Impact of formulation on solid oxygen-entrapping materials to overcome tumor hypoxia.

Tumor hypoxia, resulting from rapid tumor growth and aberrant vascular proliferation, exacerbates tumor aggressiveness and resistance to treatments like radiation and chemotherapy. To increase tumor oxygenation, we developed solid oxygen gas-entrapping materials (O2-GeMs), which were modeled after clinical brachytherapy implants, for direct tumor implantation. The objective of this study was to investigate the impact different formulations of solid O2-GeMs have on the entrapment and delivery of oxygen. Using a Parr reactor, we fabricated solid O2-GeMs using carbohydrate-based formulations used in the confectionary industry. In evaluating solid O2-GeMs manufactured from different sugars, the sucrose-containing formulation exhibited the highest oxygen concentration at 1 mg/g, as well as the fastest dissolution rate. The addition of a surface coating to the solid O2-GeMs, especially polycaprolactone, effectively prolonged the dissolution of the solid O2-GeMs. In vivo evaluation confirmed robust insertion and positioning of O2-GeMs in a malignant peripheral nerve sheath tumor, highlighting potential clinical applications.

Fluid Simulations Accelerated With 16 Bits: Approaching 4x Speedup on A64FX by Squeezing ShallowWaters.jl Into Float16.

Most Earth-system simulations run on conventional central processing units in 64-bit double precision floating-point numbers Float64, although the need for high-precision calculations in the presence of large uncertainties has been questioned. Fugaku, currently the world's fastest supercomputer, is based on A64FX microprocessors, which also support the 16-bit low-precision format Float16. We investigate the Float16 performance on A64FX with ShallowWaters.jl, the first fluid circulation model that runs entirely with 16-bit arithmetic. The model implements techniques that address precision and dynamic range issues in 16 bits. The precision-critical time integration is augmented to include compensated summation to minimize rounding errors. Such a compensated time integration is as precise but faster than mixed precision with 16 and 32-bit floats. As subnormals are inefficiently supported on A64FX the very limited range available in Float16 is 6 × 10-5 to 65,504. We develop the analysis-number format Sherlogs.jl to log the arithmetic results during the simulation. The equations in ShallowWaters.jl are then systematically rescaled to fit into Float16, using 97% of the available representable numbers. Consequently, we benchmark speedups of up to 3.8x on A64FX with Float16. Adding a compensated time integration, speedups reach up to 3.6x. Although ShallowWaters.jl is simplified compared to large Earth-system models, it shares essential algorithms and therefore shows that 16-bit calculations are indeed a competitive way to accelerate Earth-system simulations on available hardware.

Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting.

We assess the value of machine learning as an accelerator for the parameterization schemes of operational weather forecasting systems, specifically the parameterization of nonorographic gravity wave drag. Emulators of this scheme can be trained to produce stable and accurate results up to seasonal forecasting timescales. Generally, networks that are more complex produce emulators that are more accurate. By training on an increased complexity version of the existing parameterization scheme, we build emulators that produce more accurate forecasts. For medium range forecasting, we have found evidence that our emulators are more accurate than the version of the parametrization scheme that is used for operational predictions. Using the current operational CPU hardware, our emulators have a similar computational cost to the existing scheme, but are heavily limited by data movement. On GPU hardware, our emulators perform 10 times faster than the existing scheme on a CPU.