Using solute/ micro organism comparative tracer testing to investigate hydrogeological heterogeneity in a fluvioglacial sand and gravel aquifer.

Groundwater drawn from fluvioglacial sand and gravel aquifers form the principal source of drinking water in many part of central Western Europe. High population densities and widespread organic agriculture in these same areas constitute hazards that may impact the microbiological quality of many potable supplies. Tracer testing comparing two similarly sized bacteria (E.coli and P. putida) and the smaller bacteriophage (H40/1) with the response of non-reactive solute tracer (uranine) at the decametre scale revealed that all tracers broke through up to 100 times more quickly than anticipated using conventional rules of thumb. All microbiological tracer responses were less disperse than the solute, although bacterial peak relative concentrations consistently exceeded those of the solute tracer at one sampling location reflecting exclusion processes influencing micro biological tracer migration. Relative recoveries of H40/1 and E.coli proved consistent at both monitoring wells, while responses of H40/1 and P.putida differed. Examination of exposures of the upper reaches of the aquifer in nearby sand and gravel quarries revealed the aquifer to consist of laterally extensive layers of open framework (OW) gravel enveloped in finer grained gravelly sand. Granulometric analysis of these deposits suggested that the OW gravel was up to two orders of magnitude more permeable than the surrounding deposits giving rise to the preferential flow paths. By contrast fine grained lenses of silty sand within the OW gravels are suspected to play an important role in the exclusion processes that permit solutes to access them but exclude larger micro organisms.

Spatial Heterogeneity and Kinetic Regulation of Arsenic Dynamics in Mangrove Sediments: The Sundarbans, Bangladesh

The biogeochemistry of arsenic (As) in sediments is regulated by multiple factors such as particle size, dissolved organic matter (DOM), iron mobilization, and sediment binding characteristics, among others. Understanding the heterogeneity of factors affecting As deposition and the kinetics of mobilization, both horizontally and vertically, across sediment depositional environments was investigated in Sundarban mangrove ecosystems, Bengal Delta, Bangladesh. Sediment cores were collected from 3 different Sundarbans locations and As concentration down the profiles were found to be more associated with elevated Fe and Mn than with organic matter (OM). At one site chosen for field monitoring, sediment cores, pore and surface water, and in situ diffusive gradients in thin films (DGT) measurements (which were used to model As sediment pore-water concentrations and resupply from the solid phase) were sampled from four different subhabitats. Coarse-textured riverbank sediment porewaters were high in As, but with a limited resupply of As from the solid phase compared to fine-textured and high organic matter content forest floor sediments, where porewater As was low, but with much higher As resupply. Depositional environment (overbank verses forest floor) and biological activity (input of OM from forest biomass) considerably affected As dynamics over very short spatial distances in the mosaic of microhabitats that constitute a mangrove ecosystem.

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

Background: Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.

Methods: On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.

Results: Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with ‘low cancer-risk’ characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring ‘high cancer-risk” characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest ‘high cancer- risk’ cluster were different than those contributing to the classifiers for the ‘low cancer-risk’ clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.

Conclusions: The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs. © 2013 Emmert-Streib et al; licensee BioMed Central Ltd.

Compositional Divergence and Convergence in Local Communities and Spatially Structured Landscapes

Community structure depends on both deterministic and stochastic processes. However, patterns of community dissimilarity (e.g. difference in species composition) are difficult to interpret in terms of the relative roles of these processes. Local communities can be more dissimilar (divergence) than, less dissimilar (convergence) than, or as dissimilar as a hypothetical control based on either null or neutral models. However, several mechanisms may result in the same pattern, or act concurrently to generate a pattern, and much research has recently been focusing on unravelling these mechanisms and their relative contributions. Using a simulation approach, we addressed the effect of a complex but realistic spatial structure in the distribution of the niche axis and we analysed patterns of species co-occurrence and beta diversity as measured by dissimilarity indices (e.g. Jaccard index) using either expectations under a null model or neutral dynamics (i.e., based on switching off the niche effect). The strength of niche processes, dispersal, and environmental noise strongly interacted so that niche-driven dynamics may result in local communities that either diverge or converge depending on the combination of these factors. Thus, a fundamental result is that, in real systems, interacting processes of community assembly can be disentangled only by measuring traits such as niche breadth and dispersal. The ability to detect the signal of the niche was also dependent on the spatial resolution of the sampling strategy, which must account for the multiple scale spatial patterns in the niche axis. Notably, some of the patterns we observed correspond to patterns of community dissimilarities previously observed in the field and suggest mechanistic explanations for them or the data required to solve them. Our framework offers a synthesis of the patterns of community dissimilarity produced by the interaction of deterministic and stochastic determinants of community assembly in a spatially explicit and complex context.

A potential vulnerability locus for schizophrenia on chromosome 6p24-22:evidence for genetic heterogeneity

In 265 Irish pedigrees, with linkage analysis we find evidence for a vulnerability locus for schizophrenia in region 6p24-22. The greatest lod score, assuming locus heterogeneity, is 3.51 (P = 0.0002) with D6S296. Another test, the C test, also supported linkage, the strongest results being obtained with D6S296 (P = 0.00001), D6S274 (P = 0.004) and D6S285 (P = 0.006). Non-parametric analysis yielded suggestive, but substantially weaker, findings. This locus appears to influence the vulnerability to schizophrenia in roughly 15 to 30% of our pedigrees. Evidence for linkage was maximal using an intermediate phenotypic definition and declined when this definition was narrowed or was broadened to include other psychiatric disorders.

