Adaptive sampling method to monitor low-risk pathways with limited surveillance resources.

The rise of globalization has led to a sharp increase in international trade with high volumes of containers, goods, and items moving across the world. Unfortunately, these trade pathways also facilitate the movement of unwanted pests, weeds, diseases, and pathogens. Each item could contain biosecurity risk material, but it is impractical to inspect every item. Instead, inspection efforts typically focus on high-risk items. However, low risk does not imply no risk. It is crucial to monitor the low-risk pathways to ensure that they are and remain low risk. To do so, many approaches would seek to estimate the risk to some precision, but increasingly lower risks require more samples. On a low-risk pathway that can be afforded only limited inspection resources, it makes more sense to assign fewer samples to the lower risk activities. We approach the problem by introducing two thresholds. Our method focuses on letting us know whether the risk is below certain thresholds, rather than estimating the risk precisely. This method also allows us to detect a significant change in risk. Our approach typically requires less sampling than previous methods, while still providing evidence to regulators to help them efficiently and effectively allocate inspection effort.

Modelling the impact of hybrid immunity on future COVID-19 epidemic waves.

BACKGROUND: Since the emergence of SARS-CoV-2 (COVID-19), there have been multiple waves of infection and multiple rounds of vaccination rollouts. Both prior infection and vaccination can prevent future infection and reduce severity of outcomes, combining to form hybrid immunity against COVID-19 at the individual and population level. Here, we explore how different combinations of hybrid immunity affect the size and severity of near-future Omicron waves. METHODS: To investigate the role of hybrid immunity, we use an agent-based model of COVID-19 transmission with waning immunity to simulate outbreaks in populations with varied past attack rates and past vaccine coverages, basing the demographics and past histories on the World Health Organization Western Pacific Region. RESULTS: We find that if the past infection immunity is high but vaccination levels are low, then the secondary outbreak with the same variant can occur within a few months after the first outbreak; meanwhile, high vaccination levels can suppress near-term outbreaks and delay the second wave. Additionally, hybrid immunity has limited impact on future COVID-19 waves with immune-escape variants. CONCLUSIONS: Enhanced understanding of the interplay between infection and vaccine exposure can aid anticipation of future epidemic activity due to current and emergent variants, including the likely impact of responsive vaccine interventions.

Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park.

We tackle the problem of coupling a spatiotemporal model for simulating the spread and control of an invasive alien species with data coming from image processing and expert knowledge. In this study, we implement a spatially explicit optimal control model based on a reaction-diffusion equation which includes an Holling II type functional response term for modeling the density control rate. The model takes into account the budget constraint related to the control program and searches for the optimal effort allocation for the minimization of the invasive alien species density. Remote sensing and expert knowledge have been assimilated in the model to estimate the initial species distribution and its habitat suitability, empirically extracted by a land cover map of the study area. The approach has been applied to the plant species Ailanthus altissima (Mill.) Swingle within the Alta Murgia National Park. This area is one of the Natura 2000 sites under the study of the ongoing National Biodiversity Future Center (NBFC) funded by the Italian National Recovery and Resilience Plan (NRRP), and pilot site of the finished H2020 project ECOPOTENTIAL, which aimed at the integration of modeling tools and Earth Observations for a sustainable management of protected areas. Both the initial density map and the land cover map have been generated by using very high resolution satellite images and validated by means of ground truth data provided by the EU Life Alta Murgia Project (LIFE12 BIO/IT/000213), a project aimed at the eradication of A. altissima in the Alta Murgia National Park.

Understanding the impact of disease and vaccine mechanisms on the importance of optimal vaccine allocation.

Vaccination is an important epidemic intervention strategy. However, it is generally unclear how the outcomes of different vaccine strategies change depending on population characteristics, vaccine mechanisms and allocation objective. In this paper we develop a conceptual mathematical model to simulate strategies for pre-epidemic vaccination. We extend the SEIR model to incorporate a range of vaccine mechanisms and disease characteristics. We then compare the outcomes of optimal and suboptimal vaccination strategies for three public health objectives (total infections, total symptomatic infections and total deaths) using numerical optimisation. Our comparison shows that the difference in outcomes between vaccinating optimally and suboptimally depends on vaccine mechanisms, disease characteristics, and objective considered. Our modelling finds vaccines that impact transmission produce better outcomes as transmission is reduced for all strategies. For vaccines that impact the likelihood of symptomatic disease or dying due to infection, the improvement in outcome as we decrease these variables is dependent on the strategy implemented. Through a principled model-based process, this work highlights the importance of designing effective vaccine allocation strategies. We conclude that efficient allocation of resources can be just as crucial to the success of a vaccination strategy as the vaccine effectiveness and/or amount of vaccines available.

