No abstract

Many different assays are being used to measure serum GH concentrations in children with disorders of growth. We assessed four readily available methods to determine the comparability of the immunopotency estimates: standard double antibody RIA with pituitary standards from the National Hormone and Pituitary Program (assay 1) and from a commercial source (assay 2), a double antibody RIA with serum standards (assay 4), and a commercial immunoradiometric assay (assay 3). There was a high degree of relative correlation between assays (r = 0.95-0.98), but absolute potency estimates differed. Assays 1 and 2 were almost identical. Assay 3 yielded serum GH levels about 65% those of assay 1 or 2 and 80% those of assay 4. Assay 4 gave intermediate values between the low readings in assay 3 and higher values in assay 1 and 2. We conclude that substantial variation occurs in potency estimates in different GH assays. Such differences can affect the interpretation of many GH provocative and sampling studies.

Increases in the data rates of detectors for electron microscopy (EM) have outpaced increases in network, mass storage and memory bandwidth by two orders of magnitude between 2009(Weber, 2018. The LiberTEM open source platform (Clausen et al., 2020) is designed to match the growing performance requirements of EM data processing (Weber, Clausen, & Dunin-Borkowski, 2020). MotivationThe data rate of the fastest detectors for electron microscopy that are available in 2019 exceeds 50 GB/s, which is faster than the memory bandwidth of typical personal computers (PCs) at this time. Applications from ten years before that ran smoothly on a typical PC have evolved into numerical analysis of complex multidimensional datasets (Ophus, 2019) that require distributed processing on high-performance systems. Furthermore, electron microscopy is interactive and visual, and experiments performed inside electron microscopes (so-called in situ experiments) often rely on fast on-line data processing as the experimental parameters need to be adjusted based on the observation results. As a consequence, modern data processing systems for electron microscopy should be designed for very high throughput in combination with short response times for interactive GUI use and closed-loop feedback. That requires fundamental changes in the architecture and programming model, and consequently in the implementation of algorithms and user interfaces for electron microscopy applications.