes. This network contains local hubs, which have many
connections within their own communities, and connector hubs, which have
connections diversely distributed across communities. A mechanistic
understanding of these hubs and how they support cognition has not been
demonstrated. Here, we leveraged individual differences in hub connectivity and
cognition. We show that a model of hub connectivity accurately predicts the
cognitive performance of 476 individuals in four distinct tasks. Moreover,
there is a general optimal network structure for cognitive
performance--individuals with diversely connected hubs and consequent modular
brain networks exhibit increased cognitive performance, regardless of the task.
Critically, we find evidence consistent with a mechanistic model in which
connector hubs tune the connectivity of their neighbors to be more modular
while allowing for task appropriate information integration across communities,
which increases global modularity and cognitive performance
Time-Efficient Hybrid Approach for Facial Expression Recognition
Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database
Neutral Evolution of Mutational Robustness
We introduce and analyze a general model of a population evolving over a
network of selectively neutral genotypes. We show that the population's limit
distribution on the neutral network is solely determined by the network
topology and given by the principal eigenvector of the network's adjacency
matrix. Moreover, the average number of neutral mutant neighbors per individual
is given by the matrix spectral radius. This quantifies the extent to which
populations evolve mutational robustness: the insensitivity of the phenotype to
mutations. Since the average neutrality is independent of evolutionary
parameters---such as, mutation rate, population size, and selective
advantage---one can infer global statistics of neutral network topology using
simple population data available from {\it in vitro} or {\it in vivo}
evolution. Populations evolving on neutral networks of RNA secondary structures
show excellent agreement with our theoretical predictions.Comment: 7 pages, 3 figure
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