Characterization of three-dimensional spatial aggregation and association patterns of brown rot symptoms within intensively mapped sour cherry trees

Background and Aims

Characterization of spatial patterns of plant disease can provide insights into important epidemiological processes such as sources of inoculum, mechanisms of dissemination, and reproductive strategies of the pathogen population. Whilst two-dimensional patterns of disease (among plants within fields) have been studied extensively, there is limited information on three-dimensional patterns within individual plant canopies. Reported here are the detailed mapping of different symptom types of brown rot (caused by Monilinia laxa) in individual sour cherry tree (Prunus cerasus) canopies, and the application of spatial statistics to the resulting data points to determine patterns of symptom aggregation and association.

Methods

A magnetic digitizer was utilized to create detailed three-dimensional maps of three symptom types (blossom blight, shoot blight and twig canker) in eight sour cherry tree canopies during the green fruit stage of development. The resulting point patterns were analysed for aggregation (within a given symptom type) and pairwise association (between symptom types) using a three-dimensional extension of nearest-neighbour analysis.

Key Results

Symptoms of M. laxa infection were generally aggregated within the canopy volume, but there was no consistent pattern for one symptom type to be more or less aggregated than the other. Analysis of spatial association among symptom types indicated that previous year''s twig cankers may play an important role in influencing the spatial pattern of current year''s symptoms. This observation provides quantitative support for the epidemiological role of twig cankers as sources of primary inoculum within the tree.

Conclusions

Presented here is a new approach to quantify spatial patterns of plant disease in complex fruit tree canopies using point pattern analysis. This work provides a framework for quantitative analysis of three-dimensional spatial patterns within the finite tree canopy, applicable to many fields of research.     

Rapid MALDI-TOF Mass Spectrometry Strain Typing during a Large Outbreak of Shiga-Toxigenic Escherichia coli

Background

In 2011 northern Germany experienced a large outbreak of Shiga-Toxigenic Escherichia coli O104:H4. The large amount of samples sent to microbiology laboratories for epidemiological assessment highlighted the importance of fast and inexpensive typing procedures. We have therefore evaluated the applicability of a MALDI-TOF mass spectrometry based strategy for outbreak strain identification.

Methods

Specific peaks in the outbreak strain’s spectrum were identified by comparative analysis of archived pre-outbreak spectra that had been acquired for routine species-level identification. Proteins underlying these discriminatory peaks were identified by liquid chromatography tandem mass spectrometry and validated against publicly available databases. The resulting typing scheme was evaluated against PCR genotyping with 294 E. coli isolates from clinical samples collected during the outbreak.

Results

Comparative spectrum analysis revealed two characteristic peaks at m/z 6711 and m/z 10883. The underlying proteins were found to be of low prevalence among genome sequenced E. coli strains. Marker peak detection correctly classified 292 of 293 study isolates, including all 104 outbreak isolates.

Conclusions

MALDI-TOF mass spectrometry allowed for reliable outbreak strain identification during a large outbreak of Shiga-Toxigenic E. coli. The applied typing strategy could probably be adapted to other typing tasks and might facilitate epidemiological surveys as part of the routine pathogen identification workflow.     

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis.

Methods/Principal Findings

We calculated the incidence rates and standardised incidence rates of BFV disease. Moran''s I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ² = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran''s I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state.

Conclusions/Significance

This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.     

Spatially explicit analyses of anopheline mosquitoes indoor resting density: implications for malaria control

Background

The question of sampling and spatial aggregation of malaria vectors is central to vector control efforts and estimates of transmission. Spatial patterns of anopheline populations are complex because mosquitoes'' habitats and behaviors are strongly heterogeneous. Analyses of spatially referenced counts provide a powerful approach to delineate complex distribution patterns, and contributions of these methods in the study and control of malaria vectors must be carefully evaluated.

