Geographical Distribution and Pattern of Pesticides in Danish Drinking Water 2002-2018: Reducing Data Complexity.

Pesticides are a large and heterogenous group of chemicals with a complex geographic distribution in the environment. The purpose of this study was to explore the geographic distribution of pesticides in Danish drinking water and identify potential patterns in the grouping of pesticides. Our data included 899,169 analyses of 167 pesticides and metabolites, of which 55 were identified above the detection limit. Pesticide patterns were defined by (1) pesticide groups based on chemical structure and pesticide-metabolite relations and (2) an exploratory factor analysis identifying underlying patterns of related pesticides within waterworks. The geographic distribution was evaluated by mapping the pesticide categories for groups and factor components, namely those detected, quantified, above quality standards, and not analysed. We identified five and seven factor components for the periods 2002-2011 and 2012-2018, respectively. In total, 16 pesticide groups were identified, of which six were representative in space and time with regards to the number of waterworks and analyses, namely benzothiazinone, benzonitriles, organophosphates, phenoxy herbicides, triazines, and triazinones. Pesticide mapping identified areas where multiple pesticides were detected, indicating areas with a higher pesticide burden. The results contribute to a better understanding of the pesticide pattern in Danish drinking water and may contribute to exposure assessments for future epidemiological studies.

An interactive database for the investigation of high-density peptide microarray guided interaction patterns and antivenom cross-reactivity.

Snakebite envenoming is a major neglected tropical disease that affects millions of people every year. The only effective treatment against snakebite envenoming consists of unspecified cocktails of polyclonal antibodies purified from the plasma of immunized production animals. Currently, little data exists on the molecular interactions between venom-toxin epitopes and antivenom-antibody paratopes. To address this issue, high-density peptide microarray (hdpm) technology has recently been adapted to the field of toxinology. However, analysis of such valuable datasets requires expert understanding and, thus, complicates its broad application within the field. In the present study, we developed a user-friendly, and high-throughput web application named "Snake Toxin and Antivenom Binding Profiles" (STAB Profiles), to allow straight-forward analysis of hdpm datasets. To test our tool and evaluate its performance with a large dataset, we conducted hdpm assays using all African snake toxin protein sequences available in the UniProt database at the time of study design, together with eight commercial antivenoms in clinical use in Africa, thus representing the largest venom-antivenom dataset to date. Furthermore, we introduced a novel method for evaluating raw signals from a peptide microarray experiment and a data normalization protocol enabling intra-microarray and even inter-microarray chip comparisons. Finally, these data, alongside all the data from previous similar studies by Engmark et al., were preprocessed according to our newly developed protocol and made publicly available for download through the STAB Profiles web application (http://tropicalpharmacology.com/tools/stab-profiles/). With these data and our tool, we were able to gain key insights into toxin-antivenom interactions and were able to differentiate the ability of different antivenoms to interact with certain toxins of interest. The data, as well as the web application, we present in this article should be of significant value to the venom-antivenom research community. Knowledge gained from our current and future analyses of this dataset carry the potential to guide the improvement and optimization of current antivenoms for maximum patient benefit, as well as aid the development of next-generation antivenoms.