A unified classification of mimetic resemblances

The eight possible interactions of a tripartite mimicry system (model, mimic and operator) are defined in terms of positive and negative functions. Five possible states of the system are also recognized in terms of specific composition. From this an eight by five classification matrix is developed, which embraces all normally recognized mimetic situations. Each class is examined for general properties and examples. Special consideration is given to situations (Mullerian mimicry) included here, and to other systems (crypsis, deflective marks) not included, which have been treated differently elsewhere. Mimicry is defined in terms of a tripartite system of living organisms, in which a sensitive signal-receiver (the operator) misidentifies the mimic as the model. It is emphasized that the system developed here is a classification of mimetic-interactions, not a classification of species. Discussion is given on some implications of the system in relation to natural selection, and the classification of ecological interactions, convergence and genetic diversity.     

Natural selection in mimicry

Biological mimicry has served as a salient example of natural selection for over a century, providing us with a dazzling array of very different examples across many unrelated taxa. We provide a conceptual framework that brings together apparently disparate examples of mimicry in a single model for the purpose of comparing how natural selection affects models, mimics and signal receivers across different interactions. We first analyse how model–mimic resemblance likely affects the fitness of models, mimics and receivers across diverse examples. These include classic Batesian and Müllerian butterfly systems, nectarless orchids that mimic Hymenoptera or nectar‐producing plants, caterpillars that mimic inert objects unlikely to be perceived as food, plants that mimic abiotic objects like carrion or dung and aggressive mimicry where predators mimic food items of their own prey. From this, we construct a conceptual framework of the selective forces that form the basis of all mimetic interactions. These interactions between models, mimics and receivers may follow four possible evolutionary pathways in terms of the direction of selection resulting from model–mimic resemblance. Two of these pathways correspond to the selective pressures associated with what is widely regarded as Batesian and Müllerian mimicry. The other two pathways suggest mimetic interactions underpinned by distinct selective pressures that have largely remained unrecognized. Each pathway is characterized by theoretical differences in how model–mimic resemblance influences the direction of selection acting on mimics, models and signal receivers, and the potential for consequent (co)evolutionary relationships between these three protagonists. The final part of this review describes how selective forces generated through model–mimic resemblance can be opposed by the basic ecology of interacting organisms and how those forces may affect the symmetry, strength and likelihood of (co)evolution between the three protagonists within the confines of the four broad evolutionary possibilities. We provide a clear and pragmatic visualization of selection pressures that portrays how different mimicry types may evolve. This conceptual framework provides clarity on how different selective forces acting on mimics, models and receivers are likely to interact and ultimately shape the evolutionary pathways taken by mimetic interactions, as well as the constraints inherent within these interactions.     

Asymmetric cell division in the morphogenesis of <Emphasis Type="Italic">Drosophila melanogaster</Emphasis> macrochaetae

Asymmetric cell division (ACD) is the basic process which creates diversity in the cells of multi-cellular organisms. As a result of asymmetric cell division, daughter cells acquire the ability to differentiate and specialize in a given direction, which is different from that of their parent cells and from each other. This type of division is observed in a wide range of living organisms from bacteria to vertebrates. It has been shown that the molecular-genetic control mechanism of ACD is evolutionally conservative. The proteins involved in the process of ACD in different kinds of animals have a high degree of homology. Sensory organs—bristles (macrochaetae)—of Drosophila are widely used as a model system for studying the genetic control mechanisms of asymmetric division. Bristles located in an orderly manner on the head and body of the fly play the role of mechanoreceptors. Each of them consists of four specialized cells—offspring of the only sensory organ precursor cell (SOP), which differentiates from the wing imaginal disc at the larval stage of the late third age. The basic differentiation and further specialization of the daughter cells of SOP is an asymmetric division process.     

The biocontainment of a high speed disc bowl centrifuge

The containment of a high speed disc bowl centrifuge during normal operation has been examined using a mutant E. coli strain and a range of sampling devices to monitor the release of viable organisms. A discharge of a small amount of supernatant was used to provide a mimic of a release of a low number of organisms with a view to testing the sensitivity of the sampling systems used. Three sampling devices were used: settle plates, a slit sampler and an air filter sampler. These were all shown to be effective in the collection of viable organisms during the release under low pressure of 10 ml of supernatant (equivalent microbial count to 0.02 ml fermentation broth). Three runs carried out under normal operation of the centrifuge showed no release of viable organisms. The prevention of a second source of release during disassembly and cleaning of the bowl was demonstrated to be possible by the ability to clean and steam sterilize in place. The consequences of such operations are discussed in terms of the use of high speed disc bowl centrifuges for the processing of organisms under various levels of containment.     

