124,247 research outputs found
Model of Brain Activation Predicts the Neural Collective Influence Map of the Brain
Efficient complex systems have a modular structure, but modularity does not
guarantee robustness, because efficiency also requires an ingenious interplay
of the interacting modular components. The human brain is the elemental
paradigm of an efficient robust modular system interconnected as a network of
networks (NoN). Understanding the emergence of robustness in such modular
architectures from the interconnections of its parts is a long-standing
challenge that has concerned many scientists. Current models of dependencies in
NoN inspired by the power grid express interactions among modules with fragile
couplings that amplify even small shocks, thus preventing functionality.
Therefore, we introduce a model of NoN to shape the pattern of brain
activations to form a modular environment that is robust. The model predicts
the map of neural collective influencers (NCIs) in the brain, through the
optimization of the influence of the minimal set of essential nodes responsible
for broadcasting information to the whole-brain NoN. Our results suggest new
intervention protocols to control brain activity by targeting influential
neural nodes predicted by network theory.Comment: 18 pages, 5 figure
Initiating technical refinements in high-level golfers: Evidence for contradictory procedures
When developing motor skills there are several outcomes available to an athlete depending on their skill status and needs. Whereas the skill acquisition and performance literature is abundant, an under-researched outcome relates to the refinement of already acquired and well-established skills. Contrary to current recommendations for athletes to employ an external focus of attention and a representative practice design, Carson and Collins’ (2011) [Refining and regaining skills in fixation/diversification stage performers: The Five-A Model. International Review of Sport and Exercise Psychology, 4, 146–167. doi:10.1080/1750984x.2011.613682] Five-A Model requires an initial narrowed internal focus on the technical aspect needing refinement: the implication being that environments which limit external sources of information would be beneficial to achieving this task. Therefore, the purpose of this paper was to (1) provide a literature-based explanation for why techniques counter to current recommendations may be (temporarily) appropriate within the skill refinement process and (2) provide empirical evidence for such efficacy. Kinematic data and self-perception reports are provided from high level golfers attempting to consciously initiate technical refinements while executing shots onto a driving range and into a close proximity net (i.e. with limited knowledge of results). It was hypothesised that greater control over intended refinements would occur when environmental stimuli were reduced in the most unrepresentative practice condition (i.e. hitting into a net). Results confirmed this, as evidenced by reduced intra-individual movement variability for all participants’ individual refinements, despite little or no difference in mental effort reported. This research offers coaches guidance when working with performers who may find conscious recall difficult during the skill refinement process
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Learning to Discover Sparse Graphical Models
We consider structure discovery of undirected graphical models from
observational data. Inferring likely structures from few examples is a complex
task often requiring the formulation of priors and sophisticated inference
procedures. Popular methods rely on estimating a penalized maximum likelihood
of the precision matrix. However, in these approaches structure recovery is an
indirect consequence of the data-fit term, the penalty can be difficult to
adapt for domain-specific knowledge, and the inference is computationally
demanding. By contrast, it may be easier to generate training samples of data
that arise from graphs with the desired structure properties. We propose here
to leverage this latter source of information as training data to learn a
function, parametrized by a neural network that maps empirical covariance
matrices to estimated graph structures. Learning this function brings two
benefits: it implicitly models the desired structure or sparsity properties to
form suitable priors, and it can be tailored to the specific problem of edge
structure discovery, rather than maximizing data likelihood. Applying this
framework, we find our learnable graph-discovery method trained on synthetic
data generalizes well: identifying relevant edges in both synthetic and real
data, completely unknown at training time. We find that on genetics, brain
imaging, and simulation data we obtain performance generally superior to
analytical methods
Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs
Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks
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