Stochasticity is often associated with negative consequences for population dynamics since a population may die out due to random chance during periods when population size is very low (stochastic fade-out). Here we develop a coupled social-ecological model based on stochastic differential equations that includes natural expansion and harvesting of a forest ecosystem, and dynamics of conservation opinions, social norms and social learning in a human population. Our objective was to identify mechanisms that influence long-term persistence of the forest ecosystem in the presence of noise. We found that most of the model parameters had a significant influence on the time to extinction of the forest ecosystem. Increasing the social learning rate and the net benefits of conservation significantly increased the time to extinction, for instance. Most interestingly, we found a parameter regime where an increase in the amount of system stochasticity caused an increase in the mean time to extinction, instead of causing stochastic fade-out. This effect occurs for a subset of realizations, but the effect is large enough to increase the mean time to extinction across all realizations. Such "stochasticity-induced persistence" occurs when stochastic dynamics in the social system generates benefits in the forest system at crucial points in its temporal dynamics. We conclude that studying relatively simple social-ecological models has the benefit of facilitating characterization of dynamical states and thereby enabling us to formulate new hypothesis about mechanisms that could be operating in empirical social-ecological systems.

Contact tracing, case isolation, quarantine, social distancing, and other non-pharmaceutical interventions (NPIs) have been a cornerstone in managing the COVID-19 pandemic. However, their effects on disease dynamics are not fully understood. Saturation of contact tracing caused by the increase of infected individuals has been recognized as a crucial variable by healthcare systems worldwide. Here, we model this saturation process with a mechanistic and a phenomenological model and show that it induces an Allee effect which could determine an infection threshold between two alternative states-containment and outbreak. This transition was considered elsewhere as a response to the strength of NPIs, but here we show that they may be also determined by the number of infected individuals. As a consequence, timing of NPIs implementation and relaxation after containment is critical to their effectiveness. Containment strategies such as vaccination or mobility restriction may interact with contact tracing-induced Allee effect. Each strategy in isolation tends to show diminishing returns, with a less than proportional effect of the intervention on disease containment. However, when combined, their suppressing potential is enhanced. Relaxation of NPIs after disease containment--e.g. because vaccination--have to be performed in attention to avoid crossing the infection threshold required to a novel outbreak. The recognition of a contact tracing-induced Allee effect, its interaction with other NPIs and vaccination, and the existence of tipping points contributes to the understanding of several features of disease dynamics and its response to containment interventions. This knowledge may be of relevance for explaining the dynamics of diseases in different regions and, more importantly, as input for guiding the use of NPIs, vaccination campaigns, and its combination for the management of epidemic outbreaks.

We use a minimalistic mathematical model with a limited number of parameters to evaluate the impact of interindividual differences in the collective decision-making of a group. As it turns out, in most cases, heterogeneous groups are more efficient in their decision-making than homogenous ones, especially when considering small group sizes. In reality, being different disfavours the emergence of an hysteresis and a collective threshold which are characteristics of such cooperative species while keeping inter-attractions the same between individuals. Finally, when the cooperativity becomes very large, we observe an explosion of accessible stable states.

Most animals move intermittently, pausing or slowing down for short moments and short moves, and darting away towards a new location where to hover again. This pattern occurs at a range of spatial and temporal scales (thence, resembling 'scale-free'), from the quick inspection of local areas to the sum of all movements performed from birth to death. While this pattern has been extensively described, its proximate drivers remain open to debate. A current hypothesis states that underlying proximate generative mechanisms of intermittent movement of animals are linked to external stimuli, e.g., interactions with the abiotic environment, resources, and other individuals. Here we investigated a prediction from this hypothesis, using termites as a biological model. We verified whether the social background in which a termite is inserted will modify the parameters of its intermittent scale-free spatial displacement. This relates to the hypothesis because it inspects how do external stimuli coming from intraspecifics can affect this type of movement. We tracked the trajectories of a focal termite confined along its nestmates in experimental clueless arenas, every 0.5 s along about six hours. Arenas varied in group size and comprised 22 distinct caste compositions, yielding 75 trackings (> 400 h) and more than two million Cartesian coordinates. Most of these trajectories (96% or 72/75) were classified as intermittent scale free (Lévy-like), indicating prevalence of this type of movement over non-Lévy-like ones (P=1.62×10-15). Moreover, intermittent scale-free movements performed by the focal termite (i) did arise outside the foraging-searching framework, and (ii) were modified by the social context while remaining within the limits of Lévy-walk realms. That is, some limits seem to exist along with the necessary plasticity to provide room for natural selection. Moreover, by arising outside the foraging framework, Lévy-like movements are shown to have broader relevance. Studies on processes that depend on social context and movement (e.g., collective behaviour, the spread of diseases) may, hence, profit from such concepts.