Resemblance of psychotic symptoms and syndromes in affected sibling pairs from the Irish Study of High-Density Schizophrenia Families:evidence for possible etiologic heterogeneity

The authors sought to determine whether the clinical manifestations of schizophrenia and other psychotic disorders are correlated in affected sibling pairs.

It's not that I don't care, I just don't care very much: confounding between attribute non-attendance and taste heterogeneity

With the growing interest in the topic of attribute non-attendance, there is now widespread use of latent class (LC) structures aimed at capturing such behaviour, across a number of different fields. Specifically, these studies rely on a confirmatory LC model, using two separate values for each coefficient, one of which is fixed to zero while the other is estimated, and then use the obtained class probabilities as an indication of the degree of attribute non-attendance. In the present paper, we argue that this approach is in fact misguided, and that the results are likely to be affected by confounding with regular taste heterogeneity. We contrast the confirmatory model with an exploratory LC structure in which the values in both classes are estimated. We also put forward a combined latent class mixed logit model (LC-MMNL) which allows jointly for attribute non-attendance and for continuous taste heterogeneity. Across three separate case studies, the exploratory LC model clearly rejects the confirmatory LC approach and suggests that rates of non-attendance may be much lower than what is suggested by the standard model, or even zero. The combined LC-MMNL model similarly produces significant improvements in model fit, along with substantial reductions in the implied rate of attribute non-attendance, in some cases even eliminating the phenomena across the sample population. Our results thus call for a reappraisal of the large body of recent work that has implied high rates of attribute non-attendance for some attributes. Finally, we also highlight a number of general issues with attribute non-attendance, in particular relating to the computation of willingness to pay measures.

Heterogeneity assumptions in the specification of bargaining models: a study of household level trade-offs between commuting time and salary

With many real world decisions being made in conjunction with other decision makers, or single agent decisions having an influence on other members of the decision maker's immediate entourage, there is strong interest in studying the relative weight assigned to different agents in such contexts. In the present paper, we focus on the case of one member of a two person household being asked to make choices affecting the travel time and salary of both members. We highlight the presence of significant heterogeneity across individuals not just in their underlying sensitivities, but also in the relative weight they assign to their partner, and show how this weight varies across attributes. This is in contrast to existing work which uses weights assigned to individual agents at the level of the overall utility rather than for individual attributes. We also show clear evidence of a risk of confounding between heterogeneity in marginal sensitivities and heterogeneity in the weights assigned to each member. We show how this can lead to misleading model results, and argue that this may also explain past results showing bargaining or weight parameters outside the usual [0,1] range in more traditional joint decision making contexts. In terms of substantive results, we find that male respondents place more weight on their partner's travel time, while female respondents place more weight on their partner's salary.

Spatial and Temporal Changes in Soil Organic Carbon Assessment using an Integrated Geostatistical and Compositional Data Analysis Approach

The Irish and UK governments, along with other countries, have made a commitment to limit the concentrations of greenhouse gases in the atmosphere by reducing emissions from the burning of fossil fuels. This can be achieved (in part) through increasing the sequestration of CO2 from the atmosphere including monitoring the amount stored in vegetation and soils. A large proportion of soil carbon is held within peat due to the relatively high carbon density of peat and organic-rich soils. This is particularly important for a country such as Ireland, where some 16% of the land surface is covered by peat. For Northern Ireland, it has been estimated that the total amount of carbon stored in vegetation is 4.4Mt compared to 386Mt stored within peat and soils. As a result it has become increasingly important to measure and monitor changes in stores of carbon in soils. The conservation and restoration of peat covered areas, although ongoing for many years, has become increasingly important. This is summed up in current EU policy outlined by the European Commission (2012) which seeks to assess the relative contributions of the different inputs and outputs of organic carbon and organic matter to and from soil. Results are presented from the EU-funded Tellus Border Soil Carbon Project (2011 to 2013) which aimed to improve current estimates of carbon in soil and peat across Northern Ireland and the bordering counties of the Republic of Ireland.
Historical reports and previous surveys provide baseline data. To monitor change in peat depth and soil organic carbon, these historical data are integrated with more recently acquired airborne geophysical (radiometric) data and ground-based geochemical data generated by two surveys, the Tellus Project (2004-2007: covering Northern Ireland) and the EU-funded Tellus Border project (2011-2013) covering the six bordering counties of the Republic of Ireland, Donegal, Sligo, Leitrim, Cavan, Monaghan and Louth. The concept being applied is that saturated organic-rich soil and peat attenuate gamma-radiation from underlying soils and rocks. This research uses the degree of spatial correlation (coregionalization) between peat depth, soil organic carbon (SOC) and the attenuation of the radiometric signal to update a limited sampling regime of ground-based measurements with remotely acquired data. To comply with the compositional nature of the SOC data (perturbations of loss on ignition [LOI] data), a compositional data analysis approach is investigated. Contemporaneous ground-based measurements allow corroboration for the updated mapped outputs. This provides a methodology that can be used to improve estimates of soil carbon with minimal impact to sensitive habitats (like peat bogs), but with maximum output of data and knowledge.