Bioaccessibility performance data for fifty-seven elements in guidance material BGS 102

BGS 102, a guidance material for bioaccessible arsenic (As) and lead (Pb), was produced during validation of the in vitro Unified Bioaccessibility Method (UBM). This paper reports a compilation of reproducible bioaccessible guidance values for fifty-five additional elements in BGS 102, providing guidance for analysts to broaden the scope of UBM analyses for a wider range of elements based on data collected over an average of 60 separate analytical batches per element. Data are presented in categories for both gastric (STOM) and gastrointestinal (STOM + INT) extraction phases, where reproducibility, measured as relative standard deviation (RSD) was; ≤ 10% RSD for 27 elements (Mg, Al, Si, P, Ca, Cr, Mn, Co, Ni, As, Rb, Sr, Y, Ba, La, Ce, Pr, Nd, Sm, Eu, Tb, Gd, Dy, Ho, Er, Tm, Yb); between 10 and 20% RSD for 10 elements (Li, K, V, Fe, Cu, Zn, Cd, Lu, Pb, U); and ≥ 20% RSD for 19 elements in the gastric phase (Be, B, S, Ti, Ga, Se, Zr, Nb, Mo, Ag, Sn, Sb, Cs, Hf, Ta, W, Tl, Bi, Th). Two elements (Mg, Rb) met the ≤ 10% RSD criteria in the UBM gastrointestinal extraction phase due to the alkaline conditions of this phase precipitating out the majority of determinands. Certain elements, including Na, K, Zn and Se, were found to be a significant component of the extraction fluids with proportionally higher concentrations compared to the guidance material. Bioaccessible fractions (%BAF) were also calculated, but were found to be a less reproducible format for confirming the accuracy of measurements. The low concentration of some elements of interest in BGS 102, such as antimony (Sb), justifies the preparation of an alternative certified reference material (CRM). This paper presents an opportunity to broaden the scope of the UBM method to address food security issues (e.g. Fe and Zn micronutrient deficiencies) and contributions to dietary intake from extraneous dust or soil through evidence of the analytical possibilities and current limitations requiring further investigation

Limitations on the role of the hyporheic zone in chromium natural attenuation in a contaminated urban stream

The urban hyporheic zone may offer natural attenuation potential for contaminants. This potential is contaminant-specific and may be spatially and temporally variable. The aim of this study was the assessment of the natural attenuation potential of the hyporheic zone of an urban stream receiving hexavalent chromium (Cr)-rich effluents from the historical land disposal of chromite ore processing residue (COPR) in Glasgow, Scotland. The evidence based approach involved the use of a network of multilevel piezometers for best capturing potential anoxic field conditions and fine-scale spatial gradients in solute concentrations of surface water and porewater. In-situ porewater sampling was integrated with sediment sampling and reach-scale monitoring of stream water quality. The results show a sharp decrease of total dissolved (filtered <0.45 μm) Cr concentrations at the surface water – sediment boundary in all profiles, from Cr mean values of 1100 μg l−1 in surface water to 5 μg l−1 in porewater. Chromium speciation analysis indicates that no Cr(VI) was detectable in the neutral pH, moderately reducing porewater, while it was the dominant species in surface water. Evidence of historical COPR detrital grains contributed to the total Cr concentrations (size fraction <150 μm) up to 8800 mg kg−1 in the streambed sediment. The abundance in the porewater of Fe (mean value = 1700 μg l−1), mainly as Fe(II), a natural electron donor for Cr(VI) reduction to Cr(III), indicates a high natural attenuation potential of the hyporheic zone for downwelling dissolved Cr, through Cr(VI) reduction to Cr(III) and the formation of Cr(III) solids of low solubility. Authigenic Cr-rich rims on particles also documented active Cr precipitation from solution in the fine sediments. Large short-term changes of stream stage and stream water composition were not reflected in the hyporheic conservative (chloride) and reactive solute composition. This result indicates only limited surface water infiltration and suggests that small advective exchange might limit the effectiveness of the hyporheic zone for enhancing Cr surface water quality at the reach-scale. This is supported by further evidence from preliminary surface water quality synoptic sampling which shows only moderate to low downstream decrease in surface water Cr concentrations. The surface water investigation needs to be supported by combined water quality-flow monitoring and to be extended to a wider range of temporal and spatial scales to corroborate the reach-scale findings

