Sensitivity analyses for ecological regression

In many ecological regression studies investigating associations between environmental exposures and health outcomes, the observed relative risks are in the range 1.0-2.0. The interpretation of such small relative risks is difficult due to a variety of biases--some of which are unique to ecological data, since they arise from within-area variability in exposures/confounders. The potential for residual spatial dependence, due to unmeasured confounders and/or data anomalies with spatial structure, must also be considered, though it often will be of secondary importance when compared to the likely effects of unmeasured confounding and within-area variability in exposures/confounders. Methods for addressing sensitivity to these issues are described, along with an approach for assessing the implications of spatial dependence. An ecological study of the association between myocardial infarction and magnesium is critically reevaluated to determine potential sources of bias. It is argued that the sophistication of the statistical analysis should not outweigh the quality of the data, and that finessing models for spatial dependence will often not be merited in the context of ecological regression.     

Disease mapping and spatial regression with count data

In this paper, we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyze male lip cancer incidence data collected in Scotland over the period 1975-1980, and outline a number of problems with previous analyses of these data. In disease mapping studies, hierarchical models can provide robust estimation of area-level risk parameters, though care is required in the choice of covariate model, and it is important to assess the sensitivity of estimates to the spatial model chosen, and to the prior specifications on the variance parameters. Spatial ecological regression is a far more hazardous enterprise for two reasons. First, there is always the possibility of ecological bias, and this can only be alleviated by the inclusion of individual-level data. For the Scottish data, we show that the previously used mean model has limited interpretation from an individual perspective. Second, when residual spatial dependence is modeled, and if the exposure has spatial structure, then estimates of exposure association parameters will change when compared with those obtained from the independence across space model, and the data alone cannot choose the form and extent of spatial correlation that is appropriate.     

Using fuzzy logic to determine the vulnerability of marine species to climate change

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species' sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species‐specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates. We use a fuzzy logic approach to accommodate the variability in data availability and uncertainties associated with inferring vulnerability levels from climate projections and species' traits. Applying the approach to estimate the relative vulnerability and risk of impacts of climate change in 1074 exploited marine species globally, we estimated their index of vulnerability and risk of impacts to be on average 52 ± 19 SD and 66 ± 11 SD, scaling from 1 to 100, with 100 being the most vulnerable and highest risk, respectively, under the ‘business‐as‐usual' greenhouse gas emission scenario (Representative Concentration Pathway 8.5). We identified 157 species to be highly vulnerable while 294 species are identified as being at high risk of impacts. Species that are most vulnerable tend to be large‐bodied endemic species. This study suggests that the fuzzy logic framework can help estimate climate vulnerabilities and risks of exploited marine species using publicly and readily available information.     

Assessment of Human Health and Ecological Risks Posed by the Uses of Steel-Industry Slags in the Environment

Steel-industry slag, a co-product of iron and steel production, is produced and sold for use in a wide range of applications. A comprehensive study of the potential human health risks associated with the environmental applications (e.g., fill, roadbase, landscaping) of iron- and steel-making slag was performed using characterization data for 73 samples of slag collected from blast furnaces, basic oxygen furnaces, and electric arc furnaces. Characterization data were compared to regulatory health-based “screening” benchmarks to determine constituents of interest. Antimony, beryllium, cadmium, trivalent and hexavalent chromium, manganese, thallium, and vanadium were measured above screening levels and were assessed in an application-specific exposure assessment using standard U.S. Environmental Protection Agency risk assessment methods. A stochastic analysis was conducted to evaluate the variability and uncertainty in the inhalation exposure and risk estimates, and the oral bioaccessibility of certain metals in the slag was quantified. The risk assessment found no significant hazards to human health as a result of the environmental applications of steel-industry slag. However, site-specific ecological risk assessment may be required for slag applications in and around small water bodies with limited dilution volume, because high pH and aluminum were found to leach at levels that may be harmful to aquatic life     

The estimation of relative site variability among aligned homologous protein sequences

MOTIVATION: Maximum likelihood-based methods to estimate site by site substitution rate variability in aligned homologous protein sequences rely on the formulation of a phylogenetic tree and generally assume that the patterns of relative variability follow a pre-determined distribution. We present a phylogenetic tree-independent method to estimate the relative variability of individual sites within large datasets of homologous protein sequences. It is based upon two simple assumptions. Firstly that substitutions observed between two closely related sequences are likely, in general, to occur at the most variable sites. Secondly that non-conservative amino acid substitutions tend to occur at more variable sites. Our methodology makes no assumptions regarding the underlying pattern of relative variability between sites. RESULTS: We have compared, using data simulated under a non-gamma distributed model, the performance of this approach to that of a maximum likelihood method that assumes gamma distributed rates. At low mean rates of evolution our method inferred site by site relative substitution rates more accurately than the maximum likelihood approach in the absence of prior assumptions about the relationships between sequences. Our method does not directly account for the effects of mutational saturation, However, we have incorporated an 'ad-hoc' modification that allows the accurate estimation of relative site variability in fast evolving and saturated datasets.     

