CyberSec Research

In 1997, T. H. E. Meuwissen published a groundbreaking article titled 'Maximizing the response of selection with a predefined rate of inbreeding', in which he provided an optimized solution for the trade-off between genetic response and inbreeding avoidance in animal breeding. Evidently, this issue is highly relevant for the honeybee with its small breeding population sizes. However, the genetic peculiarities of bees have thus far prevented an application of the theory to this species. The present manuscript intends to fill this desideratum. It develops the necessary bee-specific theory and introduces a small R script that implements Optimum Contribution Selection (OCS) for honeybees. While researching for this manuscript, we found it rather cumbersome that even though Meuwissen's theory is 28 years old and has sparked research in many new directions, to our knowledge, there is still no comprehensive textbook on the topic. Instead, all relevant information had to be extracted from several articles, leading to a steep learning curve. We anticipate that many honeybee breeding scientists with a putative interest in OCS for honeybees have little to no experience with classical OCS. Thus, we decided to embed our new derivations into a general introduction to OCS that then specializes more and more to the honeybee case. The result are these 121 pages, of which we hope that at least the first sections can also be of use for breeding theorists concerned with other species than honeybees.

Econometrics in general, and Panel Data methods in particular, are becoming crucial in Public Health Economics and Social Policy analysis. In this discussion paper, we employ a helpful approach of Feasible Generalized Least Squares (FGLS) to assess if there are statistically relevant relationships between hemoglobin (adjusted to sea-level), weight, and height from 2007 to 2022 in children up to five years of age in Peru. By using this method, we may find a tool that allows us to confirm if the relationships considered between the target variables by the Peruvian agencies and authorities are in the right direction to fight against chronic malnutrition and stunting.

A highly pathogenic avian influenza (HPAI) panzootic has severely impacted wild bird populations worldwide, with documented (zoonotic) transmission to mammals, including humans. Ongoing HPAI outbreaks on U.S. cattle farms have raised concerns about potential spillover of virus from birds to cattle in other countries, including Denmark. In the EU, the Bird Flu Radar tool, coordinated by EFSA, monitors the spatio-temporal risk of HPAIV infection in wild bird populations. A preparedness tool to assess the spillover risk to the cattle industry is currently lacking, despite its critical importance. This study aims to assess the temporal and spatial risk of HPAI virus (HPAIV) spillover from wild birds, particularly waterfowl, into cattle populations in Denmark. To support this assessment, a spillover transmission model is developed by integrating two well-established surveillance tools, eBird and Bird Flu Radar, in combination with global cattle density data. The generated quantitative risk maps reveal the heterogeneous temporal and spatial distribution of HPAIV spillover risk from wild birds to cattle across Denmark. The highest risk periods are observed during calendar weeks 50 to 10. The estimated total number of spillover cases nationwide is 1.93 (95% CI: 0.48, 4.98) in 2024, and 0.62 cases (95% CI: 0.15, 1.25) in 2025. These risk estimates provide valuable insights to support veterinary contingency planning and enable targeted allocation of resources in highrisk areas for the early detection of HPAIV in cattle.

This work investigates how biodiversity is affected in a cyclic spatial May-Leonard model with hierarchical and non-hierarchical rules. Here we propose a generalization of the traditional rock-paper-scissors model by considering highly reactive species, i. e., species that react in a stronger manner compared to the others in respect to either competition or reproduction. These two classes of models, called here Highly Competitive and Highly Reproductive models, may lead to hierarchical and non-hierarchical dynamics, depending on the number of highly reactive species. The fundamental feature of these models is the fact that hierarchical models may as well support biodiversity, however, with a higher probability of extinction than the non-hierarchical ones, which are in fact more robust. This analysis is done by evaluating the probability of extinction as a function of mobility. In particular, we have analyzed how the dominance scheme changes depending on the highly reactive species for non-hierarchical models, where the findings lead to the conclusion that highly reactive species are usually at a disadvantage compared to the others. Moreover, we have investigated the power spectrum and the characteristic length of each species, including more information on the behavior of the several systems considered in the present work.

