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Human cells have numerous repair mechanisms to counteract various insults incurred on the DNA. Any mutation in these repair mechanisms can lead to accumulation of DNA errors and carcinogenesis. This review aims to discuss the therapeutic options in the two most common DNA repair deficient cancer syndromes, namely Lynch syndrome (hereditary non-polyposis colorectal cancer) and breast cancer susceptibility gene (BRCA) associated ovarian and breast cancer. Deficiency in DNA repair mechanisms renders these tumors with increased sensitivity to platinum agents. There has been increasing amount of information on the utility of the defects in DNA repair as targets for cancer therapy in these syndromes. Novel therapies like poly (ADP-ribose) polymerase (PARP) inhibitors are one of such example where the induction of double stranded breaks in DNA leads to tumoricidal effect in patients with homologous DNA repair deficiency. Interestingly, patients with DNA repair deficiencies tend to have a more favorable prognosis than sporadic malignancies. In microsatellite high colorectal cancer patients, this has been attributed to increased recruitment of CD8+ T lymphocytes in tumor microenvironment. However, these tumors are able to limit the host immune response by activation of immune checkpoints that seem like attractive targets of therapy in the future.

Inverse problems in fluid dynamics are ubiquitous in science and engineering, with applications ranging from electronic cooling system design to ocean modeling. We propose a general and robust approach for solving inverse problems for the steady state Navier-Stokes equations by combining deep neural networks and numerical PDE schemes. Our approach expresses numerical simulation as a computational graph with differentiable operators. We then solve inverse problems by constrained optimization, using gradients calculated from the computational graph with reverse-mode automatic differentiation. This technique enables us to model unknown physical properties using deep neural networks and embed them into the PDE model. Specifically, we express the Newton's iteration to solve the highly nonlinear Navier-Stokes equations as a computational graph. We demonstrate the effectiveness of our method by computing spatially-varying viscosity and conductivity fields with deep neural networks (DNNs) and training DNNs using partial observations of velocity fields. We show that DNNs are capable of modeling complex spatially-varying physical field with sparse and noisy data. We implement our method using ADCME, a library for solving inverse modeling problems in scientific computing using automatic differentiation.

Transcranial ultrasound stimulation (tUS) shows potential as a noninvasive brain stimulation (NIBS) technique, offering increased spatial precision compared to other NIBS techniques. However, its reported effects on primary motor cortex (M1) are limited. We aimed to better understand tUS effects in human M1 by performing tUS of the hand area of M1 (M1hand) during tonic muscle contraction of the index finger. Stimulation during muscle contraction was chosen because of the transcranial magnetic stimulation-induced phenomenon known as cortical silent period (cSP), in which transcranial magnetic stimulation (TMS) of M1hand involuntarily suppresses voluntary motor activity. Since cSP is widely considered an inhibitory phenomenon, it presents an ideal parallel for tUS, which has often been proposed to preferentially influence inhibitory interneurons. Recording electromyography (EMG) of the first dorsal interosseous (FDI) muscle, we investigated effects on muscle activity both during and after tUS. We found no change in FDI EMG activity concurrent with tUS stimulation. Using single-pulse TMS, we found no difference in M1 excitability before versus after sparsely repetitive tUS exposure. Using acoustic simulations in models made from structural MRI of the participants that matched the experimental setups, we estimated in-brain pressures and generated an estimate of cumulative tUS exposure experienced by M1hand for each subject. We were unable to find any correlation between cumulative M1hand exposure and M1 excitability change. We also present data that suggest a TMS-induced MEP always preceded a near-threshold cSP.

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