A physical characterization of biological information and communication system model of ecosystems

What is information for living organisms? An answer to this question is given on a physical basis and a contrast between genetic information and sensory information is stressed with a relation to information theory. A simple model of an environment of living organisms is investigated on the basis of communication systems model proposed by the author and a cost of information transmission is taken into consideration through capacity cost theory. It is shown that channel capacity of information theory can be interpreted as an environment, and furthermore that a large diversity of genetic messages needs a large capacity of the environment. In addition, a definition of life in terms of information is proposed and a unified view on life processes is suggested.     

Message operators: A new expression of the information

Starting from the synaptic model proposed in a previous paper (Teodorescu, 1976b), based on the idea that, in certain condition, information may be expressed in terms of the constraints in an optimization problem, the concept of an active message is defined. This is a message conveying a certain purpose, which is able to transform an initial orderly state in a new one, subject to some constraints, by choosing the optimal way. Some examples are given to show that active messages play an important role, not only in organisms, but also in communications between individuals and/or groups in social populations. The concept of a message operator, as a mathematical expression of the active message is, then, defined. It is shown that such an operator is, in fact, a pair, having as components a certain transformation and the associate optimization problem involving some information in constraints-form. The transformation is a relationship between the joint moment hypercube, expressing the initial multivariate random process (initial orderly state), and the final orderly state expressed by the joint density hypercube. By solving the optimization problem, the optimized density hypercube (i.e. the new orderly state) is obtained. An example is given to illustrate the procedure. It follows that message operators are able to express, in mathematical form, the transition from a given orderly state (say, a biological population in a quasi-chaotic state) into a new orderly state, with a different degree of orderliness. In such a transition involving three stages i.e. initial orderly state-message-final orderly state, the information is expressed, respectively, in terms of: probabilities-constraints-probabilities. Thus, Shanon's measure of information applies only in the states, since message operators have nothing to do with probabilities. To investigate the particular manner where information is involved in the constraints of the optimization problem, some main properties of the message operators are emphasized and illustrated by an example. Thus, it is shown that message operators are powerfull tools, permitting to investigate some unknown aspects of the information. This leads to a deeper understanding of the communication problems in biological systems as well as to various applications in biology.     

Becoming a Sign: The Mimic’s Activity in Biological Mimicry

From a semiotic perspective biological mimicry can be described as a tripartite system with a double structure that consists of ecological relations between species and semiotic relations of sign. In this article the focus is on the mimic who is the individual benefiting from its resemblance to the cues or signals of other species or to the environment. In establishing the mimetic resemblance the question of mimic’s activity becomes crucial, and the activity can range from the fixed bodily patterns to fully dynamic behavioural displays. The mimic’s activity can be targeted at two other participants of the mimicry system—either at the model or at the receiver. The first possibility is quite common in camouflage and there are several possibilities for mimic’s activity to occur: selecting a resting place or habitat based on conformity with the environment, changing body coloration to correspond to the surrounding environment, covering oneself with particles of the soil. In its activity aimed at the model, the mimic develops a strong semiotic connection with its specific perceptual environment or part of it and obtains a representational character. In the second possibility the activity of a mimetic organism is aimed at the receiver who is confused by the resemblance, and between the two participants an active communicative interaction is established. Such type of mimicry can be exemplified by abstract threat displays found in various groups of animals, for instance a toad’s upright posture as a response to the presence of a snake. From the semiotic viewpoint it can be interpreted as the motive of fear in the predator’s Umwelt being entered into the mimic’s subjective world and manifested in its behaviour. The mimetic organism ends up in an ambiguous position, where it needs to pretend to be something other than it is. In the final part of the article it is argued that the mimetic sign is basically a false designator as the mimic’s activity to become a sign is aimed at a specific type of signs. Rather than signifying belonging to its own species or group, a mimetic sign indicates that its carrier belongs to the type of some other species. The tension between the form and behaviour of mimetic organisms arises from the discrepancy between the type of organism that it essentially is and the type of organism that the mimetic sign it carries imposes on it.     

Population persistence under advection–diffusion in river networks

An integro-differential equation on a tree graph is used to model the time evolution and spatial distribution of a population of organisms in a river network. Individual organisms become mobile at a constant rate, and disperse according to an advection-diffusion process with coefficients that are constant on the edges of the graph. Appropriate boundary conditions are imposed at the outlet and upstream nodes of the river network. The local rates of population growth/decay and that by which the organisms become mobile, are assumed constant in time and space. Imminent extinction of the population is understood as the situation whereby the zero solution to the integro-differential equation is stable. Lower and upper bounds for the eigenvalues of the dispersion operator, and related Sturm-Liouville problems are found. The analysis yields sufficient conditions for imminent extinction and/or persistence in terms of the values of water velocity, channel length, cross-sectional area and diffusivity throughout the river network.     

