scholarly journals Conditionally positive functions andp-norm distance matrices

1991 ◽  
Vol 7 (1) ◽  
pp. 427-440 ◽  
Author(s):  
B. J. C. Baxter
Author(s):  
Asfree Gwanyanya ◽  
Christie Nicole Godsmark ◽  
Roisin Kelly-Laubscher

Abstract: Ethanolamine is a bioactive molecule found in several cells, including those in the central nervous system (CNS). In the brain, ethanolamine and ethanolamine-related molecules have emerged as prodrug moieties that can promote drug movement across the blood-brain barrier. This improvement in the ability to target drugs to the brain may also mean that in the process ethanolamine concentrations in the brain are increased enough for ethanolamine to exert its own neurological ac-tions. Ethanolamine and its associated products have various positive functions ranging from cell signaling to molecular storage, and alterations in their levels have been linked to neurodegenerative conditions such as Alzheimer’s disease. This mini-review focuses on the effects of ethanolamine in the CNS and highlights the possible implications of these effects for drug design.


2011 ◽  
Vol 435 (6) ◽  
pp. 1479-1489 ◽  
Author(s):  
R.B. Bapat ◽  
Sivaramakrishnan Sivasubramanian
Keyword(s):  

2010 ◽  
Vol 41 (2) ◽  
pp. 216-233 ◽  
Author(s):  
Willem H. J. Martens

AbstractThe positive functions of psychosis are examined. It is concluded that psychosis might have following positive and compensating functions: satisfaction of urgent needs that otherwise would remain unsatisfied; avoidance of and coping with unbearable reality, harmful influences and stress, and/or trauma; realization of urgent but otherwise unattainable goal settings; and upgrading of social-emotional and cognitive incapacities into more adequate social-emotional and cognitive awareness and functioning. The therapeutic implications of these findings are also discussed.


2001 ◽  
Vol 35 (2) ◽  
pp. 283-300 ◽  
Author(s):  
Alain Guénoche ◽  
Bruno Leclerc
Keyword(s):  

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
M Pope ◽  
P Kuklik ◽  
A Briosa E Gala ◽  
M Leo ◽  
J Paisey ◽  
...  

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Oxford Biomedical Research Centre Introduction Non-contact charge density mapping allows visualisation of whole chamber propagation during atrial fibrillation (AF). The identification of regions with repetitive or, conversely, more complex patterns of wavefront propagation may provide clues to mechanisms responsible for AF maintenance and lead to improved outcomes from catheter ablation. Our novel mapping approach based on signal recurrence plots has never been applied to whole chamber, bi-atrial recording of atrial fibrillation. Purpose To apply recurrence analysis to characterise whole chamber bi-atrial AF propagation. Methods Non-contact dipole signals from left and right atrial maps were obtained during simultaneous bi-atrial charge density mapping of AF. Signals were converted to phase and mean phase coherence calculated for the generation of recurrence distance matrices for the whole chamber and each anatomical region (6x LA and 4x RA) over the 30-second recording duration, where a value of 1 (purple, see figure panel A) represents uniform repetitive conduction, and 0 (red), irregular, non-repetitive activity. Whole chamber and regional mean recurrence values were calculated and correlated with the frequency of wavefronts of localised irregular activation patterns. Results Maps were obtained prior to ablation in 21 patients (5 paroxysmal (pAF), 16 persistent AF (persAF)) undergoing de-novo catheter ablation procedures. Whole chamber recurrence was higher in patients with pAF (0.40 ± 0.08) than persAF (0.34 ± 0.05), p < 0.0005. There was an inverse correlation between regional recurrence values and the number of localised irregular activations detected (-0.7021, p < 0.0005, figure panel B) with the lateral LA and anterior RA demonstrating the highest recurrence values in each chamber (figure panel C). Conclusion Use of recurrence distance matrices characterises global AF propagation phenotypes. Regional values are inversely correlated with the frequency of localised irregular activation patterns identified demonstrating an anatomic dependence in the level of AF propagation complexity, greatest in the anterior LA and septal RA. Comparison of strategies targeting regions with maximal vs. minimal values during catheter ablation may define an optimal approach to treatment of persistent AF. Abstract Figure. Recurrence abstract figure


Author(s):  
T. O. Petrova ◽  
I. P. Chulakov

We discuss whether on not it is possible to have interpolatory estimates in the approximation of a function $f є W^r [0,1]$ by polynomials. The problem of positive approximation is to estimate the pointwise degree of approximation of a function $f є C^r [0,1] \cap \Delta^0$ where $\Delta^0$ is the set of positive functions on [0,1]. Estimates of the form (1) for positive approximation are known ([1],[2]). The problem of monotone approximation is that of estimating the degree of approximation of a monotone nondecreasing function by monotone nondecreasing polynomials. Estimates of the form (1) for monotone approximation were proved in [3],[4],[8]. In [3],[4] is consider $r є , r > 2$. In [8] is consider $r є , r > 2$. It was proved that for monotone approximation estimates of the form (1) are fails for $r є , r > 2$. The problem of convex approximation is that of estimating the degree of approximation of a convex function by convex polynomials. The problem of convex approximation is that of estimating the degree of approximation of a convex function by convex polynomials. The problem of convex approximation is consider in ([5],[6]). In [5] is consider $r є , r > 2$. In [6] is consider $r є , r > 2$. It was proved that for convex approximation estimates of the form (1) are fails for $r є , r > 2$. In this paper the question of approximation of function $f є W^r \cap \Delta^1, r є (3,4)$ by algebraic polynomial $p_n є \Pi_n \cap \Delta^1$ is consider. The main result of the work generalize the result of work [8] for $r є (3,4)$.


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