A Bootstrap Test of Stochastic Equality of Two Populations

Effect of seasonality and microclimate on the variation in bat-fly load (Diptera: Streblidae) in a cave bat assemblage in a dry forest

Seasonality causes fluctuations in the availability of resources, affecting the presence and abundance of animal species. These fluctuations can have an impact on parasite-host relationships, which in turn can be exacerbated by microclimatic changes to which bat-flies are exposed. We characterized the bat-fly load and evaluated the effect of seasonality on five bat species in a dry forest. We evaluated variations in microclimatic conditions inside the cave Cerro Huatulco between seasons and the response of the bat-fly load. We collected 1165 bat-fly specimens belonging to 16 species from 688 bats. The obtained results indicate that the mean abundance and infestation intensity exhibited changes between seasons in Artibeus jamaicensis, Desmodus rotundus, Glossophaga soricina, and Pteronotus parnellii. In the case of the effect of microclimate conditions, we observed that prevalence is negatively correlated with temperature in G. soricina, while mean abundance and mean infestation intensity were negatively related to temperature in A. jamaicensis and G. soricina. The present study provides significant information about host-parasite relationships in a dry forest and discusses the relevance of abiotic and biotic factors that could affect host-parasite interactions, as well as the importance of each parasite load parameter for the understanding of this interaction.

Helminth fauna of the screaming hairy armadillo Chaetophractus vellerosus from Argentina: the consequence of host isolation on parasite diversity

Chaetophractus vellerosus in Argentina has a north-west and central distribution. An isolated population is located in the east of the Buenos Aires province, separated from the population of central Argentina by about 500 km. We compared the component community of C. vellerosus between the main population in central Argentina and the isolated population, evaluating the impact of the host isolation on parasite diversity. Sixteen species of helminths were found, namely Ancylostoma caninum, Aspidodera fasciata, Aspidodera scoleciformis, Mazzia bialata, Pterygodermatites chaetophracti, Pterygodermatites argentinensis, Strongyloides sp., Trichohelix tuberculata, Cruzia tentaculata, Cyclobulura superinae, Delicata ransomi, Macielia jorgei, Moennigia celinae, Orihelia anticlava (Nematoda), Mathevotaenia sp. (Cestoda) and Travassosia sp. (Acanthocephala). Only the first 8 species, however, were present in both populations. Prevalences and intensities of the shared species were similar. The parasite community of the isolated population, however, was less rich and diverse than that of the main population. The poor parasite community in the isolated population could have several causes: the founding population was composed of few specimens that were only infected with the most prevalent parasite species and intermediate hosts for parasites with indirect cycles were absent. In addition, coexistence with another Chlamyphoridae would have contributed to maintaining a group of species that are usual in armadillos along their distribution.

Biostatistics for Parasitologists - A Primer to Quantitative Parasitology

The aggregated distributions of host-parasite systems require several different infection parameters to characterize them. We advise readers how to choose infection indices with clear and distinct biological interpretations, and recommend statistical tests to compare them across samples. A user-friendly and free software is available online to overcome technical difficulties.

Bonamia in Ostrea angasi: Diagnostic performance, field prevalence and intensity

Bonamia spp. parasites threaten flat oyster (Ostrea spp.) farming worldwide. Understanding test performance is important for designing surveillance and interpreting diagnostic results. Following a pilot survey which found low Bonamia sp. intensity in farmed Ostrea angasi, we tested further oysters (n = 100-150) from each of three farms for Bonamia sp. using heart smear, histology and qPCR. We used a Bayesian Latent Class Model to assess diagnostic sensitivity (DSe) and specificity (DSp) of these tests individually or in combination, and to assess prevalence. Histology was the best individual test (DSe 0.76, DSp 0.93) compared to quantitative polymerase chain reaction (qPCR) (DSe 0.69, DSp 0.93) and heart smear (DSe 0.61, DSp 0.60). Histology combined with qPCR and defining a positive from either test as an infected case maximized test performance (DSe 0.91, DSp 0.88). Prevalence was higher at two farms in a high-density oyster growing region than at a farm cultivating oysters at lower density. Parasite intensities were lower than in New Zealand and European studies, and this is probably contributed to differences in the performance of test when compared to other studies. Understanding diagnostic test performance in different populations can support the development of improved Bonamia surveillance programs.

