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Natural killer (NK) cells are innate lymphocytes that lack antigen-specific rearranged receptors, a hallmark of adaptive lymphocytes. In some people infected with human cytomegalovirus (HCMV), an NK cell subset expressing the activating receptor NKG2C undergoes clonal-like expansion that partially resembles anti-viral adaptive responses. However, the viral ligand that drives the activation and differentiation of adaptive NKG2C + NK cells has remained unclear. Here we found that adaptive NKG2C + NK cells differentially recognized distinct HCMV strains encoding variable UL40 peptides that, in combination with pro-inflammatory signals, controlled the population expansion and differentiation of adaptive NKG2C + NK cells. Thus, we propose that polymorphic HCMV peptides contribute to shaping of the heterogeneity of adaptive NKG2C + NK cell populations among HCMV-seropositive people. NK cells constrain infection by cytomegalovirus. Romagnani and colleagues show that human NKG2C + NK cells recognize distinct HCMV UL40 viral peptides, which can vary among viral isolates. NKG2C + NK cells thereby demonstrate adaptive-like recognition that can discriminate between closely related viral strains.

In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by training a neural network. We apply physics-informed neural networks and the finite element method to estimate the diffusion coefficient governing the long term spread of molecules in the human brain from magnetic resonance images. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural network faces challenges with noisy measurements in our application. Our numerical results demonstrate that the residual of the partial differential equation after training needs to be small for accurate parameter recovery. To achieve this, we tune the weights and the norms used in the loss function and use residual based adaptive refinement of training points. We find that the diffusion coefficient estimated from magnetic resonance images with physics-informed neural networks becomes consistent with results from a finite element based approach when the residuum after training becomes small. The observations presented here are an important first step towards solving inverse problems on cohorts of patients in a semi-automated fashion with physics-informed neural networks.

In recent years, a plethora of methods combining deep neural networks and partial differential equations have been developed. A widely known and popular example are physics-informed neural networks. They solve forward and inverse problems involving partial differential equations in terms of a neural network training problem. We apply physics-informed neural networks as well as the finite element method to estimate the diffusion coefficient governing the long term, i.e. over days, spread of molecules in the human brain from a novel magnetic resonance imaging technique. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural network faces challenges with noisy measurements in our application. Our numerical results demonstrate that the residual of the partial differential equation after training needs to be small in order to obtain accurate recovery of the diffusion coefficient. To achieve this, we apply several strategies such as tuning the weights and the norms used in the loss function as well as residual based adaptive refinement and exchange of residual training points. We find that the diffusion coefficient estimated with PINNs from magnetic resonance images becomes consistent with results from a finite element based approach when the residuum after training becomes small. The observations presented in this work are an important first step towards solving inverse problems on observations from large cohorts of patients in a semi-automated fashion with physics-informed neural networks.