Genomic architecture controls spatial structuring in Amazonian birds

Large rivers are ubiquitously invoked to explain the distributional limits and speciation of the Amazon Basin's mega-diversity. However, inferences on the spatial and temporal origins of Amazonian species have narrowly focused on evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known to produce heterogeneity in differentiation across the genome. To test how these factors may influence evolutionary inferences across multiple taxa, we sequenced whole genomes of populations for three bird species that co-occur in southeastern Amazonian and exhibit different life histories linked to their propensity to maintain gene flow across the landscape. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with the species tree topology weight. Gene flow was less pervasive in regions of low recombination, making these windows more suitable for commonly used phylogenetic methods that assume a bifurcating-branching model. To corroborate that these associations were attributable to selection, we modeled the signature of adaptive alleles across the genome taking demographic history into account, and found that on average 31.6 percent of the genome showed high probability for patterns consistent with selective sweeps and linked selection directly affecting the estimation of evolutionary parameters. By implementing a comparative genomic approach we were able to disentangle the effects of intrinsic genomic characteristics and selection from the neutral processes and show how speciation hypotheses are sensitive to genomic architecture.

A Form-Finding Method for Branching Structures Based on Dynamic Relaxation

Branching structure is often used as a supporting structure of the grid shell due to its geometrical and force-transferring features, and the rationality of its shape is very important. The “physical” and “numerical” hanging models can be used for the joint form-finding of the branching structure and free-form grid shell. However, slack elements may exist in the equilibrium model which corresponds to the inefficient members in the form-found branching structure. To solve this problem, a form-finding method of branching structure based on dynamic relaxation is proposed in this study. The proposed method clusters the elements of the branching model and equalizes the axial forces of the elements in the same cluster, in other words, there are no slack elements in the equilibrium branching model. This method overcomes the defect that the equilibrium branching model may have slack elements and needs many manual adjustments during the procedure of determining the rational shape of a branching structure, and effectively prevents the inefficient members existing in the form-found structure. Numerical examples are provided to demonstrate the characteristics of the proposed method and its effectiveness is verified as well.

OFF-periods reduce the complexity of neocortical activity during sleep

Field recordings decrease their temporal complexity during slow-wave sleep (SWS), however, the neural mechanism for this decrease remains elusive. Here, we show that this complexity reduction is caused by synchronous neuronal OFF-periods by analysing in-vivo recordings from neocortical neuronal populations. We find that OFF-periods trap cortical dynamics, disrupting causal interactions and making the population activity more recurrent, deterministic, and less chaotic than during REM sleep or Wakefulness. Moreover, when we exclude OFF-periods, SWS becomes indistinguishable from Wakefulness or REM sleep. In fact, for all states, we show that the spiking activity has a universal scaling compatible with critical phenomena. We complement these results by analysing a critical branching model that replicates the experimental findings, where we show that forcing OFF-periods into a percentage of neurons suffices to generate a decrease in complexity that replicates SWS.

A Heterogeneous Branching Process with Immigration Modeling for COVID-19 Spreading in Local Communities in China

The COVID-19 pandemic spread catastrophically over the world since the spring of 2020. In this paper, a heterogeneous branching process with immigration is established to quantify the human-to-human transmission of COVID-19 in local communities, based on the temporal and structural transmission patterns extracted from public case disclosures by four provincial Health Commissions in China. With proper parameter settings, our branching model matches the actual transmission chains satisfactorily and, therefore, sheds light on the underlying COVID-19 spreading mechanism. Moreover, based on our branching model, the efficacy of home quarantine and social distancing are explored, providing a reference for the effective prevention of COVID-19 worldwide.

Stochastic effects on the establishment of mutants resistant to β-lactam antibiotics

For antibiotic resistance to arise, new resistant mutants must establish in a bacterial population before they can spread via natural selection. Understanding the stochastic factors influencing mutant establishment is crucial for a quantitative understanding of antibiotic resistance emergence. Here, we quantify the single-cell establishment probability of four Escherichia coli strains expressing β-lactamase alleles with different activity against the antibiotic cefotaxime, as a function of concentration. Using a simple branching model, we show that concentrations well below the minimal inhibitory concentration (MIC) can substantially hamper establishment, particularly for highly resistant mutants, suggesting cooperative effects due to antibiotic breakdown at high cell densities. We further show that variation among cell lineages may explain the more gradual influence of increased antibiotic concentrations on agar. Finally, we use the single-cell establishment probability to predict a strain’s MIC in the absence of social interactions, as a general reference to detect cooperative resistance effects.

On branching models with alarm triggerings

We discuss a continuous-time Markov branching model in which each individual can trigger an alarm according to a Poisson process. The model is stopped when a given number of alarms is triggered or when there are no more individuals present. Our goal is to determine the distribution of the state of the population at this stopping time. In addition, the state distribution at any fixed time is also obtained. The model is then modified to take into account the possible influence of death cases. All distributions are derived using probability-generating functions, and the approach followed is based on the construction of families of martingales.

A Predator–Prey Two-Sex Branching Process

In this paper, we present the first stochastic process to describe the interaction of predator and prey populations with sexual reproduction. Specifically, we introduce a two-type two-sex controlled branching model. This process is a two-type branching process, where the first type corresponds to the predator population and the second one to the prey population. While each population is described via a two-sex branching model, the interaction and survival of both groups is modelled through control functions depending on the current number of individuals of each type in the ecosystem. In view of their potential for the conservation of species, we provide necessary and sufficient conditions for the ultimate extinction of both species, the fixation of one of them and the coexistence of both of them. Moreover, the description of the present predator–prey two-sex branching process on the fixation events can be performed in terms of the behaviour of a one-type two-sex branching process with a random control on the number of individuals, which is also introduced and analysed.

CoVID-19 in Singapore: Impact of Contact Tracing and Self-awareness on Healthcare Demand

BackgroundA great concern around the globe now is to mitigate the COVID-19 pandemic via contact tracing. Analyzing the control strategies during the first five months of 2020 in Singapore is important to estimate the effectiveness of contacting tracing measures.MethodsWe developed a mathematical model to simulate the COVID-19 epidemic in Singapore, with local cases stratified into 5 categories according to the conditions of contact tracing and self-awareness. Key parameters of each category were estimated from local surveillance data. We also simulated a set of possible scenarios to predict the effects of contact tracing and self-awareness for the following month.FindingsDuring January 23 - March 16, 2020, the success probabilities of contact tracing and self-awareness were estimated to be 31% (95% CI 28%-33%) and 54% (95% CI 51%-57%), respectively. During March 17 - April 7, 2020, several social distancing measures (e.g., limiting mass gathering) were introduced in Singapore, which, however, were estimated with minor contribution to reduce the non-tracing reproduction number per local case (Rι,2). If contact tracing and self-awareness cannot be further improved, we predict that the COVID-19 epidemic will continue to spread in Singapore if Rι,2 ≥ 1.5.ConclusionContact tracing and self-awareness can mitigate the COVID-19 transmission, and can be one of the key strategies to ensure a sustainable reopening after lifting the lockdown.SummaryWe evaluate the efficiency of contact tracing and self-awareness in Singapore’s early-stage control of COVID-19. Then use a branching model to simulate and evaluate the possible prospective outcomes of Singapore’s COVID-19 control in different scenarios.

Parton Branching Process with Supersymmetric Particles at LHC Energies

A 2-step parton branching model is proposed to describe the potential presence of supersymmetric particles in multiplicity distributions. This model gives a reasonable description of the data obtained at 13TeV by the ATLAS Collaboration across the maximum pseudorapidity range of η<= 2.5.