4,790,623 research outputs found
The Modern Approach To The Complex Therapy Of Demodicosis
Demodicosis is one of the most common diseases of the skin. Despite the large number of scientific publications on this issue the question of the etiology and pathogenesis of this disease still remains unresolved and the development of more effective methods of treatment and prevention has not only medical but also social significance as patients preserving the working ability have actually long enough to be on outpatient and even inpatient treatment. In addition the long existence of the rash on the face that is the "business card" leads to the secondary sometimes severe neurotic disorders that results in reducing abilities, isolation, unwillingness to be in a team, family, etc.We investigated the effect of complex therapy which had been conducted by means of antiparasitic, immunomodulatory, anti-inflammatory and desensitizing drugs in patients with demodicosis, as well as mandatory adjustment of changes in the hepato- panŃreato-duodenal system. We examined 109 patients with demodicosis from 18 to 39 years old and 10 healthy individuals of the corresponding age and sex. It was established that the effectiveness of treatment of demodicosis based on the application of albendazole in the complex treatment along with immune-modulating therapy and hepatoprotection increased significantly. There has been a reliable rapid regression of clinical symptoms in most patients with demodicosis
A Generalized Approach to Complex Networks
This work describes how the formalization of complex network concepts in
terms of discrete mathematics, especially mathematical morphology, allows a
series of generalizations and important results ranging from new measurements
of the network topology to new network growth models. First, the concepts of
node degree and clustering coefficient are extended in order to characterize
not only specific nodes, but any generic subnetwork. Second, the consideration
of distance transform and rings are used to further extend those concepts in
order to obtain a signature, instead of a single scalar measurement, ranging
from the single node to whole graph scales. The enhanced discriminative
potential of such extended measurements is illustrated with respect to the
identification of correspondence between nodes in two complex networks, namely
a protein-protein interaction network and a perturbed version of it. The use of
other measurements derived from mathematical morphology are also suggested as a
means to characterize complex networks connectivity in a more comprehensive
fashion.Comment: 10 pages, 2 figur
A complex network approach to stylometry
Statistical methods have been widely employed to study the fundamental
properties of language. In recent years, methods from complex and dynamical
systems proved useful to create several language models. Despite the large
amount of studies devoted to represent texts with physical models, only a
limited number of studies have shown how the properties of the underlying
physical systems can be employed to improve the performance of natural language
processing tasks. In this paper, I address this problem by devising complex
networks methods that are able to improve the performance of current
statistical methods. Using a fuzzy classification strategy, I show that the
topological properties extracted from texts complement the traditional textual
description. In several cases, the performance obtained with hybrid approaches
outperformed the results obtained when only traditional or networked methods
were used. Because the proposed model is generic, the framework devised here
could be straightforwardly used to study similar textual applications where the
topology plays a pivotal role in the description of the interacting agents.Comment: PLoS ONE, 2015 (to appear
Stability threshold approach for complex dynamical systems
Acknowledgments This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP, and supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS). The first author thanks Dr Roman Ovsyannikov for valuable discussions regarding estimation of the mistake probability.Peer reviewedPreprintPublisher PD
A Complex Network Approach to Topographical Connections
The neuronal networks in the mammals cortex are characterized by the
coexistence of hierarchy, modularity, short and long range interactions,
spatial correlations, and topographical connections. Particularly interesting,
the latter type of organization implies special demands on the evolutionary and
ontogenetic systems in order to achieve precise maps preserving spatial
adjacencies, even at the expense of isometry. Although object of intensive
biological research, the elucidation of the main anatomic-functional purposes
of the ubiquitous topographical connections in the mammals brain remains an
elusive issue. The present work reports on how recent results from complex
network formalism can be used to quantify and model the effect of topographical
connections between neuronal cells over a number of relevant network properties
such as connectivity, adjacency, and information broadcasting. While the
topographical mapping between two cortical modules are achieved by connecting
nearest cells from each module, three kinds of network models are adopted for
implementing intracortical connections (ICC), including random,
preferential-attachment, and short-range networks. It is shown that, though
spatially uniform and simple, topographical connections between modules can
lead to major changes in the network properties, fostering more effective
intercommunication between the involved neuronal cells and modules. The
possible implications of such effects on cortical operation are discussed.Comment: 5 pages, 5 figure
A molecular approach to complex adaptive systems
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are
ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Hollandās broadcast language. In the MCS.b, no explicit fitness function or rule discovery mechanism is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS: Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this paper we present a series of experiments
focusing on the self-replication ability of these CAS. Results indicate counter intuitive outcomes as opposed to those inferred from the literature. This work highlights the current deficit of a theoretical framework for the study of Artificial Chemistries
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