اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA Brand-new Research Method of Neuroendocrine System
137
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper, we present the empirical investigation results on the neuroendocrine system by bipartite graphs. This neuroendocrine network model can describe the structural characteristic of neuroendocrine system. The act degree distribution and cumulate act degree distribution show so-called shifted power law-SPL function forms. In neuroendocrine network, the act degree stands for the number of the cells that secretes a single mediator, in which bFGF(basic fibroblast growth factor) is the largest node act degree. It is an important mitogenic cytokine, followed by TGF-beta, IL-6, IL1-beta, VEGF, IGF-1and so on. They are critical in neuroendocrine system to maintain bodily healthiness, emotional stabilization and endocrine harmony. The average act degree of neuroendocrine network is h = 3.01, It means each mediator is secreted by three cells on an average . The similarity that stand for the average probability of secreting the same mediators by all the neuroendocrine cells is s = 0.14. Our results may be used in the research of the medical treatment of neuroendocrine diseases.
قيم البحث
اقرأ أيضاً
The field of exoplanets is quickly expanding from just the detection of new planets and the measurement of their most basic parameters, such as mass, radius and orbital configuration, to the first measurements of their atmospheric characteristics, su
ch as temperature, chemical composition, albedo, dynamics and structure. Here I will overview some of the main findings on exoplanet atmospheres until September 2010, first from space and just in the past two years also from the ground.
The controllability of synchronization is an intriguing question in complex systems, in which hiearchically-organized heterogeneous elements have asymmetric and activity-dependent couplings. In this study, we introduce a simple and effective way to c
ontrol synchronization in such a complex system by changing the complexity of subsystems. We consider three Stuart-Landau oscillators as a minimal subsystem for generating various complexity, and hiearchically connect the subsystems through a mean field of their activities. Depending on the coupling signs between three oscillators, subsystems can generate ample dynamics, in which the number of attractors specify their complexity. The degree of synchronization between subsystems is then controllable by changing the complexity of subsystems. This controllable synchronization can be applied to understand the synchronization behavior of complex biological networks.
Many neuronal systems and models display a certain class of mixed mode oscillations (MMOs) consisting of periods of small amplitude oscillations interspersed with spikes. Various models with different underlying mechanisms have been proposed to generate this type of behavior. Stochast
We calculate a measure of statistical complexity from the global dynamics of electroencephalographic (EEG) signals from healthy subjects and epileptic patients, and are able to stablish a criterion to characterize the collective behavior in both grou
ps of individuals. It is found that the collective dynamics of EEG signals possess relative higher values of complexity for healthy subjects in comparison to that for epileptic patients. To interpret these results, we propose a model of a network of coupled chaotic maps where we calculate the complexity as a function of a parameter and relate this measure with the emergence of nontrivial collective behavior in the system. Our results show that the presence of nontrivial collective behavior is associated to high values of complexity; thus suggesting that similar dynamical collective process may take place in the human brain. Our findings also suggest that epilepsy is a degenerative illness related to the loss of complexity in the brain.
Immune system is the most important defense system to resist human pathogens. In this paper we present an immune model with bipartite graphs theory. We collect data through COPE database and construct an immune cell- mediators network. The act degree
distribution of this network is proved to be power-law, with index of 1.8. From our analysis, we found that some mediators with high degree are very important mediators in the process of regulating immune activity, such as TNF-alpha, IL-8, TNF-alpha receptors, CCL5, IL-6, IL-2 receptors, TNF-beta receptors, TNF-beta, IL-4 receptors, IL-1 beta, CD54 and so on. These mediators are important in immune system to regulate their activity. We also found that the assortative of the immune system is -0.27. It reveals that our immune system is non-social network. Finally we found similarity of the network is 0.13. Each two cells are similar to small extent. It reveals that many cells have its unique features. The results show that this model could describe the immune system comprehensive.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