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Uptake of synthetic low density lipoprotein by leukemic stem cells — a potential stem cell targeted drug delivery strategy

Chronic Myeloid Leukemia (CML) stem/progenitor cells, which over-express Bcr-Abl, respond to imatinib by a reversible block in proliferation without significant apoptosis. As a result, patients are unlikely to be cured owing to the persistence of leukemic quiescent stem cells (QSC) capable of initiating relapse. Previously, we have reported that intracellular levels of imatinib in primary primitive CML cells (CD34&lt;sup&gt;+&lt;/sup&gt;38&lt;sup&gt;lo/−&lt;/sup&gt;), are significantly lower than in CML progenitor cells (total CD34&lt;sup&gt;+&lt;/sup&gt;) and leukemic cell lines. The aim of this study was to determine if potentially sub-therapeutic intracellular drug concentrations in persistent leukemic QSC may be overcome by targeted drug delivery using synthetic Low Density Lipoprotein (sLDL) particles. As a first step towards this goal, however, the extent of uptake of sLDL by leukemic cell lines and CML patient stem/progenitor cells was investigated. Results with non-drug loaded particles have shown an increased and preferential uptake of sLDL by Bcr-Abl positive cell lines in comparison to Bcr-Abl negative. Furthermore, CML CD34&lt;sup&gt;+&lt;/sup&gt; and primitive CD34&lt;sup&gt;+&lt;/sup&gt;38&lt;sup&gt;lo/−&lt;/sup&gt; cells accumulated significantly higher levels of sLDL when compared with non-CML CD34&lt;sup&gt;+&lt;/sup&gt; cells. Thus, drug-loading the sLDL nanoparticles could potentially enhance intracellular drug concentrations in primitive CML cells and thus aid their eradication

Chromium speciation in foodstuffs: a review

Numerous critical reviews have evaluated exposure to toxic and carcinogenic hexavalent chromium (Cr(VI)) from a number of pathways; including workplace air, cement and packaging materials. The contribution of foodstuffs to dietary Cr(VI) has been increasingly under investigation, however no summary of this work has been carried out. The objective of this article is to review the last twenty years of chromium speciation research in foodstuffs. Alkaline extraction, used for chromium speciation in other solids, is the most widely-reported procedure. Previous measurement of Cr(VI) in foodstuffs is questionable due to the reducing power of organic matter and antioxidants, leading to the development of speciated isotope dilution mass spectrometry (SIDMS) techniques to monitor interconversions. Evaluation of the genotoxicity of trivalent chromium (Cr(III)), which acts through a different pathway to that of Cr(VI), requires reconsideration towards measurement of Cr(III), which is present at higher concentrations in foodstuffs following reduction of the more-bioavailable Cr(VI)

Special fast diffusion with slow asymptotics. Entropy method and flow on a Riemannian manifold

We consider the asymptotic behaviour of positive solutions $u(t,x)$ of the fast diffusion equation $u_t=\Delta (u^{m}/m)={\rm div} (u^{m-1}\nabla u)$ posed for x\in\RR^d, $t>0$, with a precise value for the exponent $m=(d-4)/(d-2)$. The space dimension is $d\ge 3$ so that $m<1$, and even $m=-1$ for $d=3$. This case had been left open in the general study \cite{BBDGV} since it requires quite different functional analytic methods, due in particular to the absence of a spectral gap for the operator generating the linearized evolution. The linearization of this flow is interpreted here as the heat flow of the Laplace-Beltrami operator of a suitable Riemannian Manifold (\RR^d,{\bf g}), with a metric ${\bf g}$ which is conformal to the standard \RR^d metric. Studying the pointwise heat kernel behaviour allows to prove {suitable Gagliardo-Nirenberg} inequalities associated to the generator. Such inequalities in turn allow to study the nonlinear evolution as well, and to determine its asymptotics, which is identical to the one satisfied by the linearization. In terms of the rescaled representation, which is a nonlinear Fokker--Planck equation, the convergence rate turns out to be polynomial in time. This result is in contrast with the known exponential decay of such representation for all other values of $m$.Comment: 37 page