A Regional Multiple-Stressor Rank-Based Ecological Risk Assessment for the Fjord of Port Valdez,Alaska

We conducted an ecological risk assessment of the marine environment of Port Valdez, a fjord in south-central Alaska. Because the assessment was regional rather than site-specific and contained a large number of different stressors in a variety of environments, we required a nontraditional method to estimate risks. We created a Relative Risk Model to rank and sum individual risks numerically within each subarea, from each source, and to each habitat. Application of this model involved division of Port Valdez into 11 subareas containing specific ecological and anthropogenic structures and activities. Within each subarea, the stressor sources were analyzed to estimate exposure of receptors within habitats leading to effects relevant to the chosen assessment endpoints. The subareas were analyzed and compared to form a Port-wide perspective of ecological risk. Available chemical concentrations from sediment and mussels collected from the Port were compared to various toxicological benchmarks as a partial confirmation of the risk analysis. An estimation of the risk of polycyclic aromatic hydrocarbons (PAHs) to marine invertebrates indicated low risk. The municipal boat harbor had the highest estimate, which reflected our relative risk rankings. The Relative Risk Model approach appears robust and has potential for use in situations where multiple stressors are of concern and for assessments covering broad geographic areas. In the Port Valdez assessment the approach provided relative risk rankings for chemical and physical stressors from various sources. But data were available for confirmation of risk estimates only for the chemical stressors. The rankings are relative, and extrapolation beyond the scenario in which they were developed is not warranted. Uncertainty is large, and the numerical scores collapse a multidimensional space into a single value. Use of just the numerical score out of context is more valid than with other indexes. The value of the approach lies in the relative rankings and the accounting of the components of the relative risk score.     

A flexible hierarchical framework for improving inference in area-referenced environmental health studies

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).     

How Creditable are Cancer Risk Estimates from the S.W. Taiwan Database for Arsenic in Drinking Water?

A database of cancer mortality and arsenic concentrations in village wells in an arseniasis-endemic area of southwestern Taiwan has been the predominant source of information for risk assessments of U.S. Environmental Protection Agency and two National Research Council reports on arsenic and drinking water. A limitation of the data, however, is that exposure is ecological, that is, cancer mortality cannot be matched with arsenic exposure on an individual basis, just grouped by village. The resultant potential for bias is examined by comparing dose-response analyses of villages divided into two groups, those with well concentrations in a narrow range and the remainder. The narrow range group suggests a flat or downward change in risk in the low dose range, whereas the other group indicates increasing risk. More disturbingly, however, the dose-response data are highly dispersed for both groups and the dose-response curve predicts background bladder/lung cancer levels much higher than a southwestern Taiwan comparison population. This may be due to a large variability between villages of the study area in bladder/lung cancer not directly attributable to arsenic. Including the comparison population in the dose-response analysis artificially anchors the dose-response curve at a background level inconsistent with the study population and likely just biases the slope factor upward.     

Old-growth forest is what giant pandas really need

Giant pandas (Ailuropoda melanoleuca) are an iconic conservation species, but despite significant research effort, do we understand what they really need? Estimating and mapping suitable habitat play a critical role in conservation planning and policy. But if assumptions about ecological needs are wrong, maps with misidentified suitable habitat will misguide conservation action. Here, we use an information-theoretic approach to analyse the largest, landscape-level dataset on panda habitat use to date, and challenge the prevailing wisdom about panda habitat needs. We show that pandas are associated with old-growth forest more than with any ecological variable other than bamboo. Other factors traditionally used in panda habitat models, such as topographic slope, are less important. We suggest that our findings are disparate from previous research in part because our research was conducted over a larger ecological scale than previous research conducted over more circumscribed areas within individual reserves. Thus, extrapolating from habitat studies on small scales to conservation planning on large scales may entail some risk. As the Chinese government is considering the renewal of its logging ban, it should take heed of the panda''s dependency on old growth.     

