Time Series Experiments Research Articles

Upregulation of hemolymph and tissue ferritin by dietary HgCl2 in the wax moth Galleria mellonella

AbstractThe effect of Hg treatment on hemolymph and tissue ferritin in the wax moth Galleria mellonella was examined by western blotting. At 48 h after feeding HgCl2, the level of hemolymph ferritin increased approximately 1.8‐fold over that of control insects that were not fed HgCl2, while there was a small increase in tissue ferritin. Time series experiments showed that tissue ferritin had a typically saturated pattern, with a maximum level from 24 to 72 h, although it decreased 12 h following HgCl2 feeding, while hemolymph ferritin first decreased but subsequently increased. Tissue ferritin in the fat body, gut and Malpighian tubules, the main tissues of ferritin expression, was upregulated over time following treatment with Hg, and in particular, tissue ferritin in the gut increased by a large amount at 12–48 h. The results suggest that in G. mellonella, the ferritin‐inducible mechanisms following treatment with HgCl2 are different for hemolymph and tissue ferritin, as are their biochemical properties.

Temporal Bayesian classifiers for modelling muscular dystrophy expression data

The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. In this paper we focus on data that has been generated to explore different types of muscular dystrophy.

Precipitation of heavy metals in produced water: Influence on contaminant transport and toxicity

Produced water undergoes changes in its physical chemistry including precipitation of heavy metals after being discharged and mixed with ambient seawater. Potential impacts of the precipitation of heavy metals on their transport and toxicity were studied using samples from offshore oil production sites on the Scotian Shelf off eastern Canada. Concentrations of aluminum, cadmium, chromium, cobalt, copper, iron, lead, manganese, nickel and zinc were measured in total, particulate and dissolved fractions together with Microtox tests for assessment of toxicity. Heavy metals in produced water were transformed from dissolved to particulate phase in a period of hours under oxygenated conditions, and aggregated to larger particles that settle rapidly (>100 m/day) over a few days. In addition, there was production of buoyant particles comprised of heavy metal precipitates sequestered onto oil droplets that were transported to the surface. The particulate fraction was generally more toxic than the dissolved fraction. This was evident at the mixing interface between produced water and seawater where elevated particulate and toxicity levels were observed. Laboratory studies suggest an increase in the toxicity of discharged produced water over time. Time-series experiments showed a sustained toxic response for more than a week following the oxidation of freshly discharged produced water that initially elicited little or no toxic response in the Microtox test. Chemical processes identified in this study, namely precipitation of heavy metals and consequent settling and rising fluxes of particles, will influence the toxicity, the fate and the transport of potential contaminants in the produced water. Therefore, these processes need to be considered in assessment of the environmental impact associated with offshore oil and gas operations.

Analysis of promoter regions of co-expressed genes identified by microarray analysis

BackgroundThe use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated.ResultsWe introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different dataset, one consisting of micro array data from 108 leukemias (AMLs) and a second from a time series experiment, and show that biologically relevant promoter patterns may be obtained using phylogenetic foot-printing methodology. In addition, we also found that 15% of the analyzed promoter regions contained transcription factors start sites for additional genes transcribed in the opposite direction.ConclusionPromoter clustering based on global promoter features greatly improve the identification of shared TFBS in co-expressed genes. We believe that the outlined approach may be a useful first step to identify transcription factors that contribute to specific features of gene expression profiles.

Temporal variation in the expression of the p53 protein in proliferating and differentiating murine erythroleukaemia cells

Temporal changes in the expression of p53 were investigated during hexamethylene bisacetamide (HMBA)-induced differentiation of murine erythroleukaemic (MEL) cells. Cell preparations were analysed by SDS-PAGE and western immunoblotting, which detected an immunospecific band of molecular mass 53 kDa. This analysis provided semi-quantitative information. Cell extracts were analysed further by means of ELISA techniques, using a p53-specific antibody, to provide quantitative data. In time series experiments in which cells were isolated at 15, 30 and 60 min intervals, dynamic variations in the expression of the p53 protein were detected in both the untreated and the HMBA-treated MEL cells. In all cases, the effects were complex with variations in amplitude, frequency and phasing of the rhythms. The available evidence suggests that the observed patterns, like periodic variations in other systems, are modified in rhythmic fashion with respect to period and amplitude. The results add further support for our view that it is essential to consider the dynamics when interpreting the role of p53 and cellular processes in general.

