Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. While DuNets have been successfully applied to many linear inverse problems, nonlinear problems tend to impair the performance of the method. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets -- the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems

Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. Although DuNets have been successfully applied to many linear inverse problems, their performance tends to be impaired by nonlinear problems. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets—the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA, respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems

Blocking interaction between SHP2 and PD‐1 denotes a novel opportunity for developing PD‐1 inhibitors

Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis

Nonlinear dynamic analysis on maglev train system with flexible guideway and double time-delay feedback control

In this paper, the dynamic behavior of time-delayed feedback control for maglev train system with double discrete time delays is considered with flexible guideway. Considering the maglev guideway as Beroulli-Euler beam, the mathematical model of maglev system with flexible guideway is constructed. The time delay of the two state feedback signals in the maglev system occurs simultaneously, and the values are different. The present treatment method only considers one single feedback delay, which are insufficiency. Thus, the Hopf bifurcation with double time-delay feedback of maglev train running on the flexible guideway is analyzed considering time-delayed position feedback signal τ1 and velocity feedback signal τ2. A novel method is presented to develop the double-parametric Hopf bifurcation diagram in relation to τ1 and τ2. Sufficient numerical simulations are provided to illustrate the complex dynamical behavior of the discrete delays τ1 and τ2 for maglev system and we verify the obtained theoretical analysis. Finally, the field experiments are carried out to validate the effectiveness of the Hopf bifurcation analytical method preliminarily

Prognostic risk of immune-associated signature in the microenvironment of brain gliomas

Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes

Prostate cancer risk and DNA damage: translational significance of selenium supplementation in a canine model

Daily supplementation with the essential trace mineral selenium significantly reduced prostate cancer risk in men in the Nutritional Prevention of Cancer Trial. However, the optimal intake of selenium for prostate cancer prevention is unknown. We hypothesized that selenium significantly regulates the extent of genotoxic damage within the aging prostate and that the relationship between dietary selenium intake and DNA damage is non-linear, i.e. more selenium is not necessarily better. To test this hypothesis, we conducted a randomized feeding trial in which 49 elderly beagle dogs (physiologically equivalent to 62--69-year-old men) received nutritionally adequate or supranutritional levels of selenium for 7 months, in order to mimic the range of dietary selenium intake of men in the United States. Our results demonstrate an intriguing U-shaped dose--response relationship between selenium status (toenail selenium concentration) and the extent of DNA damage (alkaline Comet assay) within the prostate. Further, we demonstrate that the concentration of selenium that minimizes DNA damage in the aging dog prostate remarkably parallels the selenium concentration in men that minimizes prostate cancer risk. By studying elderly dogs, the only non-human animal model of spontaneous prostate cancer, we have established a new approach to bridge the gap between laboratory and human studies that can be used to select the appropriate dose of anticancer agents for large-scale human cancer prevention trials. From the U-shaped dose--response, it follows that not all men will necessarily benefit from increasing their selenium intake and that measurement of baseline nutrient status should be required for all individuals in prevention trials to avoid oversupplementation

Breast cancer-derived K172N, D301V mutations abolish Na+/H+ exchanger regulatory factor 1 inhibition of platelet-derived growth factor receptor signaling

AbstractNa+/H+ exchanger regulatory factor 1 (NHERF1) is a scaffold protein known to interact with a number of cancer-related proteins. nherf1 Mutations (K172N and D301V) were recently identified in breast cancer cells. To investigate the functional properties of NHERF1, wild-type and cancer-derived nherf1 mutations were stably expressed in SKMES-1 cells respectively. NHERF1-wt overexpression suppressed the cellular malignant phenotypes, including proliferation, migration, and invasion. nherf1 Mutations (K172N and D301V) caused complete or partial loss of NHERF1 functions by affecting the PTEN/NHERF1/PDGFRβ complex formation, inactivating NHERF1 inhibition of PDGF-induced AKT and ERK activation, and attenuating the tumor-suppressor effects of NHERF1-wt. These results further demonstrated the functional consequences of breast cancer-derived nherf1 mutations (K172N and D301V), and suggested the causal role of NHERF1 in tumor development and progression

Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses. However, most of the DL models are considered as “black boxes”. There is an unmet need to demystify DL models so domain experts can trust these high-performance DL models. This has resulted in a sub-domain of AI research called explainable artificial intelligence (XAI). In the last decade, many experts have dedicated their efforts to developing novel XAI methods that are competent at visualizing and explaining the logic behind data-driven DL models. However, XAI techniques are still in their infancy for medical MRI image analysis. This study aims to outline the XAI applications that are able to interpret DL models for MRI data analysis. We first introduce several common MRI data modalities. Then, a brief history of DL models is discussed. Next, we highlight XAI frameworks and elaborate on the principles of multiple popular XAI methods. Moreover, studies on XAI applications in MRI image analysis are reviewed across the tissues/organs of the human body. A quantitative analysis is conducted to reveal the insights of MRI researchers on these XAI techniques. Finally, evaluations of XAI methods are discussed. This survey presents recent advances in the XAI domain for explaining the DL models that have been utilized in MRI applications