Regulation of GATA-3 Expression during CD4 Lineage Differentiation

GATA-3 is necessary for the development of MHC class II-restricted CD4 T cells, and its expression is increased during positive selection of these cells. TCR signals drive this upregulation, but the signaling pathways that control this process are not well understood. Using genetic and pharmacological approaches, we show that GATA-3 upregulation during thymocyte-positive selection is the result of additive inputs from the Ras/MAPK and calcineurin pathways. This upregulation requires the presence of the transcription factor c-Myb. Furthermore, we show that TH-POK can also upregulate GATA-3 in double-positive thymocytes, suggesting the existence of a positive feedback loop that contributes to lock in the initial commitment to the CD4 lineage during differentiation

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

The Ras/MAPK Pathway Is Required for Generation of iNKT Cells

iNKT cells derive from CD4+CD8+ DP thymocytes, and are selected by thymocyte-thymocyte interactions through signals from their invariant Vα14-Jα18 TCR and from the costimulatory molecules SLAMF1 and SLAMF6. Genetic studies have demonstrated the contribution of different signaling pathways to this process. Surprisingly, current models imply that the Ras/MAPK pathway, one of the critical mediators of conventional αβ T cell positive selection, is not necessary for iNKT cell development. Using mice defective at different levels of this pathway our results refute this paradigm, and demonstrate that Ras, and its downstream effectors Egr-1 and Egr-2 are required for positive selection of iNKT cells. Interestingly our results also show that there are differences in the contributions of several of these molecules to the development of iNKT and conventional αβ T cells

Novel Data Compression Algorithm for Transmission Line Condition Monitoring

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data

Novel Data Compression Algorithm for Transmission Line Condition Monitoring

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data

Egr1^-/-; Egr2^f/f<i>lck</i>cre mice have a complete block in iNKT development.

<p>(<b>a</b>) Thymic profile of littermate wild-type (WT), or Egr1^-/-;Egr2^f/f<i>lck</i>cre (DKO) mice <b>(b)</b> Percentages and absolute numbers of iNKT cells in the thymus (T), spleen (S) and liver mononuclear cells (L) of WT and EgrDKO mice stained with CD4, CD8, PBS57-loaded CD1d tetramer and TCRβ. <b>(c)</b> Percentages and absolute numbers of iNKT cell subpopulations in gated thymic TCR-βhiPBS57-CD1dtet⁺ of WT and EgrDKO mice. Results representative of five independent pairs in three independent experiments. The bar graphs show the average and SEM of all the experiments. Significance as assessed using the unpaired t-test. ***<0.001, **<0.01</p

dnRas mice lack iNKT cells.

<p>(<b>a</b>) Thymic profile of WT and dnRas mice (<b>b</b>) Percentages and absolute numbers of iNKT cells in the thymus (T), spleen (S) and liver mononuclear cells (L) of normal littermate controls (WT) and dnRas mice stained with CD4, CD8, PBS57-loaded CD1d tetramer and TCRβ. (<b>c</b>) Percentages and absolute numbers of iNKT cell subpopulations in gated TCR^hiPBS57-CD1dtet⁺ thymocytes from WT and dnRas mice. Results representative of nine independent dnRas and WT pairs analyzed in five experiments<b>,</b> except for the CD44/NK1.1 histograms (n = 2). The bar graphs show the average and SEM of all the experiments. Significance as assessed using a two-tailed unpaired t-test. ***<0.001, **<0.01. (<b>d</b>) Gating strategy used to sort the different populations. (<b>e</b>) Expression of Egr-1, Egr-2 and Id3 in sorted Tet⁺ HSA^hi and Tet⁺ HSA^lo from normal littermate control (WT) and dnRas mice. Bar graphs show relative expression of dnRas compared to WT for three independent experiments. Each experiment was an independent sort of a WT and dnRas pair. Expression in each experiment was normalized to the expression levelis in Tet⁺ HSA^hi WT cells. Significance as assessed using a two-tailed unpaired t-test. **<0.01.</p

Egr1 and Egr2 contribute in a quantitatively different manner to iNKT cell development.

<p>(<b>a</b>) Contribution of WT and Egr1^-/- (Egr1KO) cells to the iNKT compartment in thymus (T), spleen (S) and liver (L) of mixed bone marrow chimeras generated by injecting Egr1^-/- (CD45.2) and F1(C57BL/6xB6-LY5.2/Cr) (CD45.1;CD45.2) bone marrow cells into lethally irradiated B6-LY5.2/Cr recipient mice (CD45.1). Mean and SEM is shown on the right (n = 5). Significance was assessed using a paired t-test. **<0.01, *<0.05. This is one out of two independent experiments. <b>(b)</b> Percentages and absolute numbers of iNKT cells in the thymus (T), spleen (S) and liver mononuclear cells (L) of WT and Egr-2^f/f-<i>lck</i>-Cre (EGR2KO) mice stained with CD4, CD8, PBS57-loaded CD1d tetramer and TCRβ. <b>(c)</b> Percentages and absolute numbers of iNKT cell subpopulations in gated TCR-β^hiPBS57-CD1dtet⁺ thymocytes from WT and EGR2KO mice. Results representative of five independent pairs in three independent experiments. The bar graphs show the average and SEM of all the experiments. Significance was assessed using an unpaired t-test. ***<0.001, **<0.01.</p

Defects in Slamf1, Slamf6 and CD1d expression in dnRas, but not Egr-1,2 double knockout mice.

<p>Slamf1, Slamf3, Slamf6, Slamf5 and CD1d expression levels in DP thymocytes mice were assessed by flow cytometry. Shown are representative histograms, and the mean and SEM of the normalized MFI of DP populations. In <b>(A)</b> WT vs. dnRas (n = 5). In <b>(B)</b> WT vs. Egr1^-/-;Egr2^f/f-<i>lck</i>-Cre (Egr-DKO) (n = 3). <b>(C)</b> SAP and Bcl_xL expression levels in DP thymocytes from WT or dnRas mice were assessed by intracellular flow cytometry. Shown are representative histograms, and the mean and SEM of the normalized MFI of DP populations (n = 5). To normalize the MFI, we averaged the MFI for the WT mice in each experiment and considered that value 1. The bar graphs show the average and SEM of all the experiments. Significance was assessed using the unpaired t-test ***<0.001, **<0.01 *<0.05<b>.</b></p