Visual and ocular changes associated with exposure to two tertiary amines

Aims: To determine if exposure to dimethylisopropanolamine (DMIPA) and dimethylaminoethanol (DMAE) in a label printing plant was associated with visual disturbances and/or ocular changes. Methods: Questionnaires, eye examinations (visual acuity, contrast sensitivity at 2.5% and 1.25% contrast, slit lamp biomicroscopy, and pachymetry), and industrial hygiene monitoring for DMIPA and DMAE were performed over a two week period. Results: Eighty nine per cent of line workers reported having experienced blurry vision while at work in the past 12 months, compared to 12.5% of prime workers. A total of 108 full shift personal breathing zone (PBZ) air samples for the amines were collected. The mean time weighted average (TWA) concentration of DMIPA was significantly higher in the line division than in the prime division, as was the mean TWA concentration for total amines. The mean TWA concentration of DMAE was higher in the prime division than the line division. Higher levels of total amines were associated with increased risk of reporting blurry vision, halo vision, and blue-grey vision. The risk of corneal opacity rose with increasing exposure to total amines. The prevalence of corneal opacity also increased with increasing concentration of total amines. Median corneal thickness increased with increasing grades of corneal opacity. There was a statistically significant relation between total amine concentration and increased risk of reduced bilateral visual acuity and 2.5% contrast sensitivity. Conclusions: Exposure to tertiary amines was associated with blurry, halo, and blue-grey vision, corneal opacity, and decrements in visual acuity and contrast sensitivity at 2.5% contrast

Cross-comparison of state of the art neuromorphological simulators on modern CPUs and GPUs using the Brain Scaffold Builder

ABSTRACTA variety of software simulators exist for neuronal networks, and a subset of these tools allow the scientist to model neurons in high morphological detail. The scalability of such simulation tools over a wide range in neuronal networks sizes and cell complexities is predominantly limited by effective allocation of components of such simulations over computational nodes, and the overhead in communication between them. In order to have more scalable simulation software, it is therefore important to develop a robust benchmarking strategy that allows insight into specific computational bottlenecks for models of realistic size and complexity. In this study, we demonstrate the use of the Brain Scaffold Builder (BSB; De Schepper et al., 2021) as a framework for performing such benchmarks. We perform a comparison between the well-known neuromorphological simulator NEURON (Carnevale and Hines, 2006), and Arbor (Abi Akar et al., 2019), a new simulation library developed within the framework of the Human Brain Project. The BSB can construct identical neuromorphological and network setups of highly spatially and biophysically detailed networks for each simulator. This ensures good coverage of feature support in each simulator, and realistic workloads. After validating the outputs of the BSB generated models, we execute the simulations on a variety of hardware configurations consisting of two types of nodes (GPU and CPU). We investigate performance of two different network models, one suited for a single machine, and one for distributed simulation. We investigate performance across different mechanisms, mechanism classes, mechanism combinations, and cell types. Our benchmarks show that, depending on the distribution scheme deployed by Arbor, a speed-up with respect to NEURON of between 60 and 400 can be achieved. Additionally Arbor can be up to two orders of magnitude more energy efficient.</jats:p