Higher-order interactions are prevalent in real-world complex systems and exert unique influences on system evolution that cannot be captured by pairwise interactions. We incorporate game transitions into the higher-order prisoner's dilemma game model, where these transitions consistently promote cooperation. Moreover, in systems with game transitions, the fraction of higher-order interactions has a dual impact, either enhancing the emergence and persistence of cooperation or facilitating invasions that promote defection within an otherwise cooperative system.

The highly pathogenic avian influenza (HPAI) H5 clade 2.3.4.4b has triggered an unprecedented global panzootic. As the frequency and scale of HPAI H5 outbreaks continue to rise, understanding how wild birds contribute to shape the global virus spread across regions, affecting poultry, domestic and wild mammals, is increasingly critical. In this review, we examine ecological and evolutionary studies to map the global transmission routes of HPAI H5 viruses, identify key wild bird species involved in viral dissemination, and explore infection patterns, including mortality and survival. We also highlight major remaining knowledge gaps that hinder a full understanding of wild birds role in viral dynamics, which must be addressed to enhance surveillance strategies and refine risk assessment models aimed at preventing future outbreaks in wildlife, domestic animals and safeguard public health.

We introduce an individual-based model for structured populations undergoing demographic bottlenecks, i.e. drastic reductions in population size that last many generations and can have arbitrary shapes. We first show that the (non-Markovian) allele-frequency process converges to a Markovian diffusion process with jumps in a suitable relaxation of the Skorokhod J1 topology. Backward in time we find that genealogies of samples of individuals are described by multi-type $\Xi$-coalescents presenting multiple simultaneous mergers with simultaneous migrations. These coalescents are also moment-duals of the limiting jump diffusions. We then show through a numerical study that our model is flexible and can predict various shapes for the site frequency spectrum, consistent with real data, using a small number of interpretable parameters.

Free-ranging dogs (Canis familiaris) thrive in diverse landscapes, including those heavily modified by humans. This study investigated the influence of resource availability on their spatial ecology across 52 rural and 41 urban sites, comparing urban and rural environments. Census-based surveys were conducted to understand the distribution of dogs and resources, while territory-based observations were carried out across different seasons to capture temporal variability in dog populations and resource availability. Dog and resource density were significantly higher in urban areas, supporting the Resource Dispersion Hypothesis (RDH). Territory size (TS) varied seasonally, decreasing significantly (by 21%) post-mating, likely reflecting shifts in resource demands and distribution. TS was positively correlated with resource heterogeneity, dispersion, patch richness, and male-to-female ratio, but not with group size, which remained stable across seasons and resource gradients. This suggests that while resource availability and sex ratio influence space use, social factors play a key role in shaping group dynamics. These findings highlight the complex interplay between resource availability, social behaviour, and human influences in shaping the spatial ecology of free-ranging dogs and have important implications for their management and disease control, informing targeted interventions such as spay/neuter programs and responsible waste management in both urban and rural landscapes.

We propose and analyze a model for the dynamics of the flow into and out of a nest for the arboreal turtle ant $\textit{Cephalotes goniodontus}$ during foraging to investigate a possible mechanism for the emergence of oscillations. In our model, there is mixed dynamic feedback between the flow of ants between different behavioral compartments and the concentration of pheromone along trails. On one hand, the ants deposit pheromone along the trail, which provides a positive feedback by increasing rates of return to the nest. On the other hand, pheromone evaporation is a source of negative feedback, as it depletes the pheromone and inhibits the return rate. We prove that the model is globally asymptotically stable in the absence of pheromone feedback. Then we show that pheromone feedback can lead to a loss of stability of the equilibrium and onset of sustained oscillations in the flow in and out of the nest via a Hopf bifurcation. This analysis sheds light on a potential key mechanism that enables arboreal turtle ants to effectively optimize their trail networks to minimize traveled path lengths and eliminate graph cycles.