Independent component analysis reveals new and biologically significant structures in micro array data.

BackgroundAn alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes.ResultsWe applied independent component analysis (ICA) to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology (GO) categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes.ConclusionAlthough the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA.

Dynamics and Characterization of Refractory Dissolved Organic Matter Produced by a Pure Bacterial Culture in an Experimental Predator-Prey System

We studied the effects of a bacterium (Pseudomonas chlororaphis) and a bactivorous protozoan (Uronema sp.) on transformations of labile dissolved organic carbon (DOC). In 36-day time series experiments, bacteria were grown on glucose both with and without protozoa. We measured bulk organic carbon pools and used electrospray ionization mass spectrometry to characterize dissolved organic matter on a molecular level. Bacteria rapidly utilized glucose, depleting it to nondetectable levels and producing new DOC compounds of higher molecular weight within 2 days. Some of these new compounds, representing 3 to 5% of the initial glucose-C, were refractory and persisted for over a month. Other new compounds were produced and subsequently used by bacteria during the lag and exponential growth phases, pointing to a dynamic cycling of organic compounds. Grazers caused a temporary spike in the DOC concentration consisting of labile compounds subsequently utilized by the bacteria. Grazing did not increase the complexity of the DOC pool already established by the bacteria but did continually decrease the particulate organic carbon pool and expedited the conversion of glucose-C to CO2. After 36 days, 29% of initial glucose-C remained in pure bacteria cultures, while only 6% remained in cultures where a grazer was present. In this study the bacteria were the primary shapers of the complex DOC continuum, suggesting higher trophic levels possibly have less of an impact on the qualitative composition of DOC than previously assumed.

Correction of scaling mismatches in oligonucleotide microarray data.

BackgroundGene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive.ResultsWe explain how scaling mismatches occur in data summarized by the popular MAS5 (GCOS; Affymetrix) algorithm, and propose a simple recursive algorithm to correct them. Its principle is to identify a set of constant genes and to use this set to rescale the microarray signals. We study the properties of the algorithm using artificially generated data and apply it to experimental data. We show that the set of constant genes it generates can be used to rescale data from other experiments, provided that the underlying system is similar to the original. We also demonstrate, using a simple example, that the method can successfully correct existing imbalancesin the data.ConclusionThe set of constant genes obtained for a given experiment can be applied to other experiments, provided the systems studied are sufficiently similar. This type of rescaling is especially relevant in systems biology applications using microarray data.

Applying dynamic Bayesian networks to perturbed gene expression data.

BackgroundA central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics, allowing to deal with the stochastic aspects of gene expressions and noisy measurements in a natural way, Bayesian networks appear attractive in the field of inferring gene interactions structure from microarray experiments data. However, the basic formalism has some disadvantages, e.g. it is sometimes hard to distinguish between the origin and the target of an interaction. Two kinds of microarray experiments yield data particularly rich in information regarding the direction of interactions: time series and perturbation experiments. In order to correctly handle them, the basic formalism must be modified. For example, dynamic Bayesian networks (DBN) apply to time series microarray data. To our knowledge the DBN technique has not been applied in the context of perturbation experiments.ResultsWe extend the framework of dynamic Bayesian networks in order to incorporate perturbations. Moreover, an exact algorithm for inferring an optimal network is proposed and a discretization method specialized for time series data from perturbation experiments is introduced. We apply our procedure to realistic simulations data. The results are compared with those obtained by standard DBN learning techniques. Moreover, the advantages of using exact learning algorithm instead of heuristic methods are analyzed.ConclusionWe show that the quality of inferred networks dramatically improves when using data from perturbation experiments. We also conclude that the exact algorithm should be used when it is possible, i.e. when considered set of genes is small enough.

STEM: a tool for the analysis of short time series gene expression data

BackgroundTime series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.ResultsWe introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology.ConclusionThe unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .

