Epidemiology and control of onchocerciasis: The threshold biting rate of savannah onchocerciasis in Africa

Control of onchocerciasis currently focuses on community-directed treatment with the microfilaricide ivermectin which effectively kills Onchocerca volvulus microfilariae in the human host. The feasibility of elimination by this control strategy has recently been reported for some foci in Africa which has rekindled discussions on evaluating the threshold conditions of elimination of onchocerciasis. We developed a stochastic model based on a master equation which predicts, based on data from West and Central Africa, that elimination of savannah onchocerciasis can be expected around a threshold biting rate of 730 bites per person per year, ranging region-specifically roughly from 230 to 2300 bites per person and year. The threshold values give rise to optimism that elimination of onchocerciasis is feasible, but the associated measures of parasite prevalence and density suggest that onchocerciasis can remain endemic at very low infection intensities. Endemicity at a low level is a risk factor for elimination strategies, and we point to the necessity of investigating these issues on the basis of breakpoints which refer to threshold conditions based on parasite prevalence and density.     

Statistical Properties and Control Algorithms of Recursive Quantile Estimators

The present study is an extension of the investigations made by Grieszbach and Schack (1993) where the recursive estimators of the quantile were introduced. Attention is focused on statistical properties and on the controlling of these estimators in order to reduce their variance and to improve their capability of adaptation. Using methods of stochastic approximation, several control algorithms have been developed, where both the consistent and the adaptive estimation are considered. Due to the recursive computation formula the estimators are suitable for the analysis of large data sets and for sets whose elements are obtained sequentially. In this study, application examples from the analysis of EEG‐records are presented, where quantiles are used as threshold values.     

The Plasmodium bottleneck: malaria parasite losses in the mosquito vector

Nearly one million people are killed every year by the malaria parasite Plasmodium. Although the disease-causing forms of the parasite exist only in the human blood, mosquitoes of the genus Anopheles are the obligate vector for transmission. Here, we review the parasite life cycle in the vector and highlight the human and mosquito contributions that limit malaria parasite development in the mosquito host. We address parasite killing in its mosquito host and bottlenecks in parasite numbers that might guide intervention strategies to prevent transmission.     

Malaria pathogenesis: a jigsaw with an increasing number of pieces

Plasmodium falciparum malaria remains as one of the most devastating global health problems of today. It is estimated that around 150 million individuals get the disease every year and of these 2-3 million die from it. Our knowledge of the mechanisms underlying the pathology has expanded greatly over the last decades, but many aspects of the molecular biology, immunology and epidemiology that govern the pathogenesis and spread of this parasite are still unclear. As new insights are gained we are also revealing a challenging biological complexity. Piecing this information together is the key to vaccine development and production of new antimalarial drugs.     

Mosquito midgut barriers to malaria parasite development

Malaria is one of the deadliest infectious diseases and kills more than one million people every year. For transmission to occur, the malaria parasite has to complete an elaborate developmental program in hostile mosquito environment. Thus, understanding the molecular mechanisms by which mosquitoes limit the parasite development may lead to new methods for controlling malaria. There has been considerable progress during the last decade in this research area. This review focuses on the mosquito response to midgut invasion of the malaria parasite and examines the role of mosquito digestive enzymes, peritrophic matrix and microvillar proteins as barriers to parasite development.     

A comparison of diversity estimators applied to a database of host–parasite associations

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second-order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

Estimating the Antimicrobial Log Reduction: Part 2. Presence/Absence Assays

This is part 2 of a pair of papers on antimicrobial assays conducted to estimate the log reduction (LR), in the density of viable microbes, attributable to the germicide. Two alternative definitions of LR were defined in part 1, one based on the mean of the log-transformed densities; the other is based on the logarithm of the mean of densities. In this paper, we evaluate statistical methods for estimating LR from an antimicrobial assay in which the responses are presence/absence observations at each dilution in a series of dilutions. We provide a model for the presence/absence data, and, for each definition of LR, we derive the maximum likelihood estimator (mle). Using computer simulation methods, we compare the mle to several alternative estimators, including an estimator based on averaging the log-transformed most probable number (mpn) values. Standard error formulas for the estimators are also derived and evaluated using computer simulations. This investigation results in the following recommendations. If the parameter of interest is based on the mean of log-transformed densities, then the results favor use of the log-transformed mpn method. If, however, the parameter of interest is based on the logarithm of the mean of densities, then the results show that the mle should be used.     

