Quantification of host-parasite interactions: sheep and their nematodes

The objective of this dissertation is to use modelling and statistical approaches to expand our knowledge of the immune responses against gastrointestinal nematode infections, to assess the impact of nematode infection, and to use our improved knowledge to examine novel means of selective breeding in farm animals (sheep) as a control strategy. To expand the knowledge of the host immune response against infection, Chapter 2 of this dissertation focuses on immunoglobulin A (IgA), an antibody that binds nematode molecules, and its transfer through the body from the abomasal mucus (i.e. site of infection, where it is produced) to the blood plasma, where it is typically measured. These findings have been published in Parasitology (Prada Jimenez de Cisneros et al., 2014a). The implications of low levels of infection in adult milking ewes, which are more resistant than lambs, were also studied. A relationship is generated between infection levels using parasitological data and production data. There were however limitations in the dataset, which are discussed at the end of Chapter 3. Parasite resistance in adults sheep at low levels of infection was also studied, especially since the most common parasitological marker of disease is the faecal egg count (i.e. number of nematode eggs in the animals faeces) which is subject to substantial measurement error, among other limitations. Chapter 4 analyses a dataset of adult animals with low infection levels using a zero inflated binomial model (ZINB) and extends the model by including other evidence of parasite resistance to discriminate between exposed and unexposed animals. To examine selective breeding, an individual-based data-driven immunogenetically explicit mathematical model was developed. One application of this model is to compare the efficacy of two selective breeding schemes, each based on a different marker for disease, namely faecal egg counts and plasma IgA. This work has been published in Journal of the Royal Society Interface (Prada Jimenez de Cisneros et al., 2014b). The model can be extended to create a distribution for the variation in larval intake that best fits the field data. This allows the partitioning of the variation in adult worm burden into different components. The purpose is to quantify the contribution of the immune response and larval intake to determine which of the two accounts for more of the variation in the level of infection. The model can be also extended to explore selection schemes in the two components of the immune response (i.e. namely the IgA mediated and IgE mediated immune response) and estimate animal size at the end of the grazing season

MSQNet: Actor-agnostic Action Recognition with Multi-modal Query

Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and single-label classification whilst neglecting other available information sources (e.g., class name text) and the concurrent occurrence of multiple actions. To overcome these limitations, we propose a new approach called 'actor-agnostic multi-modal multi-label action recognition,' which offers a unified solution for various types of actors, including humans and animals. We further formulate a novel Multi-modal Semantic Query Network (MSQNet) model in a transformer-based object detection framework (e.g., DETR), characterized by leveraging visual and textual modalities to represent the action classes better. The elimination of actor-specific model designs is a key advantage, as it removes the need for actor pose estimation altogether. Extensive experiments on five publicly available benchmarks show that our MSQNet consistently outperforms the prior arts of actor-specific alternatives on human and animal single- and multi-label action recognition tasks by up to 50%. Code will be released at https://github.com/mondalanindya/MSQNet

The use of Immunodiagnostic Techniques in Sheep for the Epidemiological Surveillance of Cystic Echinococcosis

Objective: Cystic echinococcosis (CE) is a parasitic zoonosis caused by Echinococcus granulosussensu lato. Immunodiagnostic techniques such as Western blot (WB) or enzyme-linkedimmunosorbent assay (ELISA), with different antigens, can be applied to the diagnosis ofsheep for epidemiological surveillance purposes in control programs. However, its use islimited by the existence of antigenic cross-reactivity between different species of taeniidaepresent in sheep. Therefore, the usefulness of establishing surveillance systems based on theidentification of infection present in a livestock establishment, known as the (Epidemiological)Implementation Unit (IU), needs to be evaluated.Materials and Methods: A new ELISA diagnostic technique has been recently developed andvalidated using the recombinant EgAgB8/2 antigen for the detection of antibodies against E.granulosus. To determine detection of infection at the IU level using information from thisdiagnostic technique, simulations were carried out to evaluate the sample size required toclassify IUs as likely infected, using outputs from a recently developed Bayesian latent classanalysis model.Results: Relatively small samples sizes (between 14-29) are sufficient to achieve a highprobability of detection (above 80%), across a range of prevalence, with the recentlyrecommended Optical Density cut-off value for this novel ELISA (0.496), which optimizesdiagnostic sensitivity and specificity.Conclusions: This diagnostic technique could be potentially used to identify the prevalence ofinfection in an area under control, measured as the percentage of IUs with the presence ofinfected sheep (infection present), or to individually identify the IU with ongoing transmission,given the presence of infected lambs, on which control measures should be intensified.Fil: Poggio, Thelma Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Ciencia y Tecnología "Dr. César Milstein". Fundación Pablo Cassará. Instituto de Ciencia y Tecnología "Dr. César Milstein"; ArgentinaFil: Mujica, Guillermo Bernardo. Ministerio de Salud de Rio Negro; ArgentinaFil: Prada, Joaquin M.. University of Surrey; Reino UnidoFil: Larrieu, Edmundo Juan. Universidad Nacional de La Pampa. Facultad de Ciencias Veterinarias; Argentin