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19.

BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS: In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS: We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION: For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

Measuring the photostability of agrochemicals on leaves: understanding the balance between loss processes and foliar uptake.

BACKGROUND: Photostability is an important property in agrochemicals, impacting their biological efficacy, environmental fate and registrability. As such, it is a property that is routinely measured during the development of new active ingredients and their formulations. To make these measurements, compounds are typically exposed to simulated sunlight after application to a glass substrate. While useful, these measurements neglect key factors that influence photostability under true field conditions. Most importantly, they neglect the fact that compounds are applied to living plant tissue, and that uptake and movement within this tissue provides a mechanism to protect compounds from photodegradation. RESULTS: In this work, we introduce a new photostability assay incorporating leaf tissue as a substrate, designed to run at medium throughput under standardized laboratory conditions. Using three test cases, we demonstrate that our leaf-disc-based assays provides quantitatively different photochemical loss profiles to an assay employing a glass substrate. And we also demonstrate that these different loss profiles are intimately linked to the physical properties of the compounds, the effect that those properties have on foliar uptake and, thereby, the availability of the active ingredient on the leaf surface. CONCLUSIONS: The method presented provides a quick and simple measure of the interplay between abiotic loss processes and foliar uptake, supplying additional information to facilitate the interpretation of biological efficacy data. The comparison of loss between glass slides and leaves also provides a better understanding of when intrinsic photodegradation is likely to be a good model for a compound's behaviour under field conditions. © 2023 Society of Chemical Industry.

Analysis of sloppiness in model simulations: Unveiling parameter uncertainty when mathematical models are fitted to data.

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

Dissecting Solvent Effects on Hydrogen Bonding.

The experimental isolation of H-bond energetics from the typically dominant influence of the solvent remains challenging. Here we use synthetic molecular balances to quantify amine/amide H-bonds in competitive solvents. Over 200 conformational free energy differences were determined using 24 H-bonding balances in 9 solvents spanning a wide polarity range. The correlations between experimental interaction energies and gas-phase computed energies exhibited wild solvent-dependent variation. However, excellent correlations were found between the same computed energies and the experimental data following empirical dissection of solvent effects using Hunter's α/ß solvation model. In addition to facilitating the direct comparison of experimental and computational data, changes in the fitted donor and acceptor constants reveal the energetics of secondary local interactions such as competing H-bonds.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Optimal allocation of PCR tests to minimise disease transmission through contact tracing and quarantine.

PCR testing is a crucial capability for managing disease outbreaks, but it is also a limited resource and must be used carefully to ensure the information gain from testing is valuable. Testing has two broad uses for informing public health policy, namely to track epidemic dynamics and to reduce transmission by identifying and managing cases. In this work we develop a modelling framework to examine the effects of test allocation in an epidemic, with a focus on using testing to minimise transmission. Using the COVID-19 pandemic as an example, we examine how the number of tests conducted per day relates to reduction in disease transmission, in the context of logistical constraints on the testing system. We show that if daily testing is above the routine capacity of a testing system, which can cause delays, then those delays can undermine efforts to reduce transmission through contact tracing and quarantine. This work highlights that the two goals of aiming to reduce transmission and aiming to identify all cases are different, and it is possible that focusing on one may undermine achieving the other. To develop an effective strategy, the goals must be clear and performance metrics must match the goals of the testing strategy. If metrics do not match the objectives of the strategy, then those metrics may incentivise actions that undermine achieving the objectives.

Managing ecosystems in a sea of uncertainty: invasive species management and assisted colonizations.