Dealing with gene expression missing data

Compared evaluation of different methods is presented for estimating missing values in microarray data: weighted K-nearest neighbours imputation (KNNimpute), regression-based methods such as local least squares imputation (LLSimpute) and partial least squares imputation (PLSimpute) and Bayesian principal component analysis (BPCA). The influence in prediction accuracy of some factors, such as methods' parameters, type of data relationships used in the estimation process (i.e. row-wise, column-wise or both), missing rate and pattern and type of experiment [time series (TS), non-time series (NTS) or mixed (MIX) experiments] is elucidated. Improvements based on the iterative use of data (iterative LLS and PLS imputation--ILLSimpute and IPLSimpute), the need to perform initial imputations (modified PLS and Helland PLS imputation--MPLSimpute and HPLSimpute) and the type of relationships employed (KNNarray, LLSarray, HPLSarray and alternating PLS--APLSimpute) are proposed. Overall, it is shown that data set properties (type of experiment, missing rate and pattern) affect the data similarity structure, therefore influencing the methods' performance. LLSimpute and ILLSimpute are preferable in the presence of data with a stronger similarity structure (TS and MIX experiments), whereas PLS-based methods (MPLSimpute, IPLSimpute and APLSimpute) are preferable when estimating NTS missing data.

Microstructure evolution of aqueous fluid‐bearing wehrlites: Implications for the fluid distribution in polymineralic rocks

The microstructure of aqueous fluid‐bearing wehrlites was experimentally studied in order to investigate the fluid distribution in polymineralic rocks. Wehrlites of various forsterite/diopside ratios were synthesized with 1.0–1.5 wt % water at 1200°C and 1.2 GPa for 1.5–668 hours. Conspicuous grain growth, drag, and coagulation of pore fluids were observed in the time series experiments. The pore fluids were classified into two types: those surrounded only by a single phase (G‐type) and those surrounded by both forsterite and diopside (I‐type). In the short time runs the relative volume fraction of I‐type pores in all the pores, FI, agrees well with the value calculated from an ideal pore distribution model in which all the grains are randomly distributed. The FI of wehrlites with various forsterite/diopside ratios increased with average grain size and reached a steady state value at 80–90% in about 160–163 hours. The increase of FI is explained by “interphase boundary trapping”: G‐type pore fluids move faster than I‐type, and the number of G‐type becoming I‐type by being trapped into the interphase boundary per unit time is larger than that of I‐type pores transforming to G‐type. The experimental results suggest that large fractions of pore fluids might be distributed at the grain junctions surrounded by more than two mineral phases in natural rocks as a result of grain growth. The connectivity of the fluid network in polymineralic rocks may be strongly influenced by modal composition of the rocks as well as by the dihedral angles among the fluid and two mineral phases.

Monitoring the Dynamics of Transcription and Translation Within the Time Course of Recombinant E. coli Cultivations

Background Among the key objectives for the optimization of recombinant protein production on industrial scale high yield of the product is one of the most important issues. To achieve high quantities of recombinant protein strong host/vector systems are utilized, generally leading to an overburden of the host cell metabolism. To cope with different types of stress cells have evolved complex regulatory entities acting on the highest level of metabolic regulation, such as the stringent response network. Highly specific signal molecules of these regulatory networks, like sigma factors and ppGpp, as well as global regulators like ArcA, Crp and Fis enable accurate up-and downregulation of specific genes [1,2]. The thereby altered expression profile confers increased resistance to adverse conditions. As the exploitation of the cell factory will always approach the physiological limits of the cellular machinery the shortas well as the long-term impact of high recombinant gene expression rates have to be investigated. Therefore key variables of metabolic stress to define and thus quantify the actual metabolic load of host cell metabolism can be inferred from variations of genome and proteome patterns. Due to the availability of whole genome E. coli microarrays this technology provides an efficient tool for screening overall transcriptional changes in the course of fermentation processes. 2D-Fluorescence Differencel Gel Electrophoresis (DIGE, Amersham Biosciences) and MALDI-MS are established to identify changes on proteome level. A combination of these three methods (microarrays, DIGE and MS) paired with the application of time series experiments during a recombinant fermentation process provide an insight into the black box of cell metabolism and improve the understanding of cellular physiology.

