Retinoic acid proximalizes level-specific properties responsible for intercalary regeneration in axolotl limbs

Development ◽  
1988 ◽  
Vol 104 (4) ◽  
pp. 703-712 ◽  
Author(s):  
K. Crawford ◽  
D.L. Stocum
Keyword(s):  
Retinoic Acid ◽  
Normal Cell ◽  
Recognition System ◽  
Cellular Mechanism ◽  
Recognition Mechanism ◽  
Amputation Level ◽  
Intercalary Regeneration ◽  
Cellular Recognition

The objective of this study was to determine whether retinoic acid (RA) coordinately proximalizes positional memory and the cellular recognition system that detects pattern discontinuity in regenerating amphibian limbs. The strategy was to test the capacity of RA-treated blastemas to evoke intercalary regeneration when grafted to an amputation level proximal to their level of origin. Control wrist and ankle, or elbow and knee blastemas treated with the retinoid solvent, dimethylsulphoxide, evoked intercalary regeneration as effectively as untreated blastemas, when grafted to the midstylopodial amputation surface of host limbs. RA-treated wrist and ankle or elbow and knee blastemas were proximalized and formed complete limbs that were at an angle to, or continuous with, the midstylopodium of the host limb. No intercalary regeneration, from either graft or host, was observed in these cases. The results indicate that the cellular mechanism that recognizes disparities between non-neighbouring cells and initiates intercalary regeneration is coordinately proximalized with positional memory. Thus the recognition mechanism and positional memory are directly related. Intercalary regeneration and corrective displacement (affinophoresis), both of which restore a pattern of normal cell neighbours by different means in regenerating axolotl limbs, appear to use the same mechanism to recognize pattern discontinuity.

Small peptide–mediated self-recognition prevents cannibalism in predatory nematodes

Science ◽  
2019 ◽  
Vol 364 (6435) ◽  
pp. 86-89 ◽  
Author(s):  
James W. Lightfoot ◽  
Martin Wilecki ◽  
Christian Rödelsperger ◽  
Eduardo Moreno ◽  
Vladislav Susoy ◽  
...  
Keyword(s):  
Hypervariable Region ◽  
Recognition System ◽  
Natural World ◽  
Single Amino Acid ◽  
Biological Processes ◽  
Small Peptide ◽  
Recognition Mechanism ◽  
Self Recognition ◽  
C Terminus ◽  
Molecular Machinery

Self-recognition is observed abundantly throughout the natural world, regulating diverse biological processes. Although ubiquitous, often little is known of the associated molecular machinery, and so far, organismal self-recognition has never been described in nematodes. We investigated the predatory nematode Pristionchus pacificus and, through interactions with its prey, revealed a self-recognition mechanism acting on the nematode surface, capable of distinguishing self-progeny from closely related strains. We identified the small peptide SELF-1, which is composed of an invariant domain and a hypervariable C terminus, as a key component of self-recognition. Modifications to the hypervariable region, including single–amino acid substitutions, are sufficient to eliminate self-recognition. Thus, the P. pacificus self-recognition system enables this nematode to avoid cannibalism while promoting the killing of competing nematodes.

scholarly journals Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system

2018 ◽  
Author(s):  
Katarína Bod’bvá ◽  
Tadeas Priklopil ◽  
David L. Field ◽  
Nicholas H. Barton ◽  
Melinda Pickup
Keyword(s):  
Genetic Model ◽  
Haplotype Diversity ◽  
Recognition System ◽  
Stochastic Simulations ◽  
Self Incompatibility ◽  
Recognition Mechanism ◽  
Evolutionary Pathway ◽  
Self Pollination ◽  
Self Recognition ◽  
Evolutionary Pathways

AbstractSelf-incompatibility (SI) is a genetically based recognition system that functions to prevent self-fertilization and mating among related plants. An enduring puzzle in SI is how the high diversity observed in nature arises and is maintained. Based on the underlying recognition mechanism, SI can be classified into two main groups: self- and non-self recognition. Most work has focused on diversification within self-recognition systems despite expected differences between the two groups in the evolutionary pathways and outcomes of diversification. Here, we use a deterministic population genetic model and stochastic simulations to investigate how novel S-haplotypes evolve in a gametophytic non-self recognition (SRNase/S Locus F-box (SLF)) SI system. For this model the pathways for diversification involve either the maintenance or breakdown of SI and can vary in the order of mutations of the female (SRNase) and male (SLF) components. We show analytically that diversification can occur with high inbreeding depression and self-pollination, but this varies with evolutionary pathway and level of completeness (which determines the number of potential mating partners in the population), and in general is more likely for lower haplotype number. The conditions for diversification are broader in stochastic simulations of finite population size. However, the number of haplotypes observed under high inbreeding and moderate to high self-pollination is less than that commonly observed in nature. Diversification was observed through pathways that maintain SI as well as through self-compatible intermediates. Yet the lifespan of diversified haplotypes was sensitive to their level of completeness. By examining diversification in a non-self recognition SI system, this model extends our understanding of the evolution and maintenance of haplotype diversity observed in a recognition system common in flowering plants.

