Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises.

Biological materials exhibit complex structure-property relationships which are designed by nature\'s evolution over millions of years. Unlocking the fundamental physical principles behind these relationships is crucial for creating bioinspired materials and structures with advanced functionalities. The eggshell is a remarkable example with a well-designed structure to balance the trade-off as it provides mechanical protection while still being easy for hatching. In this study, we investigate the underlying mechanical design principles of chicken eggshells under various loading conditions through a combination of experiments and simulations. The unique geometry and structure of the eggshell play a critical role in achieving an excellent balance between mechanical toughness and ease of hatching. The effects of eggshell membranes are elucidated to tune the mechanical properties of the eggshell to further enhance this balance. Moreover, a mechanics-based three-index model is proposed based on these design principles, suggesting the optimal eggshell thickness design to improve survivability across a broad range of avian species with varying egg sizes. The survivability-design relationships hold great potential for the development of improved structural materials for applications in sports safety equipment and the packaging industry. STATEMENT OF SIGNIFICANCE: The fundamental physical principles underlying the complex structure-property relationships in biological materials are uncovered in this study, with a particular focus on chicken eggshells as a prime example. Through the investigation of their mechanical design, we reveal the critical role of eggshell geometry and structure in achieving a balance between toughness and ease of hatching. Specifically, the crack resting effect is observed, making the eggshell easier to break from the inside than from the outside. Additionally, we explore the influence of eggshell membranes on this balance, contributing to the enhancement of the eggshell\'s mechanical properties. For the first time, we propose a three-index model that uncovers the underlying principles governing the evolution of eggshell thickness. This model suggests optimal thickness designs for diverse avian species, with the goal of enhancing egg survivability. These findings can guide the development of improved structural materials with advanced functionalities, enabling greater safety and efficiency in a wide range of applications.

The objective of this study is to create a reliable predictive model for the electrochemical performance of self-powered biosensors that rely on urea-based biological energy sources. Specifically, this model focuses on the development of a human energy harvesting model based on the utilization of urea found in sweat, which will enable the development of self-powered biosensors. In the process, the potential of urea hydrolysis in the presence of a urease enzyme is employed as a bioreaction for self-powered biosensors. The enzymatic reaction yields a positive potential difference that can be harnessed to power biofuel cells (BFCs) and act as an energy source for biosensors. This process provides the energy required for self-powered biosensors as biofuel cells (BFCs). To this end, initially, the platinum electrodes are modified by multi-walled carbon nanotubes to increase their conductivity. After stabilizing the urease enzyme on the surface of the platinum electrode, the amount of electrical current produced in the process is measured. The optimal design of the experiments is performed based on the Taguchi method to investigate the effect of urea concentration, buffer concentration, and pH on the generated electrical current. A general equation is employed as a prediction model and its coefficients calculated using an evolutionary strategy. Also, the evaluation of effective parameters is performed based on error rates. The obtained results show that the established model predicts the electrical current in terms of urea concentration, buffer concentration, and pH with high accuracy.

Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.

Synonymous codon bias in the viral genome affects protein translation and gene expression, suggesting that the synonymous codon mutant plays an essential role in influencing virulence and evolution. However, how the recessive mutant form contributes to virus evolvability remains elusive. In this paper, we characterize how the Senecavirus A (SVA), a picornavirus, utilizes synonymous codon mutations to influence its evolution, resulting in the adaptive evolution of the virus to adverse environments. The phylogenetic tree and Median-joining (MJ)-Network of these SVA lineages worldwide were constructed to reveal SVA three-stage genetic development clusters. Furthermore, we analyzed the codon bias of the SVA genome of selected strains and found that SVA could increase the GC content of the third base of some amino acid synonymous codons to enhance the viral RNA adaptive evolution. Our results highlight the impact of recessive mutation of virus codon bias on the evolution of the SVA and uncover a previously underappreciated evolutionary strategy for SVA. They also underline the importance of understanding the genetic evolution of SVA and how SVA adapts to the adverse effects of external stress.

Microbial communities are continuously exposed to unpredictable changes in their environment. To thrive in such dynamic habitats, microorganisms have developed the ability to readily switch phenotypes, resulting in a number of differently adapted subpopulations expressing various traits. In evolutionary biology, a particular case of phenotypic heterogeneity that evolved in an unpredictably changing environment has been defined as bet-hedging. Bet-hedging is a risk-spreading strategy where isogenic populations stochastically (randomly) diversify their phenotypes, often resulting in maladapted individuals that suffer lower reproductive success. This fitness trade-off in a specific environment may have a selective advantage upon the sudden environmental shift. Thus, a bet-hedging strategy allows populations to persist in very dynamic habitats, but with a particular fitness cost. In recent years, numerous examples of phenotypic heterogeneity in different microorganisms have been observed, some suggesting bet-hedging. Here, we highlight the latest reports concerning bet-hedging phenomena in various microorganisms to show how versatile this strategy is within the microbial realms. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.

