arXiv subjects

Jian-Hua Mao

Publications and source records attributed to Jian-Hua Mao.

3 recordsLinked to original sources

arXiv2026

Radiation damage to normal mammalian tissue in vivo with laser-driven protons at ultra-high instantaneous dose rate

The differential sparing of normal tissues relative to tumor control observed at ultra-high dose rates, referred to as the FLASH effect, has recently gained considerable attention. The therapeutic advantages of FLASH radiotherapy are expected to be further amplified through the use of protons and ions, which enable precise dose deposition at tumor depth while minimizing irradiation of healthy tissues proximal and distal to the target. Nevertheless, the mechanism underlying this sparing effect remains poorly understood. Laser-driven proton accelerators are capable of delivering uniquely high instantaneous dose rates in ultrashort bunches. Here, we report the first in vivo investigation of normal tissue response to laser-driven proton irradiation, with exposures to 8 MeV protons, delivering total doses up to 50 Gy at 2 Gy per laser shot. Our findings reveal a reduction in tissue swelling following laser-driven proton treatment compared with X-ray irradiations at conventional dose rates. RNA sequencing identified differential gene expression associated with immune and epidermal programs following laser-driven proton irradiations at two different dose levels.

physics.med-ph↗

arXiv2022

Online Charge Measurement for Petawatt Laser-Driven Ion Acceleration

Laser-driven ion beams have gained considerable attention for their potential use in multidisciplinary research and technology. Pre-clinical studies into their radiobiological effectiveness have established the prospect of using laser-driven ion beams for radiotherapy. In particular, research into the beneficial effects of ultra-high instantaneous dose rates is enabled by the high ion bunch charge and uniquely short bunch lengths present for laser-driven ion beams. Such studies require reliable, online dosimetry methods to monitor the bunch charge for every laser shot to ensure that the prescribed dose is accurately applied to the biological sample. In this paper we present the first successful use of an Integrating Current Transformer (ICT) for laser-driven ion accelerators. This is a non-invasive diagnostic to measure the charge of the accelerated ion bunch. It enables online dose measurements in radiobiological experiments and facilitates ion beam tuning, in particular, optimization of the laser ion source and alignment of the proton transport beamline. We present the ICT implementation and the correlation with other diagnostics such as radiochromic films, a Thomson parabola spectrometer and a scintillator.

physics.acc-ph↗

arXiv2017

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, modular, and scalable framework for enhanced model selection and estimation. Methods based on UoI perform model selection and model estimation through intersection and union operations, respectively. We show that UoI-based methods achieve low-variance and nearly unbiased estimation of a small number of interpretable features, while maintaining high-quality prediction accuracy. We perform extensive numerical investigation to evaluate a UoI algorithm ($UoI_{Lasso}$) on synthetic and real data. In doing so, we demonstrate the extraction of interpretable functional networks from human electrophysiology recordings as well as accurate prediction of phenotypes from genotype-phenotype data with reduced features. We also show (with the $UoI_{L1Logistic}$ and $UoI_{CUR}$ variants of the basic framework) improved prediction parsimony for classification and matrix factorization on several benchmark biomedical data sets. These results suggest that methods based on the UoI framework could improve interpretation and prediction in data-driven discovery across scientific fields.

stat.ML↗