Understanding Mimicry – with Special Reference to Vocal Mimicry

The term mimicry was introduced to biology in 1862 by Henry Walter Bates in his evolutionary explanation of deceptive communication in nature, based on a three‐part interaction system of a mimicked organism or object (called model), a mimicking organism (called mimic), and one or more organisms as selecting agents. Bates gave two incongruous definitions of mimicry: one from the viewpoint of a natural agent that selects for, and in consequence is deceived by, the close resemblance of a toxic model's warning signal and the similar appearance of a palatable mimic, and another one from the viewpoint of a human taxonomist who under an evolutionary aspect focuses on convergent resemblance between model and mimic. Later definitions of Müllerian (F. Müller), arithmetic (A. Wallace) and social (M. Moynihan) mimicry abolish deception in the natural selecting agent, rely on the convergence criterion alone, fuse the roles of model and mimic but have to accept a mix of homologous and convergent resemblance amongst them for a functional explanation. The definition of vocal mimicry (E. Armstrong) refers to a learned resemblance between mimic and heterospecific model by character duplication (no convergence), so far without known (deceived or not deceived) natural selecting agents. It excludes Batesian vocal mimicry. The functional ethological understanding of mimicry as a tripartite communication system (W. Wickler) is consistent with Bates' concept and accepts deception as key element of Batesian mimicry beyond homologous and convergent resemblances. Deception is seen as caused by the divergence between a sign and its meaning for the natural selecting agent. This understanding covers mimicry in all behaviour domains, provides a generally applicable definition of mimic and model so far missing in any mimicry concept, and it distinguishes – still in line with Henry Bates – cultural from genetically determined model‐mimic‐resemblance; this applies to vocal mimicry in particular. Convergently evolved model‐mimic‐resemblance, not essential in Batesian mimicry but mandatory for its alternatives, marks a fundamental distinction between Batesian mimicry (including Mimesis) and all other conceptualized mimicries and accounts for the non‐existence of a unified meaning of the term mimicry. However, character convergence does not help to explain the mere existence of mimicry phenomena and is irrelevant for their permanence in nature. I therefore propose to remove the convergence argument from any mimicry definition.     

Modelling neural informational propagation and functional auditory sensory memory with temporal multi-scale operators

In this paper we prove that both diffusion and the leaky integrators cascade based transport mechanisms have as their inherent property the effect of temporal multi-scaling. The two transport mechanisms are modeled not as convolution based algorithms but as causal physical processes. This implies that propagation of information through a neural map may act as a mechanism for achieving temporal multi-scale analysis in the auditory system. Specifically, we are interested in the effects of such a transport process on the formation and the dynamics of auditory sensory memory. Two temporal models of information propagation are discussed and compared in terms of their ability to model auditory sensory memory effects and the biological plausibility of their structure: the causal diffusion based operator (CD) and the leaky integrator cascade based operator (LINC). We show that temporal multi-scale representations achieved by both models exhibit the effects similar to those of auditory sensory memory (filtering, time delay and binding of information). As regards higher-level functions of auditory sensory memory such as change detection, the LINC operator seems to be a biologically more plausible solution for modeling temporal cortical processing.     

Sequence similarity and functional relationship among eukaryotic ZIP and CDF transporters

ZIP （ZRT/IRT-like Protein） and CDF （Cation Diffusion Facilitator） are two large metal transporter families mainly transporting zinc into and out of the cytosol. Several ZIP and CDF transporters have been characterized in mammals and various model organisms, such as yeast, nematode, fruit fly, and zebrafish, and many candidate genes have been identified by genome projects. Unexpected functions of ZIP and CDF transporters have been recently reported in some model organisms, leading to major advances in our understanding of the functions of mammalian counterparts. Here, we review the recent information on the sequence similarity and functional relationship among eukaryotic ZIP and CDF transporters obtained from the representative model organisms.     

Quantum-like model of processing of information in the brain based on classical electromagnetic field

We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations.     

