Acta Biotheoretica最新文献

Transitive inference (TI) refers to social cognition that facilitates the discernment of unknown relationships between individuals using known relationships. It is extensively reported that TI evolves in animals living in a large group because TI could assess relative rank without deducing all dyadic relationships, which averts costly fights. The relationships in a large group become so complex that social cognition may not be developed adequately to handle such complexity. If members apply TI to all possible members in the group, TI requires extremely highly developed cognitive abilities especially in a large group. Instead of developing cognitive abilities significantly, animals may apply simplified TI we call reference TI in this study as heuristic approaches. The reference TI allows members to recognize and remember social interactions only among a set of reference members rather than all potential members. Our study assumes that information processes in the reference TI comprises (1) the number of reference members based on which individuals infer transitively, (2) the number of reference members shared by the same strategists, and (3) memory capacity. We examined how information processes evolve in a large group using evolutionary simulations in the hawk–dove game. Information processes with almost any numbers of reference members could evolve in a large group as long as the numbers of shared reference member are high because information from the others’ experiences is shared. TI dominates immediate inference, which assesses relative rank on direct interactions, because TI could establish social hierarchy more rapidly applying information from others’ experiences.

In this article we analyse the issue of what accounts for developmental potential, i.e., the possible phenotypes a developing organism can manifest during ontogeny. We shall argue in favour of two theses. First, although the developing organism is the unit of development, the complete causal basis for its potential to develop does neither lie entirely in itself as a whole nor in any specific part of itself (such as its genome). Thus, the extra-organismal environment must be counted as one of the three necessary, partial and complementary causal bases for development potential. Secondly, we shall defend a constructivist view of the developmental process. If the genome, the developing organism and the extra-organismal environment are to be counted as proper elements of the causal basis for an organism’s developmental potential, the latter is not a given. Rather, it is the result of an interaction-based construction, a process sometimes generating genuine developmental novelty. We will thus argue for an interactionist multi-causal basis view of developmental potential construction. We contend that our view provides a biologically tenable and metaphysically coherent account of developmental dynamics.

In this work we propose the partial Wiener index as one possible measure of branching in phylogenetic evolutionary trees. We establish the connection between the generalized Robinson–Foulds (RF) metric for measuring the similarity of phylogenetic trees and partial Wiener indices by expressing the number of conflicting pairs of edges in the generalized RF metric in terms of partial Wiener indices. To do so we compute the minimum and maximum value of the partial Wiener index (Wleft(T,r, nright)), where (T) is a binary rooted tree with root (r) and (n) leaves. Moreover, under the Yule probabilistic model, we show how to compute the expected value of (Wleft(T,r, nright)). As a direct consequence, we give exact formulas for the upper bound and the expected number of conflicting pairs. By doing so we provide a better theoretical understanding of the computational complexity of the generalized RF metric.

The paper is devoted to a conceptual model of cell patterning, based on a generalized notion of the epigenetic code of a cell determining its state. We introduce the concept of signaling depending both upon the spatial distance between cells and the distance between their cell states (s-distance); signaling can repel cells in the space of cell states (s-space) or attract them. The influence of different types of repelling signaling on the evolution of cells is considered. Stabilizing signaling, namely a signaling monotonically decreasing with s-distance, causes the restoring of cell states after perturbations; destabilizing signaling, i.e., the one in which the signaling monotonically increases with s-distance, causes the appearance of pairs of cells with alternating cell states (one close to the state conventionally called “head”, and another close to the “tail” state). Non-monotonic (in s-space) signaling splits the cells into groups. The model shows that different types of signaling may provide different types of cellular patterns. General principles for applying this model to complex cellular structures are discussed.

A modified version of the SEIR model with the effects of vaccination and inter-state movement is proposed to simulate the spread of COVID-19 in Malaysia. A mathematical analysis of the proposed model was performed to derive the basic reproduction number. To enhance the model’s forecasting capabilities, the model parameters were estimated using the Nelder–Mead simplex method by fitting the model outputs to the observed data. Our results showed a good fit between the model outputs and available data, where the model was then able to perform short-term predictions. In line with the rapid vaccination program, our model predicted that the COVID-19 cases in the country would decrease by the end of August. Furthermore, our findings indicated that relaxing travel restrictions from a highly vaccinated region to a low vaccinated region would result in an epidemic outbreak.

We propose a framework for the description of the effects of vaccinations on the spreading of an epidemic disease. Different vaccines can be dosed, each providing different immunization times and immunization levels. Differences due to individuals’ ages are accounted for through the introduction of either a continuous age structure or a discrete set of age classes. Extensions to gender differences or to distinguish fragile individuals can also be considered. Within this setting, vaccination strategies can be simulated, tested and compared, as is explicitly described through numerical integrations.

We extend the comparatively simple processes of group symmetry-breaking in physical systems to groupoid/equivalence class phase transitions characterizing adiabatically, piecewise stationary, information transmission in prebiotic, biological, and social phenomena: High vs. Low probability paths (rightarrow) Interior and Exterior Interact (rightarrow) Multiple Interacting Tunable Workspaces Application to nonstationary processes seems possible via generalizations of the symmetry algebra, for example, to semigroupoids. The dynamic probability models explored here can be transformed into statistical tools for the analysis of real-time and other data across a spectrum of important disciplines confronted by biological and other forms of cognition and their dysfunctions.

In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing.

In this work, we study and analyze the aggregate death counts of COVID-19 reported by the United States Centers for Disease Control and Prevention (CDC) for the fifty states in the United States. To do this, we derive a stochastic model describing the cumulative number of deaths reported daily by CDC from the first time Covid-19 death is recorded to June 20, 2021 in the United States, and provide a forecast for the death cases. The stochastic model derived in this work performs better than existing deterministic logistic models because it is able to capture irregularities in the sample path of the aggregate death counts. The probability distribution of the aggregate death counts is derived, analyzed, and used to estimate the count’s per capita initial growth rate, carrying capacity, and the expected value for each given day as at the time this research is conducted. Using this distribution, we estimate the expected first passage time when the aggregate death count is slowing down. Our result shows that the expected aggregate death count is slowing down in all states as at the time this analysis is conducted (June 2021). A formula for predicting the end of Covid-19 deaths is derived. The daily expected death count for each states is plotted as a function of time. The probability density function for the current day, together with the forecast and its confidence interval for the next four days, and the root mean square error for our simulation results are estimated.