Methodology/Principal Findings

We used correlograms, directional variograms, Local Indicators of Spatial Association (LISA) and the Spatial Analysis by Distance IndicEs (SADIE) to examine spatial patterns of Indoor Resting Densities (IRD) in two dominant malaria vectors sampled with a 5×5 km grid over a 2500 km² area in the forest domain of Cameroon. SADIE analyses revealed that the distribution of Anopheles gambiae was different from regular or random, whereas there was no evidence of spatial pattern in Anopheles funestus (Ia = 1.644, Pa<0.05 and Ia = 1.464, Pa>0.05, respectively). Correlograms and variograms showed significant spatial autocorrelations at small distance lags, and indicated the presence of large clusters of similar values of abundance in An. gambiae while An. funestus was characterized by smaller clusters. The examination of spatial patterns at a finer spatial scale with SADIE and LISA identified several patches of higher than average IRD (hot spots) and clusters of lower than average IRD (cold spots) for the two species. Significant changes occurred in the overall spatial pattern, spatial trends and clusters when IRDs were aggregated at the house level rather than the locality level. All spatial analyses unveiled scale-dependent patterns that could not be identified by traditional aggregation indices.

Conclusions/Significance

Our study illustrates the importance of spatial analyses in unraveling the complex spatial patterns of malaria vectors, and highlights the potential contributions of these methods in malaria control.     

Bayesian spatio-temporal modeling of Schistosoma japonicum prevalence data in the absence of a diagnostic 'gold' standard

Background

Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques.

Methodology/Principal Findings

We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People''s Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the ‘true’ prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River.

Conclusion/Significance

Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.     

Whole Genome Sequencing versus Traditional Genotyping for Investigation of a Mycobacterium tuberculosis Outbreak: A Longitudinal Molecular Epidemiological Study

Background

Understanding Mycobacterium tuberculosis (Mtb) transmission is essential to guide efficient tuberculosis control strategies. Traditional strain typing lacks sufficient discriminatory power to resolve large outbreaks. Here, we tested the potential of using next generation genome sequencing for identification of outbreak-related transmission chains.

Methods and Findings

During long-term (1997 to 2010) prospective population-based molecular epidemiological surveillance comprising a total of 2,301 patients, we identified a large outbreak caused by an Mtb strain of the Haarlem lineage. The main performance outcome measure of whole genome sequencing (WGS) analyses was the degree of correlation of the WGS analyses with contact tracing data and the spatio-temporal distribution of the outbreak cases. WGS analyses of the 86 isolates revealed 85 single nucleotide polymorphisms (SNPs), subdividing the outbreak into seven genome clusters (two to 24 isolates each), plus 36 unique SNP profiles. WGS results showed that the first outbreak isolates detected in 1997 were falsely clustered by classical genotyping. In 1998, one clone (termed “Hamburg clone”) started expanding, apparently independently from differences in the social environment of early cases. Genome-based clustering patterns were in better accordance with contact tracing data and the geographical distribution of the cases than clustering patterns based on classical genotyping. A maximum of three SNPs were identified in eight confirmed human-to-human transmission chains, involving 31 patients. We estimated the Mtb genome evolutionary rate at 0.4 mutations per genome per year. This rate suggests that Mtb grows in its natural host with a doubling time of approximately 22 h (400 generations per year). Based on the genome variation discovered, emergence of the Hamburg clone was dated back to a period between 1993 and 1997, hence shortly before the discovery of the outbreak through epidemiological surveillance.

Conclusions

Our findings suggest that WGS is superior to conventional genotyping for Mtb pathogen tracing and investigating micro-epidemics. WGS provides a measure of Mtb genome evolution over time in its natural host context. Please see later in the article for the Editors'' Summary     

Investigating the effects of topography and clonality on genetic structuring within a large Norwegian population of Arabidopsis lyrata

Background and Aims

The gene flow through pollen or seeds governs the extent of spatial genetic structure in plant populations. Another factor that can contribute to this pattern is clonal growth. The perennial species Arabidopsis lyrata ssp. petraea (Brassicaceae) is a self-incompatible, clonal species found in disjunctive populations in central and northern Europe.

Methods

Fourteen microsatellite markers were employed to study the level of kinship and clonality in a high-altitude mountain valley at Spiterstulen, Norway. The population has a continuous distribution along the banks of the River Visa for about 1·5 km. A total of 17 (10 m × 10 m) squares were laid out in a north–south transect following the river on both sides.