Insights into the immunopathogenesis of malaria using mouse models

Malaria kills approximately 1-2 million people every year, mostly in sub-Saharan Africa and in Asia. These deaths are at the most severe end of a scale of pathologies affecting approximately 500 million people per year. Much of the pathogenesis of malaria is caused by inappropriate or excessive immune responses mounted by the body to eliminate malaria parasites. In this review, we examine the evidence that immunopathology is responsible for malaria disease in the context of what we have learnt from animal models of malaria. In particular, we look in detail at the processes involved in endothelial cell damage leading to syndromes such as cerebral malaria, as well as generalised systemic manifestations such as anaemia, cachexia and problems with thermoregulation of the body. We also consider malaria in light of the variation of the severity of disease observed among people, and discuss the contribution from animal models to our understanding of this variation. Finally, we discuss some of the implications of immunopathology, and of host and parasite genetic variation, for the design and implementation of anti-malarial vaccines.     

Robust Estimation of Area Under ROC Curve Using Auxiliary Variables in the Presence of Missing Biomarker Values

Summary In medical research, the receiver operating characteristic (ROC) curves can be used to evaluate the performance of biomarkers for diagnosing diseases or predicting the risk of developing a disease in the future. The area under the ROC curve (ROC AUC), as a summary measure of ROC curves, is widely utilized, especially when comparing multiple ROC curves. In observational studies, the estimation of the AUC is often complicated by the presence of missing biomarker values, which means that the existing estimators of the AUC are potentially biased. In this article, we develop robust statistical methods for estimating the ROC AUC and the proposed methods use information from auxiliary variables that are potentially predictive of the missingness of the biomarkers or the missing biomarker values. We are particularly interested in auxiliary variables that are predictive of the missing biomarker values. In the case of missing at random (MAR), that is, missingness of biomarker values only depends on the observed data, our estimators have the attractive feature of being consistent if one correctly specifies, conditional on auxiliary variables and disease status, either the model for the probabilities of being missing or the model for the biomarker values. In the case of missing not at random (MNAR), that is, missingness may depend on the unobserved biomarker values, we propose a sensitivity analysis to assess the impact of MNAR on the estimation of the ROC AUC. The asymptotic properties of the proposed estimators are studied and their finite‐sample behaviors are evaluated in simulation studies. The methods are further illustrated using data from a study of maternal depression during pregnancy.     

Statistical surveillance of epidemics: peak detection of influenza in Sweden

A statistical surveillance system gives a signal as soon as data give enough evidence of an important event. We consider on-line surveillance systems for detecting changes in influenza incidence. One important feature of the influenza cycle is the start of the influenza season, and another one is the change to a decline (the peak). In this report we discuss statistical methods for on-line peak detection. One motive for doing this is the need for health resource planning. Surveillance systems were adapted for Swedish data on laboratory verified diagnoses of influenza. In Sweden, the parameters of the influenza cycles vary too much from year to year for parametric methods to be useful. We suggest a non-parametric method based on the monotonicity properties of the increase and decline around a peak. A Monte Carlo study indicated that this method has useful stochastic properties. The method was applied to Swedish data on laboratory verified diagnoses of influenza for seven periods.     