Managing ecosystems in the face of complex species interactions, and the associated uncertainty, presents a considerable ecological challenge. Altering those interactions via actions such as invasive species management or conservation translocations can result in unintended consequences, supporting the need to be able to make more informed decisions in the face of this uncertainty. We demonstrate the utility of ecosystem models to reduce uncertainty and inform future ecosystem management. We use Phillip Island, Australia, as a case study to investigate the impacts of two invasive species management options and consider whether a critically endangered mammal is likely to establish a population in the presence of invasive species. Qualitative models are used to determine the effects of apex predator removal (feral cats) and invasive prey removal (rabbits, rats, and mice). We extend this approach using Ensemble Ecosystem Models to consider how suppression, rather than eradication influences the species community; and consider whether an introduction of the critically endangered eastern barred bandicoot is likely to be successful in the presence of invasive species. Our analysis revealed the potential for unintended outcomes associated with feral cat control operations, with rats and rabbits expected to increase in abundance. A strategy based on managing prey species appeared to have the most ecosystem-wide benefits, with rodent control showing more favorable responses than a rabbit control strategy. Eastern barred bandicoots were predicted to persist under all feral cat control levels (including no control). Managing ecosystems is a complex and imprecise process. However, qualitative modeling and ensemble ecosystem modeling address uncertainty and are capable of improving and optimizing management practices. Our analysis shows that the best conservation outcomes may not always be associated with the top-down control of apex predators, and land managers should think more broadly in relation to managing bottom-up processes as well. Challenges faced in continuing to conserve biodiversity mean new, bolder, conservation actions are needed. We suggest that endangered species are capable of surviving in the presence of feral cats, potentially opening the door for more conservation translocations.

Combating ecosystem collapse from the tropics to the Antarctic.

Globally, collapse of ecosystems-potentially irreversible change to ecosystem structure, composition and function-imperils biodiversity, human health and well-being. We examine the current state and recent trajectories of 19 ecosystems, spanning 58° of latitude across 7.7 M km2 , from Australia's coral reefs to terrestrial Antarctica. Pressures from global climate change and regional human impacts, occurring as chronic 'presses' and/or acute 'pulses', drive ecosystem collapse. Ecosystem responses to 5-17 pressures were categorised as four collapse profiles-abrupt, smooth, stepped and fluctuating. The manifestation of widespread ecosystem collapse is a stark warning of the necessity to take action. We present a three-step assessment and management framework (3As Pathway Awareness, Anticipation and Action) to aid strategic and effective mitigation to alleviate further degradation to help secure our future.

Persistence as an Optimal Hedging Strategy.

Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.

Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry.

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear only once, the multiplicity of ways in which many compounds can be represented means that these databases require methods for standardizing the representation of chemistry. This is commonly achieved through the use of "Chemistry Business Rules", sets of predefined rules that describe the "house style" of the database in question. At Syngenta, the historical approach to the design of chemistry business rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, we overturn that convention. Through the use of quantum chemistry calculations, we define a set of chemistry business rules for tautomer standardization that reproduces gas-phase energetic preferences. We go on to show that, compared to our historic approach, this method yields tautomers that are in better agreement with those observed experimentally in condensed phases and that are better suited for use in predictive models.

Designing combination therapies using multiple optimal controls.

Strategic management of populations of interacting biological species routinely requires interventions combining multiple treatments or therapies. This is important in key research areas such as ecology, epidemiology, wound healing and oncology. Despite the well developed theory and techniques for determining single optimal controls, there is limited practical guidance supporting implementation of combination therapies. In this work we use optimal control theory to calculate optimal strategies for applying combination therapies to a model of acute myeloid leukaemia. We present a versatile framework to systematically explore the trade-offs that arise in designing combination therapy protocols using optimal control. We consider various combinations of continuous and bang-bang (discrete) controls, and we investigate how the control dynamics interact and respond to changes in the weighting and form of the pay-off characterising optimality. We demonstrate that the optimal controls respond non-linearly to treatment strength and control parameters, due to the interactions between species. We discuss challenges in appropriately characterising optimality in a multiple control setting and provide practical guidance for applying multiple optimal controls. Code used in this work to implement multiple optimal controls is available on GitHub.

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data.

Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

A novel approach to assessing the ecosystem-wide impacts of reintroductions.

