A Learnable Prior Improves Inverse Tumor Growth Modeling.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.

University of North Carolina adolescent and young adult cancer program: reach and characteristics of care.

Cancer care organizations often struggle to adequately address the unique needs of adolescent and young adult cancer patients, resulting in poorer outcomes compared with other age groups. Creation of adolescent and young adult cancer programs serves to bridge this gap and improve quality of care for this population. We aimed to describe the evolution and impact of the University of North Carolina at Chapel Hill's Adolescent and Young Adult Cancer Program.

Evaluation of Ergot Resistance in Hard Red Spring Wheat in North Dakota.

Higher levels of ergot ( [Fr.] Tul.) were reported in North Dakota hard red spring wheat (HRSW) in 2018, leading to questions pertaining to management and cultivar resistance.

A Learnable Prior Improves Inverse Tumor Growth Modeling.

Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.