Primate malarias: Diversity, distribution and insights for zoonotic Plasmodium

Protozoans within the genus Plasmodium are well-known as the causative agents of malaria in humans. Numerous Plasmodium species parasites also infect a wide range of non-human primate hosts in tropical and sub-tropical regions worldwide. Studying this diversity can provide critical insight into our understanding of human malarias, as several human malaria species are a result of host switches from non-human primates. Current spillover of a monkey malaria, Plasmodium knowlesi, in Southeast Asia highlights the permeability of species barriers in Plasmodium. Also recently, surveys of apes in Africa uncovered a previously undescribed diversity of Plasmodium in chimpanzees and gorillas. Therefore, we carried out a meta-analysis to quantify the global distribution, host range, and diversity of known non-human primate malaria species. We used published records of Plasmodium parasites found in non-human primates to estimate the total diversity of non-human primate malarias globally. We estimate that at least three undescribed primate malaria species exist in sampled primates, and many more likely exist in unstudied species. The diversity of malaria parasites is especially uncertain in regions of low sampling such as Madagascar, and taxonomic groups such as African Old World Monkeys and gibbons. Presence–absence data of malaria across primates enables us to highlight the close association of forested regions and non-human primate malarias. This distribution potentially reflects a long coevolution of primates, forest-adapted mosquitoes, and malaria parasites. The diversity and distribution of primate malaria are an essential prerequisite to understanding the mechanisms and circumstances that allow Plasmodium to jump species barriers, both in the evolution of malaria parasites and current cases of spillover into humans

The effects of subcurative praziquantel treatment on life-history traits and trade-offs in drug-resistant Schistosoma mansoni

Natural selection acts on all organisms, including parasites, to maximise reproductive fitness. Drug resistance traits are often associated with life-history costs in the absence of treatment. Schistosomiasis control programmes rely on mass drug administration to reduce human morbidity and mortality. Although hotspots of reduced drug efficacy have been reported, resistance is not widespread. Using Bayesian State-Space Models (SSMs) fitted to data from an in vivo laboratory system, we tested the hypothesis that the spread of resistant Schistosoma may be limited by life-history costs not present in susceptible counterparts. Schistosoma mansoni parasites from a praziquantel–susceptible (S), a praziquantel–resistant (R) or a mixed line of originally resistant and susceptible parasites (RS) were exposed to a range of praziquantel doses. Parasite numbers at each life stage were quantified in their molluscan intermediate and murine definitive hosts across four generations, and SSMs were used to estimate key life-history parameters for each experimental group over time. Model outputs illustrated that parasite adult survival and fecundity in the murine host decreased across all lines, including R, with increasing drug pressure. Trade-offs between adult survival and fecundity were observed in all untreated lines, and these remained strong in S with praziquantel pressure. In contrast, trade-offs between adult survival and fecundity were lost under praziquantel pressure in R. As expected, parasite life-history traits within the molluscan host were complex, but trade-offs were demonstrated between parasite establishment and cercarial output. The observed trade-offs between generations within hosts, which were modified by praziquantel treatment in the R line, could limit the spread of R parasites under praziquantel pressure. Whilst such complex life-history costs may be difficult to detect using standard empirical methods, we demonstrate that SSMs provide robust estimates of life history parameters, aiding our understanding of costs and trade-offs of resistant parasites within this system and beyond

Entrance examinations in teacher education

"Erstmals in einem Lehrerstudiengang an einer deutschen Universität wurden an der Otto-Friedrich-Universität Bamberg Studienplätze nach der Eignung für Lehrerstudium und Lehrerberuf vergeben. Das ca. 30-minütige teilstrukturierte Auswahlgespräch bezog sich auf vier für das Studienfeld spezifische Kriterien." Forschungsmethode: Befragung; empirisch-quantitativ; empirisch. (Autorenreferat, IAB-Doku)"In teacher training entrance examinations are spread worldwide. For the first time at a German University however in 2002 the department for Primary Teacher Education in Bamberg developed and administered a respecting assessment procedure. Among other questions applicants were asked about their motivational background and prior pedagogical experiences and had to comment a short videographed lesson-section." (author\u27s abstract, IAB-Doku

Introduction to special issue: Advancing disease ecology through eDNA monitoring of infectious agents

This special issue focuses on the applications of environmental DNA (eDNA) sequencing for the detection and monitoring of disease‐causing agents, including viruses, bacteria, protozoans, myxozoans, fungi, trematodes, and arthropods. We explore the impact of eDNA technologies, such as metabarcoding and qPCR, in understanding the dynamics of pathogens in various environments as well as their implications for conservation, biosecurity, and veterinary and agricultural health under the “One Health” framework. This issue addresses how molecular sequencing provides innovative solutions to the challenges faced by conventional parasite and pathogen detection methods, enabling a more comprehensive understanding of the spatiotemporal dynamics of disease agents. Finally, we discuss the challenges in eDNA applications, such as primer development and taxonomic resolution, and the opportunities for future research in advancing eDNA methodologies for infectious disease studies. This issue highlights the growing importance of eDNA surveillance in understanding and managing the health of ecosystems and at‐risk species

Cross-species pathogen spillover across ecosystem boundaries: mechanisms and theory