Cerebrospinal fluid (CSF) flows through the perivascular spaces (PVSs) surrounding cerebral arteries. Revealing the mechanisms driving that flow could bring improved understanding of brain waste transport and insights for disorders including Alzheimer's disease and stroke. In vivo velocity measurements of CSF in surface PVSs in mice have been used to argue that flow is driven primarily by the pulsatile motion of artery walls - perivascular pumping. However, fluid dynamics theory and simulation have predicted that perivascular pumping produces flows differing from in vivo observations starkly, particularly in the phase and relative amplitude of flow oscillation. We show that coupling theoretical and simulated flows to more realistic end boundary conditions, using resistance and compliance values measured in mice instead of using periodic boundaries, results in velocities that match observations more closely in phase and relative amplitude of oscillation, while preserving the existing agreement in mean flow speed. This quantitative agreement among theory, simulation, and in vivo measurement further supports the idea that perivascular pumping is an important CSF driver in physiological conditions.

The first step in statistical inference of the evolutionary histories of species is developing a probability model that describes the mutation process as accurately and realistically as possible. A major complication of this inference is that different loci on the genome can have histories that diverge from the common species history and each other. The multispecies coalescent process is commonly used to model one source of this divergence, incomplete lineage sorting, or ILS. In Chifman and Kubatko (2015), the authors computed the site pattern probabilities for four taxa under a full probability model based on the Jukes-Cantor substitution model when the molecular clock holds. This paper generalizes that work to a relaxed clock model, allowing for mutation rates to differ among species. This will enable better phylogentic inference in cases where the molecular clock does not hold.

Releasing mosquitoes transinfected with the endosymbiotic bacterium Wolbachia is a novel strategy for interrupting vector-borne pathogen transmission. Following its success in controlling arboviruses spread by Aedes aegypti, this technology is being adapted for anopheline malaria vectors. However, antagonistic interactions between Wolbachia and naturally resident Asaia bacteria in malaria vectors have been demonstrated experimentally, potentially jeopardising Wolbachia biocontrol. We developed the first mathematical model accounting for interspecific competition between endosymbionts to assess the feasibility of this novel strategy for controlling malaria. First, Asaia prevalences among natural mosquito populations were compared with simulations parametrized with rates of Asaia transmission reported from laboratory studies. Discrepancies between projections and natural Asaia prevalences indicated potential overestimation of Asaia transmissibility in artificial laboratory settings. With parametrization that matches natural Asaia prevalence, simulations identified redundancies in Asaia's many infection routes (vertical, sexual and environmental). This resilience was only overcome when Wolbachia conferred very high resistance to environmental infection with Asaia, resulting in Wolbachia fixation and Asaia exclusion. Wolbachia's simulated spread was prevented when its maternal transmission was impeded in coinfected mosquitoes and the pre-control Asaia prevalence was beyond a threshold of 60-75%. This theoretical assessment highlights critical next steps in laboratory experiments to inform this strategy's feasibility.