Combined static and dynamic analysis for determining the quality of time-series expression profiles

Expression profiling of time-series experiments is widely used to study biological systems. However, determining the quality of the resulting profiles remains a fundamental problem. Because of inadequate sampling rates, the effect of arrest-and-release methods and loss of synchronization, the measurements obtained from a series of time points may not accurately represent the underlying expression profiles. To solve this, we propose an approach that combines time-series and static (average) expression data analysis--for each gene, we determine whether its temporal expression profile can be reconciled with its static expression levels. We show that by combining synchronized and unsynchronized human cell cycle data, we can identify many cycling genes that are missed when using only time-series data. The algorithm also correctly distinguishes cycling genes from genes that specifically react to an environmental stimulus even if they share similar temporal expression profiles. Experimental validation of these results shows the utility of this analytical approach for determining the accuracy of gene expression patterns.

Effect of Different Carbon Sources on Community Composition of Bacterial Enrichments from Soil

Soil is a highly heterogeneous matrix, which can contain thousands of different bacterial species per gram. Only a small component of this diversity (maybe <1%) is commonly captured using standard isolation techniques, although indications are that a larger proportion of the soil community is in fact culturable. Better isolation techniques yielding greater bacterial diversity would be of benefit for understanding the metabolic activity and capability of many soil microorganisms. We studied the response of soil bacterial communities to carbon source enrichment in small matrices by means of terminal restriction fragment length polymorphism (TRFLP) analysis. The community composition of replicate enrichments from soil displayed high variability, likely attributable to soil heterogeneity. An analysis of TRFLP data indicated that enrichment on structurally similar carbon sources selected for similar bacterial communities. The same analysis indicated that communities first enriched on glucose or benzoate and subsequently transferred into medium containing an alternate carbon source retained a distinct community signature induced by the carbon source used in the primary enrichment. Enrichment on leucine presented a selective challenge that was able to override the imprint left by primary enrichment on acetate. In a time series experiment community change was most rapid 18 hours after inoculation, corresponding to exponential growth. Community composition did not stabilize even 4 days after secondary enrichment. Four different soil types were enriched on four different carbon sources. TRFLP analysis indicated that in three out of four cases communities enriched on the same carbon source were more similar regardless of which soil type was used. Conversely, the garden soil samples yielded similar enrichment communities regardless of the enrichment carbon source. Our results indicate that in order to maximize the diversity of bacteria recovered from the environment, multiple enrichments should be performed using a chemically diverse set of carbon sources.

A community-wide intercomparison exercise for the determination of dissolved iron in seawater

The first large-scale international intercomparison of analytical methods for the determination of dissolved iron in seawater was carried out between October 2000 and December 2002. The exercise was conducted as a rigorously “blind” comparison of 7 analytical techniques by 24 international laboratories. The comparison was based on a large volume (700 L), filtered surface seawater sample collected from the South Atlantic Ocean (the “IRONAGES” sample), which was acidified, mixed and bottled at sea. Two 1-L sample bottles were sent to each participant. Integrity and blindness were achieved by having the experiment designed and carried out by a small team, and overseen by an independent data manager. Storage, homogeneity and time-series stability experiments conducted over 2.5 years showed that inter-bottle variability of the IRONAGES sample was good (< 7%), although there was a decrease in iron concentration in the bottles over time (0.8–0.5 nM) before a stable value was observed. This raises questions over the suitability of sample acidification and storage. For the complete dataset of 45 results (after excluding 3 outliers not passing the screening criteria), the mean concentration of dissolved iron in the IRONAGES sample was 0.59 ± 0.21 nM, representing a coefficient of variation (%CV) for analytical comparability (“community precision”) of 36% (1s), a significant improvement over earlier exercises. Within-run precision (5–10%), inter-run precision (15%) and inter-bottle homogeneity (< 7%) were much better than overall analytical comparability, implying the presence of: (1) random variability (inherent to all intercomparison exercises); (2) errors in quantification of the analytical blank; and (3) systematic inter-method variability, perhaps related to secondary sample treatment (e.g. measurement of different physicochemical fractions of iron present in seawater) in the community dataset. By grouping all results for the same method, analyses performed using flow injection-luminol chemiluminescence (with FeII detection after sample reduction) [Bowie, A.R., Achterberg, E.P., Mantoura, R.F.C., Worsfold, P.J., 1998. Determination of sub-nanomolar levels of iron in seawater using flow injection with chemiluminescence detection. Anal. Chim. Acta 361, 189–200] and flow injection-catalytic spectrophotometry (using the reagent DPD) [Measures, C.I., Yuan, J., Resing, J.A., 1995. Determination of iron in seawater by flow injection analysis using in-line preconcentration and spectrophotometric detection. Mar. Chem. 50, 3–12] gave significantly ( P = 0.05) higher dissolved iron concentrations than analyses performed using isotope dilution ICPMS [Wu, J.F., Boyle, E.A., 1998. Determination of iron in seawater by high-resolution isotope dilution inductively coupled plasma mass spectrometry after Mg(OH) 2 co-precipitation. Anal. Chim. Acta 367, 183–191]. There was, however, evidence of scatter within each method group (CV up to 59%), implying that better uniformity in procedures may be required. This paper does not identify individual data and should not be viewed as an evaluation of single laboratories. Rather it summarises the status of dissolved iron analysis in seawater by the international community at the start of the 21st century, and can be used to inform future exercises including the SAFE iron intercomparison study in the North Pacific in October 2004.