Divergent Allele Advantage provides a quantitative model for maintaining alleles with a wide range of intrinsic merits

The Major Histocompatibility Complex (MHC) is the most genetically diverse region of the genome in most vertebrates. Some form of balancing selection is necessary to account for the extreme diversity, but the precise mechanism of balancing selection is unknown. Due to the way MHC molecules determine immune recognition, overdominance (also referred to as heterozygote advantage) has been suggested as the main driving force behind this unrivalled diversity. However, both theoretical results and simulation models have shown that overdominance in its classical form cannot maintain large numbers of alleles unless all alleles confer unrealistically similar levels of fitness. There is increasing evidence that heterozygotes containing genetically divergent alleles allow for broader antigen presentation to immune cells, providing a selective mechanism for MHC polymorphism. By framing competing models of overdominance within a general framework, we show that a model based on Divergent Allele Advantage (DAA) provides a superior mechanism for maintaining alleles with a wide range of intrinsic merits, as intrinsically less fit MHC alleles that are more divergent can survive under DAA. Specifically, our results demonstrate that a quantitative mechanism built from the Divergent Allele Advantage hypothesis is able to maintain polymorphism in the MHC. Applying such a model to both livestock breeding and conservation could provide a better way of identifying superior heterozygotes and quantifying the advantages of genetic diversity at the MHC

PANINI : Pangenome Neighbour Identification for Bacterial Populations

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here, we introduce PANINI (Pangenome Neighbour Identification for Bacterial Populations), a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding based on the t-SNE (t-distributed stochastic neighbour embedding) algorithm. PANINI is browser-based and integrates with the Microreact platform for rapid online visualization and exploration of both core and accessory genome evolutionary signals, together with relevant epidemiological, geographical, temporal and other metadata. Several case studies with single- and multi-clone pneumococcal populations are presented to demonstrate the ability to identify biologically important signals from gene content data. PANINI is available at http://panini.pathogen.watch and code at http://gitlab.com/cgps/panini.Peer reviewe

Impact of Changes in Detection Effort on Control of Visceral Leishmaniasis in the Indian Subcontinent.

BACKGROUND: Control of visceral leishmaniasis (VL) on the Indian subcontinent relies on prompt detection and treatment of symptomatic cases. Detection efforts influence the observed VL incidence and how well it reflects the underlying true incidence. As control targets are defined in terms of observed cases, there is an urgent need to understand how changes in detection delay and population coverage of improved detection affect VL control. METHODS: Using a mathematical model for transmission and control of VL, we predict the impact of reduced detection delays and/or increased population coverage of the detection programs on observed and true VL incidence and mortality. RESULTS: Improved case detection, either by higher coverage or reduced detection delay, causes an initial rise in observed VL incidence before a reduction. Relaxation of improved detection may lead to an apparent temporary (1 year) reduction in VL incidence, but comes with a high risk of resurging infection levels. Duration of symptoms in detected cases shows an unequivocal association with detection effort. CONCLUSIONS: VL incidence on its own is not a reliable indicator of the performance of case detection programs. Duration of symptoms in detected cases can be used as an additional marker of the performance of case detection programs

Translating from egg- to antigen-based indicators for Schistosoma mansoni elimination targets: A Bayesian latent class analysis study

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.Schistosomiasis is a parasitic disease affecting over 240-million people. World Health Organization (WHO) targets for Schistosoma mansoni elimination are based on Kato-Katz egg counts, without translation to the widely used, urine-based, point-of-care circulating cathodic antigen diagnostic (POC-CCA). We aimed to standardize POC-CCA score interpretation and translate them to Kato-Katz-based standards, broadening diagnostic utility in progress towards elimination. A Bayesian latent-class model was fit to data from 210 school-aged-children over four timepoints pre- to six-months-post-treatment. We used 1) Kato-Katz and established POC-CCA scoring (Negative, Trace, +, ++ and +++), and 2) Kato-Katz and G-Scores (a new, alternative POC-CCA scoring (G1 to G10)). We established the functional relationship between Kato-Katz counts and POC-CCA scores, and the score-associated probability of true infection. This was combined with measures of sensitivity, specificity, and the area under the curve to determine the optimal POC-CCA scoring system and positivity threshold. A simulation parametrized with model estimates established antigen-based elimination targets. True infection was associated with POC-CCA scores of ≥ + or ≥G3. POC-CCA scores cannot predict Kato-Katz counts because low infection intensities saturate the POC-CCA cassettes. Post-treatment POC-CCA sensitivity/specificity fluctuations indicate a changing relationship between egg excretion and antigen levels (living worms). Elimination targets can be identified by the POC-CCA score distribution in a population. A population with ≤2% ++/+++, or ≤0.5% G7 and above, indicates achieving current WHO Kato-Katz-based elimination targets. Population-level POC-CCA scores can be used to access WHO elimination targets prior to treatment. Caution should be exercised on an individual level and following treatment, as POC-CCAs lack resolution to discern between WHO Kato-Katz-based moderate- and high-intensity-infection categories, with limited use in certain settings and evaluations

Comparing antigenaemia- and microfilaraemia as criteria for stopping decisions in lymphatic filariasis elimination programmes in Africa

