CyberSec Research

Aquaculture is pivotal for global food security but faces significant challenges from infectious diseases, particularly those caused by Streptococcus species such as Streptococcus iniae and Streptococcus agalactiae. These pathogens induce severe systemic infections in various fish species, resulting in high morbidity and mortality rates. This review consolidates current knowledge on the epidemiology, pathogenesis, and clinical manifestations of these infections in fish and provides a comprehensive analysis of multifaceted control and prebention strategies. Advancements in genetic engineering and selective breeding are highlighted, demonstrating significant potential in developing disease-resistant fish strains through technologies like CRISPR-Cas9 and genomic selection. We examine the impact of farming practices on disease prevalence, emphasizing the roles of stocking density, feeding regimes, and biosecurity measures. The integration of big data analytics and IoT technologies is shown to revolutionize disease monitoring and management, enabling real-time surveillance and predictive modeling for timely interventions. Progress in vaccine development, including subunit, DNA, and recombinant protein vaccines, highlights the importance of tailored immunoprophylactic strategies. Furthermore, this review emphasizes the One-Health approach and the essential collaboration among industry, academia, and government to address the interconnected health of humans, animals, and the environment. This holistic strategy, supported by advanced technologies and collaborative efforts, promises to enhance the sustainability and productivity of aquaculture systems. Future research directions advocate for continued innovation and interdisciplinary partnerships to overcome the persistent challenges of streptococcal infections in aquaculture.

Phylogenetic tree shapes capture fundamental signatures of evolution. We consider ``ranked'' tree shapes, which are equipped with a total order on the internal nodes compatible the tree graph. Recent work has established an elegant bijection of ranked tree shapes and a class of integer matrices, called \textbf{F}-matrices, defined by simple inequalities. This formulation is for isochronous ranked tree shapes, where all leaves share the same sampling time, such as in the study of ancient human demography from present-day individuals. Another important style of phylogenetics concerns trees where the ``timing'' of events is by branch length rather than calendar time. This style of tree, called a rooted phylogram, is output by popular maximum-likelihood methods. These trees are broadly relevant, such as to study the affinity maturation of B cells in the immune system. Discretizing time in a rooted phylogram gives a fully heterochronous ranked tree shape, where leaves are part of the total order. Here we extend the \textbf{F}-matrix framework to such fully heterochronous ranked tree shapes. We establish an explicit bijection between a class of \textbf{F}-matrices and the space of such tree shapes. The matrix representation has the key feature that values at any entry are highly constrained via four previous entries, enabling straightforward enumeration of all valid tree shapes. We also use this framework to develop probabilistic models on ranked tree shapes. Our work extends understanding of combinatorial objects that have a rich history in the literature: isochronous ranked tree shapes are related to alternating permutations that Andr\'e studied over 130 years ago, and Poupard found (nearly 40 years ago) that fully heterochronous ranked tree shapes are counted by the reduced tangent numbers.

The classical Maximum-Entropy Principle (MEP) based on Shannon entropy is widely used to construct least-biased probability distributions from partial information. However, the Shore-Johnson axioms that single out the Shannon functional hinge on strong system independence, an assumption often violated in real-world, strongly correlated systems. We provide a self-contained guide to when and why practitioners should abandon the Shannon form in favour of the one-parameter Uffink-Jizba-Korbel (UJK) family of generalized entropies. After reviewing the Shore and Johnson axioms from an applied perspective, we recall the most commonly used entropy functionals and locate them within the UJK family. The need for generalized entropies is made clear with two applications, one rooted in economics and the other in ecology. A simple mathematical model worked out in detail shows the power of generalized maximum entropy approaches in dealing with cases where strong system independence does not hold. We conclude with practical guidelines for choosing an entropy measure and reporting results so that analyses remain transparent and reproducible.

We introduce a general diploid population model with self-fertilization and possible overlapping generations, and study the genealogy of a sample of $n$ genes as the population size $N$ tends to infinity. Unlike traditional approach in coalescent theory which considers the unconditional (annealed) law of the gene genealogies averaged over the population pedigree, here we study the conditional (quenched) law of gene genealogies given the pedigree. We focus on the case of high selfing probability and obtain that this conditional law converges to a random probability measure, given by the random law of a system of coalescing random walks on an exchangeable fragmentation-coalescence process of \cite{berestycki04}. This system contains the system of coalescing random walks on the ancestral recombination graph as a special case, and it sheds new light on the site-frequency spectrum (SFS) of genetic data by specifying how SFS depends on the pedigree. The convergence result is proved by means of a general characterization of weak convergence for random measures on the Skorokhod space with paths taking values in a locally compact Polish space.