Adsorption of REE(III)-humate complexes onto MnO 2: Experimental evidence for cerium anomaly and lanthanide tetrad effect suppression

Experiments were conducted to evaluate the impact of organic complexation on the development of Ce anomalies and the lanthanide tetrad effect during the adsorption of rare-earth elements (REE) onto MnO 2. Two types of aqueous solutions—NaCl and NaNO 3—were tested at pH 5 and 7.5. Time-series experiments indicate that a steady-state is reached within less than 10 h when REE occur as free inorganic species, whereas steady state is not reached before 10 d when REE occur as REE-humate complexes. The distribution coefficients (K d REE) between suspended MnO 2 and solution show no or only very weak positive Ce anomaly or lanthanide tetrad effect when REE occur as humate complexes, unlike the results obtained in experiments with REE occurring as free inorganic species. Monitoring of dissolved organic carbon (DOC) concentrations show that log K d REE organic /K d DOC ratios are close to 1.0, implying that the REE and humate remain bound to each other upon adsorption. Most likely, the Ce anomaly reduction/suppression in the organic experiments arises from a combination of two processes: (i) inability of MnO 2 to oxidize Ce(III) because of shielding of MnO 2 surfaces by humate molecules and (ii) Ce(IV) cannot be preferentially removed from solution due to quantitative complexation of the REE by organic matter. We suggest that the lack of lanthanide tetrad effect arises because the adsorption of REE-humate complexes onto MnO 2 occurs dominantly via the humate side of the complexes (anionic adsorption), thereby preventing expression of the differences in Racah parameters for 4f electron repulsion between REE and the oxide surface. The results presented here explain why, despite the development of strongly oxidizing conditions and the presence of MnO 2 in the aquifer, no (or insignificant) negative Ce anomalies are observed in organic-rich waters. The present study demonstrates experimentally that the Ce anomaly cannot be used as a reliable proxy of redox conditions in organic-rich waters or in precipitates formed at equilibrium with organic-rich waters.

TEMPERATURE EFFECTS ON IRON REDUCTION IN A HYDRIC SOIL

For soils to be considered hydric, they must demonstrate both saturation and anaerobic conditions in the upper part of the soil. Although several technologies are available for monitoring of soil water tables, documentation of reducing conditions is more problematic. This has led to recent interest in the use of IRIS (indicator of reduction in soils) tubes. IRIS tubes are lengths of PVC pipe coated with ferrihydrite paint, which are inserted into the soil to document reducing conditions. Observations from preliminary studies led us to postulate soil temperature will affect the degree and rate of reduction and removal of ferrihydrite from the IRIS tubes. To quantify the impact of soil temperature on ferrihydrite reduction and removal from IRIS tubes, a time series experiment was designed such that IRIS tubes were placed into a hydric soil for time periods ranging from 7 to 28 days, between February and June, 2004. Water tables were monitored daily, Eh was measured at depths of 10, 20, 30, 40 and 50, cm on a weekly basis, and soil temperature was recorded every 4 hours. Removal of ferrihydrite from IRIS tubes was not constant during periods of anaerobiosis and was related to soil temperature. At temperatures below 2 °C, there was essentially no measurable ferrihydrite reduction, even though soil redox potentials fell within the Fe(II) stability field. As soil temperatures increased between 2 and 8 °C, the quantity of ferrihydrite reduction increased with time. At soil temperatures between 8 and 20 °C, substantial (35% to 45%) ferrihydrite paint was reduced and removed from IRIS tubes within 7 days. For a given temperature, there was greater reduction of ferrihydrite in zones closer to the soil surface, presumably due to higher amounts of oxidizable organic matter in near surface horizons.