Automatic Voice-Based Recognition For Automotive Headlights Beam Control

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
W. Astuti ◽  
S. Tan ◽  
M.I. Solihin ◽  
R.S. Vincent ◽  
B. Michael
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Recognition System ◽  
Support Vector ◽  
Recognition Mechanism ◽  
Training Time ◽  
Performance Accuracy ◽  
Vector Machines ◽  
Mel Frequency Cepstral Coefficient ◽  
The Voice

Driving comfort plays an important role in modern automotive technologies. One of the ways of comforting the driver is the voice-based recognition to control car headlights. The driver uttered a ‘specific word’ that is taken as an input to the proposed voice-based recognition system. The proposed mechanism then determines if the signal was either ‘high beam’ or ‘low beam’ to control the car headlights. To activate the headlight’s beam, this voice recognised signal is sent to a processing board. Mel Frequency Cepstral Coefficient (MFCC) is used in the recognition mechanism to extract the uttered word before being fed into Artificial Neural Networks (ANN) and Support Vector Machines (SVM) as a classification engine. The proposed automatic voice-based recognition was evaluated via experimental work. The results show that the proposed automatic voice-based recognition for headlights activation control involving MFCC feature works effectively in which SVM gives slightly better performance accuracy when compared to ANN. In addition to a lesser training time, the resulting accuracy using SVM in the training and testing phase is 93.595% and 91.74% respectively. Meanwhile, ANN has an accuracy of 89.39% and 88.16% in the training and testing respectively.

scholarly journals CGA: A New Feature Selection Model for Visual Human Action Recognition

2020 ◽  
Author(s):  
Ritam Guha ◽  
Ali Hussain Kha ◽  
Pawan Kumar Singh ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Image Understanding ◽  
Recognition Mechanism ◽  
New Feature ◽  
Original Feature

Abstract Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel Feature Selection (FS) model called Co-operative Genetic Algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of co-operative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets such as Weizmann, KTH, UCF11, HMDB51 and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering nearly 50 to 60% of the original feature vector.

Stimulated Virus Production in Vinblastine and Cytochalasin-Induced Multinucleate Cells

1970 ◽  
Vol 28 ◽  
pp. 186-187
Author(s):  
Krishan Awtar
Keyword(s):  
Cell Division ◽  
Normal Cell ◽  
Virus Production ◽  
Multinucleate Cells ◽  
Daughter Cells ◽  
Cell Divisions ◽  
Nuclear Membranes ◽  
Division 2 ◽  
Vinblastine Sulfate

Exposure of cells to low sublethal but mitosis-arresting doses of vinblastine sulfate (Velban) results in the initial arrest of cells in mitosis followed by their subsequent return to an “interphase“-like stage. A large number of these cells reform their nuclear membranes and form large multimicronucleated cells, some containing as many as 25 or more micronuclei (1). Formation of large multinucleate cells is also caused by cytochalasin, by causing the fusion of daughter cells at the end of an otherwise .normal cell division (2). By the repetition of this process through subsequent cell divisions, large cells with 6 or more nuclei are formed.

Electron Microscopy and image analysis of nucleo-protein complexes

1994 ◽  
Vol 52 ◽  
pp. 88-89
Author(s):  
E. H. Egelman ◽  
X. Yu
Keyword(s):  
Electron Microscopy ◽  
Image Analysis ◽  
Normal Cell ◽  
Genetic Recombination ◽  
Protein Complexes ◽  
Chromosomal Rearrangements ◽  
Reca Protein ◽  
E Coli ◽  
Helical Polymer ◽  
Specific Protease

The RecA protein of E. coli has been shown to mediate genetic recombination, regulate its own synthesis, control the expression of other genes, act as a specific protease, form a helical polymer and have an ATPase activity, among other observed properties. The unusual filament formed by the RecA protein on DNA has not previously been shown to exist outside of bacteria. Within this filament, the 36 Å pitch of B-form DNA is extended to about 95 Å, the pitch of the RecA helix. We have now establishedthat similar nucleo-protein complexes are formed by bacteriophage and yeast proteins, and availableevidence suggests that this structure is universal across all of biology, including humans. Thus, understanding the function of the RecA protein will reveal basic mechanisms, in existence inall organisms, that are at the foundation of general genetic recombination and repair.Recombination at this moment is assuming an importance far greater than just pure biology. The association between chromosomal rearrangements and neoplasms has become stronger and stronger, and these rearrangements are most likely products of the recombinatory apparatus of the normal cell. Further, damage to DNA appears to be a major cause of cancer.

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