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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.