A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments

MOTIVATION: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed. RESULTS: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by (log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes (as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Dual screening

We discuss the problem of screening a general population for characteristics such as HIV or drug use. Our main approach is Bayesian, which allows for the incorporation of prior information about parameters. In the particular problem we consider, there is currently no information in the data for estimating the sensitivity of the screening test, and consequently, the prevalence of the characteristic among screened negatives cannot be estimated from the collected data alone. Our inferences are straightforward to obtain using Gibbs sampling techniques, and they are valid for large or small samples and for arbitrary prevalence or accuracy of screening tests. We also develop the maximum-likelihood approach using the EM algorithm.     

Methods for Improving Human Gut Microbiome Data by Reducing Variability through Sample Processing and Storage of Stool

Gut microbiome community analysis is used to understand many diseases like inflammatory bowel disease, obesity, and diabetes. Sampling methods are an important consideration for human microbiome research, yet are not emphasized in many studies. In this study, we demonstrate that the preparation, handling, and storage of human faeces are critical processes that alter the outcomes of downstream DNA-based bacterial community analyses via qPCR. We found that stool subsampling resulted in large variability of gut microbiome data due to different microenvironments harbouring various taxa within an individual stool. However, we reduced intra-sample variability by homogenizing the entire stool sample in liquid nitrogen and subsampling from the resulting crushed powder prior to DNA extraction. We experimentally determined that the bacterial taxa varied with room temperature storage beyond 15 minutes and beyond three days storage in a domestic frost-free freezer. While freeze thawing only had an effect on bacterial taxa abundance beyond four cycles, the use of samples stored in RNAlater should be avoided as overall DNA yields were reduced as well as the detection of bacterial taxa. Overall we provide solutions for processing and storing human stool samples that reduce variability of microbiome data. We recommend that stool is frozen within 15 minutes of being defecated, stored in a domestic frost-free freezer for less than three days, and homogenized prior to DNA extraction. Adoption of these simple protocols will have a significant and positive impact on future human microbiome research.     

A theory for ecological survey methods to map individual distributions

Spatially explicit approaches are widely recommended for ecosystem management. The quality of the data, such as presence/absence or habitat maps, affects the management actions recommended and is, therefore, key to management success. However, available data are often biased and incomplete. Previous studies have advanced ways to resolve data bias and missing data, but questions remain about how we design ecological surveys to develop a dataset through field surveys. Ecological surveys may have multiple spatial scales, including the spatial extent of the target ecosystem (observation window), the resolution for mapping individual distributions (mapping unit), and the survey area within each mapping unit (sampling unit). We developed an ecological survey method for mapping individual distributions by applying spatially explicit stochastic models. We used spatial point processes to describe individual spatial placements using either random or clustering processes. We then designed ecological surveys with different spatial scales and individual detectability. We found that the choice of mapping unit affected the presence mapped fraction, and the fraction of the total individuals covered by the presence mapped patches. Tradeoffs were found between these quantities and the map resolution, associated with equivalent asymptotic behaviors for both metrics at sufficiently small and large mapping unit scales. Our approach enabled consideration of the effect of multiple spatial scales in surveys, and estimation of the survey outcomes such as the presence mapped fraction and the number of individuals situated in the presence detected units. The developed theory may facilitate management decision-making and inform the design of monitoring and data gathering.     

Applying an animal‐centric approach to improve ecological restoration

Traditionally, ecological restoration is based on re‐establishing patterns of vegetation communities with the expectation that wildlife will recolonize, restoring the ecological function. However, in many restoration projects, wildlife fails to recolonize, even when vegetation is restored, in many cases because revegetated habitats lack the critical features required by wildlife. We present a new approach to restoration, based on a detailed understanding of ecological process, the mechanisms by which wildlife respond to landscape patterns. Our animal‐centric approach involves measuring the risk‐sensitive decision‐making of individual animals as they balance searching for food, mates, and breeding sites with avoiding being eaten by predators and relates this to fine‐scale habitat and landscape structure. The outcome of these decisions can be measured in occupancy of habitat, the information on which conventional restoration is based. Incorporating landscape genetics allows retrospective assessment of the outcome of dispersal decisions by individual animals on a deeper time frame and at regional scales. Fine‐scale connectivity models can be parameterized with these multiscale spatial and temporal data to direct restoration efforts. We are translating this novel approach to practice in the large Midlands restoration project (4 years, AUD $6 million) in Tasmania, Australia, in partnership with Greening Australia. More than 200 years of intensive agricultural practice in this National Biodiversity Hotspot has resulted in extensive landscape modification, high densities of feral cats, and decline of many native mammals. Our research–practice partnership will alter the way that restoration is done, leading hopefully to successful restoration of wildlife, gene flow, and ecological function.     

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.     

