Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems

Detection of false paths in logical circuits by joint analysis of the AND/OR trees and SSBDD-graphs

Consideration was given to the problem of time verification of the combinational circuits, namely, to the problem of determining the false paths. The delays arising in the false paths do not manifest themselves in the circuit operational mode. At determination of the maximal circuit delay as a whole it is recommendable to detect and disregard such paths. It was proposed to reduce the problem of detecting a false path to the search of a test pattern for the stuck-at 0.1 faults of the character of the equivalent normal form corresponding to this path. Search of the test patterns comes to analyzing the conjunctions of the equivalent normal form represented compactly by the AND-OR trees and the structurally synthesized binary decision diagrams. The joint analysis of the AND-OR trees and such diagrams was oriented to reducing the computer burden at seeking the test patterns

Inflammasome-independent role of AIM2 in suppressing colon tumorigenesis via DNA-PK and Akt

The inflammasome activates caspase-1 and the release of interleukin-1β (IL-1β) and IL-18, and several inflammasomes protect against intestinal inflammation and colitis-associated colon cancer (CAC) in animal models. The absent in melanoma 2 (AIM2) inflammasome is activated by double-stranded DNA, and AIM2 expression is reduced in several types of cancer, but the mechanism by which AIM2 restricts tumor growth remains unclear. We found that Aim2-deficient mice had greater tumor load than Asc-deficient mice in the azoxymethane/dextran sodium sulfate (AOM/DSS) model of colorectal cancer. Tumor burden was also higher in Aim2−/−/ApcMin/+ than in APCMin/+ mice. The effects of AIM2 on CAC were independent of inflammasome activation and IL-1β and were primarily mediated by a non–bone marrow source of AIM2. In resting cells, AIM2 physically interacted with and limited activation of DNA-dependent protein kinase (DNA-PK), a PI3K-related family member that promotes Akt phosphorylation, whereas loss of AIM2 promoted DNA-PK–mediated Akt activation. AIM2 reduced Akt activation and tumor burden in colorectal cancer models, while an Akt inhibitor reduced tumor load in Aim2−/− mice. These findings suggest that Akt inhibitors could be used to treat AIM2-deficient human cancers