UC Santa Cruz

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

Computerized microscopes improve repeatability, throughput, antisepsis, data analysis and data sharing in the biological laboratory, but these machines are cost-prohibitive in most academic environments. This is a barrier into collecting the large and consistent datasets required for machine learning analyses of microscopy data. We demonstrate hardware modifications and software to bring the features of modern computerized microscopes to decades-old legacy laboratory inverted microscopes. We demonstrate automation of X-Y positioning, focus stacking, image acquisition and image storage.

Systems hosting multiple giant planets are important laboratories for understanding planetary formation and migration processes. We present a nearly decade-long Doppler spectroscopy campaign from the HIRES instrument on the Keck-I telescope to characterize the two transiting giant planets orbiting Kepler-511 on orbits of 27 days and 297 days. The radial velocity measurements yield precise masses for both planets: 0.10 0 − 0.039 + 0.036 (2.6σ) and 0.4 4 − 0.12 + 0.11 (4σ) Jupiter masses, respectively. We use these masses to infer their bulk metallicities (i.e., metal mass fraction 0.87 ± 0.03 and 0.22 ± 0.04, respectively). Strikingly, both planets contain approximately 25-30 Earth masses of heavy elements but have very different amounts of hydrogen and helium. Envelope mass loss cannot account for this difference due to the relatively large orbital distance and mass of the inner planet. We conclude that the outer planet underwent runaway gas accretion while the inner planet did not. This bifurcation in accretion histories is likely a result of the accretion of gas with very different metallicities by the two planets or the late formation of the inner planet from a merger of sub-Neptunes. Kepler-511 uniquely demonstrates how giant planet formation can produce dramatically different outcomes even for planets in the same system.

Classification of genetic variants remains an obstacle to realizing the full potential of clinical genetic sequencing. Because of their ability to interrogate large numbers of variants, multiplexed assays of variant effect (MAVEs) and computational tools are viewed as a critical part of the solution to variant classification uncertainty. However, the (joint) performance of these assays and tools on novel variants has not been established. Transformation of the qualitative classification guidelines developed by the American College of Medical Genetics and Genomics (ACMG) into a quantitative Bayesian point system enables empirical validation of strength of evidence assigned to evidence criteria. Here, we derived a maximum likelihood estimate (MLE) model that converts frequentist odds ratios calculated from case-control data to proportions pathogenic and applied this model to functional assays, alone and in combination with computational tools across several domains of BRCA1. Furthermore, we defined exceptionally conserved ancestral residues (ECARs) and interrogated the performance of assays and tools at these residues in BRCA1. We found that missense substitutions in BRCA1 that fall at ECARs are disproportionately likely to be pathogenic with effect sizes similar to that of protein truncating variants. In contrast, for substitutions falling at non-ECAR positions, concordant predictions of pathogenicity from functional assay and computational tool often fail to meet the additive assumptions of strength in ACMG guidelines. Thus, collectively, we conclude that strengths of evidence assigned by expert opinion in the ACMG guidelines are not universally applicable and require empirical validation.