Viruses are known to have some of the highest and most diverse mutation rates found in any biological replicator, with single-stranded (ss) RNA viruses evolving the fastest, and double-stranded (ds) DNA viruses having rates approaching those of bacteria. As mutation rates are tightly and negatively correlated with genome size, selection is a clear driver of viral evolution. However, the role of intragenomic interactions as drivers of viral evolution is still unclear. To understand how these two processes affect the long-term evolution of viruses infecting humans, we comprehensively analyzed ssRNA, ssDNA, dsRNA, and dsDNA viruses, to find which virus types and which functions show evidence for episodic diversifying selection and correlated evolution. We show that selection mostly affects single stranded viruses, that correlated evolution is more prevalent in DNA viruses, and that both processes, taken independently, mostly affect viral replication. However, the genes that are jointly affected by both processes are involved in key aspects of their life cycle, favoring viral stability over proliferation. We further show that both evolutionary processes are intimately linked at the amino acid level, which suggests that it is the joint action of selection and correlated evolution, and not just selection, that shapes the evolutionary trajectories of viruses-and possibly of their epidemiological potential.

Backward or subcritical bifurcation is usually considered an undesirable phenomenon in epidemiology since control measures require a reduction in R0 not below one but below a much smaller value. However, there are contexts for which a backward or subcritical bifurcation is not a bad thing; it can even be desirable. Such is the case for any characteristic that can be passed to the next generation (genetically fixed or not) and that increases the effective reproductive rate of the host or the total number of individuals. In the present work, we study an epidemiological model consisting of two classes, susceptible and \"infected\" individuals; the model considers a characteristic that is passed from \"infected\" to \"susceptible\" by direct \"contact,\" for instance increased fecundity. We analyze conditions for the appearance of a backward or subcritical bifurcation. We discuss the advantage for the population under infection, since the total number of individuals increases at equilibrium. If one takes that as a proxy for increased fitness, it would increase the species\' ecological success. One key element in the model is the fact that \"susceptible\" individuals have \"susceptible\" descendants, but \"infected\" individuals can have \"infected\" descendants as well as \"susceptible\" ones. A somehow rare addition for epidemiological models, the fact that \"infected\" individuals reproduce more rapidly than the susceptible ones, leads to unexpected consequences. Facilitating the \"inoculation\" increases the total population size, i.e., the backward or subcritical bifurcation appears, with desirable consequences for the population. We show that an increase in the number of susceptible newborns is the main reason for the appearance of a backward or subcritical bifurcation, which induces a bigger population size. We analyze the effect of different combinations of susceptible/infected birth rates. This kind of phenomenon has been observed for bacterial infections in several insects-bacteria and nematodes-bacteria interactions; in particular, it has been intensely studied in interactions of wasps and flies with the genus Wolbachia. It has also been shown in amphibians.

UNASSIGNED: A profile-comparison method with position-specific scoring matrix (PSSM) is among the most accurate alignment methods. Currently, cosine similarity and correlation coefficients are used as scoring functions of dynamic programming to calculate similarity between PSSMs. However, it is unclear whether these functions are optimal for profile alignment methods. By definition, these functions cannot capture nonlinear relationships between profiles. Therefore, we attempted to discover a novel scoring function, which was more suitable for the profile-comparison method than existing functions, using neural networks.
UNASSIGNED: Although neural networks required derivative-of-cost functions, the problem being addressed in this study lacked them. Therefore, we implemented a novel derivative-free neural network by combining a conventional neural network with an evolutionary strategy optimization method used as a solver. Using this novel neural network system, we optimized the scoring function to align remote sequence pairs. Our results showed that the pairwise-profile aligner using the novel scoring function significantly improved both alignment sensitivity and precision relative to aligners using existing functions.
UNASSIGNED: We developed and implemented a novel derivative-free neural network and aligner (Nepal) for optimizing sequence alignments. Nepal improved alignment quality by adapting to remote sequence alignments and increasing the expressiveness of similarity scores. Additionally, this novel scoring function can be realized using a simple matrix operation and easily incorporated into other aligners. Moreover our scoring function could potentially improve the performance of homology detection and/or multiple-sequence alignment of remote homologous sequences. The goal of the study was to provide a novel scoring function for profile alignment method and develop a novel learning system capable of addressing derivative-free problems. Our system is capable of optimizing the performance of other sophisticated methods and solving problems without derivative-of-cost functions, which do not always exist in practical problems. Our results demonstrated the usefulness of this optimization method for derivative-free problems.

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology.