Possible health impacts of naturally occurring uptake of aristolochic acids by maize and cucumber roots: links to the etiology of endemic (Balkan) nephropathy

Aristolochic acids (AAs) are nephrotoxic and carcinogenic derivatives found in several Aristolochia species. To date, the toxicity of AAs has been inferred only from the effects observed in patients suffering from a kidney disease called “aristolochic acid nephropathy” (AAN, formerly known as “Chinese herbs nephropathy”). More recently, the chronic poisoning with Aristolochia seeds has been considered to be the main cause of Balkan endemic nephropathy, another form of chronic renal failure resembling AAN. So far, it was assumed that AAs can enter the human food chain only through ethnobotanical use (intentional or accidental) of herbs containing self-produced AAs. We hypothesized that the roots of some crops growing in fields where Aristolochia species grew over several seasons may take up certain amounts of AAs from the soil, and thus become a secondary source of food poisoning. To verify this possibility, maize plant (Zea mays) and cucumber (Cucumis sativus) were used as a model to substantiate the possible significance of naturally occurring AAs’ root uptake in food chain contamination. This study showed that the roots of maize plant and cucumber are capable of absorbing AAs from nutrient solution, consequently producing strong peaks on ultraviolet HPLC chromatograms of plant extracts. This uptake resulted in even higher concentrations of AAs in the roots compared to the nutrient solutions. To further validate the measurement of AA content in the root material, we also measured their concentrations in nutrient solutions before and after the plant treatment. Decreased concentrations of both AAI and AAII were found in nutrient solutions after plant growth. During this short-term experiment, there were much lower concentrations of AAs in the leaves than in the roots. The question is whether these plants are capable of transferring significant amounts of AAs from the roots into edible parts of the plant during prolonged experiments.     

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.     

Evaluating Spatio-temporal Complexities of Forest Management: An Integrated Agent-based Modeling and GIS Approach

The objective of this study is to integrate agent-based modeling and geographic information systems (GIS) for examining how interactions within forest management lead to patterns of land-cover change. Specifically, this study evaluates how management agents behave in the presence of variable timber prices, harvesting costs, and accessibility to timber and how their actions influence the spatial characteristics of the forest landscape over time. The GIS calculates the average harvested patch size, number of patches, and total harvested area as measures of emergent patterns resulting from agent actions. The results from the agent-based GIS model reveal that good economic conditions lead to few but large harvested patches, while deteriorating conditions will see more patches of smaller size if forest companies have access to high-quality timber. This study emphasizes the need for a complex systems approach to forest management as the model illustrates how system elements interact in a manner to produce emergent spatial patterns over time.     

Hydrochemical characteristics of mine waters from abandoned mining sites in Serbia and their impact on surface water quality

Upon completion of exploration and extraction of mineral resources, many mining sites have been abandoned without previously putting environmental protection measures in place. As a consequence, mine waters originating from such sites are discharged freely into surface water. Regional scale analyses were conducted to determine the hydrochemical characteristics of mine waters from abandoned sites featuring metal (Cu, Pb–Zn, Au, Fe, Sb, Mo, Bi, Hg) deposits, non-metallic minerals (coal, Mg, F, B) and uranium. The study included 80 mine water samples from 59 abandoned mining sites. Their cation composition was dominated by Ca²⁺, while the most common anions were found to be SO₄ ^2? and HCO₃ ^?. Strong correlations were established between the pH level and metal (Fe, Mn, Zn, Cu) concentrations in the mine waters. Hierarchical cluster analysis was applied to parameters generally indicative of pollution, such as pH, TDS, SO₄ ^2?, Fe total, and As total. Following this approach, mine water samples were grouped into three main clusters and six subclusters, depending on their potential environmental impact. Principal component analysis was used to group together variables that share the same variance. The extracted principal components indicated that sulfide oxidation and weathering of silicate and carbonate rocks were the primary processes, while pH buffering, adsorption and ion exchange were secondary drivers of the chemical composition of the analyzed mine waters. Surface waters, which received the mine waters, were examined. Analysis showed increases of sulfate and metal concentrations and general degradation of surface water quality.     

Learning habitat models for the diatom community in Lake Prespa

Habitat suitability modelling studies the influence of abiotic factors on the abundance or diversity of a given taxonomic group of organisms. In this work, we investigate the effect of the environmental conditions of Lake Prespa (Republic of Macedonia) on diatom communities. The data contain measurements of physical and chemical properties of the environment as well as the relative abundances of 116 diatom taxa. In addition, we create a separate dataset that contains information only about the top 10 most abundant diatoms. We use two machine learning techniques to model the data: regression trees and multi-target regression trees. We learn a regression tree for each taxon separately (from the top 10 most abundant) to identify the environmental conditions that influence the abundance of the given diatom taxon. We learn two multi-target regression trees: one for modelling the complete community and the other for the top 10 most abundant diatoms. The multi-target regression trees approach is able to detect the conditions that affect the structure of a diatom community (as compared to other approaches that can model only a single target variable). We interpret and compare the obtained models. The models present knowledge about the influence of metallic ions and nutrients on the structure of the diatom community, which is consistent with, but further extends existing expert knowledge.     

Using data mining to predict soil quality after application of biosolids in agriculture

The amount of biosolids recycled in agriculture has steadily increased during the last decades. However, few models are available to predict the accompanying risks, mainly due to the presence of trace element and organic contaminants, and benefits for soil fertility of their application. This paper deals with using data mining to assess the benefits and risks of biosolids application in agriculture. The analyzed data come from a 10-yr field experiment in northeast France focusing on the effects of biosolid application and mineral fertilization on soil fertility and contamination. Biosolids were applied at agriculturally recommended rates. Biosolids had a significant effect on soil fertility, causing in particular a persistent increase in plant-available phosphorus (P) relative to plots receiving mineral fertilizer. However, soil fertility at seeding and crop management method had greater effects than biosolid application on soil fertility at harvest, especially soil nitrogen (N) content. Levels of trace elements and organic contaminants in soils remained below legal threshold values. Levels of extractable metals correlated more strongly than total metal levels with other factors. Levels of organic contaminants, particularly polycyclic aromatic hydrocarbons, were linked to total metal levels in biosolids and treated soil. This study confirmed that biosolid application at rates recommended for agriculture is a safe option for increasing soil fertility. However, the quality of the biosolids selected has to be taken into account. The results also indicate the power of data mining in examining links between parameters in complex data sets.     

Evaluating forest management practices using a GIS-based cellular automata modeling approach with multispectral imagery

The objective of this study was to develop an integrated geographic information system (GIS) cellular automata (CA) model for simulating insect-induced tree mortality patterns in order to evaluate the influence of different forest management activities to control insect outbreaks. High-resolution multispectral images were used to determine susceptibility of trees to attack, whereas the GIS-based CA model simulated the effectiveness of clear-cuts and thinning practices for reducing insect-induced tree mortality. The results indicate that thinning susceptible forests should be more effective than clear-cutting for reducing tree loss to insect outbreaks. This study demonstrates the benefits of an integrated approach for understanding and evaluating forest management activities and expresses the need for spatial analysis and modeling for improving forest management practices.