Insights into the primary radiation damage of silicon by a machine learning interatomic potential

We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. Impact statement Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.Peer reviewe

The Increased Level of Serum p53 in Hepatitis B-Associated Liver Cirrhosis

BACKGROUND: The ability of tumour suppressor protein p53 (P53) to regulate cell cycle processes can be modulated by hepatitis B virus (HBV). While preliminary evidences indicates the involvement of protein-x of HBV (HBx) in altering p53 DNA binding, no further data have been accumulated for the significance of serum p53 in chronic hepatitis B virus infected patients. METHODS: 72 non-cirrhotic and 19 cirrhotic patients infected by HBV were enrolled for the analysis in this study. Enzyme linked immunosorbent assay (ELISA) was performed to study the concentrations of serum p53 protein. The tertiary structures of HBx and P53 were docked by Z-dock and Hex servers for in-silico protein-protein interaction analysis. RESULTS: There was a significant association between the serum p53 and cirrhosis (OR=1.81 95 CI: 1.017-3.2, P=0.044). Cirrhotic patients had higher level of serum p53 compare with chronic infection of HBV (1.98+/-1.22 vs. 1.29+/-0.72 U/ml, P=0.05). No evidence of correlation was seen between the different variables such as age, gender, log viral load, serum alkaline phosphatase (ALP) and alanine aminotransferase (ALT) with serum p53. Tertiary model shows that the amino acid residues from Arg110 to Lys132 of the N-terminal of P53 which is critical for ubiquitination, are bonded to a region in N- terminal of HBx amino acid residues from Arg19 to Ser33. CONCLUSION: There is an increase in serum p53 in HBV-related cirrhosis patients. In this case, HBx might be responsible for such higher concentration of p53 through HBx-p53 protein-protein interaction, as is shown by molecular modeling approach

A NOVEL APPROACH TO FIND OPTIMIZED NEUTRON ENERGY GROUP STRUCTURE IN MOX THERMAL LATTICES USING SWARM INTELLIGENCE

Energy group structure has a significant effect on the results of multigroup transport calculations. It is known that UO2–PUO2 (MOX) is a recently developed fuel which consumes recycled plutonium. For such fuel which contains various resonant nuclides, the selection of energy group structure is more crucial comparing to the UO2 fuels. In this paper, in order to improve the accuracy of the integral results in MOX thermal lattices calculated by WIMSD-5B code, a swarm intelligence method is employed to optimize the energy group structure of WIMS library. In this process, the NJOY code system is used to generate the 69 group cross sections of WIMS code for the specified energy structure. In addition, the multiplication factor and spectral indices are compared against the results of continuous energy MCNP-4C code for evaluating the energy group structure. Calculations performed in four different types of H2O moderated UO2–PuO2 (MOX) lattices show that the optimized energy structure obtains more accurate results in comparison with the WIMS original structure

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we employ a Gaussian approximation machine-learning potential (GAP) to study the primary damage in silicon with close to ab initio precision level. We report detailed analysis of cascade simulations derived from our modified Si GAP, which has already shown its reliability for simulating radiation damage in silicon. Major differences in the picture of primary damage predicted by machine-learning potential compared to classical potentials are atomic mixing, defect state at the heat spike phase, defect clustering, and recrystallization rate. Atomic mixing is higher in the GAP description by a factor of two. GAP shows considerably higher number of coordination defects at the heat spike phase and the number of displaced atoms is noticeably greater in GAP. Surviving defects are dominantly isolated defects and small clusters, rather than large clusters, in GAP's prediction. The pattern by which the cascades are evolving is also different in GAP, having more expanded form compared to the locally compact form with classical potentials. Moreover, recovery of the generated defects at the heat spike phase take places with higher efficiency in GAP. We also provide the attributes of the new defect cluster that we had introduced in our previous study. A cluster of four defects, in which a central vacancy is surrounded by three split interstitials, where the surrounding atoms are all 4-folded bonded. The cluster shows higher occurrence in simulations with the GAP potential. The formation energy of the defect is 5.57 eV and it remains stable up to 700 K, at least for 30 ps. The Arrhenius equation predicts the lifetime of the cluster to be 0.0725 mu s at room temperature.Peer reviewe

The investigation of phosphatidylinositol 3-kinase (PI3K) isoforms which express by human prostate cancer cell lines, PC3 and DU145

Background: From Two highly metastatic prostate cancer cell lines, DU145 expresses PTEN gene while PC3 is null for it. PTEN is a tumor suppressor gene whose primary function is lipid phosphatase herewith PTEN antagonizes the PI3K activity. Phosphatidylinositol 3- kinase (PI3K) is involved in modulating basement membrane's protein degradation, angiogenesis and neovascularization making possible cellular migration. There are several articles reporting up-regulation of PI3K gene in different kinds of metastatic cancers. Since despite of bearing functional PTEN, DU145 cell line manifest highly metastatic potential (as well as PC3 cells) it was interesting for us to know if there was any differences in PI3K isoforms expression patterns between these two cell lines. Materials and Methods: Total RNA was extracted from the cells and mRNA content was analyzed (through three different reverse transcriptase enzymes) using RT-PCR method. Results: Unexpectedly data showed both of the cell lines express identical isoforms. Here, as the first report, we introduce P110α catalytic subunit and P85 adapter protein from class IA, PI3K-C2 from class II and Vps34p from class III of PI3K super family as PI3K isoforms which expressed by PC3 and DU145 cells. Conclusions: We propose to search for DU145 metastatic potential in inequality of PI3K isoforms supply. In this regard we are going to quantify each isoform mRNA individually for both PC3 and DU145 cells using Real-Time RT-PCR method