Variability and its types: the differential approach

One of the key aims of current biology is to describe adequately the tremendous multiplicity of living organisms. This aim requires clear terminological apparatus. However, many terms traditionally used in such descriptions are rather vague and ambiguous. In particular, this relates to ‘variability’ and ‘variation’. In the present review, we carried out a critical analysis of these terms. We demonstrate that the widely accepted tradition to consider them as almost synonymous is incorrect. Moreover, both terms are initially ambiguous and thus are poorly suitable for biologists. To avoid this ambiguity, we clearly delineate three phenomena: 1) biological changeability, 2) certain biological changes, and 3) biological diversity. There is an obvious three-component relation between them: changeability realizes in certain biological changes (metamorphoses, mutations, modifications, etc.) which in turn result in biological diversity. Herein, the first component is entirely dynamic (the ability of living organisms to undergo various changes), the second aggregates both dynamic and static aspects (a certain event leads to a specific state), and the third is represented by some static ‘cadaster’, which describes the state of a given biological unity at some point of time. We classified different types of changeability. To create such classification, autonomous aspects of changeability should be distinguished and each of them should be considered separately. This approach (we name it ‘the differential concept of changeability’) allows successful resolution of multiple terminological problems in current biology.

On connected component Markov point processes

We note some interesting properties of the class of point processes which are Markov with respect to the ‘connected component’ relation. Results in the literature imply that this class is closed under random translation and independent cluster generation with almost surely non-empty clusters. We further prove that it is closed under superposition. A wide range of examples is also given.

On connected component Markov point processes

We note some interesting properties of the class of point processes which are Markov with respect to the ‘connected component’ relation. Results in the literature imply that this class is closed under random translation and independent cluster generation with almost surely non-empty clusters. We further prove that it is closed under superposition. A wide range of examples is also given.