A Multi-Scale Electromagnetic Particle Code with Adaptive Mesh Refinement and Its Parallelization

AbstractSpace plasma phenomena occur in multi-scale processes from the electron scale to the magnetohydrodynamic scale. In order to investigate such multi-scale phenomena including plasma kinetic effects, we started to develop a new electromagnetic Particle-In-Cell (PIC) code with Adaptive Mesh Refinement (AMR) technique. AMR can realize high-resolution calculation saving computer resources by generating and removing hierarchical cells dynamically. In the parallelization, we adopt domain decomposition method and for good locality preserving and dynamical load balancing, we will use the Morton ordered curve. In the PIC method, particle calculation occupies most of the total calculation time. In our AMR-PIC code, time step intervals are also refined. To realize the load balancing between processes in the domain decomposition scheme, it is the most essential to consider the number of particle calculation loops for each cell among all hierarchical levels as a work weight for each processor. Therefore, we calculate the work weights based on the cost of particle calculation and hierarchical levels of each cell. Then we decompose the domain according to the Morton curve and the work weight, so that each processor has approximately the same amount of work. By performing a simple one-dimensional simulation, we confirmed that the dynamic load balancing is achieved and the computation time is reduced by introducing the dynamic domain decomposition scheme

A RUNX-targeted gene switch-off approach modulates the BIRC5/PIF1-p21 pathway and reduces glioblastoma growth in mice

Glioblastoma is the most common adult brain tumour, representing a high degree of malignancy. Transcription factors such as RUNX1 are believed to be involved in the malignancy of glioblastoma. RUNX1 functions as an oncogene or tumour suppressor gene with diverse target genes. Details of the effects of RUNX1 on the acquisition of malignancy in glioblastoma remain unclear. Here, we show that RUNX1 downregulates p21 by enhancing expressions of BIRC5 and PIF1, conferring anti-apoptotic properties on glioblastoma. A gene switch-off therapy using alkylating agent-conjugated pyrrole-imidazole polyamides, designed to fit the RUNX1 DNA groove, decreased expression levels of BIRC5 and PIF1 and induced apoptosis and cell cycle arrest via p21. The RUNX1-BIRC5/PIF1-p21 pathway appears to reflect refractory characteristics of glioblastoma and thus holds promise as a therapeutic target. RUNX gene switch-off therapy may represent a novel treatment for glioblastoma

Validation of non‐muscle‐invasive bladder cancer risk stratification updated in the 2021 European Association of Urology guidelines

Abstract Objective The objective of this study is to validate the predictive ability of the 2021 European Association of Urology (EAU) risk model compared to that of existing risk models, including the 2019 EAU model and risk scoring tables of the European Organization for Research and Treatment of Cancer, Club Urologico Espanol de Tratamiento Oncologico, and Japanese Nishinihon Uro‐oncology Extensive Collaboration Group. Patients and methods This retrospective multi‐institutional database study included two cohorts—3024 patients receiving intravesical bacillus Calmette–Guerin (BCG) treatment (BCG cohort) and 789 patients not receiving BCG treatment (non‐BCG cohort). The Kaplan–Meier estimate and log‐rank test were used to visualize and compare oncological survival outcomes after transurethral surgery among the risk groups. Harrell's concordance index (C‐index) was used to evaluate the predictive ability of the models. Results We observed a risk shift from the 2019 EAU risk grouping to the 2021 EAU risk grouping in a substantial number of patients. For progression, the C‐index of the 2021 EAU model was significantly higher than that of the 2019 EAU model in both the BCG (0.617 vs. 0.572; P = 0.011) and non‐BCG (0.718 vs. 0.560; P < 0.001) cohorts. According to the 2021 EAU model, 731 (24%) and 130 (16%) patients in the BCG and non‐BCG cohorts, respectively, were considered to have a very high risk. Survival analysis showed no significant differences among the five very high‐risk subgroups in both cohorts. A major limitation was potential selection bias owing to the retrospective nature of this study. Conclusions The updated 2021 EAU model showed better stratification than the three existing risk models, especially for progression, in both cohorts, determining the most appropriate postoperative treatment and identifying patients requiring intensified surveillance or early cystectomy