The epidemiological dynamics of Mycoplasma pneumoniae are characterized by complex and poorly understood multiannual cycles, posing challenges for forecasting. Using Bayesian methods to fit a seasonally forced transmission model to long-term surveillance data from Denmark (1958-1995, 2010-2025), we investigate the mechanisms driving recurrent outbreaks of M. pneumoniae. The period of the multiannual cycles (predominantly approx. 5 years in Denmark) are explained as a consequence of the interaction of two time-scales in the system, one intrinsic and one extrinsic (seasonal). While it provides an excellent fit to shorter time series (a few decades), we find that the deterministic model eventually settles into an annual cycle, failing to reproduce the observed 4-5-year periodicity long-term. Upon further analysis, the system is found to exhibit transient chaos and thus high sensitivity to stochasticity. We show that environmental (but not purely demographic) stochasticity can sustain the multi-year cycles via stochastic resonance. The disruptive effects of COVID-19 non-pharmaceutical interventions (NPIs) on M. pneumoniae circulation constitute a natural experiment on the effects of large perturbations. Consequently, the effects of NPIs are included in the model and medium-term predictions are explored. Our findings highlight the intrinsic sensitivity of M. pneumoniae dynamics to perturbations and interventions, underscoring the limitations of deterministic epidemic models for long-term prediction. More generally, our results emphasize the potential role of stochasticity as a driver of complex cycles across endemic and recurring pathogens.

In 2019, Yoshida et al. developed tropical Principal Component Analysis (PCA), that is, an analogue of the classical PCA in the setting of tropical geometry and applied it to visualize a set of gene trees over a space of phylogenetic trees which is an union of lower dimensional polyhedral cones in an Euclidean space with its dimension $m(m-1)/2$ where $m$ is the number of leaves. In this paper, we introduce a projected gradient descent method to estimate the tropical principal polytope over the space of phylogenetic trees and we apply it to apicomplexa dataset. With computational experiment against MCMC samplers, we show that our projected gradient descent works very well.

Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pairwise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.

We study a fast-slow version of the Bazykin-Berezovskaya predator-prey model with Allee effect evolving on two timescales, through the lenses of Geometric Singular Perturbation Theory (GSPT). The system we consider is in non-standard form. We completely characterize its dynamics, providing explicit threshold quantities to distinguish between a rich variety of possible asymptotic behaviors. Moreover, we propose numerical results to illustrate our findings. Lastly, we comment on the real-world interpretation of these results, in an economic framework and in the context of predator-prey models.

Genomic data can be used to reconstruct population size over thousands of generations, using a new class of algorithms (SMC methods). These analyses often show a recent decline in $N_e$ (effective size), which at face value implies a conservation or demographic crisis: a population crash and loss of genetic diversity. This interpretation is frequently mistaken. Here we outline how SMC methods work, why they generate this misleading signal, and suggest simple approaches for exploiting the rich information produced by these algorithms. In most species, genomic patterns reflect major changes in the species' range and subdivision over tens or hundreds of thousands of years. Consequently, collaboration between geneticists, palaeoecologists, palaeoclimatologists, and geologists is crucial for evaluating the outputs of SMC algorithms.

To accurately represent disease spread, epidemiological models must account for the complex network topology and contact heterogeneity. Traditionally, most studies have used random heterogeneous networks, which ignore correlations between the nodes' degrees. Yet, many real-world networks exhibit degree assortativity - the tendency for nodes with similar degrees to connect. Here we explore the effect degree assortativity (or disassortativity) has on long-term dynamics and disease extinction in the realm of the susceptible-infected-susceptible model on heterogeneous networks. We derive analytical results for the mean time to extinction (MTE) in assortative networks with weak heterogeneity, and show that increased assortativity reduces the MTE and that assortativity and degree heterogeneity are interchangeable with regard to their impact on the MTE. Our analytical results are verified using the weighted ensemble numerical method, on both synthetic and real-world networks. Notably, this method allows us to go beyond the capabilities of traditional numerical tools, enabling us to study rare events in large assortative networks, which were previously inaccessible.