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping is a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms such as random forest (RF) models have been developed for such tasks as they are capable of modelling non-linear relationships using a range of datasets and determining the importance of predictor variables, offering multiple benefits to traditional techniques such as kriging. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with auxiliary environmental covariates. The model was applied to predict the total concentration (mg kg-1 ) of 56 elements, soil pH and organic matter content, as well as to assess prediction uncertainty using 466 soil samples in western Kenya (Watts et al 2021). The results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work due to their contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH was assessed as an important parameter to define soil chemical reactions. Algorithm performance was evaluated to determine the importance of each predictor variable and the model’s response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating the out-of-bag predicted values. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of auxiliary values did improve model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agricommunity utilise the geochemical maps presented here, and the webtool, for assessing the relationship between environmental geochemistry and endemic diseases and preventable micronutrient deficiency

Predictive geochemical mapping using machine learning in western Kenya

Digital soil mapping techniques represent a cost-effective method for obtaining detailed information regarding the spatial distribution of chemical elements in soils. Machine learning (ML) algorithms using random forest (RF) models have been developed for classification, pattern recognition and regression tasks, they are capable of modelling non-linear relationships using a range of datasets, identifying hierarchical relationships, and determining the importance of predictor variables. In this study, we describe a framework for spatial prediction based on RF modelling where inverse distance weighted (IDW) predictors are used in conjunction with ancillary environmental covariates. The model was applied to predict the total concentration (mg kg−1) and assess the prediction uncertainty of 56 elements, soil pH and organic matter content using 466 soil samples in western Kenya; the results of iodine (I), selenium (Se), zinc (Zn) and soil pH are highlighted in this work. These elements were selected due to contrasting biogeochemical cycles and widespread dietary deficiencies in sub-Saharan Africa, whilst soil pH is an important parameter controlling soil chemical reactions. Algorithm performance was evaluated determining the relative importance of each predictor variable and the model's response using partial dependence profiles. The accuracy and precision of each RF model were assessed by evaluating out-of-bag predicted values. The models R2 values range from 0.31 to 0.64 whilst CCC values range from 0.51 to 0.77. The IDW predictor variables had the greatest impact on assessing the distribution of soil properties in the study area, however, the inclusion of ancillary environmental data improved model performance for all soil properties. The results presented in this paper highlight the benefits of ML algorithms which can incorporate multiple layers of data for spatial prediction, uncertainty assessment and attributing variable importance. Additional research is now required to ensure health practitioners and the agri-community utilise the geochemical maps presented here for assessing the relationship between environmental geochemistry, endemic diseases and preventable micronutrient deficiency

Artisanal gold mining in Kakamega and Vihiga counties, Kenya: potential human exposure and health risk

Artisanal and small-scale gold mining (ASGM) represents 20% of gold supply and 90% of gold mining workforce globally, which operates in highly informal setups. Pollutants from mined ores and chemicals introduced during gold processing pose occupational and inadvertent health risks to the extent that has not been well elucidated in Africa. Trace and major elements were analysed using inductively coupled plasma mass spectrometry in soil, sediment and water samples from 19 ASGM villages in Kakamega and Vihiga counties. Associated health risks for residents and ASGM workers were assessed. This paper focuses on As, Cd, Cr, Hg, Ni and Pb for which 96% of soil samples from mining and ore processing sites had As concentrations up to 7937 times higher than the US EPA 12 mg kg−1 standard for residential soils. Soil Cr, Hg and Ni concentrations in 98%, 49% and 68% of the samples exceeded respective USEPA and CCME standards, with 1–72% bioaccessibility. Twenty-five percentage of community drinking water sources were higher than the WHO 10 µg L−1 drinking water guideline. Pollution indices indicated significant enrichment and pollution of soils, sediment and water in decreasing order of As > Cr > Hg > Ni > Pb > Cd. The study revealed increased risks of non-cancer health effects (98.6) and cancer in adults (4.93 × 10−2) and children (1.75 × 10−1). The findings will help environment managers and public health authorities better understand the potential health risks in ASGM and support evidence-based interventions in ASGM processes, industrial hygiene and formulation of public health policy to protect residents and ASGM workers’ health in Kenya