Molecularly barcoded Zika virus libraries to probe in vivo evolutionary dynamics

Defining the complex dynamics of Zika virus (ZIKV) infection in pregnancy and during transmission between vertebrate hosts and mosquito vectors is critical for a thorough understanding of viral transmission, pathogenesis, immune evasion, and potential reservoir establishment. Within-host viral diversity in ZIKV infection is low, which makes it difficult to evaluate infection dynamics. To overcome this biological hurdle, we constructed a molecularly barcoded ZIKV. This virus stock consists of a “synthetic swarm” whose members are genetically identical except for a run of eight consecutive degenerate codons, which creates approximately 64,000 theoretical nucleotide combinations that all encode the same amino acids. Deep sequencing this region of the ZIKV genome enables counting of individual barcodes to quantify the number and relative proportions of viral lineages present within a host. Here we used these molecularly barcoded ZIKV variants to study the dynamics of ZIKV infection in pregnant and non-pregnant macaques as well as during mosquito infection/transmission. The barcoded virus had no discernible fitness defects in vivo, and the proportions of individual barcoded virus templates remained stable throughout the duration of acute plasma viremia. ZIKV RNA also was detected in maternal plasma from a pregnant animal infected with barcoded virus for 67 days. The complexity of the virus population declined precipitously 8 days following infection of the dam, consistent with the timing of typical resolution of ZIKV in non-pregnant macaques and remained low for the subsequent duration of viremia. Our approach showed that synthetic swarm viruses can be used to probe the composition of ZIKV populations over time in vivo to understand vertical transmission, persistent reservoirs, bottlenecks, and evolutionary dynamics.

Understanding the complex dynamics of Zika virus (ZIKV) infection during pregnancy and during transmission to and from vertebrate host and mosquito vector is critical for a thorough understanding of viral transmission, pathogenesis, immune evasion, and reservoir establishment. We sought to develop a virus model system for use in nonhuman primates and mosquitoes that allows for the genetic discrimination of molecularly cloned viruses. This “synthetic swarm” of viruses incorporates a molecular barcode that allows for tracking and monitoring individual viral lineages during infection. Here we infected rhesus macaques with this virus to study the dynamics of ZIKV infection in nonhuman primates as well as during mosquito infection/transmission. We found that the proportions of individual barcoded viruses remained relatively stable during acute infection in pregnant and nonpregnant animals. However, in a pregnant animal, the complexity of the virus population declined precipitously 8 days following infection, consistent with the timing of typical resolution of ZIKV in non-pregnant macaques and remained low for the subsequent duration of viremia.

Inference for the relative treatment effect with the density ratio model

Consider the problem of estimating and testing the relative treatment effect between two populations based on a random sample from each distribution. Under the well–established normal theory, inference is based on analysis of variance methods. However, there are many examples of skewed data which show that normal theory is not applicable. Then the problem of inference regarding the treatment effect can be attacked by standard nonparametric methods. In this paper, we propose a semiparametric model, the so–called density ratio model, which specifies that the log–likelihood ratio of two densities is linear in some parameters. For testing hypotheses regarding the relative treatment effect, a robust test is obtained by employing the density ratio model for a suitable Box–Cox transformation of the data. The transformation, along with the density ratio model, are estimated by maximum empirical likelihood. The new test procedure is studied theoretically and it is applied to real and simulated data. It is further compared with some nonparametric competitors, and it is found to have relatively high power across a wide variety of distributions, including those outside the density ratio family.

The t test and beyond: Recommendations for testing the central tendencies of two independent samples in research on speech, language and hearing pathology

PURPOSE

In this Tutorial we compare current practice of the analysis of data obtained in designs involving two independent samples with new developments in statistics and evidence on the behavior of conventional statistics. We included t tests, non-parametric alternatives, such as the Wilcoxon-Mann-Whitney test, and recently developed approaches, known as bootstrapping and randomization tests. The relative use of the different statistics is illustrated on the basis of counts carried out in three journals on disordered communication in the time interval 2005-2013: Clinical Linguistics & Phonetics, Journal of Communication Disorders and Journal of Speech, Language and Hearing Research. A number of recommendations are given to guide the researcher in the presentation and analysis of her/his data.

CONCLUSIONS

The main messages are (a) that researchers should present more relevant features of their data (means, medians, SD, skewness, tailedness, outliers etc.), (b) not routinely use conventional non-parametric tests like Wilcoxon-Mann-Whitney test in case one or more of the assumptions of t tests are not met, and (c) should consider using less conventional, but robust statistics which have been developed and tested in the last decades.

Controlling the Type I error rate by using the nonparametric bootstrap when comparing means

Of the several tests for comparing population means, the best known are the ANOVA, Welch, Brown-Forsythe, and James tests. Each performs appropriately only in certain conditions, and none performs well in every setting. Researchers, therefore, have to select the appropriate procedure and run the risk of making a bad selection and, consequently, of erroneous conclusions. It would be desirable to have a test that performs well in any situation and so obviate preliminary analysis of data. We assess and compare several tests for equality of means in a simulation study, including non-parametric bootstrap techniques, finding that the bootstrap ANOVA and bootstrap Brown-Forsythe tests exhibit a similar and exceptionally good behaviour.