Key Results

It is shown that clonal growth is far more common than previously shown in this species, although the overall size of the genets is small (mean diameter = 6·4 cm). Across the whole population there is no indication of isolation by distance, and spatial genetic structure is only visible on fine spatial scales. In addition, no effect of the river on the spatial distribution of genotypes was found.

Conclusions

Unexpectedly, the data show that populations of small perennials like A. lyrata can behave like panmictic units across relatively large areas at local sites, as opposed to earlier findings in central Europe.     

Genotyping of Burkholderia mallei from an Outbreak of Glanders in Bahrain Suggests Multiple Introduction Events

Background

Glanders, caused by the gram-negative bacterium Burkholderia mallei, is a highly infectious zoonotic disease of solipeds causing severe disease in animals and men. Although eradicated from many Western countries, it recently emerged in Asia, the Middle-East, Africa, and South America. Due to its rareness, little is known about outbreak dynamics of the disease and its epidemiology.

Methodology/Principal Findings

We investigated a recent outbreak of glanders in Bahrain by applying high resolution genotyping (multiple locus variable number of tandem repeats, MLVA) and comparative whole genome sequencing to B. mallei isolated from infected horses and a camel. These results were compared to samples obtained from an outbreak in the United Arab Emirates in 2004, and further placed into a broader phylogeographic context based on previously published B. mallei data. The samples from the outbreak in Bahrain separated into two distinct clusters, suggesting a complex epidemiological background and evidence for the involvement of multiple B. mallei strains. Additionally, the samples from Bahrain were more closely related to B. mallei isolated from horses in the United Arab Emirates in 2004 than other B. mallei which is suggestive of repeated importation to the region from similar geographic sources.

Conclusion/Significance

High-resolution genotyping and comparative whole genome analysis revealed the same phylogenetic patterns among our samples. The close relationship of the Dubai/UAE B. mallei populations to each other may be indicative of a similar geographic origin that has yet to be identified for the infecting strains. The recent emergence of glanders in combination with worldwide horse trading might pose a new risk for human infections.     

Gene flow in environmental Legionella pneumophila leads to genetic and pathogenic heterogeneity within a Legionnaires’ disease outbreak

Background

Legionnaires’ disease is a severe form of pneumonia caused by the environmental bacterium Legionella pneumophila. Outbreaks commonly affect people with known risk factors, but the genetic and pathogenic complexity of L. pneumophila within an outbreak is not well understood. Here, we investigate the etiology of the major Legionnaires’ disease outbreak that occurred in Edinburgh, UK, in 2012, by examining the evolutionary history, genome content, and virulence of L. pneumophila clinical isolates.

Results

Our high resolution genomic approach reveals that the outbreak was caused by multiple genetic subtypes of L. pneumophila, the majority of which had diversified from a single progenitor through mutation, recombination, and horizontal gene transfer within an environmental reservoir prior to release. In addition, we discover that some patients were infected with multiple L. pneumophila subtypes, a finding which can affect the certainty of source attribution. Importantly, variation in the complement of type IV secretion systems encoded by different genetic subtypes correlates with virulence in a Galleria mellonella model of infection, revealing variation in pathogenic potential among the outbreak source population of L. pneumophila.

Conclusions

Taken together, our study indicates previously cryptic levels of pathogen heterogeneity within a Legionnaires’ disease outbreak, a discovery that impacts on source attribution for future outbreak investigations. Furthermore, our data suggest that in addition to host immune status, pathogen diversity may be an important influence on the clinical outcome of individual outbreak infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0504-1) contains supplementary material, which is available to authorized users.     

Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions

Objective

Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients'' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity.

Methods

Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern.

Results

Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns.

Conclusion

This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.     

Modeling the spatial distribution and fruiting pattern of a key tree species in a neotropical forest: methodology and potential applications

Background

The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we demonstrate how advances in statistics permit the combination of sparse ground sampling with remote sensing imagery to generate biological relevant, spatially and temporally explicit distributions of food resources. We illustrate our procedure by creating a detailed simulation model of fruit production patterns for Dipteryx oleifera, a keystone tree species, on Barro Colorado Island (BCI), Panama.

