E3 ubiquitin ligase Cbl-b is critical for establishing the threshold for T cell activation, and is essential for induction of T cell anergy. Recent studies suggest that Cbl-b is involved in the development of inducible CD4+CD25+ regulatory T cells (iTregs). In this study, we report that the optimal induction of Foxp3 by naïve CD4+CD25− T cells requires suboptimal TCR triggering. In the absence of Cbl-b, the TCR strength for optimal Foxp3 induction is down-regulated in vitro. Using TCR transgenic Rag−/− mice in combination with Cbl-b deficiency, we show that in vivo iTreg development is also controlled by Cbl-b via tuning the TCR strength. Furthermore, we show that Akt-2 but not Akt-1 regulates Foxp3 expression downstream of Cbl-b. Therefore, we demonstrate that Cbl-b regulates the fate of iTregs via controlling the threshold for T cell activation.

In this work, a super-resolution approach based on generative adversary network (GAN) was used to interpolate (up-sample) ultrasound radio-frequency (RF) echo data along the lateral (perpendicular to the acoustic beam direction) direction before motion estimation. Our primary objective was to investigate the feasibility of using a GAN-based super-solution approach to improve lateral resolution in the RF data as a means of improving strain image quality in quasi-static ultrasound strain elastography (QUSE). Unlike natural scene photographs, axial (parallel to the acoustic beam direction) resolution is significantly higher than that of lateral resolution in ultrasound RF data. To better handle RF data, we first modified a superresolution generative adversary network (SRGAN) model developed by the computer vision community. We named the modified SRGAN model as super-resolution radio-frequency neural network (SRRFNN) model. Our preliminary experiments showed that, compared with axial strain elastograms obtained using the original ultrasound RF data, axial strain elastograms using ultrasound RF data up-sampled by the proposed SRRFNN model were improved. Based on the Wilcoxon rank-sum tests, such improvements were statistically significant (p < 0.05) for large deformation (3-5%). Also, the proposed SRRFNN model outperformed a commonly-used method (i.e. bi-cubic interpolation used in MATLAB [Mathworks Inc., MA, USA]) in terms of improving axial strain elastograms. We concluded that applying the proposed (SRRFNN) model was feasible and good-quality strain elastography data could be obtained in in vivo tumor-bearing breast ultrasound data. INDEX TERMS Generative adversarial network, motion tracking, super-resolution, quasi-static ultrasound strain elastography.

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