Understanding the dynamics of the spread of diseases within populations is critical for effective public health interventions. We extend the classical SIR model by incorporating additional complexities such as the introduction of a cure and migration between cities. Our framework leverages a system of differential equations to simulate disease transmission across a network of interconnected cities, capturing more realistic patterns. We present theoretical results on the convergence of population sizes in the migration framework (in the absence of deaths). We also run numerical simulations to understand how the timing of the introduction of the cure affects mortality rates. Our numerical results explain how localized interventions affect the spread of the disease across cities. In summary, this work advances the modeling of epidemics to a more local scope, offering a more expressive tool for epidemiological research and public health planning.

Mapping habitat quality, based on factors like host availability and environmental suitability, is a common approach to determining which locations are important for the spread of a species. Mapping habitat connectivity takes geographic analyses a step further, evaluating the potential roles of locations in biological invasions, pandemics, or species conservation. Locations with high habitat quality may play a minor role in species spread if they are geographically isolated. Yet, a location with lower habitat quality may play a major role in a species' spread if it acts as a bridge between regions that would otherwise be physically fragmented. Here we introduce the geohabnet R package, which evaluates the potential importance of locations for the spread of species through habitat landscapes. geohabnet incorporates key factors such as dispersal probabilities and habitat availability in a network framework, for better understanding habitat connectivity for host-dependent species, such as pathogens, arthropod pests, or pollinators. geohabnet uses publicly available or user-provided datasets, six network centrality metrics, and a user-selected geographic scale. We provide examples using geohabnet for surveillance prioritization of emerging plant pests in Africa and the Americas. These examples illustrate how users can apply geohabnet for their species of interest and generate maps of the estimated importance of geographic locations for species spread. geohabnet provides a quick, open-source, and reproducible baseline to quantify a species' habitat connectivity across a wide range of geographic scales and evaluates potential scenarios for the expansion of a species through habitat landscapes. geohabnet supports biosecurity programs, invasion science, and conservation biology when prioritizing management efforts for transboundary pathogens, pests, or endangered species.

Evolutionary systems must learn to generalize, often extrapolating from a limited set of selective conditions to anticipate future environmental changes. The mechanisms enabling such generalization remain poorly understood, despite their importance to predict ecological robustness, drug resistance, or design future-proof vaccination strategies. Here, we demonstrate that annealed population heterogeneity, wherein distinct individuals in the population experience different instances of a complex environment over time, can act as a form of implicit regularization and facilitate evolutionary generalization. Mathematically, annealed heterogeneity introduces a variance-weighted demographic noise term that penalizes across-environment fitness variance and effectively rescales the population size, thereby biasing evolution toward generalist solutions. This process is indeed analogous to a variant of the mini-batching strategy employed in stochastic gradient descent, where an effective multiplicative noise produces an inductive bias by triggering noise-induced transitions. Through numerical simulations and theoretical analysis we discuss the conditions under which variation in how individuals experience environmental selection can naturally promote evolutionary strategies that generalize across environments and anticipate novel challenges.

We study the effect of intratumor heterogeneity in the likelihood of cancer cells moving from a primary tumor to other sites in the human body, generating a metastatic process. We model different scenarios of competition between tumor cells using a static evolutionary game in which cells compete for nutrients and oxygen and might choose to stay and proliferate in the primary tumor or opt to a motility strategy in order to find resources in a metastatic site. The theoretical results found in the evolutionarily equilibrium in the mathematical model are in line with the empirical results observed in oncology, namely, the coexistence of both primary and metastatic tumors and the conditions that favor a metastatic process. Particularly, the model finds mathematical support for what is empirically observed in punctuated and branching cancers for the specific case of clear cell renal cell carcinomas: motility of cells is larger in punctuated cancers if the proportion of BAP1 mutations remain below a given cell proportion threshold.

