Human and ecological risk assessment of a crop protection chemical: a case study with the azole fungicide epoxiconazole.

Conventional risk assessments for crop protection chemicals compare the potential for causing toxicity (hazard identification) to anticipated exposure. New regulatory approaches have been proposed that would exclude exposure assessment and just focus on hazard identification based on endocrine disruption. This review comprises a critical analysis of hazard, focusing on the relative sensitivity of endocrine and non-endocrine endpoints, using a class of crop protection chemicals, the azole fungicides. These were selected because they are widely used on important crops (e.g. grains) and thereby can contact target and non-target plants and enter the food chain of humans and wildlife. Inhibition of lanosterol 14α-demethylase (CYP51) mediates the antifungal effect. Inhibition of other CYPs, such as aromatase (CYP19), can lead to numerous toxicological effects, which are also evident from high dose human exposures to therapeutic azoles. Because of its widespread use and substantial database, epoxiconazole was selected as a representative azole fungicide. Our critical analysis concluded that anticipated human exposure to epoxiconazole would yield a margin of safety of at least three orders of magnitude for reproductive effects observed in laboratory rodent studies that are postulated to be endocrine-driven (i.e. fetal resorptions). The most sensitive ecological species is the aquatic plant Lemna (duckweed), for which the margin of safety is less protective than for human health. For humans and wildlife, endocrine disruption is not the most sensitive endpoint. It is concluded that conventional risk assessment, considering anticipated exposure levels, will be protective of both human and ecological health. Although the toxic mechanisms of other azole compounds may be similar, large differences in potency will require a case-by-case risk assessment.

The applicability of using different energy levels in CT imaging for differentiation or identification of dental restorative materials.

PURPOSE The goal of this study was to investigate whether different computed tomography (CT) energy levels could supply additional information for the differentiation of dental materials for forensic investigations. METHODS Nine different commonly used restorative dental materials were investigated in this study. A total of 75 human third molars were filled with the restorative dental materials and then scanned using the forensic reference phantom in singlesource mode. The mean Hounsfield unit values and standard deviations (SDs) of each material were calculated at 120, 80 and 140 kVp. RESULTS Most of the dental materials could be differentiated at 120 kVp. We found that greater X-ray density of a material resulted in higher SDs and that the material volume could influence the measurements. CONCLUSION Differentiation of dental materials in CT was possible in many cases using single-energy CT scans at 120 kVp. Because of the number of dental restorative materials available and scanner and scan parameter dependence, as well as the CT imaging artifacts, the identification (in contrast to differentiation) was problematic.

Immune-Deficient Drosophila melanogaster: A Model for the Innate Immune Response to Human Fungal Pathogens

We explored the host-pathogen interactions of the human opportunistic fungus Candida albicans using Drosophila melanogaster. We established that a Drosophila strain devoid of functional Toll receptor is highly susceptible to the human pathogen C. albicans. Using this sensitive strain, we have been able to show that a set of specific C. albicans mutants of different virulence in mammalian infection models are also impaired in virulence in Drosophila and remarkably display the same rank order of virulence. This immunodeficient insect model also revealed virulence properties undetected in an immunocompetent murine model of infection. The genetic systems available in both host and pathogen will enable the identification of host-specific components and C. albicans genes involved in the host-fungal interplay.

A RAB3GAP1 SINE Insertion in Alaskan Huskies with Polyneuropathy, Ocular Abnormalities and Neuronal Vacuolation (POANV) Resembling Human Warburg Micro Syndrome 1 (WARBM1)

We observed a hereditary phenotype in Alaskan Huskies, which was characterized by polyneuropathy with ocular abnormalities and neuronal vacuolation (POANV). The affected dogs developed a progressive severe ataxia, which led to euthanasia between 8 and 16 months of age. The pedigrees were consistent with a monogenic autosomal recessive inheritance. We localized the causative genetic defect to a 4 Mb interval on chromosome 19 by a combined linkage and homozygosity mapping approach. Whole genome sequencing of one affected dog, an obligate carrier and an unrelated control revealed a 218 bp SINE insertion into exon 7 of the RAB3GAP1 gene. The SINE insertion was perfectly associated with the disease phenotype in a cohort of 43 Alaskan Huskies and it was absent from 541 control dogs of diverse other breeds. The SINE insertion induced aberrant splicing and led to a transcript with a greatly altered exon 7. RAB3GAP1 loss-of-function variants in humans cause Warburg Micro Syndrome 1 (WARBM1), which is characterized by additional developmental defects compared to canine POANV, whereas Rab3gap1 deficient mice have a much milder phenotype than either humans or dogs. Thus the RAB3GAP1 mutant Alaskan Huskies provide an interesting intermediate phenotype that may help to better understand the function of RAB3GAP1 in development. Furthermore, the identification of the presumed causative genetic variant will enable genetic testing to avoid the non-intentional breeding of affected dogs.

Effect of boundary conditions on yield properties of human femoral trabecular bone

Trabecular bone plays an important mechanical role in bone fractures and implant stability. Homogenized nonlinear finite element (FE) analysis of whole bones can deliver improved fracture risk and implant loosening assessment. Such simulations require the knowledge of mechanical properties such as an appropriate yield behavior and criterion for trabecular bone. Identification of a complete yield surface is extremely difficult experimentally but can be achieved in silico by using micro-FE analysis on cubical trabecular volume elements. Nevertheless, the influence of the boundary conditions (BCs), which are applied to such volume elements, on the obtained yield properties remains unknown. Therefore, this study compared homogenized yield properties along 17 load cases of 126 human femoral trabecular cubic specimens computed with classical kinematic uniform BCs (KUBCs) and a new set of mixed uniform BCs, namely periodicity-compatible mixed uniform BCs (PMUBCs). In stress space, PMUBCs lead to 7–72 % lower yield stresses compared to KUBCs. The yield surfaces obtained with both KUBCs and PMUBCs demonstrate a pressure-sensitive ellipsoidal shape. A volume fraction and fabric-based quadric yield function successfully fitted the yield surfaces of both BCs with a correlation coefficient R2≥0.93. As expected, yield strains show only a weak dependency on bone volume fraction and fabric. The role of the two BCs in homogenized FE analysis of whole bones will need to be investigated and validated with experimental results at the whole bone level in future studies.

Towards Automatic Identification of Discourse Markers in Dialogs: the Case of 'Like'

This article discusses the detection of discourse markers (DM) in dialog transcriptions, by human annotators and by automated means. After a theoretical discussion of the definition of DMs and their relevance to natural language processing, we focus on the role of like as a DM. Results from experiments with human annotators show that detection of DMs is a difficult but reliable task, which requires prosodic information from soundtracks. Then, several types of features are defined for automatic disambiguation of like: collocations, part-of-speech tags and duration-based features. Decision-tree learning shows that for like, nearly 70% precision can be reached, with near 100% recall, mainly using collocation filters. Similar results hold for well, with about 91% precision at 100% recall.