Epidemiology of Brazilian spotted fever in the Atlantic Forest, state of São Paulo, Brazil

The tick-borne bacteriumRickettsia rickettsiiis the aetiological agent of Brazilian spotted fever (BSF). The present study evaluated tick infestations on wild and domestic animals, and the rickettsial infection in these animals and their ticks in 7 forest areas adjacent to human communities in the São Paulo Metropolitan Area (SPMA). The results were compared to ecological traits of each sampled area. Two main tick species,Amblyomma aureolatumandRhipicephalus sanguineus,were collected from dogs. The major ticks found on small mammals and birds wereIxodes loricatusandAmblyomma longirostre, respectively. Both anti-R. rickettsiiantibodies andR. rickettsii-infected ticks were detected on dogs from only 2 areas in the southern part of the SPMA, which were considered to be endemic for BSF; the remaining 5 areas were considered to be non-endemic. Ecologically, the BSF-endemic areas clearly differed from the non-endemic areas by the presence of significantly more degraded forest patches in the former. The present results corroborate historical observations that have indicated that all human cases of BSF in the SPMA were contracted in the southern part of this metropolitan area. However, not all forest patches in the southern part of the SPMA were shown to be associated with BSF endemism.

Gastrointestinal helminths of pipistrelle bats (Pipistrellus pipistrellus/Pipistrellus pygmaeus) (Chiroptera: Vespertilionidae) of England

Although bats are one of the most successful and diverse of mammalian orders, studies that focus upon bat endoparasites are limited. To further knowledge of bat parasitology, pipistrelle bats (Pipistrellus pipistrellus and P. pygmaeus) were acquired from across the Greater Manchester and Lancashire region of England and examined for gastrointestinal helminths using morphological and molecular analyses. Sixty-eight of 90 adult/juvenile bats (76% prevalence) were infected with at least 1 species of helminth and mean helminth abundance was 48·2 (+/-7·0). All helminths were digenean trematodes and the following species were identified in 51 P. pipistrellus specimens (prevalence in parentheses): Lecithodendrium linstowi (80·4%), L. spathulatum (19·6%), Prosthodendrium sp. (35·3%), Plagiorchis koreanus (29·4%) and Pycnoporus heteroporus (9·8%). Statistical analyses, incorporating multifactorial models, showed that male bats exhibited a significantly more aggregated helminth distribution and lower abundance than females. Positive associations were observed between L. linstowi and L. spathulatum, Prosthodendrium sp. and P. heteroporus and between L. spathulatum and P. koreanus. A revised phylogeny of bat-associated Lecithodendriidae, incorporating novel L. spathulatum and Prosthodendrium sp. 28S rRNA sequences, separated the controversial clade formed by L. linstowi and P. hurkovaae. Further studies are likely to assist the understanding of bat-parasite/pathogen relationships, helminth infracommunity structures and phylogenetics.

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

BACKGROUND

Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.

RESULTS

To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.

CONCLUSIONS

We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.

Improving Lesion-Symptom Mapping

Measures of brain activation (e.g., changes in scalp electrical potentials) have become the most popular method for inferring brain function. However, examining brain disruption (e.g., examining behavior after brain injury) can complement activation studies. Activation techniques identify regions involved with a task, whereas disruption techniques are able to discover which regions are crucial for a task. Voxel-based lesion mapping can be used to determine relationships between behavioral measures and the location of brain injury, revealing the function of brain regions. Lesion mapping can also correlate the effectiveness of neurosurgery with the location of brain resection, identifying optimal surgical targets. Traditionally, voxel-based lesion mapping has employed the chi-square test when the clinical measure is binomial and the Student's t test when measures are continuous. Here we suggest that the Liebermeister approach for binomial data is more sensitive than the chi-square test. We also suggest that a test described by Brunner and Munzel is more appropriate than the t test for nonbinomial data because clinical and neuropsychological data often violate the assumptions of the t test. We test our hypotheses comparing statistical tests using both simulated data and data obtained from a sample of stroke patients with disturbed spatial perception. We also developed software to implement these tests (MRIcron), made freely available to the scientific community.

Rank-order versus mean based statistics for neuroimaging

Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-Parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency--for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects.

A bootstrap test for the analysis of microarray experiments with a very small number of replications

In microarray studies it is common that the number of replications (i.e. the sample size) is small and that the distribution of expression values differs from normality. In this situation, permutation and bootstrap tests may be appropriate for the identification of differentially expressed genes. However, unlike bootstrap tests, permutation tests are not suitable for very small sample sizes, such as three per group. A variety of different bootstrap tests exists. For example, it is possible to adjust the data to have a common mean before the bootstrap samples are drawn. For small significance levels, which can occur when a large number of genes is investigated, the original bootstrap test, as well as a bootstrap test suggested for the Behrens-Fisher problem, have no power in cases of very small sample sizes. In contrast, the modified test based on adjusted data is powerful. Using a Monte Carlo simulation study, we demonstrate that the difference in power can be huge. In addition, the different tests are illustrated using microarray data.