Pathogen spillover between different host species is the trigger for many infectious disease outbreaks and emergence events, and ecosystem boundary areas have been suggested as spatial hotspots of spillover. This hypothesis is largely based on suspected higher rates of zoonotic disease spillover and emergence in fragmented landscapes and other areas where humans live in close vicinity to wildlife. For example, Ebola virus outbreaks have been linked to contacts between humans and infected wildlife at the rural-forest border, and spillover of yellow fever via mosquito vectors happens at the interface between forest and human settlements. Because spillover involves complex interactions between multiple species and is difficult to observe directly, empirical studies are scarce, particularly those that quantify underlying mechanisms. In this review, we identify and explore potential ecological mechanisms affecting spillover of pathogens (and parasites in general) at ecosystem boundaries. We borrow the concept of ‘permeability’ from animal movement ecology as a measure of the likelihood that hosts and parasites are present in an ecosystem boundary region. We then discuss how different mechanisms operating at the levels of organisms and ecosystems might affect permeability and spillover. This review is a step towards developing a general theory of cross-species parasite spillover across ecosystem boundaries with the eventual aim of improving predictions of spillover risk in heterogeneous landscapes

The problem of scale in the prediction and management of pathogen spillover

Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions

Schistosomiasis Control: Leave No Age Group Behind

Despite accelerating progress towards schistosomiasis control in sub-Saharan Africa, several age groups have been eclipsed by current treatment and monitoring strategies that mainly focus on school-aged children. As schistosomiasis poses a threat to people of all ages, unfortunate gaps exist in current treatment coverage and associated monitoring efforts, preventing subsequent health benefits to preschool-aged children as well as certain adolescents and adults. Expanding access to younger ages through the forthcoming pediatric praziquantel formulation and improving treatment coverage in older ages is essential. This should occur alongside formal inclusion of these groups in large-scale monitoring and evaluation activities. Current omission of these age groups from treatment and monitoring exacerbates health inequities and has long-term consequences for sustainable schistosomiasis control

Dynamic and integrative approaches to understanding pathogen spillover

No abstract available

Für wen gebe ich mein Urteil ab? Der systematische Einfluss des Fragebogenadressaten auf Kausalattributionsgewichtungen bei geschlossenen Antwortformaten

Die Fragebogenforschung belegt, dass Respondenten durch Kontextinformationen eines Fragebogens systematisch in ihrem Antwortverhalten beeinflusst werden. So zeigten Norenzayan und Schwarz (1999), dass Probanden bei freier Antwortmöglichkeit eher persönlichkeitsbezogene Ursachen zur Erklärung von Straftaten nennen, wenn der Fragebogen scheinbar von einem Institut für Persönlichkeitsforschung (verglichen mit einem Institut für Sozialforschung) erstellt wurde. Hierzu diskutierte Erklärungen sind einerseits Konversationsmaximen, die einen Bezug zwischen Adressat und Gesagtem induzieren, andererseits kognitive Primings, die selektive kognitive Aktivierungen und damit Verfügbarkeiten bedingen sollen. Die vorliegende Studie untersucht diese Erklärungsalternativen, indem sie erstmals in einem analogen Studiendesign persönlichkeitsbezogene und soziale Gründe in geschlossenen Antwortformaten vorgibt und gewichten lässt. Mögliche Gewichtungsunterschiede sind somit nicht mittels kognitiver Verfügbarkeit erklärbar. Eine Kovarianzanalyse (Alter, Geschlecht und die Big-Five-Persönlichkeitsdimensionen als Kovariaten) belegt im Einklang mit den Konversationsmaximen eine signifikant stärkere Bedeutungszuschreibung für persönlichkeitsbezogene Ursachen unter der Bedingung „Institut für Persönlichkeitsforschung“ im Vergleich zu „Institut für Sozialforschung“ und einer Kontrollbedingung („Institut für Kriminologie“)

The impact of storage conditions on human stool 16S rRNA microbiome composition and diversity

Background: Multiple factors can influence stool sample integrity upon sample collection. Preservation of faecal samples for microbiome studies is therefore an important step, particularly in tropical regions where resources are limited and high temperatures may significantly influence microbiota profiles. Freezing is the accepted standard to preserve faecal samples however, cold chain methods are often unfeasible in fieldwork scenarios particularly in low and middle-income countries and alternatives are required. This study therefore aimed to address the impact of different preservative methods, time-to-freezing at ambient tropical temperatures, and stool heterogeneity on stool microbiome diversity and composition under real-life physical environments found in resource-limited fieldwork conditions. Methods: Inner and outer stool samples collected from one specimen obtained from three children were stored using different storage preservation methods (raw, ethanol and RNAlater) in a Ugandan field setting. Mixed stool was also stored using these techniques and frozen at different time-to-freezing intervals post-collection from 0–32 h. Metataxonomic profiling was used to profile samples, targeting the V1–V2 regions of 16S rRNA with samples run on a MiSeq platform. Reads were trimmed, combined and aligned to the Greengenes database. Microbial diversity and composition data were generated and analysed using Quantitative Insights Into Microbial Ecology and R software. Results: Child donor was the greatest predictor of microbiome variation between the stool samples, with all samples remaining identifiable to their child of origin despite the stool being stored under a variety of conditions. However, significant differences were observed in composition and diversity between preservation techniques, but intra-preservation technique variation was minimal for all preservation methods, and across the time-to-freezing range (0–32 h) used. Stool heterogeneity yielded no apparent microbiome differences. Conclusions: Stool collected in a fieldwork setting for comparative microbiome analyses should ideally be stored as consistently as possible using the same preservation method throughout