Methodology and Principal Findings

Aerial photographs providing GPS positions for large, canopy trees, the complete census of a 50-ha and 25-ha area, diameter at breast height data from haphazardly sampled trees and long-term phenology data from six trees were used to fit 1) a point process model of tree spatial distribution and 2) a generalized linear mixed-effect model of temporal variation of fruit production. The fitted parameters from these models are then used to create a stochastic simulation model which incorporates spatio-temporal variations of D. oleifera fruit availability on BCI.

Conclusions and Significance

We present a framework that can provide a statistical characterization of the habitat that can be included in agent-based models of animal movements. When environmental heterogeneity cannot be exhaustively mapped, this approach can be a powerful alternative. The results of our model on the spatio-temporal variation in D. oleifera fruit availability will be used to understand behavioral and movement patterns of several species on BCI.     

Genome analysis of Campylobacter jejuni strains isolated from a waterborne outbreak

Background

Waterborne Campylobacter jejuni outbreaks are common in the Nordic countries, and PFGE (pulsed field gel electrophoresis) remains the genotyping method of choice in outbreak investigations. However, PFGE cannot assess the clonal relationship between isolates, leading to difficulties in molecular epidemiological investigations. Here, we explored the applicability of whole genome sequencing to outbreak investigation by re-analysing three C. jejuni strains (one isolated from water and two from patients) from an earlier resolved Finnish waterborne outbreak from the year 2000.

Results

One of the patient strains had the same PFGE profile, as well as an identical overall gene synteny and three polymorphisms in comparison with the water strain. However, the other patient isolate, which showed only minor differences in the PFGE pattern relative to the water strain, harboured several polymorphisms as well as rearrangements in the integrated element CJIE2. We reconstructed the genealogy of these strains with ClonalFrame including in the analysis four C. jejuni isolated from chicken in 2012 having the same PFGE profile and sequence type as the outbreak strains. The three outbreak strains exhibited a paraphyletic relationship, implying that the drinking water from 2000 was probably contaminated with at least two different, but related, C. jejuni strains.

Conclusions

Our results emphasize the capability of whole genome sequencing to unambiguously resolve the clonal relationship between isolates of C. jejuni in an outbreak situation and evaluate the diversity of the C. jejuni population.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-768) contains supplementary material, which is available to authorized users.     

The Use of Bivariate Spatial Modeling of Questionnaire and Parasitology Data to Predict the Distribution of Schistosoma haematobium in Coastal Kenya

Background

Questionnaires of reported blood in urine (BIU) distributed through the existing school system provide a rapid and reliable method to classify schools according to the prevalence of Schistosoma haematobium, thereby helping in the targeting of schistosomiasis control. However, not all schools return questionnaires and it is unclear whether treatment is warranted in such schools. This study investigates the use of bivariate spatial modelling of available and multiple data sources to predict the prevalence of S. haematobium at every school along the Kenyan coast.

Methodology

Data from a questionnaire survey conducted by the Kenya Ministry of Education in Coast Province in 2009 were combined with available parasitological and environmental data in a Bayesian bivariate spatial model. This modeled the relationship between BIU data and environmental covariates, as well as the relationship between BIU and S. haematobium infection prevalence, to predict S. haematobium infection prevalence at all schools in the study region. Validation procedures were implemented to assess the predictive accuracy of endemicity classification.

Principal Findings

The prevalence of BIU was negatively correlated with distance to nearest river and there was considerable residual spatial correlation at small (∼15 km) spatial scales. There was a predictable relationship between the prevalence of reported BIU and S. haematobium infection. The final model exhibited excellent sensitivity (0.94) but moderate specificity (0.69) in identifying low (<10%) prevalence schools, and had poor performance in differentiating between moderate and high prevalence schools (sensitivity 0.5, specificity 1).

Conclusions

Schistosomiasis is highly focal and there is a need to target treatment on a school-by-school basis. The use of bivariate spatial modelling can supplement questionnaire data to identify schools requiring mass treatment, but is unable to distinguish between moderate and high prevalence schools.     