We propose a mathematical model of Antimicrobial Resistance in the host to predict the failure of two antagonists of bacterial growth: the immune response and a single-antibiotic therapy. After characterising the initial bacterial load that cannot be cleared by the immune system alone, we define the success set of initial conditions for which an infection-free equilibrium can be reached by a viable single-antibiotic therapy, and we provide a rigorously defined inner approximation of the set. Finally, we propose an optimal control approach to design and compare successful single-drug therapies.

The U.S. launched the Secure Pork Supply (SPS) Plan for Continuity of Business, a voluntary program providing foreign animal disease (FAD) guidance and setting biosecurity standards to maintain business continuity amid FAD outbreaks. The role of biosecurity in disease prevention is well recognized, yet the U.S. swine industry lacks knowledge of individual farm biosecurity plans and the efficacy of existing measures. We describe a multi-sector initiative that formed the Rapid Access Biosecurity (RAB) app consortium with the swine industry, government, and academia. We (i) summarized 7,625 farms using RABapp, (ii) mapped U.S. commercial swine coverage and areas of limited biosecurity, and (iii) examined associations between biosecurity and occurrences of porcine reproductive and respiratory syndrome virus (PRRSV) and porcine epidemic diarrhea virus (PEDV). RABapp, used in 31 states, covers ~47% of U.S. commercial swine. Of 307 Agricultural Statistics Districts with swine, 78% (238) had <50% of those animals in RABapp. We used a mixed-effects logistic regression model, accounting for production company and farm type (breeding vs. non-breeding). Requiring footwear/clothing changes, having multiple carcass disposal locations, hosting other businesses, and greater distance to swine farms reduced infection odds. Rendering carcasses, manure pit storage or land application, multiple perimeter buffer areas, and a larger animal housing area increased risk. This study leveraged RABapp to assess U.S. swine farm biosecurity, revealing gaps in SPS plan adoption that create vulnerable regions. Some biosecurity practices (e.g., footwear changes) lowered PRRSV/PEDV risk, while certain disposal and manure practices increased it. Targeted biosecurity measures and broader RABapp adoption can bolster industry resilience against foreign animal diseases.

The rise in chronic diseases over the last century presents a significant health and economic burden globally. Here we apply evolutionary medicine and life history theory to better understand their development. We highlight an imbalanced metabolic axis of growth and proliferation (anabolic) versus maintenance and dormancy (catabolic), focusing on major mechanisms including IGF-1, mTOR, AMPK, and Klotho. We also relate this axis to the hyperfunction theory of aging, which similarly implicates anabolic mechanisms like mTOR in aging and disease. Next, we highlight the Brain-Body Energy Conservation model, which connects the hyperfunction theory with energetic trade-offs that induce hypofunction and catabolic health risks like impaired immunity. Finally, we discuss how modern environmental mismatches exacerbate this process. Following our review, we discuss future research directions to better understand health risk. This includes studying IGF-1, mTOR, AMPK, and Klotho and how they relate to health and aging in human subsistence populations, including with lifestyle shifts. It also includes understanding their role in the developmental origins of health and disease as well as the social determinants of health disparities. Further, we discuss the need for future studies on exceptionally long-lived species to understand potentially underappreciated trade-offs and costs that come with their longevity. We close with considering possible implications for therapeutics, including (1) compensatory pathways counteracting treatments, (2) a Goldilocks zone, in which suppressing anabolic metabolism too far introduces catabolic health risks, and (3) species constraints, in which therapeutics tested in shorter lived species with greater anabolic imbalance will be less effective in humans.