Temporal Dynamics and Severity of Cucurbit Downy Mildew Epidemics as Affected by Chemical Control and Cucurbit Host Type

Cucurbit downy mildew caused by the oomycete Pseudoperonospora cubensis is an important disease that affects members of Cucurbitaceae family globally. However, temporal dynamics of the disease have not been characterized at the field scale to understand how control strategies influence disease epidemics. Disease severity was assessed visually on cucumber and summer squash treated with weekly alternation of chlorothalonil with either cymoxanil, fluopicolide or propamocarb, during the 2018 spring season and 2019 and 2020 fall seasons in North Carolina, and the 2018 and 2020 fall seasons in South Carolina. Disease onset was observed around mid-June during the spring season and early September during the fall season, followed by a rapid increase in severity until mid-July in the spring season and late September or mid-October in the fall season, typical of polycyclic epidemics. The Gompertz, logistic and monomolecular growth models were fitted to disease severity using linear regression and parameter estimates used to compare the effects of fungicide treatment and cucurbit host type on disease progress. The Gompertz and logistic models were more appropriate than the monomolecular model in describing temporal dynamics of cucurbit downy mildew, with the Gompertz model providing the best description for 34 of the 44 epidemics examined. Fungicide treatment and host type significantly (P < 0.0001) affected standardized area under disease progress curve (sAUDPC), final disease severity (Final DS) and weighted mean absolute rates of disease progress (ρ), with these variables, in most cases, being significantly (P < 0.05) lower in fungicide treated plots than in untreated control plots. Except in a few cases, sAUDPC, Final DS and ρ were lower in cases where chlorothalonil was alternated with fluopicolide or propamocarb than in cases where chlorothalonil alternated with cymoxanil or when chlorothalonil was applied alone. These results characterized the temporal progress of cucurbit downy mildew and provided an improved understanding of the dynamics of the disease at the field level. Parameters of disease progress obtained from this study could serve as inputs in simulation studies to assess the efficacy of fungicide alternation in managing fungicide resistance in this pathosystem.

Annotated catalogue of type specimens of helminths

This catalog contains basic information on the helminth type specimens stored in the Institute's Collection, namely, inventory numbers, inventory and status of a type material; the type host, type localization and type location are also indicated. Information is also given on the collection date and collector name, as well as material amount and its preservation quality. For each species, bibliographical references to the original publication and illustrations of type specimens taken from the original sources are given. Where necessary, the descriptions were supplemented with notes regarding the name taxonomic status, fairness or reliability of the published data, the need for nomenclature amendments, etc.

Monitoring the patterns of submission and presence of tick-borne pathogens in Ixodes scapularis collected from humans and companion animals in Ontario, Canada (2011–2017)

Background The universal nature of the human–companion animal relationship and their shared ticks and tick-borne pathogens offers an opportunity for improving public and veterinary health surveillance. With this in mind, we describe the spatiotemporal trends for blacklegged tick (Ixodes scapularis) submissions from humans and companion animals in Ontario, along with pathogen prevalence. Methods We tested tick samples submitted through passive surveillance (2011–2017) from humans and companion animals for Borrelia burgdorferi, Borrelia miyamotoi, Anaplasma phagocytophilum and Babesia microti. We describe pathogen prevalence in ticks from humans and from companion animals and constructed univariable Poisson and negative binomial regression models to explore the spatiotemporal relationship between the rates of tick submissions by host type. Results During the study, there were 17,230 blacklegged tick samples submitted from humans and 4375 from companion animals. Tick submission rates from companion animals were higher than expected in several public health units (PHUs) lacking established tick populations, potentially indicating newly emerging populations. Pathogen prevalence in ticks was higher in PHUs where established blacklegged tick populations exist. Borrelia burgdorferi prevalence was higher in ticks collected from humans (maximum likelihood estimate, MLE = 17.5%; 95% confidence interval, CI 16.97–18.09%) than from companion animals (9.9%, 95% CI 9.15–10.78%). There was no difference in pathogen prevalence in ticks by host type for the remaining pathogens, which were found in less than 1% of tested ticks. The most common co-infection B. burgdorferi + B. miyamotoi occurred in 0.11% of blacklegged ticks from humans and animals combined. Borrelia burgdorferi prevalence was higher in unengorged (21.9%, 95% CI 21.12–22.65%) than engorged ticks (10.0%, 95% CI 9.45–10.56%). There were no consistent and significant spatiotemporal relationships detected via regression models between the annual rates of submission of each host type. Conclusions While B. burgdorferi has been present in blacklegged ticks in Ontario for several decades, other tick-borne pathogens are also present at low prevalence. Blacklegged tick and pathogen surveillance data can be used to monitor risk in human and companion animal populations, and efforts are under consideration to unite surveillance efforts for the different target populations. Graphic Abstract

An Unsupervised Algorithm for Host Identification in Flaviviruses

Early characterization of emerging viruses is essential to control their spread, such as the Zika Virus outbreak in 2014. Among other non-viral factors, host information is essential for the surveillance and control of virus spread. Flaviviruses (genus Flavivirus), akin to other viruses, are modulated by high mutation rates and selective forces to adapt their codon usage to that of their hosts. However, a major challenge is the identification of potential hosts for novel viruses. Usually, potential hosts of emerging zoonotic viruses are identified after several confirmed cases. This is inefficient for deterring future outbreaks. In this paper, we introduce an algorithm to identify the host range of a virus from its raw genome sequences. The proposed strategy relies on comparing codon usage frequencies across viruses and hosts, by means of a normalized Codon Adaptation Index (CAI). We have tested our algorithm on 94 flaviviruses and 16 potential hosts. This novel method is able to distinguish between arthropod and vertebrate hosts for several flaviviruses with high values of accuracy (virus group 91.9% and host type 86.1%) and specificity (virus group 94.9% and host type 79.6%), in comparison to empirical observations. Overall, this algorithm may be useful as a complementary tool to current phylogenetic methods in monitoring current and future viral outbreaks by understanding host–virus relationships.