A realistic rat brain model was used to simulate current density and electric field distributions under frequencies characteristic of sleeping states (0.8, 5, and 12 Hz). Two anode-electrode setups were simulated: plate vs. screws-anode, both with a cephalic cathode. Our simulations showed that these frequencies have limited impact on electric field and current density; however, the highest frequency evidenced higher values for both variables. The type of electrode setup had a greater effect on current distribution and induced fields. In that sense, the screws setup resulted in higher values of the modeled variables. The numeric results obtained are within the range of available data for rodent models using the finite elements method. These modeled effects should be analyzed regarding anatomical consequences (depth of penetration of the currents) and purpose of the experiment (i.e., entrainment of brain oscillations) in the context of sleep research.

In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline Syndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa, which has caused a dramatic impact from both socio-economic and environmental points of view. The main players involved in OQDS are represented by the insect vector, Philaenus spumarius, its host plants (olive trees and weeds) and the bacterium, X. fastidiosa. Current agronomic practices are mainly based on uprooting the sick olive trees and their surrounding ones, with later installment of olive cultivars more resistant to the bacterium infection. Unfortunately, both of these practices are having an undesirable impact on the environment (most of these olive trees were monumental ones) and on the economy. Based on a mathematical model expressed in terms of a nontrivial system of ordinary differential equations, our analysis has provided a clear picture of all possible steady states (feasible equilibria) and their stability properties, corresponding to a variety of different parameter scenarios; all of this has been illustrated by a set of computational experiments. A significant original contribution of this paper is the proof of the global asymptotic stability of each of the feasible equilibria under its existence assumptions, a fact that excludes multiple equilibria under the given conditions. It has emerged that the removal of a suitable amount of weed biomass (host plants of the juvenile stages of the insect vector of X. fastidiosa) from olive orchards and surrounding areas leads to the eradication of the epidemic, without requiring neither the removal nor the substitution of the existing olive trees.

As the COVID-19 pandemic continues, genetic mutations in SARS-CoV-2 emerge, and some of them are found more contagious than the previously identified strains, acting as the major mechanism for many large-scale epidemics. The transmission advantage of mutated variants is widely believed as an innate biological feature that is difficult to be altered by artificial factors. In this study, we explore how non-pharmaceutical interventions (NPI) may affect transmission advantage. A two-strain compartmental epidemic model is proposed and simulated to investigate the biological mechanism of the relationships among different NPIs, the changes in transmissibility of each strain and transmission advantage. Although the NPIs are effective in flattening the epidemic curve, we demonstrate that NPIs probably lead to a decline in transmission advantage, which is likely to occur if the NPIs become intensive. Our findings uncover the mechanistic relationship between NPIs and transmission advantage dynamically, and highlight the important role of NPIs not only in controlling the intensity of epidemics but also in slowing or even containing the growth of the proportion of variants.

Leadership is a widespread phenomena in social organisms and it is recognised to facilitate coordination between individuals. While the role of leadership in group foraging or swarm movement is well understood, it is not clear if leaders would also benefit more complex forms of coordination. In particular, a number of organisms coordinate by consensus decision-making, where individuals explicitly communicate their opinions until they converge toward a common decision. Taking inspiration from physical sciences, we extend a consensus formation model to integrate leaders, which we define by three traits: persuasiveness, talkativeness, and stubbornness. We use numerical simulations to investigate the effect of the number of leaders and their characteristics on the time a group spends to reach consensus, and the bias in the final decision. We show that having a minority of influential individuals (leaders) and a majority of influenceable individuals (followers) reduces the time to reach consensus, but biases the decision towards the preferences of the leaders. This effect emerges solely from the differences in individuals' personality traits, with the most determinant trait being the talkativeness of the individuals. Overall, we provide a comprehensive investigation of the effects of leaders and their traits on consensus decision-making.

Alzheimer's disease (AD) is one of the most common dementia, and its pathogenesis has not been clarified. The failure of amyloid targeted therapy has led us to rethink the pathogenesis of AD. There is growing evidence that complex diseases usually involve the impairment of multiple biological functions, rather than focus on several single genes. Protein-protein interaction network (PPIN) has been recognized as an important tool for identifying and predicting disease biomarkers. It is a great challenge to design network-based classification method for identifying effective, stable and interpretable biomarkers to distinguish the disease phenotype based on gene expression profile data. In this study, we used graph Laplacian regularization method to introduce topology information of PPIN, which can reveal the damaged networks involved in disease from heterogeneous gene expression profile data and identify disease-related biomarkers. The results in three AD datasets showed that the biomarkers identified by our method can not only distinguish the sample categories more accurately, but also help researchers understand the biological meaning behind complex diseases.