Morphology and distribution of blood fluke eggs and associated pathology in the gills of cultured Pacific bluefin tuna, Thunnus orientalis

Infestations of blood flukes of the genus Cardicola have been observed in juvenile Pacific bluefin tuna (PBT) cultured in Japan. Infected fish harbor large numbers of parasite eggs in their gills. Although the link between blood fluke infection and juvenile mortality is not clear, accumulation of parasite eggs appears to be pathogenic to the fish. We investigated the origins, general morphology/distribution, and histopathology of these eggs in artificially produced 0 yr old PBT. Dead and live fish were sampled on several occasions from two culture facilities in Wakayama prefecture, Japan. The number of eggs in each gill filament was enumerated under a microscope. In addition, we estimated the total number of eggs by dissolving the gills in a weak NaOH solution. We observed two morphologically distinct egg types in the gill filaments, smaller, oval shaped eggs in the gill lamellae and larger, crescent shaped eggs that occurred primarily in the filamentary arteries. Based on the ITS2 sequence, the ovoid and crescent shaped eggs were identified as C. orientalis and C. opisthorchis, respectively. Eggs of the former species were more abundant (maximum: 6400 per filament) than the latter (maximum: 1400), but the number was highly variable among filaments. The eggs of the latter species were relatively evenly distributed among the filaments. In a heavily infected individual, we estimated a total of >4.5 million eggs were present in the gills on one side of the fish. The number of eggs from the two species was positively correlated to each other and the dead fish tended to harbor more eggs than the live fish. Histological observation revealed host responses around the eggs, including encapsulation by fibroblasts and nodule formation, as seen in response to other aporocotylid eggs. In addition, we observed widespread fusion of gill lamellae and blockage of the filamentary arteries in some instances. Our results provide information that can be used for routine diagnosis of Cardicola blood flukes in cultured tuna and suggest they represent a risk to juvenile PBT.     

Estimating the mean abundance and feeding rate of a temporal ectoparasite in the wild: Afrocimex constrictus (Heteroptera: Cimicidae)

The feeding frequency of blood-feeding invertebrates in the wild is largely unknown but is an important predictor for the potential of disease transmission and for estimating the effects blood feeding may have on the host population. We present a method to estimate the mean feeding frequency per individual parasite from the frequency distribution of fed and unfed individuals in the wild. We used three populations of the cimicid species, Afrocimex constrictus, that parasitises the fruit bat Rousettus aegyptiacus. We found that the area occupied by a bug refugium was a good predictor of the number of bugs in that refugia. The estimated parasite population sizes ranged from ca. 25,000 to 3 million bugs. Their mean abundance was 1-15 bugs per host individual. Preventing feeding by bugs in their natural habitat showed that bugs took approximately 20 days to return to an unfed stage. A formula is presented by which the distribution of digestion stages in the samples was used to calculate that A. constrictus feeds approximately every 7-10 days. The dry weight of a full blood meal was approximated as 13.3 mg. Therefore A. constrictus is estimated to draw an average of 1-28 microL blood per host per day. We suggest that any of our methods can be adjusted to be used in other haematophagous insects to estimate host and parasite population size, mean parasite abundance and blood meal size as well as mean feeding frequency in the wild, including the bed bug species that parasitise humans.     

Estimating the Stochastic Bifurcation Structure of Cellular Networks

High throughput measurement of gene expression at single-cell resolution, combined with systematic perturbation of environmental or cellular variables, provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters. In dynamical systems theory, this information is the subject of bifurcation analysis, which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model. Since cellular networks are inherently noisy, we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems. We demonstrate how statistical methods for density estimation, in particular, mixture density and conditional mixture density estimators, can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae. These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network, and provide a framework that might be useful to extract information needed for the development of quantitative network models.     

Properties of crowding indices and statistical tools to analyze parasite crowding data

Crowding, i.e., the size of the infrapopulation inhabiting an individual host, is a major component of parasites' environment, which often influences both morphological and life-history characters (the so-called density-dependent characters) in different parasite taxa. Although crowding equals intensity in case of a single parasite individual, mean intensity of the host population does not define mean crowding of the parasite population. Crowding indices are notoriously hard to handle statistically because of the inherently large number of nonindependent values in data. In this study, we aim to investigate the apparently paradox features of crowding indices and to make some proposals and also to introduce statistical methods to calculate confidence intervals and 1-sample and 2-sample tests for mean crowding. All methods described in this study are supported by the freely distributed statistical software Quantitative Parasitology.     

