Cadmium in the environment and high risk population groups

Cadmium has been widely used in various industries for the past fifty years, with current world production standing at around 16,755 tonnes per year. Very little cadmium is ever recycled and the ultimate fate of all cadmium is the environment. In view of reports that cadmium in the environment is increasing, this thesis aims to identify population groups 'at risk' of receiving dietary intakes of cadmium up to or above the current Food and Agricultural Organisation/World Health Organisation maximum tolerable intake of 70 ug/day. The study involves the investigation of one hundred households (260 individuals) who grow a large proportion of their vegetable diet in garden soils in the Borough of Walsall, part of an urban/industrial area in the United Kingdom. Measurements were made of the cadmium levels in atmospheric deposition, soil, house dust, diet and urine from the participants. Atmospheric deposition of cadmium was found to be comparable with other urban/industrial areas in the European Community, with deposition rates as high as 209 g ha-1 yr-1. The garden soils of the study households were found to contain up to 33 mg kg-1 total cadmium, eleven times the highest level usually found in agricultural soils. Dietary intakes of cadmium by the residents from food were calculated to be as high as 68 ug/day. It is suggested that with intakes from other sources, such as air, adventitious ingestion, smoking and occupational exposure, total intakes of cadmium may reach or exceed the FAO/WHO limit. Urinary excretion of cadmium amongst a non-smoking, non-occupationally exposed sub-group of the study population was found to be significantly higher than that of a similar urban population who did not rely on home-produced vegetables. The results from this research indicate that present levels of cadmium in urban/industrial areas can increase dietary intakes and body burdens of cadmium. As cadmium serves no useful biological function and has been found to be highly toxic, it is recommended that policy measures to reduce human exposure on the European scale be considered.

AllerTOP - a server for in silico prediction of allergens

Background: Allergy is a form of hypersensitivity to normally innocuous substances, such as dust, pollen, foods or drugs. Allergens are small antigens that commonly provoke an IgE antibody response. There are two types of bioinformatics-based allergen prediction. The first approach follows FAO/WHO Codex alimentarius guidelines and searches for sequence similarity. The second approach is based on identifying conserved allergenicity-related linear motifs. Both approaches assume that allergenicity is a linearly coded property. In the present study, we applied ACC pre-processing to sets of known allergens, developing alignment-independent models for allergen recognition based on the main chemical properties of amino acid sequences.Results: A set of 684 food, 1,156 inhalant and 555 toxin allergens was collected from several databases. A set of non-allergens from the same species were selected to mirror the allergen set. The amino acids in the protein sequences were described by three z-descriptors (z1, z2 and z3) and by auto- and cross-covariance (ACC) transformation were converted into uniform vectors. Each protein was presented as a vector of 45 variables. Five machine learning methods for classification were applied in the study to derive models for allergen prediction. The methods were: discriminant analysis by partial least squares (DA-PLS), logistic regression (LR), decision tree (DT), naïve Bayes (NB) and k nearest neighbours (kNN). The best performing model was derived by kNN at k = 3. It was optimized, cross-validated and implemented in a server named AllerTOP, freely accessible at http://www.pharmfac.net/allertop. AllerTOP also predicts the most probable route of exposure. In comparison to other servers for allergen prediction, AllerTOP outperforms them with 94% sensitivity.Conclusions: AllerTOP is the first alignment-free server for in silico prediction of allergens based on the main physicochemical properties of proteins. Significantly, as well allergenicity AllerTOP is able to predict the route of allergen exposure: food, inhalant or toxin. © 2013 Dimitrov et al.; licensee BioMed Central Ltd.

Land resource requirements for bioenergy in India

This study presents a two stage process to determine suitable areas to grow fuel crops: i) FAO Agro Ecological Zones (AEZ) procedure is applied to four Indian states of different geographical characteristics; and ii) Modelling the growth of candidate crops with GEPIC water and nutrient model, which is used to determine potential yield of candidate crops in areas where irrigation water is brackish or soil is saline. Absence of digital soil maps, paucity of readily available climate data and knowledge of detailed requirements of candidate crops are some of the major problems, of which, a series of detailed maps will evaluate true potential of biofuels in India.