Quantitative spectroscopy analysis of prokaryotic cells: vegetative cells and spores

Multiwavelength ultraviolet/visible (UV-Vis) spectra of microorganisms and cell suspensions contain quantitative information on properties such as number, size, shape, chemical composition, and internal structure of the suspended particles. These properties are essential for the identification and classification of microorganisms and cells. The complexity of microorganisms in terms of their chemical composition and internal structure make the interpretation of their spectral signature a difficult task. In this paper, a model is proposed for the quantitative interpretation of spectral patterns resulting from transmission measurements of prokaryotic microorganism suspensions. It is also demonstrated that different organisms give rise to spectral differences that may be used for their identification and classification. The proposed interpretation model is based on light scattering theory, spectral deconvolution techniques, and on the approximation of the frequency dependent optical properties of the basic constituents of living organisms. The quantitative deconvolution in terms of the interpretation model yields critical information necessary for the detection and identification of microorganisms, such as size, dry mass, dipicolinic acid concentration, nucleotide concentration, and an average representation of the internal scattering elements of the organisms. E. coli, P. agglomerans, B. subtilis spores, and vegetative cells and spores of Bacillus globigii are used as case studies. It is concluded that spectroscopy techniques coupled with effective interpretation models are applicable to a wide range of cell types found in diverse environments.     

Asymmetric cell division in the morphogenesis of Drosophila melanogaster macrochaetae

Asymmetric cell division (ACD) is the basic process which creates diversity in the cells of multicellular organisms. As a result of asymmetric cell division, daughter cells acquire the ability to differentiate and specialize in a given direction, which is different from that of their parent cells and from each other. This type of division is observed in a wide range of living organisms from bacteria to vertebrates. It has been shown that the molecular-genetic control mechanism of ACD is evolutionally conservative. The proteins involved in the process of ACD in different kinds of animals have a high degree of homology. Sensory organs--setae (macrochaetae)--of Drosophila are widely used as a model system for studying the genetic control mechanisms of asymmetric division. Setae located in an orderly manner on the head and body of the fly play the role of mechanoreceptors. Each of them consists of four specialized cells--offspring of the only sensory organ precursor cell (SOPC), which differentiates from the imaginal wing disc at the larval stage of the late third age. The basic differentiation and further specialization of the daughter cells of SOPC is an asymmetric division process. In this summary, experimental data on genes and their products controlling asymmetric division of SOPC and daughter cells, and also the specialization of the latter, have been systemized. The basic mechanisms which determine the time cells enter into asymmetric mitosis and which provides the structural characteristics of the asymmetric division process--the polar distribution of protein determinants Numb and Neuralized--the orientation of the mitotic spindle in relation to these determinants, and the uneven segregation of the determinants into the daughter cells that determines the direction of their development have been discussed.     

Allelopathy of plasmid-bearing and plasmid-free organisms competing for two complementary resources in a chemostat

We consider a model of competition between plasmid-bearing and plasmid-free organisms for two complementary nutrients in a chemostat. We assume that the plasmid-bearing organism produces an allelopathic agent at the cost of its reproductive abilities which is lethal to plasmid-free organism. Our analysis leads to different thresholds in terms of the model parameters acting as conditions under which the organisms associated with the system cannot thrive even in the absence of competition. Local stability of the system is obtained in the absence of one or both the organisms. Also, global stability of the system is obtained in the presence of both the organisms. Computer simulations have been carried out to illustrate various analytical results.     

Allelopathy of plasmid-bearing and plasmid-free organisms competing for two complementary resources in a chemostat

We consider a model of competition between plasmid-bearing and plasmid-free organisms for two complementary nutrients in a chemostat. We assume that the plasmid-bearing organism produces an allelopathic agent at the cost of its reproductive abilities which is lethal to plasmid-free organism. Our analysis leads to different thresholds in terms of the model parameters acting as conditions under which the organisms associated with the system cannot thrive even in the absence of competition. Local stability of the system is obtained in the absence of one or both the organisms. Also, global stability of the system is obtained in the presence of both the organisms. Computer simulations have been carried out to illustrate various analytical results.     

Mimicry in plant-parasitic fungi

Mimicry is the close resemblance of one living organism (the mimic) to another (the model), leading to misidentification by a third organism (the operator). Similar to other organism groups, certain species of plant-parasitic fungi are known to engage in mimetic relationships, thereby increasing their fitness. In some cases, fungal infection can lead to the formation of flower mimics (pseudo flowers) that attract insect pollinators via visual and/or olfactory cues; these insects then either transmit fungal gametes to accomplish outcrossing (e.g. in some heterothallic rust fungi belonging to the genera Puccinia and Uromyces) or vector infectious spores to healthy plants, thereby spreading disease (e.g. in the anther smut fungus Microbotryum violaceum and the mummy berry pathogen Monilinia vaccinii-corymbosi). In what is termed aggressive mimicry, some specialized plant-parasitic fungi are able to mimic host structures or host molecules to gain access to resources. An example is M. vaccinii-corymbosi, whose conidia and germ tubes, respectively, mimic host pollen grains and pollen tubes anatomically and physiologically, allowing the pathogen to gain entry into the host's ovary via stigma and style. We review these and other examples of mimicry by plant-parasitic fungi and some of the mechanisms, signals, and evolutionary implications.