A mathematical model of the evolution of free radical concentrations in a cell under ionizing radiation

2011 ◽  
Vol 66 (5) ◽  
pp. 489-491
Author(s):  
O. I. Vasilenko ◽  
G. V. Petrunkin
Keyword(s):  
Mathematical Model ◽  
Free Radical ◽  
Ionizing Radiation ◽  
A Cell

Radioelectrochemical conversion of the energy of ionizing radiation in a cell with a semiconducting electrode

1984 ◽  
Vol 57 (5) ◽  
pp. 800-801
Author(s):  
M. D. Krotova ◽  
A. A. Revina ◽  
Yu. V. Pleskov ◽  
A. M. Morozov ◽  
G. E. Zakharov ◽  
...  
Keyword(s):  
Ionizing Radiation ◽  
A Cell ◽  
Semiconducting Electrode

scholarly journals SETI, Evolution and Human History Merged into a Mathematical Model

2013 ◽  
Vol 12 (3) ◽  
pp. 218-245 ◽  
Author(s):  
Claudio Maccone
Keyword(s):  
Mathematical Model ◽  
Exponential Growth ◽  
Numerical Estimate ◽  
Mean Value ◽  
Darwinian Evolution ◽  
Human History ◽  
The Galaxy ◽  
Positive Time ◽  
A Cell ◽  
Geometric Locus

AbstractIn this paper we propose a new mathematical model capable of merging Darwinian Evolution, Human History and SETI into a single mathematical scheme:(1) Darwinian Evolution over the last 3.5 billion years is defined as one particular realization of a certain stochastic process called Geometric Brownian Motion (GBM). This GBM yields the fluctuations in time of the number of species living on Earth. Its mean value curve is an increasing exponential curve, i.e. the exponential growth of Evolution.(2) In 2008 this author provided the statistical generalization of the Drake equation yielding the number N of communicating ET civilizations in the Galaxy. N was shown to follow the lognormal probability distribution.(3) We call “b-lognormals” those lognormals starting at any positive time b (“birth”) larger than zero. Then the exponential growth curve becomes the geometric locus of the peaks of a one-parameter family of b-lognormals: this is our way to re-define Cladistics.(4) b-lognormals may be also be interpreted as the lifespan of any living being (a cell, or an animal, a plant, a human, or even the historic lifetime of any civilization). Applying this new mathematical apparatus to Human History, leads to the discovery of the exponential progress between Ancient Greece and the current USA as the envelope of all b-lognormals of Western Civilizations over a period of 2500 years.(5) We then invoke Shannon's Information Theory. The b-lognormals' entropy turns out to be the index of “development level” reached by each historic civilization. We thus get a numerical estimate of the entropy difference between any two civilizations, like the Aztec-Spaniard difference in 1519.(6) In conclusion, we have derived a mathematical scheme capable of estimating how much more advanced than Humans an Alien Civilization will be when the SETI scientists will detect the first hints about ETs.

Commentary on radiation-induced bystander effects

2004 ◽  
Vol 23 (2) ◽  
pp. 91-94 ◽  
Author(s):  
Eric G Wright
Keyword(s):  
Free Radical ◽  
Ionizing Radiation ◽  
Genomic Instability ◽  
Inflammatory Responses ◽  
Genetic Alterations ◽  
Bystander Effects ◽  
Intercellular Signalling ◽  
Radiation Induced ◽  
Radical Generation ◽  
In Cells

The paradigm of genetic alterations being restricted to direct DNA damage after exposure to ionizing radiation has been challenged by observations in which effects of ionizing radiation arise in cells that in themselves receive no radiation exposure. These effects are demonstrated in cells that are the descendants of irradiated cells (radiation-induced genomic instability) or in cells that are in contact with irradiated cells or receive certain signals from irradiated cells (radiation-induced bystander effects). Bystander signals may be transmitted either by direct intercellular communication through gap junctions, or by diffusible factors, such as cytokines released from irradiated cells. In both phenomena, the untargeted effects of ionizing radiation appear to be associated with free radical-mediated processes. There is evidence that radiation-induced genomic instability may be a consequence of, and in some cell systems may also produce, bystander interactions involving intercellular signalling, production of cytokines and free radical generation. These processes are also features of inflammatory responses that are known to have the potential for both bystander-mediated and persisting damage as well as for conferring a predisposition to malignancy. Thus, radiation-induced genomic instability and untargeted bystander effects may reflect interrelated aspects of inflammatory type responses to radiation-induced stress and injury and contribute to the variety of the pathological consequences of radiation exposures.

scholarly journals (In)validating experimentally derived knowledge about influenza A defective interfering particles

2016 ◽  
Vol 13 (124) ◽  
pp. 20160412 ◽  
Author(s):  
Laura E. Liao ◽  
Shingo Iwami ◽  
Catherine A. A. Beauchemin
Keyword(s):  
Mathematical Model ◽  
Experimental Method ◽  
Influenza A Virus ◽  
Influenza A ◽  
Infectious Virus ◽  
Full Length ◽  
Prior Work ◽  
Defective Interfering Particles ◽  
A Cell ◽  
Eclipse Phase

A defective interfering particle (DIP) in the context of influenza A virus is a virion with a significantly shortened RNA segment substituting one of eight full-length parent RNA segments, such that it is preferentially amplified. Hence, a cell co-infected with DIPs will produce mainly DIPs, suppressing infectious virus yields and affecting infection kinetics. Unfortunately, the quantification of DIPs contained in a sample is difficult because they are indistinguishable from standard virus (STV). Using a mathematical model, we investigated the standard experimental method for counting DIPs based on the reduction in STV yield (Bellett & Cooper, 1959, Journal of General Microbiology 21 , 498–509 ( doi:10.1099/00221287-21-3-498 )). We found the method is valid for counting DIPs provided that: (i) an STV-infected cell's co-infection window is approximately half its eclipse phase (it blocks infection by other virions before it begins producing progeny virions), (ii) a cell co-infected by STV and DIP produces less than 1 STV per 1000 DIPs and (iii) a high MOI of STV stock (more than 4 PFU per cell) is added to perform the assay. Prior work makes no mention of these criteria such that the method has been applied incorrectly in several publications discussed herein. We determined influenza A virus meets these criteria, making the method suitable for counting influenza A DIPs.

scholarly journals Mathematical Model of a Cell Size Checkpoint

2010 ◽  
Vol 6 (12) ◽  
pp. e1001036 ◽  
Author(s):  
Marco Vilela ◽  
Jeffrey J. Morgan ◽  
Paul A. Lindahl
Keyword(s):  
Mathematical Model ◽  
Cell Size ◽  
A Cell
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