Using Stool Antigen to Screen for Helicobacter pylori in Immigrants and Refugees from High Prevalence Countries Is Relatively Cost Effective in Reducing the Burden of Gastric Cancer and Peptic Ulceration

Objectives

Refugees and immigrants from developing countries settling in industrialised countries have a high prevalence of Helicobacter pylori (H. pylori). Screening these groups for H. pylori and use of eradication therapy to reduce the future burden of gastric cancer and peptic ulcer disease is not currently recommended in most countries. We investigated whether a screening and eradication approach would be cost effective in high prevalence populations.

Methods

Nine different screening and follow-up strategies for asymptomatic immigrants from high H. pylori prevalence areas were compared with the current approach of no screening. Cost effectiveness comparisons assumed population prevalence''s of H. pylori of 25%, 50% or 75%. The main outcome measure was the net cost for each cancer prevented for each strategy. Total costs of each strategy and net costs including savings from reductions in ulcers and gastric cancer were also calculated.

Results

Stool antigen testing with repeat testing after treatment was the most cost effective approach relative to others, for each prevalence value. The net cost per cancer prevented with this strategy was US$111,800 (assuming 75% prevalence), $132,300 (50%) and $193,900 (25%). A test and treat strategy using stool antigen remained relatively cost effective, even when the prevalence was 25%.

Conclusions

H. pylori screening and eradication can be an effective strategy for reducing rates of gastric cancer and peptic ulcers in high prevalence populations and our data suggest that use of stool antigen testing is the most cost effective approach.     

Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis

Background

The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.

Objectives

One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.

Materials and Methods

Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.

Results

Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease''s distribution and minimum land surface temperature have also been confirmed via the application of these methods.

Conclusions

Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.     

Effect of altitude on the genetic structure of an Alpine grass, Poa hiemata

Background and Aims

The persistence of plants inhabiting restricted alpine areas under climate change will depend upon many factors including levels of genetic variation in adaptive traits, population structure, and breeding system.

Methods

Using microsatellite markers, the genetic structure of populations of a relatively common alpine grass, Poa hiemata, is examined across three altitudinal gradients within the restricted Australian alpine zone where this species has previously been shown to exhibit local adaptation across a narrow altitudinal gradient.

Key Results

Genetic variation across six microsatellite markers revealed genetic structuring along altitudinal transects, and a reduction in genetic variation at high and low altitude extremes relative to sites central within transects. There was less genetic variation among transect sites compared with altitudinal gradients within transects, even though distances among transects were relatively larger. Central sites within transects were less differentiated than those at extremes.

Conclusions

These patterns suggest higher rates of gene flow among sites at similar altitudes than along transects, a process that could assist altitudinal adaptation. Patterns of spatial autocorrelation and isolation by distance changed with altitude and may reflect altered patterns of dispersal via pollen and/or seed. There was evidence for selfing and clonality in neighbouring plants. Levels of gene flow along transects were insufficient to prevent adaptive changes in morphological traits, given previously measured levels of selection.Key words: Poa hiemata, genetic structure, altitudinal gradient, microsatellite, gene flow, climate change     

Micro-Geographical Heterogeneity in Schistosoma mansoni and S. haematobium Infection and Morbidity in a Co-Endemic Community in Northern Senegal

Background

Schistosoma mansoni and S. haematobium are co-endemic in many areas in Africa. Yet, little is known about the micro-geographical distribution of these two infections or associated disease within such foci. Such knowledge could give important insights into the drivers of infection and disease and as such better tailor schistosomiasis control and elimination efforts.

Methodology

In a co-endemic farming community in northern Senegal (346 children (0–19 y) and 253 adults (20–85 y); n = 599 in total), we studied the spatial distribution of S. mansoni and S. haematobium single and mixed infections (by microscopy), S. mansoni-specific hepatic fibrosis, S. haematobium-specific urinary tract morbidity (by ultrasound) and water contact behavior (by questionnaire). The Kulldorff''s scan statistic was used to detect spatial clusters of infection and morbidity, adjusted for the spatial distribution of gender and age.