Clay mineral weathering and contaminant dynamics in a caustic aqueous system: I. Wet chemistry and aging effects

Caustic high level radioactive waste induces mineral weathering reactions that can influence the fate of radionuclides released in the vicinity of leaking storage tanks. The uptake and release of Cs I and Sr II were studied in batch reactors of 2:1 layer-type silicates—illite (Il), vermiculite (Vm) and montmorillonite (Mt)—under geochemical conditions characteristic of leaking tank waste at the Hanford Site in WA (0.05 m Al T, 2 m Na +, 1 m NO 3 −, pH ∼14, Cs and Sr present as co-contaminants). Time series (0 to 369 d) experiments were conducted at 298 K, with initial [Cs] 0 and [Sr] 0 concentrations from 10 −5 to 10 −3 mol kg −1. Clay mineral type affected the rates of (i) hydroxide promoted dissolution of Si, Al and Fe, (ii) precipitation of secondary solids and (iii) uptake of Cs and Sr. Initial Si release to solution followed the order Mt > Vm > Il. An abrupt decrease in soluble Si and/or Al after 33 d for Mt and Vm systems, and after 190 d for Il suspensions was concurrent with accumulation of secondary aluminosilicate precipitates. Strontium uptake exceeded that of Cs in both rate and extent, although sorbed Cs was generally more recalcitrant to subsequent desorption and dissolution. After 369 d reaction time, reacted Il, Vm and Mt solids retained up to 17, 47 and 14 mmol kg −1 (0.18, 0.24 and 0.02 μmol m −2) of Cs, and 0, 27 and 22 mmol kg −1 (0, 0.14 and 0.03 μmol m −2) Sr, respectively, which were not removed in subsequent Mg exchange or oxalic acid dissolution reactions. Solubility of Al and Si decreased with initial Cs and Sr concentration in Mt and Il, but not in Vm. High co-contaminant sorption to the Vm clay, therefore, appears to diminish the influence of those ions on mineral transformation rates.

Clay mineral weathering and contaminant dynamics in a caustic aqueous system: II. Mineral transformation and microscale partitioning

Microscopic and spectroscopic studies were conducted to assess mineral transformation processes in aqueous suspensions of illite (Il), vermiculite (Vm) and montmorillonite (Mt) that were subjected to weathering in a simulated high-level radioactive tank waste leachate (0.05 m Al T, 2 m Na +, 1 m NO 3 −, pH ∼14, Cs + and Sr 2+ present as co-contaminants). Time series (0 to 369 d) experiments were conducted at 298 K, with initial [Cs] 0 and [Sr] 0 concentrations from 10 −5 to 10 − mol kg −. Incongruent clay dissolution resulted in an accumulation of secondary aluminosilicate precipitates identified as nitrate-sodalite, nitrate-cancrinite and zeolite X, by molecular spectroscopy and electron microscopy (XRD, IR, NMR, SEM-EDS and TEM-EDS). Contaminant fate was dependent on competing uptake to parent clays and weathering products. TEM-EDS results indicated that high Il affinity for Cs was due to adsorption at frayed edge sites. The Il system also comprised Sr-rich aluminous precipitates after 369 d reaction time. In Mt systems, Cs and Sr were co-precipitated into increasingly recalcitrant spheroidal precipitates over the course of the experiment, whereas contaminant association with montmorillonite platelets was less prevalent. In contrast, Cs and Sr were found in association with weathered Vm particles despite the formation of spheroidal aluminosilicate precipitates that were comparable to those formed from Mt dissolution.