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  1. ; ; ( , Acta materialia)
    Refractory multi-element alloys (RMEA) with body-centered cubic (bcc) structure have been the object of much research over the last decade due to their high potential as candidate materials for high- temperature applications. Most of these alloys display a remarkable strength at high temperatures, which cannot be explained by the standard model of bcc plasticity based on thermally-activated screw disloca- tion motion. Several works have pointed to chemical energy fluctuations as an essential aspect of RMEA strength that is not captured by standard models. In this work, we quantify the contribution of screw dis- locations to the strength of equiatomic Nb-Ta-Vmore »alloys using a kinetic Monte Carlo model fitted to solu- tion energetics obtained from atomistic calculations. In agreement with molecular dynamics simulations, we find that chemical energy fluctuations along the dislocation line lead to measurable concentrations of kinks in equilibrium in a wide temperature range. A fraction of these form cross-kink configurations, which are ultimately found to control screw dislocation motion and material strength. Our simulations (i) confirm that the evolution of cross kinks and self-pinning are strong contributors to the so-called ‘cocktail’ effect in this alloy at low temperature, and (ii) substantiate the notion that screw dislocation plasticity alone cannot explain the high temperature strength of bcc RMEA.« less
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Health Data Science)
    Background . New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods . We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results . A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2more »patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions . Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.« less
    Full Text Available 
  3. ; ; ; ; ; ; ; ( , International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Clinical Chemistry)
    Abstract Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory testing results obtained within 2 daysmore »before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.« less
    Full Text Available 
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available March 1, 2023
  6. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
    Free, publicly-accessible full text available December 1, 2022
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available September 1, 2022
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review C)
    Free, publicly-accessible full text available August 1, 2022
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available August 1, 2022
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Full Text Available