Sample pooling obscures diversity patterns in intertidal ciliate community composition and structure

The aim of this study was to assess the effect of sample pooling on the portrayal of ciliate community structure and composition in intertidal sediment samples. Molecular ciliate community profiles were obtained from nine biological replicates distributed in three discrete sampling plots and from samples that were pooled prior to RNA extraction using terminal restriction fragment polymorphism (T-RFLP) analyses of SSU rRNA. Comparing the individual replicates of one sampling plot with each other, we found a differential variability among the individual biological replicates. T-RFLP profiles of pooled samples displayed a significantly different community composition compared with the cumulative individual biological replicate samples. We conclude that sample pooling obscures diversity patterns in ciliate and possibly also other microbial eukaryote studies. However, differences between pooled samples and replicates were less pronounced when community structure was analyzed. We found that the most abundant T-RFLP peaks were generally shared between biological replicates and pooled samples. Assuming that the most abundant taxa in an ecosystem under study are also the ones driving ecosystem processes, sample pooling may still be effective for the analyses of ecological key players.     

Evidence for diversity in transcriptional profiles of single hematopoietic stem cells

Hematopoietic stem cells replenish all the cells of the blood throughout the lifetime of an animal. Although thousands of stem cells reside in the bone marrow, only a few contribute to blood production at any given time. Nothing is known about the differences between individual stem cells that dictate their particular state of activation readiness. To examine such differences between individual stem cells, we determined the global gene expression profile of 12 single stem cells using microarrays. We showed that at least half of the genetic expression variability between 12 single cells profiled was due to biological variation in 44% of the genes analyzed. We also identified specific genes with high biological variance that are candidates for influencing the state of readiness of individual hematopoietic stem cells, and confirmed the variability of a subset of these genes using single-cell real-time PCR. Because apparent variation of some genes is likely due to technical factors, we estimated the degree of biological versus technical variation for each gene using identical RNA samples containing an RNA amount equivalent to that of single cells. This enabled us to identify a large cohort of genes with low technical variability whose expression can be reliably measured on the arrays at the single-cell level. These data have established that gene expression of individual stem cells varies widely, despite extremely high phenotypic homogeneity. Some of this variation is in key regulators of stem cell activity, which could account for the differential responses of particular stem cells to exogenous stimuli. The capacity to accurately interrogate individual cells for global gene expression will facilitate a systems approach to biological processes at a single-cell level.     

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.     

LCA of Multicrystalline Silicon Photovoltaic Systems - Part 1: Present Situation and Future Perspectives (8 pp)

- Part 1: Present Situation and Future Perspectives Part 2: Application on an Island Economy Goal, Scope and Background Incorporation of exposure and risk concepts into life cycle impact assessment (LCIA) is often impaired by the number of sources and the complexity of site-specific impact assessment, especially when input-output (I-O) analysis is used to evaluate upstream processes. This makes it difficult to interpret LCIA outputs, especially in policy contexts. In this study, we develop an LCIA tool which takes into account the geographical variability in both emissions and exposure, and which can be applied to all economic sectors in I-O analysis. Our method relies on screening-level risk calculations and methods to estimate population exposure per unit of emissions from specific geographic locations. Methods We propose a simplified impact assessment approach using the concept of intake fraction, which is the fraction of a pollutant or its precursor emitted that is eventually inhaled or ingested by the population. Instead of running a complex site-specific exposure analysis, intake fractions allow for the accounting of the regional variability in exposure due to meteorological factors and population density without much computational burden. We calculate sector-specific intake fractions using previously-derived regression models and apply these values to the supply chain emissions to screen for the sectors whose emissions largely contribute to the total exposures. Thus, the analytical steps are simplified by relying on these screening-level risk calculations. We estimate population exposure per unit emissions from specific geographic locations only for the facilities and pollutants that pass an initial screening analysis. We test our analytical approach with reference to the case of increasing insulation for new single-family homes in the US. We quantify the public health costs from increasing insulation manufacturing and compare them with the benefits from energy savings, focusing on mortality and morbidity associated with exposure to primary and secondary fine particles (PM2.5) as well as cancer risk associated with exposure to toxic air pollutants. We estimate health impacts using concentration-response functions from the published literature and compare the costs and benefits of the program by assigning monetary values to the health risks. In the second part of this paper, we present the results of our case study and consider the implications for incorporating exposure and risk concepts into I-O LCA. Conclusions We have presented a methodology to incorporate regional variability in emissions and exposure into input-output LCA, using reduced-form information about the relationship between emissions and population exposure, along with standard input-output analysis and risk assessment methods. The location-weighted intake fractions can overcome the difficulty in incorporation of regional exposure in LCIA.