High Percentage Inorganic Arsenic Content of Mining Impacted and Nonimpacted Chinese Rice

Two approaches were undertaken to characterize the arsenic (As) content of Chinese rice. First, a national market basket survey (n = 240) was conducted in provincial capitals, sourcing grain from China's premier rice production areas. Second, to reflect rural diets, paddy rice (n = 195) directly from farmers fields were collected from three regions in Hunan, a key rice producing province located in southern China. Two of the sites were within mining and smeltery districts, and the third was devoid of large-scale metal processing industries. Arsenic levels were determined in all the samples while a subset (n = 33) were characterized for As species, using a new simple and rapid extraction method suitable for use with Hamilton PRP-X100 anion exchange columns and HPLC-ICP-MS. The vast majority (85%) of the market rice grains possessed total As levels <150 ng g(-1). The rice collected from mine-impacted regions, however, were found to be highly enriched in As, reaching concentrations of up to 624 ng g(-1). Inorganic As (As(i)) was the predominant species detected in all of the speciated grain, with As(i) levels in some samples exceeding 300 ng g(-1). The As(i) concentration in polished and unpolished Chinese rice was successfully predicted from total As levels. The mean baseline concentrations for As(i) in Chinese market rice based on this survey were estimated to be 96 ng g(-1) while levels in mine-impacted areas were higher with ca. 50% of the rice in one region predicted to fail the national standard.

Spatial patterns and autocorrelation in the response of microarthropods to soil pollutants: The example of oribatid mites in an abandoned mining and smelting area

Although exogenous factors such as pollutants can act on endogenous drivers (e.g. dispersion) of populations and create spatially autocorrelated distributions, most statistical techniques assume independence of error terms. As there are no studies on metal soil pollutants and microarthropods that explicitly analyse this key issue, we completed a field study of the correlation between Oribatida and metal concentrations in litter, organic matter and soil in an attempt to account for spatial patterns of both metals and mites. The 50-m wide study area had homogenous macroscopic features, steep Pb and Cu gradients and high levels of Zn and Cd. Spatial models failed to detect metal-oribatid relationships because the observed latitudinal and longitudinal gradients in oribatid assemblages were independent of the collinear gradients in the concentration of metals. It is therefore hypothesised that other spatially variable factors (e.g. fungi, reduced macrofauna) affect oribatid assemblages, which may be influenced by metals only indirectly. (C) 2009 Elsevier Ltd. All rights reserved.

Arsenic Speciation and Localization in Horticultural Produce Grown in a Historically Impacted Mining Region

A field and market basket study (similar to 1300 samples) of locally grown fruits and vegetables from historically mined regions of southwest (SW) England (Cornwall and Devon), and as reference, a market basket study of similarly locally grown produce from the northeast (NE) of Scotland (Aberdeenshire) was conducted to determine the concentration of total and inorganic arsenic present in produce from these two geogenically different areas of the U.K. On average 98.5% of the total arsenic found was present in the inorganic form. For both the market basket and the field survey, the highest total arsenic was present in open leaf structure produce (i.e., kale, chard, lettuce, greens, and spinach) being most likely to soil/dust contamination of the open leaf structure. The concentration of total arsenic in potatoes, swedes, and carrots was lower in peeled produce compared to unpeeled produce. For baked potatoes, the concentration of total arsenic in the skin was higher compared to the total arsenic concentration of the potato flesh, this difference in localization being confirmed by laser ablation inductively coupled plasma mass spectroscopy (LA-ICP-MS). For all above ground produce (e.g., apples), peeling did not have a significant effect on the concentration of total arsenic present.

In silico mining identifies IGFBP3 as a novel target of methylation in prostate cancer

Promoter hypermethylation is central in deregulating gene expression in cancer. Identification of novel methylation targets in specific cancers provides a basis for their use as biomarkers of disease occurrence and progression. We developed an in silico strategy to globally identify potential targets of promoter hypermethylation in prostate cancer by screening for 5' CpG islands in 631 genes that were reported as downregulated in prostate cancer. A virtual archive of 338 potential targets of methylation was produced. One candidate, IGFBP3, was selected for investigation, along with glutathione-S-transferase pi (GSTP1), a well-known methylation target in prostate cancer. Methylation of IGFBP3 was detected by quantitative methylation-specific PCR in 49/79 primary prostate adenocarcinoma and 7/14 adjacent preinvasive high-grade prostatic intraepithelial neoplasia, but in only 5/37 benign prostatic hyperplasia (P < 0.0001) and in 0/39 histologically normal adjacent prostate tissue, which implies that methylation of IGFBP3 may be involved in the early stages of prostate cancer development. Hypermethylation of IGFBP3 was only detected in samples that also demonstrated methylation of GSTP1 and was also correlated with Gleason score > or =7 (P=0.01), indicating that it has potential as a prognostic marker. In addition, pharmacological demethylation induced strong expression of IGFBP3 in LNCaP prostate cancer cells. Our concept of a methylation candidate gene bank was successful in identifying a novel target of frequent hypermethylation in early-stage prostate cancer. Evaluation of further relevant genes could contribute towards a methylation signature of this disease.

Mining Money: the generative capacities of scientific and legal knowledge practices in the making of resource economies

In 1997 a scandal associated with Bre-X, a junior mining firm, and its prospecting activities in Indonesia, exposed to public scrutiny the ways in which mineral exploration firms acquire, assess and report on scientific claims about the natural environment. At stake here was not just how investors understood the provisional nature of scientific knowledge, but also evidence of fraud. Contemporaneous mining scandals not only included the salting of cores, but also unreliable proprietary sample preparation and assay methods, mis-representations of visual field estimates as drilling results and ‘overly optimistic’ geological reports. This paper reports on initiatives taken in the wake of these scandals and prompted by the Mining Standards Task Force (TSE/OSC 1999). For regulators, mandated to increase investor confidence in Canada’s leading role within the global mining industry, efforts focused first and foremost upon identifying and removing sources of error and wilfulness within the production and circulation of scientific knowledge claims. A common goal cross-cutting these initiatives was ‘a faithful representation of nature’ (Daston and Galison 2010), however, as the paper argues, this was manifest in an assemblage of practices governed by distinct and rival regulative visions of science and the making of markets in claims about ‘nature’. These ‘practices of fidelity’, it is argued, can be consequential in shaping the spatial and temporal dynamics of the marketization of nature.

Fast Rule Mining over Multi-dimensional Windows

Association rule mining is an indispensable tool for discovering
insights from large databases and data warehouses.
The data in a warehouse being multi-dimensional, it is often
useful to mine rules over subsets of data defined by selections
over the dimensions. Such interactive rule mining
over multi-dimensional query windows is difficult since rule
mining is computationally expensive. Current methods using
pre-computation of frequent itemsets require counting
of some itemsets by revisiting the transaction database at
query time, which is very expensive. We develop a method
(RMW) that identifies the minimal set of itemsets to compute
and store for each cell, so that rule mining over any
query window may be performed without going back to the
transaction database. We give formal proofs that the set of
itemsets chosen by RMW is sufficient to answer any query
and also prove that it is the optimal set to be computed
for 1 dimensional queries. We demonstrate through an extensive
empirical evaluation that RMW achieves extremely
fast query response time compared to existing methods, with
only moderate overhead in pre-computation and storage

Fast Mining of Interesting Phrases from Subsets of Text Corpora

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specified using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplified list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, confirming the superiority of the proposed method.