Functional analysis of proteins involved in Plasmodium falciparum merozoite invasion of red blood cells

Plasmodium falciparum causes the most lethal form of malaria in humans and is responsible for over two million deaths per year. The development of a vaccine against this parasite is an urgent priority and potential protein targets include those on the surface of the asexual merozoite stage, the form that invades the host erythrocyte. The development of methods to transfect P. falciparum has enabled the construction of gain-of-function and loss-of-function mutants and provided new strategies to analyse the role of parasite proteins. In this review, we describe the use of this technology to examine the role of merozoite antigens in erythrocyte invasion and to address their potential as vaccine candidates.     

Functional analysis of Plasmodium falciparum merozoite antigens: implications for erythrocyte invasion and vaccine development

Malaria is a major human health problem and is responsible for over 2 million deaths per year. It is caused by a number of species of the genus Plasmodium, and Plasmodium falciparum is the causative agent of the most lethal form. Consequently, the development of a vaccine against this parasite is a priority. There are a number of stages of the parasite life cycle that are being targeted for the development of vaccines. Important candidate antigens include proteins on the surface of the asexual merozoite stage, the form that invades the host erythrocyte. The development of methods to manipulate the genome of Plasmodium species has enabled the construction of gain-of-function and loss-of-function mutants and provided new strategies to analyse the role of parasite proteins. This has provided new information on the role of merozoite antigens in erythrocyte invasion and also allows new approaches to address their potential as vaccine candidates.     

Ratio estimation with measurement error in the auxiliary variate

Summary .  With auxiliary information that is well correlated with the primary variable of interest, ratio estimation of the finite population total may be much more efficient than alternative estimators that do not make use of the auxiliary variate. The well-known properties of ratio estimators are perturbed when the auxiliary variate is measured with error. In this contribution we examine the effect of measurement error in the auxiliary variate on the design-based statistical properties of three common ratio estimators. We examine the case of systematic measurement error as well as measurement error that varies according to a fixed distribution. Aside from presenting expressions for the bias and variance of these estimators when they are contaminated with measurement error we provide numerical results based on a specific population. Under systematic measurement error, the biasing effect is asymmetric around zero, and precision may be improved or degraded depending on the magnitude of the error. Under variable measurement error, bias of the conventional ratio-of-means estimator increased slightly with increasing error dispersion, but far less than the increased bias of the conventional mean-of-ratios estimator. In similar fashion, the variance of the mean-of-ratios estimator incurs a greater loss of precision with increasing error dispersion compared with the other estimators we examine. Overall, the ratio-of-means estimator appears to be remarkably resistant to the effects of measurement error in the auxiliary variate.     

Peroxiredoxins in malaria parasites: parasitologic aspects

Malaria is one of the most debilitating and life threatening diseases in tropical regions of the world. Over 500 million clinical cases occur, and 2-3 million people die of the disease each year. Because Plasmodium lacks genuine glutathione peroxidase and catalase, the two major antioxidant enzymes in the eukaryotic cell, malaria parasites are likely to utilize members of the peroxiredoxin (Prx) family as the principal enzymes to reduce peroxides, which increase in the parasite cell due to metabolism and parasitism during parasite development. In addition to its function of protecting macromolecules from H(2)O(2), Prx has also been reported to regulate H(2)O(2) as second messenger in transmission of redox signals, which mediate cell proliferation, differentiation, and apoptosis. In the malaria parasite, several lines of experimental data have suggested that the parasite uses Prxs as multifunctional molecules to adapt themselves to asexual and sexual development. In this review, we summarize the accumulated knowledge on the Prx family with respect to their functions in mammalian cells and their possible function(s) in malaria parasites.