Contamination of the environment with endocrine disrupting chemicals (EDCs) is a major health concern. The presence of estrogenic compounds in water and their deleterious effect are well documented. However, detection and monitoring of other classes of EDCs is limited. Here we utilize a high-throughput live cell assay based on sub-cellular relocalization of GFP-tagged glucocorticoid and androgen receptors (GFP-GR and GFP-AR), in combination with gene transcription analysis, to screen for glucocorticoid and androgen activity in water samples. We report previously unrecognized glucocorticoid activity in 27%, and androgen activity in 35% of tested water sources from 14 states in the US. Steroids of both classes impact body development, metabolism, and interfere with reproductive, endocrine, and immune systems. This prevalent contamination could negatively affect wildlife and human populations.

The representation of the environment of a mobile robot by line models is a popular alternative to occupancy grid maps. Line maps require significantly less memory than occupancy grids and therefore scale better with the size of the environment. They furthermore are more accurate since they do not suffer from discretization problems. In the past a variety of techniques for learning line maps from range data have been developed. These techniques differ in various aspects such as the way lines are extracted from range scans, how the lines are updated upon sensory input. There furthermore are techniques that are able to operate online, whereas others postprocess the data. In this paper we compare three different techniques for learning line models with respect to various parameters such as efficiency and quality of the resulting maps. Experimental results illustrate the advantages and the disadvantages of the different techniques.

Objectives There have been conflicting reports of altered vaginal microbiota and infection susceptibility associated with contraception use. The objectives of this study were to determine if intrauterine contraception altered the vaginal microbiota and to compare the effects of a copper intrauterine device (Cu-IUD) and a levonorgestrel intrauterine system (LNG-IUS) on the vaginal microbiota. Study Design DNA was isolated from the vaginal swab samples of 76 women using Cu-IUD (n=36) or LNG-IUS (n=40) collected prior to insertion of intrauterine contraception (baseline) and at 6 months. A third swab from approximately 12 months following insertion was available for 69 (Cu-IUD, n=33; LNG-IUS, n=36) of these women. The V4 region of the bacterial 16S rRNA-encoding gene was amplified from the vaginal swab DNA and sequenced. The 16S rRNA gene sequences were processed and analyzed using the software package mothur to compare the structure and dynamics of the vaginal bacterial communities. Results The vaginal microbiota from individuals in this study clustered into 3 major vaginal bacterial community types: one dominated by Lactobacillus iners, one dominated by Lactobacillus crispatus and one community type that was not dominated by a single Lactobacillus species. Changes in the vaginal bacterial community composition were not associated with the use of Cu-IUD or LNG-IUS. Additionally, we did not observe a clear difference in vaginal microbiota stability with Cu-IUD versus LNG-IUS use. Conclusions Although the vaginal microbiota can be highly dynamic, alterations in the community associated with the use of intrauterine contraception (Cu-IUD or LNG-IUS) were not detected over 12 months. Implications We found no evidence that intrauterine contraception (Cu-IUD or LNG-IUS) altered the vaginal microbiota composition. Therefore, the use of intrauterine contraception is unlikely to shift the composition of the vaginal microbiota such that infection susceptibility is altered.

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