Collective systems that self-organise to maximise the group's ability to collect and distribute information can be successful in environments with high spatial and temporal variation. Such organisations are abundant in nature, as sharing information is a key benefit of many biological collective systems, and have been influential in the design of many artificial collectives such as swarm robotics. Understanding how these systems may be spatially distributed to optimise their collective potential is therefore of importance in both ecology and in collective systems design. Here, we develop a mathematical model which uses an optimisation framework to determine the higher-order spatial structure of a collective that optimises group-level knowledge transfer. The domain of the objective function is a set of weighted simplicial sets, which can fully represent the spatial structure from a topological perspective. By varying the parameters within the objective function and the constraints, we determine how the optimal spatial structure may vary when individuals differ in their information gathering ability and how this variation differs in the context of resource constraints. Our key findings are that the amount of resources in the environment can lead to specific subgroup sizes being optimal for the group as a whole when individuals are homogeneous in their information gathering abilities. Further, when there is variation in information gathering abilities, our model implies that the sharing of space between smaller subgroups of the population, rather than the whole population, is optimal for collective knowledge sharing. Our results have applications across diverse contexts from behavioural ecology to bio-inspired collective systems design.

Rabies continues to pose a significant zoonotic threat, particularly in areas with high populations of domestic dogs that serve as viral reservoirs. This study conducts a comparative analysis of Stochastic Continuous-Time Markov Chain (CTMC) and deterministic models to gain insights into rabies persistence within human and canine populations. By employing a multitype branching process, the stochastic threshold for rabies persistence was determined, revealing important insights into how stochasticity influences extinction probabilities. The stochastic model utilized 10,000 sample paths to estimate the probabilities of rabies outbreaks, offering a rigorous assessment of the variability in disease occurrences. Additionally, the study introduces a novel mathematical formulation of rabies transmission dynamics, which includes environmental reservoirs, free-ranging dogs, and domestic dogs as essential transmission factors. The basic reproduction number ($\mathcal{R}_0$) was derived and analyzed within stochastic frameworks, effectively bridging the gap between these two modeling approaches. Numerical simulations confirmed that the results from the stochastic model closely aligned with those from the deterministic model, while also highlighting the importance of stochasticity in scenarios with low infection rates. Ultimately, the study advocates for a comprehensive approach to rabies control that integrates both the predictable trends identified through deterministic models and the impact of random events emphasized by stochastic models.

The Swadesh approach for determining the temporal separation between two languages relies on the stochastic process of words replacement (when a complete new word emerges to represent a given concept). It is well known that the basic assumptions of the Swadesh approach are often unrealistic due to various contamination phenomena and misjudgments (horizontal transfers, variations over time and space of the replacement rate, incorrect assessments of cognacy relationships, presence of synonyms, and so on). All of this means that the results cannot be completely correct. More importantly, even in the unrealistic case that all basic assumptions are satisfied, simple mathematics places limits on the accuracy of estimating the temporal separation between two languages. These limits, which are purely probabilistic in nature and which are often neglected in lexicostatistical studies, are analyzed in detail in this article. Furthermore, in this work we highlight that the evolution of a language's lexicon is also driven by another stochastic process: gradual lexical modification of words. We show that this process equally also represents a major contribution to the reshaping of the vocabulary of languages over the centuries and we also show, from a purely probabilistic perspective, that taking into account this second random process significantly increases the precision in determining the temporal separation between two languages.

In this paper we investigate the asymptotic behavior of some SIR models incorporating demography, bounded random transmission coefficient and a time-dependent vaccination strategy targeting the susceptible population. In this setting, we establish the existence and uniqueness of non-negative global solution of the models and derive conditions under which either the disease is eradicated or becomes endemic. In addition, the theoretical results are further illustrated by several numerical simulations.

In this work, we prove the existence of a 2-cycle in an integrodifference equation with a Laplace kernel and logistic growth function, connecting two non-trivial fixed points of the second iterate of the logistic map in the non-chaotic regime. This model was first studied by Kot (1992), and the 2-cycle we establish corresponds to one numerically observed by Bourgeois, Leblanc, and Lutscher (2018) for the Ricker growth function. We provide strong evidence that the 2-cycle for the Ricker growth function can be rigorously proven using a similar approach. Finally, we present numerical results indicating that both 2-cycles exhibit spectral stability.

