Improving cluster-based methods for investigating potential for insect pest species establishment: region-specific risk factors

Existing cluster-based methods for investigating insect species assemblages or profiles of a region to indicate the risk of new insect pest invasion have a major limitation in that they assign the same species risk factors to each region in a cluster. Clearly regions assigned to the same cluster have different degrees of similarity with respect to their species profile or assemblage. This study addresses this concern by applying weighting factors to the cluster elements used to calculate regional risk factors, thereby producing region-specific risk factors. Using a database of the global distribution of crop insect pest species, we found that we were able to produce highly differentiated region-specific risk factors for insect pests. We did this by weighting cluster elements by their Euclidean distance from the target region. Using this approach meant that risk weightings were derived that were more realistic, as they were specific to the pest profile or species assemblage of each region. This weighting method provides an improved tool for estimating the potential invasion risk posed by exotic species given that they have an opportunity to establish in a target region

Objective functions for comparing simulations with insect trap catch data

Targeted surveillance of high risk invasion sites using insect traps is becoming an important tool in border biosecurity, aiding in early detection and subsequent monitoring of eradication attempts. The mark-release-recapture technique is widely used to study the dispersal of insects, and to generate unbiased estimates of population density. It may also be used in the biosecurity context to quantify the efficacy of surveillance and eradication monitoring systems. Marked painted apple moths were released at three different locations in Auckland, New Zealand over six weeks during a recent eradication campaign. The results of the mark-release-recapture experiment were used to parameterise a process-based mechanistic dispersal model in order to understand the moth dispersal pattern in relation to wind patterns, and to provide biosecurity agencies with an ability to predict moth dispersal patterns. A genetic algorithm was used to fit some model parameters. Different objective functions were tested: 1) Cohen’s Kappa test, 2) the sum of squared difference on trap catches, 3) the sum of squared difference weighted by distance from the release site, 4) the sum of squared difference weighted on distance between best-fit paired data. The genetic algorithm proved to be a powerful fitting method, but the model results were highly dependant on the objective function used. Objective functions for fitting spatial data need to characterise spatial patterns as well as density (ie. recapture rate). For fitting stochastic models to datasets derived from stochastic spatial processes, objective functions need to accommodate the fact that a perfect fit is practically impossible, even if the models are the same. Applied on mark-release-recapture data, the Cohen’s Kappa test and the sum of squared difference on trap catches captured respectively the distance component of the spatial pattern and the density component adequately but failed to capture both requirements whereas the sum of squared difference weighted by distance from the release site did. However, in order to integrate the stochastic error generated by the model underlying stochastic process, only the sum of squared difference weighted on distance between best-fit paired data was adequate. The relevance of each of the fitting methods is detailed, and their respective strengths and weaknesses are discussed in relation to their ability to capture the spatial patterns of insect recaptures

Individual-based modelling of moth dispersal to improve biosecurity incursion response

1. Some biosecurity systems aimed at reducing the impacts of invasive alien species that employ sentinel trapping systems to detect the presence of unwanted organisms. Once detected, the next challenge is to locate the source population of the invasive species. Tools that can direct search efforts towards the most likely sources of a trapped invasive alien species can improve the chance of rapidly delimiting and eradicating the local population and may help to identify the original introduction pathway. Ground-based detection and delimitation surveys can be very expensive, and methods to focus search efforts to those areas most likely to contain the target organisms can make these efforts more effective and efficient. 2. An individual-based semi-mechanistic model was developed to simulate the spatio-temporal dispersal patterns of an invasive moth. The model combines appetitive and pheromone anemotaxis behaviours in response towind, temperature and pheromone conditions. The modelwas trained using data from a series ofmark-release-recapture experiments on painted applemoth Teia anartoides. 3. The model was used to create hindcast simulations by reversing the time course of environmental conditions. The ability of the model to encompass the release location was evaluated using individual trap locations as starting points for the hindcast simulations. 4. The hindcast modelling generated a pattern of moth flights that successfully encompassed the origin from 86%of trap locations, representing 95%of the 1464 recaptures observed in the mark- release-recapture experiments. 5. Comparing the guided search area defined using the hindcast model with the area of a simple point-diffusion search strategy revealed an optimized search strategy that combined searching a circle of 1 km radius around the trap followed by the area indicated by hindcast model predictions. 6. Synthesis and applications. Incorporating this novel moth dispersal model into biosecurity sentinel systems will allow incursion managers to direct search effort for the proximal source of the incursion towards those areas most likely to contain a local infestation. Such targeted effort should reduce the costs and time taken to detect the proximal source of an incursion. (Résumé d'auteur

Invasive alien species in the food chain : advancing risk assessment models to address climate change, economics and uncertainty

Economic globalization depends on the movement of people and goods between countries. As these exchanges increase, so does the potential for translocation of harmful pests, weeds, and pathogens capable of impacting our crops, livestock and natural resources (Hulme 2009), with concomitant impacts on global food security (Cook et al. 2011)

Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering

For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk

Responding positively to plant defences, a candidate key trait for invasion success in the New Zealand grass grub Costelytra zealandica

Occasionally, exotic plant introductions lead to the emergence of an invasive insect within its native geographical range. Such emergence could be explained by a pre-adaptation of the insect to break through the defences of the new encountered host. We investigated the fitness responses of two New Zealand endemic scarabs (Costelytra brunneum and C. zealandica ) when given a diet of an exotic pasture species, Trifolium repens, whose defences were artificially triggered by the phytohormone jasmonic acid. We found differential fitness responses between the two species when they were exposed to a defence-induced diet. We observed a significant weight increase in the invasive species C.zealandica when it was fed with treated roots compared with untreated controls, whereas no significant weight increase was observed in the non-invasive C.brunneum compared with the control treatments. Our study suggests that C. zealandica has a pre-existing ability to tolerate the defence chemicals of its exotic host and, more interestingly, to benefit from them, which may explain why this species has become a serious pest of pasture throughout its native geographical ran

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570