Molecular Evolutionary Analysis of the Influenza A(H1N1)pdm, May–September, 2009: Temporal and Spatial Spreading Profile of the Viruses in Japan

BACKGROUND: In March 2009, pandemic influenza A(H1N1) (A(H1N1)pdm) emerged in Mexico and the United States. In Japan, since the first outbreak of A(H1N1)pdm in Osaka and Hyogo Prefectures occurred in the middle of May 2009, the virus had spread over 16 of 47 prefectures as of June 4, 2009. METHODS/PRINCIPAL FINDINGS: We analyzed all-segment concatenated genome sequences of 75 isolates of A(H1N1)pdm viruses in Japan, and compared them with 163 full-genome sequences in the world. Two analyzing methods, distance-based and Bayesian coalescent MCMC inferences were adopted to elucidate an evolutionary relationship of the viruses in the world and Japan. Regardless of the method, the viruses in the world were classified into four distinct clusters with a few exceptions. Cluster 1 was originated earlier than cluster 2, while cluster 2 was more widely spread around the world. The other two clusters (clusters 1.2 and 1.3) were suggested to be distinct reassortants with different types of segment assortments. The viruses in Japan seemed to be a multiple origin, which were derived from approximately 28 transported cases. Twelve cases were associated with monophyletic groups consisting of Japanese viruses, which were referred to as micro-clade. While most of the micro-clades belonged to the cluster 2, the clade of the first cases of infection in Japan originated from cluster 1.2. Micro-clades of Osaka/Kobe and the Fukuoka cases, both of which were school-wide outbreaks, were eradicated. Time of most recent common ancestor (tMRCA) for each micro-clade demonstrated that some distinct viruses were transmitted in Japan between late May and early June, 2009, and appeared to spread nation-wide throughout summer. CONCLUSIONS: Our results suggest that many viruses were transmitted from abroad in late May 2009 irrespective of preventive actions against the pandemic influenza, and that the influenza A(H1N1)pdm had become a pandemic stage in June 2009 in Japan

Integrated code framework for operation scenario development with the global-optimizer-based iterative solver GOTRESS

新古典・乱流輸送と無矛盾な分布を得るために、大域的最適化手法を用いた全く新しい解法に基づく反復型輸送方程式ソルバーGOTRESSを開発している。解を直接探索するため、偏微分方程式を離散化する手法では扱いが困難であった硬い輸送モデルを上手く扱える。平衡コードACCOMEや加熱コードOFMCと連携させることで、GOTRESSを核とした統合モデルGOTRESS+を開発した。GOTRESS+は定常状態におけるJT-60SAの運転シナリオをTOPICSより高速に予測する事が出来る

Integrated code framework for operation scenario development with the global-optimizer-based iterative solver GOTRESS

新古典・乱流輸送と無矛盾な分布を得るために、大域的最適化手法を用いた全く新しい解法に基づく反復型輸送方程式ソルバーGOTRESSを開発している。解を直接探索するため、偏微分方程式を離散化する手法では扱いが困難であった硬い輸送モデルを上手く扱える。平衡コードACCOMEや加熱コードOFMCと連携させることで、GOTRESSを核とした統合モデルGOTRESS+を開発した。GOTRESS+は定常状態におけるJT-60SAの運転シナリオをTOPICSより高速に予測する事が出来る。46th European Physical Society Conference on Plasma Physics (EPS 2019

Quasilinear turbulent particle and heat transport modelling with a neural-network- based approach founded on gyrokinetic calculations and experimental data

A novel quasilinear turbulent transport model DeKANIS has been constructed founded on the gyrokinetic analysis of JT-60U plasmas. DeKANIS predicts particle and heat fluxes fast with a neural network (NN) based approach and distinguishes diffusive and non-diffusive transport processes. The original model only considered particle transport, but its capability has been extended to cover multi-channel turbulent transport. To solve a set of particle and heat transport equations stably in integrated codes with DeKANIS, the NN model embedded in DeKANIS has been modified. DeKANIS originally determined turbulent saturation levels semi-empirically based on JT-60U experimental data, but now it can also estimate them using a theory-based saturation rule. The new saturation model is still partly connected to experimental data, but it offers the potential for applying DeKANIS independently of the device