Principal Findings

Schistosoma mansoni and S. haematobium infection densities clustered in different sections of the community (p = 0.002 and p = 0.023, respectively), possibly related to heterogeneities in the use of different water contact sites. While the distribution of urinary tract morbidity was homogeneous, a strong geospatial cluster was found for severe hepatic fibrosis (p = 0.001). Particularly those people living adjacent to the most frequently used water contact site were more at risk for more advanced morbidity (RR = 6.3; p = 0.043).

Conclusions/Significance

Schistosoma infection and associated disease showed important micro-geographical heterogeneities with divergent patterns for S. mansoni and S. haematobium in this Senegalese community. Further in depth investigations are needed to confirm and explain our observations. The present study indicates that local geospatial patterns should be taken into account in both research and control of schistosomiasis. The observed extreme focality of schistosomiasis even at community level, suggests that current strategies may not suffice to move from morbidity control to elimination of schistosomiasis, and calls for less uniform measures at a finer scale.     

Spatial genetic structure of Aquilegia taxa endemic to the island of Sardinia

Background and Aims

The Mediterranean Basin is one of the most important regions for the Earth''s plant biodiversity; however, the scarcity of studies on fine scale patterns of genetic variation in this region is striking. Here, an assessment is made of the spatial genetic structure of all known locations of the three Sardinian endemic species of Aquilegia in order to determine the relative roles of gene flow and genetic drift as underlying evolutionary forces canalizing the divergence of Sardinian Aquilegia taxa, and to see if the spatial genetic structure found fits the current taxonomic differentiation of these taxa.

Methods

DNA from 89 individuals from all known locations of Aquilegia across Sardinia was analysed by means of amplified fragment length polymorphism (AFLP) markers. Both principal co-ordinates analysis (PCoA) and Bayesian clustering analyses were used to determine the spatial genetic structure irrespective of any taxonomic affiliation. Historical effects of gene flow and genetic drift were assessed by checking for the existence of isolation-by-distance patterns.

Key Results

STRUCTURE and PCoA analyses revealed a pattern of genetic variation geographically structured into four spatial genetic groups. No migration–drift equilibrium was detected for Aquilegia in Sardinia, when analysed either as a whole or in individual groups. The scenario approached a Case III pattern sensu Hutchinson and Templeton, which is associated with extreme isolation conditions where genetic drift has historically played a dominant role over gene flow.

Conclusions

The pattern of genetic variation of Sardinian taxa of Aquilegia indicates that genetic drift has been historically more influential than gene flow on population structure of Sardinian species of Aquilegia. Limited seed dispersal and divergent selection imposed by habitat conditions have been probably the main causes reinforcing post-Pleistocene geographical isolation of Aquilegia populations. The spatial genetic structure found here is not fully compatible with current taxonomic affiliations of Sardinian Aquilegia taxa. This is probably a consequence of the uncoupling between morphological and genetic patterns of differentiation frequently found in recently radiated taxa.     

Outbreak of Multi-Drug Resistant Pseudomonas aeruginosa Bloodstream Infection in the Haematology Unit of a South African Academic Hospital

Objective

To describe an outbreak of multi-resistant Pseudomonas aeruginosa bloodstream infections (MRPA-BSI) that occurred in the haematology ward of a tertiary academic hospital in Cape Town, South Africa, and determine risk factors for acquisition of MRPA-BSI.

Methods

The outbreak investigation included a search for additional cases, review of patient records, environmental and staff screening, molecular typing using pulsed-field gel electrophoresis (PFGE) and Multi-locus sequencing (MLST) and a retrospective case-control study.

Results

Ten MRPA-BSI cases occurred in the haematology ward between January 2010 and January 2011. The case fatality rate was 80%. Staff screening specimens were negative for MRPA and an environmental source was not identified. PFGE showed that 9/10 isolates were related. MLST showed that 3 of these 9 isolates belonged to Sequence type (ST) 233 while the unrelated isolate belonged to ST260.

Conclusion

We have described an outbreak of MRPA-BSI occurring over an extended period of time among neutropenic haematology patients. Molecular typing confirms that the outbreak was predominantly due to a single strain. The source of the outbreak was not identified, but the outbreak appears to have been controlled following intensive infection control measures.