Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.Comment: Accepted in Advances in Neural Information Processing Systems 36 (NeurIPS 2023). 55 pages, 21 figures. v2: Corrected co-author name. Keywords: Super-resolution, 3D, Neural Scaling, Physics-informed Loss, Computational Fluid Dynamics, Partial Differential Equations, Turbulent Reacting Flows, Direct Numerical Simulation, Fluid Mechanics, Combustio

The impact of perioperative blood transfusion on survival and recurrence after radical prostatectomy for prostate cancer: A systematic review and meta-analysis

Objective: Conflicting data have been reported regarding the association between perioperative blood transfusion (PBT) and clinical outcomes for prostate cancer patients. We conducted a systematic review and meta-analysis to evaluate the impact of PBT on cancer survival and recurrence for patients who underwent radical prostatectomy (RP). Methods: A systematic review of PubMed, EMBASE, and Cochrane libraries was performed to identify all eligible studies that evaluate the association between PBT and clinical outcomes for prostate cancer patients undergoing RP. The analyzed outcomes were overall survival (OS) and recurrence-free survival (RFS) at 3, 5, and 10 years. Results: A total of eight articles met our criteria. Meta-analysis indicated that prostate cancer patients with PBT had decreased OS (hazard ratio [HR] =1.51, 95% confidence interval [CI], 1.22–1.85, P < 0.01; HR = 1.57, 95% CI, 1.33–1.85, P < 0.01; HR = 1.55, 95% CI, 1.03–2.33, P = 0.04) and RFS (HR = 1.67, 95% CI, 1.37–2.04, P < 0.01; HR = 1.42, 95% CI, 1.23–1.63, P < 0.01; HR = 1.37, 95% CI, 1.03–1.83, P = 0.03) at 3, 5, and 10 years after surgery compared with those without PBT. Conclusions: The findings from the current meta-analysis demonstrate that PBT was associated with adverse survival and recurrence outcomes for prostate cancer patients undergoing RP