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  1. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

    Abstract Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selectedmore » the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain ( https://centilebrain.org/#/brainAGE2 ), an open‐science, web‐based platform for individualized neuroimaging metrics.« less
  2. Scalable Full-Stack Benchmarks for Quantum Computers

  3. Advances in benchmarking and round robin testing for PEM water electrolysis: Reference protocol and hardware

    While the number of publications in the PEM water electrolysis community increases each year, no common ground concerning reference hardware (test cells and test bench) and testing protocols has been yet established. This would, however, be necessary for the comparability of experimental results. First attempts for such reference hardware and procedures have been made in the framework of the Task 30 Electrolysis within the Technology Collaboration Programme on Advanced Fuel Cells (AFC TCP) of the International Energy Agency (IEA). Since then, improvements of both the test hardware (test cell and components) as well as the measurement protocol were identified, andmore » a revised methodology and key results based on a comprehensive measurement series have been obtained. A detailed protocol for testing commercial reference components with a reference laboratory test cell developed in-house by Fraunhofer ISE is presented. For evaluation of the protocol and the hardware, it was tested at three different institutions at the same time. Impedance spectroscopic and polarization data was acquired and analyzed. The obtained differences in performance were calculated to give the community an expectation window to compare own data to. Finally, the importance of a thorough temperature control and the conditioning phase are demonstrated.« less
  4. Verification and validation of the open-source plasma fluid code: Zapdos

    Zapdos is an open-source finite element plasma fluid solver based on the MOOSE multiphysics framework. This paper outlines Zapdos verification, benchmarking, and validation efforts for 1D and 2D RF capacitively coupled plasma discharge models for mid-range pressures (0.1 - 1 Torr). The verification process involved using the method of manufactured solutions to assess Zapdos spatial and temporal error convergence. L2 errors ranged from 10-2 to 10-4, while the convergence’s slope were in agreement with the predicted slopes for the tested variable and time integration orders. The benchmarking process involved comparisons to previously results from the validated finite element code, LSODI.more » These works included 1D and 2D simulations for a range of plasma parameters (densities, temperatures, voltage, etc.). For the 1D cases, Zapdos and LSODI results were in very good agreement. In the 2D cases, variable behaviors matched, with slight discrepancies in peak values. The validation process involved comparisons to experimental works including electron density measurements by microwave interferometry and metastable density measurements by planar laser-induced fluorescence imaging. Results shown reasonable agreement at higher pressure, with results starting to diverge at low pressures. Furthermore, probable causes for this diverges are the limitation of the fluid assumption for plasmas at low pressure, or the need for more robust boundary conditions. Overall, Zapdos shown reasonable results for the verification, benchmarking, and validation efforts, and Zapdos can be downloaded at https://github.com/shannon-lab/zapdos.« less
  5. The Good, the Bad, and the Ugly: Pseudopotential Inconsistency Errors in Molecular Applications of Density Functional Theory

    The pseudopotential (PP) approximation is one of the most common techniques in computational chemistry. Despite its long history, the development of custom PPs has not tracked with the explosion of different density functional approximations (DFAs). As a result, the use of PPs with exchange/correlation models for which they were not developed is widespread, although this practice is known to be theoretically unsound. The extent of PP inconsistency errors (PPIEs) associated with this practice has not been systematically explored across the types of energy differences commonly evaluated in chemical applications. Here, we evaluate PPIEs for a number of PPs and DFAsmore » across 196 chemically relevant systems of both transition-metal and main-group elements, as represented by the W4-11, TMC34, and S22 data sets. Near the complete basis set limit, these PPs are found to cleanly approach all-electron (AE) results for noncovalent interactions but introduce root-mean-squared errors (RMSEs) upwards of 15 kcal mol–1 into predictions of covalent bond energies for a number of popular DFAs. We achieve significant improvements through the use of empirical atom- and DFA-specific PP corrections, indicating considerable systematicity of the PPIEs. The results of this work have implications for chemical modeling in both molecular contexts and for DFA design, which we discuss.« less
  6. Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

    Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as onmore » their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.« less
  7. Glassy Carbon Substrate Oxidation Effects on Electrode Stability for Oxygen Evolution Reaction Catalysis Stability Benchmarking

    Employing benchmarking metrics to capture the activity and stability of electrocatalysts for the oxygen evolution reaction (OER) in acid is a critical practice that enables meaningful comparison of catalyst material candidates reported throughout the literature. In this work, we find that ubiquitously used glassy carbon electrode substrates oxidize under typical OER operating conditions, forming a pacified, electrically insulating, and oxygen-rich surface layer that causes drastic loss of current density over the course of extended chronoamperometric stability tests at an anodic potential of 1.7 VRHE. We show that the experimentally observed stability of glassy carbon-based electrodes is approximately two orders ofmore » magnitude lower than that expected solely from dissolution-based catalyst intrinsic stability of Ir-based catalysts. We additionally find that glassy carbon-based electrode stability measured by chronoamperometric holds is greatly impacted by catalyst loading, with high catalyst loadings improving the stability of the overall electrode via a protective effect on the glassy carbon substrate. Altogether, our investigation highlights that glassy carbon is not electrochemically inert under OER conditions on the timescale of common stability tests, which can cause electrodes to exhibit performance losses that do not reflect the intrinsic stability of the actual catalyst material being investigated. In light of our findings, we underscore the usefulness of metrics, such as the S-number, to reflect intrinsic catalyst material stability.« less
  8. Benchmarking embedded chain breaking in quantum annealing*

    Quantum annealing solves combinatorial optimization problems by finding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of themore » quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors.« less
  9. Metallic Fuel Performance Benchmarks for Versatile Test Reactor Applications

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