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1.
J Dev Biol ; 11(2)2023 May 24.
Article in English | MEDLINE | ID: mdl-37367476

ABSTRACT

Osteoderms are bony structures that develop within the dermal layer of the skin in vertebrates and are very often found in different lizard families. Lizard osteoderms are diverse in topography, morphology, and microstructure. Of particular interest are the compound osteoderms of skinks, which are a complex of several bone elements known as osteodermites. We present new data on the development and regeneration of compound osteoderms based on the results of a histological and Computed Microtomography (micro-CT) study of a scincid lizard: Eurylepis taeniolata. The specimens studied are stored in the herpetological collections of the Saint-Petersburg State University and Zoological Institute of the Russian Academy of Sciences located in St. Petersburg, Russia. The topography of osteoderms in the integuments of the original tail area and its regenerated part was studied. A comparative histological description of the original and regenerated osteoderms of Eurylepis taeniolata is presented for the first time. The first description of the development of compound osteoderm microstructure in the process of caudal regeneration is also presented.

2.
J Dev Biol ; 9(3)2021 Aug 16.
Article in English | MEDLINE | ID: mdl-34449652

ABSTRACT

We present a review of the data on the intervertebral autotomy and regeneration of agamid lizards based on an analysis of information obtained over a 35-year period after the publication of thorough reviews (Arnold, 1984, 1988 and Bellairs, Bryant, 1985). It is supplemented by our own studies of 869 specimens of agamid lizards (Sauria, Agamidae) stored in the herpetological collections of the Zoological Institute of the Russian Academy of Sciences (St. Petersburg, Russia) and the Zoological Museum of the Moscow State University (Moscow, Russia), represented by 31 species of 16 genera. The manifestations of the ability for autotomy and regeneration in phylogenetic lineages within the family-Leiolepidinae, Amphibolurinae, Agaminae, Draconinae-are considered. A comparative morphological analysis of the structure of the caudal vertebrae was carried out using the Computer Microtomography Methods (micro-CT) in the following ecomorphological types of agama: (1) with developed abilities to caudal autotomy and regeneration, (2) with the ability to caudal autotomy but without regeneration and (3) without the ability to autotomy. The phenomenon of intervertebral autotomy (urotomy) in snakes is considered too. Possible ways of evolution of the ability to caudal autotomy as a defense strategy against predators are discussed in the phylogenetic context.

3.
Proc Natl Acad Sci U S A ; 118(5)2021 02 02.
Article in English | MEDLINE | ID: mdl-33495346

ABSTRACT

Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google's ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance.

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