We consider populations evolving according to natural selection, mutation, and recombination, and assume that the genomes of all or a representative selection of individuals are known. We pose the problem if it is possible to infer fitness parameters and genotype fitness order from such data. We tested this hypothesis in simulated populations. We delineate parameter ranges where this is possible and other ranges where it is not.Our work provides a framework for determining when fitness inference is feasible from population-wide, whole-genome, time-stratified data and highlights settings where it is not. We give a brief survey of biological model organisms and human pathogens that fit into this framework.

Species distribution models (SDMs), which aim to predict species occurrence based on environmental variables, are widely used to monitor and respond to biodiversity change. Recent deep learning advances for SDMs have been shown to perform well on complex and heterogeneous datasets, but their effectiveness remains limited by spatial biases in the data. In this paper, we revisit deep SDMs from a Bayesian perspective and introduce BATIS, a novel and practical framework wherein prior predictions are updated iteratively using limited observational data. Models must appropriately capture both aleatoric and epistemic uncertainty to effectively combine fine-grained local insights with broader ecological patterns. We benchmark an extensive set of uncertainty quantification approaches on a novel dataset including citizen science observations from the eBird platform. Our empirical study shows how Bayesian deep learning approaches can greatly improve the reliability of SDMs in data-scarce locations, which can contribute to ecological understanding and conservation efforts.

Human bone marrow stromal cells (BMSC) include skeletal stem cells with ground-breaking therapeutic potential. However, BMSC colonies have very heterogeneous in vivo behaviour, due to their different potency; this unpredictability is the greatest hurdle to the development of skeletal regeneration therapies. Colony-level heterogeneity urges a fundamental question: how is it possible that one colony as a collective unit behaves differently from another one? If cell-to-cell variability were just an uncorrelated random process, a million cells in a transplant-bound colony would be enough to yield statistical homogeneity, hence washing out any colony-level traits. A possible answer is that the differences between two originating cells are transmitted to their progenies and collectively persist through an hereditary mechanism. But non-genetic inheritance remains an elusive notion, both at the experimental and at the theoretical level. Here, we prove that heterogeneity in the lineage topology of BMSC clonal colonies is determined by heritable traits that regulate cell-cycle exit. The cornerstone of this result is the definition of a novel entropy of the colony, which measures the hereditary ramifications in the distribution of inactive cells across different branches of the proliferation tree. We measure the entropy in 32 clonal colonies, obtained from single-cell lineage tracing experiments, and show that in the greatest majority of clones this entropy is decisively smaller than that of the corresponding non-hereditary lineage. This result indicates that hereditary epigenetic factors play a major role in determining cycle exit of bone marrow stromal cells.

Many Mendelian randomization (MR) papers have been conducted only in people of European ancestry, limiting transportability of results to the global population. Expanding MR to diverse ancestry groups is essential to ensure equitable biomedical insights, yet presents analytical and conceptual challenges. This review examines the practical challenges of MR analyses beyond the European only context, including use of data from multi-ancestry, mismatched ancestry, and admixed populations. We explain how apparent heterogeneity in MR estimates between populations can arise from differences in genetic variant frequencies and correlation patterns, as well as from differences in the distribution of phenotypic variables, complicating the detection of true differences in the causal pathway. We summarize published strategies for selecting genetic instruments and performing analyses when working with limited ancestry-specific data, discussing the assumptions needed in each case for incorporating external data from different ancestry populations. We conclude that differences in MR estimates by ancestry group should be interpreted cautiously, with consideration of how the identified differences may arise due to social and cultural factors. Corroborating evidence of a biological mechanism altering the causal pathway is needed to support a conclusion of differing causal pathways between ancestry groups.

We design a linear chain trick algorithm for dynamical systems for which we have oscillatory time histories in the distributed time delay. We make use of this algorithmic framework to analyse memory effects in disease evolution in a population. The modelling is based on a susceptible-infected-recovered SIR - model and on a susceptible-exposed-infected-recovered SEIR - model through a kernel that dampens the activity based on the recent history of infectious individuals. This corresponds to adaptive behavior in the population or through governmental non-pharmaceutical interventions. We use the linear chain trick to show that such a model may be written in a Markovian way, and we analyze the stability of the system. We find that the adaptive behavior gives rise to either a stable equilibrium point or a stable limit cycle for a close to constant number of susceptibles, i.e.\ locally in time. We also show that the attack rate for this model is lower than it would be without the dampening, although the adaptive behavior disappears as time goes to infinity and the number of infected goes to zero.