BACKGROUND: Mass drug administration (MDA) is the main strategy towards lymphatic filariasis (LF) elimination. Progress is monitored by assessing microfilaraemia (Mf) or circulating filarial antigenaemia (CFA) prevalence, the latter being more practical for field surveys. The current criterion for stopping MDA requires \u3c2% CFA prevalence in 6- to 7-year olds, but this criterion is not evidence-based. We used mathematical modelling to investigate the validity of different thresholds regarding testing method and age group for African MDA programmes using ivermectin plus albendazole. METHODOLGY/PRINCIPAL FINDINGS: We verified that our model captures observed patterns in Mf and CFA prevalence during annual MDA, assuming that CFA tests are positive if at least one adult worm is present. We then assessed how well elimination can be predicted from CFA prevalence in 6-7-year-old children or from Mf or CFA prevalence in the 5+ or 15+ population, and determined safe (\u3e95% positive predictive value) thresholds for stopping MDA. The model captured trends in Mf and CFA prevalences reasonably well. Elimination cannot be predicted with sufficient certainty from CFA prevalence in 6-7-year olds. Resurgence may still occur if all children are antigen-negative, irrespective of the number tested. Mf-based criteria also show unfavourable results (PPV \u3c95% or unpractically low threshold). CFA prevalences in the 5+ or 15+ population are the best predictors, and post-MDA threshold values for stopping MDA can be as high as 10% for 15+. These thresholds are robust for various alternative assumptions regarding baseline endemicity, biological parameters and sampling strategies. CONCLUSIONS/SIGNIFICANCE: For African areas with moderate to high pre-treatment Mf prevalence that have had 6 or more rounds of annual ivermectin/albendazole MDA with adequate coverage, we recommend to adopt a CFA threshold prevalence of 10% in adults (15+) for stopping MDA. This could be combined with Mf testing of CFA positives to ensure absence of a significant Mf reservoir for transmission

Mapping the evidence of the effects of environmental factors on the prevalence of antibiotic resistance in the non-built environment: Protocol for a systematic evidence map

Background: Human, animal, and environmental health are increasingly threatened by the emergence and spread of antibiotic resistance. Inappropriate use of antibiotic treatments commonly contributes to this threat, but it is also becoming apparent that multiple, interconnected environmental factors can play a significant role. Thus, a One Health approach is required for a comprehensive understanding of the environmental dimensions of antibiotic resistance and inform science-based decisions and actions. The broad and multidisciplinary nature of the problem poses several open questions drawing upon a wide heterogeneous range of studies. Objective: This study seeks to collect and catalogue the evidence of the potential effects of environmental factors on the abundance or detection of antibiotic resistance determinants in the outdoor environment, i.e., antibiotic resistant bacteria and mobile genetic elements carrying antibiotic resistance genes, and the effect on those caused by local environmental conditions of either natural or anthropogenic origin. Methods: Here, we describe the protocol for a systematic evidence map to address this, which will be performed in adherence to best practice guidelines. We will search the literature from 1990 to present, using the following electronic databases: MEDLINE, Embase, and the Web of Science Core Collection as well as the grey literature. We shall include full-text, scientific articles published in English. Reviewers will work in pairs to screen title, abstract and keywords first and then full-text documents. Data extraction will adhere to a code book purposely designed. Risk of bias assessment will not be conducted as part of this SEM. We will combine tables, graphs, and other suitable visualisation techniques to compile a database i) of studies investigating the factors associated with the prevalence of antibiotic resistance in the environment and ii) map the distribution, network, cross-disciplinarity, impact and trends in the literature.This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. The funder had no role in the development of this protocol.info:eu-repo/semantics/publishedVersio

Reproducibility matters: intra- and inter-sample variation of the point-of-care circulating cathodic antigen test (POC-CCA) in two Schistosoma mansoni endemic areas in Uganda

Over 240 million people are infected with schistosomiasis. Detecting Schistosoma mansoni eggs in stool using Kato–Katz thick smears (Kato-Katzs) is highly specific but lacks sensitivity. The urine-based point-of-care circulating cathodic antigen test (POC-CCA) has higher sensitivity, but issues include specificity, discrepancy between batches and interpretation of trace results. A semi-quantitative G-score and latent class analyses making no assumptions about trace readings have helped address some of these issues. However, intra-sample and inter-sample variation remains unknown for POC-CCAs. We collected 3 days of stool and urine from 349 and 621 participants, from high- and moderate-endemicity areas, respectively. We performed duplicate Kato-Katzs and one POC-CCA per sample. In the high-endemicity community, we also performed three POC-CCA technical replicates on one urine sample per participant. Latent class analysis was performed to estimate the relative contribution of intra- (test technical reproducibility) and inter-sample (day-to-day) variation on sensitivity and specificity. Within-sample variation for Kato-Katzs was higher than between-sample, with the opposite true for POC-CCAs. A POC-CCA G3 threshold most accurately assesses individual infections. However, to reach the WHO target product profile of the required 95% specificity for prevalence and monitoring and evaluation, a threshold of G4 is needed, but at the cost of reducing sensitivity. This article is part of the theme issue ‘Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs’