2R-Cell: A Universal Cell for an Easy and Safe SOFC Operation

2019 ◽  
Vol 35 (1) ◽  
pp. 393-402 ◽  
Author(s):  
Raphaël Ihringer
Keyword(s):  
Universal Cell

Design of a fault-tolerant universal cell

1992 ◽  
Vol 72 (3) ◽  
pp. 467-470 ◽  
Author(s):  
P. K. LALA ◽  
F. BU-SABA ◽  
A. XIE ◽  
K. C. YARLAGADDA
Keyword(s):  
Fault Tolerant ◽  
Universal Cell

Cell biology: Yeast as the universal cell

Nature ◽  
1985 ◽  
Vol 316 (6030) ◽  
pp. 678-679 ◽  
Author(s):  
Ira Herskowitz
Keyword(s):  
Cell Biology ◽  
Universal Cell

scholarly journals Universal cell type identifier based on number theory

2018 ◽  
Vol 93 (6) ◽  
pp. 649-652 ◽  
Author(s):  
Antonio Cosma
Keyword(s):  
Number Theory ◽  
Cell Type ◽  
Universal Cell

scholarly journals Petri net implementations by a universal cell space

1982 ◽  
Vol 53 (1-2) ◽  
pp. 121-138 ◽  
Author(s):  
Ulrich Golze ◽  
Lutz Priese
Keyword(s):  
Petri Net ◽  
Universal Cell

scholarly journals Universal cell capture by immobilized antimicrobial peptide plantaricin

2015 ◽  
Vol 101 ◽  
pp. 18-22 ◽  
Author(s):  
Saadet Albayrak Guralp ◽  
Ilkay Hilal Gubbuk ◽  
Semahat Kucukkolbasi ◽  
Erdogan Gulari
Keyword(s):  
Antimicrobial Peptide ◽  
Cell Capture ◽  
Universal Cell

scholarly journals The Origin(s) of Cell: Some Radical New Hypotheses

2020 ◽  
Author(s):  
Shiping Tang
Keyword(s):  
Natural Selection ◽  
Survival Rate ◽  
Horizontal Gene Transfer ◽  
Biological Evolution ◽  
Differential Survival ◽  
Starting Point ◽  
Evolution Of Life ◽  
Lynn Margulis ◽  
Variation And Selection ◽  
Universal Cell

The central mechanism of biological evolution, variation-selection-inheritance (VSI), is one of the most universal mechanisms known. Much of our understanding of VSI, however, has been dominated by the Neo-Darwinian Modern Synthesis with a rather narrow understanding of what constitutes variation, selection, and inheritance. This unduly narrow understanding of VSI might have been a key cause behind our failure to adequately explain some critical puzzles in biological evolution, from the origin of the first cell to the origin of the eukaryotes, the puzzling biology of metabolism, apoptosis, aging, and cancer in metazoan.I broaden our understanding of VSI, in a spirit that is somewhat similar to several recent contributions and then extend this broadened view of VSI to its natural starting point: the origin of the First Universal Cell Ancestor (FUCA). I advance three principal arguments. First, survival comes before replication. Before the coming of reproducer and replicator, there must be survivors, to paraphrase Szathmary and Maynard Smith (1997). Second, natural selection, especially the non-Darwinian kind, can operate without replication or even metabolism, as long as different molecules, complexes, and vesicles have differential survival rate within a system. Third, merger and acquisition, via breaking-and-re-encapsulation, endocytosis, endosymbiosis, and processes similar to them, had been a far more powerful force of variation and selection in the pre-Darwinian period of evolution that led to LUCA and long before eukaryogenesis. Endosymbiosis therefore had been a far more foundational force than even Lynn Margulis and many of her supporters have appreciated. Our thesis thus goes beyond Woese’s emphasis of horizontal gene transfer (HGT) and actually subsumes HGT with Margulis’ emphasis of endosymbiosis. Combing these three new perspectives with other perspectives and evidence sheds important new light upon the origin of FUCA, the singular water-shedding moment in the evolution of life.

CLASSIFYING WITH A TWO NEURON CNN

2012 ◽  
Vol 22 (02) ◽  
pp. 1250035
Author(s):  
MIREIA VINYOLES-SERRA ◽  
XAVIER VILASÍS-CARDONA
Keyword(s):  
Steady State ◽  
Turing Machine ◽  
Boolean Functions ◽  
Functional Relation ◽  
Input Output ◽  
Classification Problems ◽  
Universal Turing Machine ◽  
Binary Input ◽  
Universal Cell

We analyze the two neuron CNN for the particular parameter range where the system converges to constant outputs. The functional relation between the external inputs and the steady state values of the neuron states is found and proves to be useful to solve classification problems. In fact, an exhaustive classification of the binary input–output relations that can be achieved by a two neuron CNN is established. From this relation, we propose an algorithm relating the CNN parameters and each one of the different classification problems. As an illustration, we attempt to implement the header action of a universal Turing machine and Boolean functions. Our results are compared to the CNN universal cell.

Sign in / Sign up

Export Citation Format

Share Document