Spatial self-organization, a common feature of multi-species communities, can provide important insights into ecosystem structure and resilience. As environmental conditions gradually worsen (e.g., resource depletion, erosion intensified by storms, drought), some ecological systems collapse to an irreversible state once a tipping point is reached. Spatial patterning may be one way for them to cope with such changes. We use a mathematical model to describe self-organization of an eroding marsh shoreline based on three-way interactions between sediment volume and two ecosystem engineers - smooth cordgrass Spartina alterniflora and ribbed mussels Geukensia demissa. Our model indicates that scale-dependent interactions between multiple ecosystem engineers drive the self-organization of eroding marsh edges and regulate the spatial scale of shoreline morphology. Spatial self-organization of the marsh edge increases the system's productivity, allows it to withstand erosion, and delays degradation that otherwise would occur in the absence of strong species interactions. Further, changes in wavelength and variance of the spatial patterns give insight into marsh recession. Finally, we find that the presence of mussels in the system modulates the spatial scale of the patterns, generates patterns with shorter wavelengths, and allows the system to tolerate a greater level of erosion. Although previous studies suggest that self-organization can emerge from local interactions and can result in increased ecosystem persistence and stability in various ecosystems, our findings extend these concepts to coastal salt marshes, emphasizing the importance of the ecosystem engineers, smooth cordgrass and ribbed mussels, and demonstrating the potential value of self-organization for ecosystem management and restoration.

Weber's law states that the ratio of the smallest perceptual change in an input signal and the background signal is constant. The law is observed across the perception of weight, light intensity, and sound intensity and pitch. To explain Weber's law observed in steady-state responses, two models of perception have been proposed, namely the logarithmic and the linear model. This paper argues in favour of the linear model, which requires the sensory system to generate linear input-output relationship over several orders of magnitude. To this end, a four-node motif (FNM) is constructed from first principles whose series provides almost linear relationship between input signal and the output over arbitrary range of input signal. Mathematical analysis into the origin of this quasi-linear relationship shows that the series of coherent type-1 feed-forward loop (C1-FFL) is able to provide perfectly linear input-output relationship over arbitrary range of input signal. FNM also reproduces the neuronal data of numerosity detection study on the monkey. The series of FNM also provides a mechanism for sensitive detection over arbitrary range of input signal when the output has an upper limit. Further, the series of FNM provides a general basis for a class of bow-tie architecture where the number of receptors is much lower than the range of input signal and the "decoded output". Besides (quasi-)linear input-output relationship, another example of this class of bow-tie architecture that the series of FNM is able to produce is absorption spectra of cone opsins of humans. Further, the series of FNM and C1-FFL, both, can compute logarithm over arbitrary range of input signal.

Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals. Extracting information on survival and space use from observation data, in particular dead recovery data, requires explicitly identifying the observation process. We build a fully stochastic model for animals marked in populations of origin, which were found dead in spatially discrete destination areas. The model acts on the population level and includes parameters for use of space, survival and recovery probability. It is based on the division coefficient and the multinomial reencounter model. We use a likelihood-based approach, derive Restricted Maximum Likelihood-like estimates for all parameters and prove their existence and uniqueness. In a simulation study we demonstrate the performance of the model by using Bayesian estimators derived by the Markov chain Monte Carlo method. We obtain unbiased estimates for survival and recovery probability if the sample size is large enough. Moreover, we apply the model to real-world data of European robins Erithacus rubecula ringed at a stopover site. We obtain annual survival estimates for different spatially discrete non-breeding areas. Additionally, we can reproduce already known patterns of use of space for this species.