Reintroducing a species to an ecosystem can have significant impacts on the recipient ecological community. Although reintroductions can have striking and positive outcomes, they also carry risks; many well-intentioned conservation actions have had surprising and unsatisfactory outcomes. A range of network-based mathematical methods has been developed to make quantitative predictions of how communities will respond to management interventions. These methods are based on the limited knowledge of which species interact with each other and in what way. However, expert knowledge isn't perfect and can only take models so far. Fortunately, other types of data, such as abundance time series, is often available, but, to date, no quantitative method exists to integrate these various data types into these models, allowing more precise ecosystem-wide predictions. In this paper, we develop mathematical methods that combine time-series data of multiple species with knowledge of species interactions and we apply it to proposed reintroductions at Booderee National Park in Australia. There have been large fluctuations in species abundances at Booderee National Park in recent history, following intense feral fox (Vulpes vulpes) control, including the local extinction of the greater glider (Petauroides volans). These fluctuations can provide information about the system isn't readily obtained from a stable system, and we use them to inform models that we then use to predict potential outcomes of eastern quoll (Dasyurus viverrinus) and long-nosed potoroo (Potorous tridactylus) reintroductions. One of the key species of conservation concern in the park is the Eastern Bristlebird (Dasyornis brachypterus), and we find that long-nosed potoroo introduction would have very little impact on the Eastern Bristlebird population, while the eastern quoll introduction increased the likelihood of Eastern Bristlebird decline, although that depends on the strength and form of any possible interaction.

Single Step Double-walled Nanoencapsulation (SSDN).

A quick fabrication method for making double-walled (DW) polymeric nanospheres is presented. The process uses sequential precipitation of two polymers. By choosing an appropriate solvent and non-solvent polymer pair, and engineering two sequential phase inversions which induces first precipitation of the core polymer followed by precipitation of the shell polymer, DW nanospheres can be created instantaneously. A series of DW formulations were prepared with various core and shell polymers, then characterized using laser diffraction particle sizing, scanning electron microscopy, atomic force microscopy, Fourier transform infrared spectroscopy, and differential scanning calorimetry (DSC). Atomic force microscopy (AFM) imaging confirmed existence of a single core polymer coated with a second polymer. Insulin (3.3% loading) was used as a model drug to assess its release profile from core (PLGA) and shell (PBMAD) polymers and resulted with a tri-phase release profile in vitro for two months. Current approaches for producing DW nanoparticles (NPs) are limited by the complexity and time involved. Additional issues include aggregation and entrapment of multiple spheres and the undesired formation of heterogeneous coatings. Therefore, the technique presented here is advantageous because it can produce NPs with distinct, core-shell morphologies through a rapid, spontaneous, self-assembly process. This method not only produces DW NPs, but can also be used to encapsulate therapeutic drug. Furthermore, modification of this process to other core and shell polymers is feasible using the general guidelines provided in this paper.

Beyond dose: Pulsed antibiotic treatment schedules can maintain individual benefit while reducing resistance.

The emergence of treatment-resistant microbes is a key challenge for disease treatment and a leading threat to human health and wellbeing. New drugs are always in development, but microbes regularly and rapidly acquire resistance. We must consider if altering how we administer drugs at the individual level could slow development of resistance. Here we use mathematical models to show that exposing microbes to drug pulses could greatly reduce resistance without increasing individual pathogen load. Our results stem from two key factors: the presence of antibiotics creates a selection pressure for antibiotic resistant microbes, and large populations of bacteria are more likely to harbor drug resistance than small populations. Drug pulsing targets these factors simultaneously. Short duration pulses minimize the time during which there is selection for resistance, and high drug concentrations minimize pathogen abundance. Our work provides a theoretical basis for the design of in vitro and in vivo experiments to test how drug pulsing might reduce the impact of drug resistant infections.

Scalable total synthesis and comprehensive structure-activity relationship studies of the phytotoxin coronatine.

Natural phytotoxins are valuable starting points for agrochemical design. Acting as a jasmonate agonist, coronatine represents an attractive herbicidal lead with novel mode of action, and has been an important synthetic target for agrochemical development. However, both restricted access to quantities of coronatine and a lack of a suitably scalable and flexible synthetic approach to its constituent natural product components, coronafacic and coronamic acids, has frustrated development of this target. Here, we report gram-scale production of coronafacic acid that allows a comprehensive structure-activity relationship study of this target. Biological assessment of a >120 member library combined with computational studies have revealed the key determinants of potency, rationalising hypotheses held for decades, and allowing future rational design of new herbicidal leads based on this template.