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Continued discoveries of negative regulators of inflammatory signaling provide detailed molecular insights into peripheral tolerance and anti-tumor immunity. Accumulating evidence indicates that peripheral tolerance is maintained at multiple levels of immune responses by negative regulators of proinflammatory signaling, soluble anti-inflammatory factors, inhibitory surface receptors & ligands, and regulatory cell subsets. This review provides a global overview of these regulatory machineries that work in concert to maintain peripheral tolerance at cellular and host levels, focusing on the direct and indirect regulation of T cells. The recent success of checkpoint blockade immunotherapy (CBI) has initiated a dramatic shift in the paradigm of cancer treatment. Unprecedented responses to CBI have highlighted the central role of T cells in both anti-tumor immunity and peripheral tolerance and underscored the importance of T cell exhaustion in cancer. We discuss the therapeutic implications of modulating the negative regulators of T cell function for tumor immunotherapy with an emphasis on inhibitory surface receptors & ligands-central players in T cell exhaustion and targets of checkpoint blockade immunotherapies. We then introduce a Threshold Model for Immune Activation-the concept that these regulatory mechanisms contribute to defining a set threshold of immunogenic (proinflammatory) signaling required to elicit an anti-tumor or autoimmune response. We demonstrate the value of the Threshold Model in understanding clinical responses and immune related adverse events in the context of peripheral tolerance, tumor immunity, and the era of Checkpoint Blockade Immunotherapy.

Cracks in civil structures are important signs of structural degradation and may indicate the inception of catastrophic failure. However, most of studies that have employed deep learning models for automatic crack detection are limited to high computational demand and require a large amount of labeled data. Long training time is not friendly to model update, and large amount of training data is usually unavailable in real applications. To bridge this gap, the innovation of this study lies in developing a hybrid method that comprises transfer learning (TL) and low‐rank dictionary learning (LRDL) for fast crack detection on concrete surfaces. Benefiting from the availability of preextracted features in TL and a limited number of parameters in LRDL, the training time can be significantly minimized without GPU acceleration. Experimental results showed that the time for training a dictionary only takes 25.33 s. Moreover, this new hybrid method reduces the demand for labeled data during training. It achieved an accuracy of 99.68% with only 20% labeled data. Three large‐scale images captured under varying conditions (e.g., uneven lighting conditions and very thin cracks) were further used to assess the crack detection performance. These advantages help to implement the proposed TL‐LRDL method on resource‐limited computers, such as battery‐powered UAVs, UGVs, and scarce processing capability of AR headsets. [ABSTRACT FROM AUTHOR]

This study explored the effects of cloud-based m-learning on students' creative processes and products in engineering design. A nonequivalent pretest-posttest design was adopted, and 62 university students from Taipei City, Taiwan, were recruited as research participants in the study. The results showed that cloud-based m-learning had a positive effect on creative performance in engineering design. It also improved the creative process in terms of presenting problems and of generating and validating responses. Additionally, cloud-based m-learning effectively improved overall performance, novelty, value and elaboration in designing creative products.

Background: The monitoring of immunotherapies is still based on changes in the tumor size in imaging, with a long evaluation period and low sensitivity. Purpose: To investigate the effectiveness of diffusion kurtosis imaging (DKI) and dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) in assessing the therapeutic efficacy of anti‐programmed death‐1 (PD‐1) therapy in a mouse triple negative breast cancer (TNBC) model. Study Type: Prospective. Animal Model: A total of 54 BALB/c mouse subcutaneous 4 T1 transplantation models of TNBC. Field Strength/Sequence: A 3.0‐T; turbo spin echo (TSE) T2‐weighted imaging, DKI with seven b values (0, 500, 1000, 1500, 2000, 2500, and 3000 sec/mm2) and T1‐twist DCE acquisition series. Assessment: DKI and DCE‐MRI parameters were evaluated by two radiologists independently. Regions of interest (ROIs) were drawn manually on the maximum cross‐sectional area of the lesion; care was taken to avoid necrotic areas. The tumor cell density, the CD45 and CD31 levels were analyzed by two pathologists. Statistical Tests: The two‐tailed unpaired t‐test, Mann–Whitney U test, Fisher's exact test and Pearson correlation coefficient were performed. A P < 0.05 was considered statistically significant. Results: The apparent diffusion coefficient (ADC), mean diffusivity (MD), Ktrans and Kep values were significantly different between the two groups at each time point after treatment. There were significant differences in the mean kurtosis (MK) and Ve values between the two groups at 5 and 10 days after treatment but no significant differences at 15 days (P = 0.317 and 0.183, respectively). The ADC and MD values were significantly correlated with tumor cell density (ADC, r = −0.833; MD, r = 0.890) and the CD45 level (ADC, r = 0.720; MD, r = 0.718). The Ktrans and Kep values were significantly correlated with the CD31 level (Ktrans, r = 0.820; Kep, r = 0.683). Data Conclusion: DKI and DCE‐MRI could reflect the changes in tumor microstructure and tumor tissue vasculature after anti‐PD‐1 therapy, respectively. Level of Evidence: 1 Technical Efficacy: Stage 4. [ABSTRACT FROM AUTHOR]