No abstract

No abstract

No abstract

No abstract

No abstract

In our previous work, we have reported the expression of immunoglobulin (Ig) molecules by numerous epithelial cancer cells and hyperplastic epithelial cells. In the present study, we extended our investigation to study the frequencies of expression of IgG and IgA in some types of oral epithelial tumor cells, and analyzed the oral tumor-derived V regions characteristic of Ig gamma chain gene transcripts by immunohistochemistry, in situ hybridization, laser capture microdissection-correlated reverse-transcription polymerase chain reaction, and sequencing. IgG and IgA immunoreactivity was prominent in the cytoplasmic or plasma membrane or secretion of malignant cells, pleomorphic adenoma tumor cells, and some normal glandular epithelial cells or squamous cells adjacent to tumors. More importantly, rearranged Ig gene transcripts were identified in these tumor cells, and in some normal glandular epithelial cells, the V-D-J region sequences revealed that IgG transcripts in 2 tested oral tumors were oligoclonal. These results support that the phenomenon of Ig could also be expressed in oral cavity epithelial tumor cells.

There are two principle approaches for learning in artificial intelligence: error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide complementary learning capabilities for versatile learning scenarios. At the same time, neuromorphic computing holds great promise, but still needs plenty of useful algorithms and algorithm-hardware co-designs to fully exploit its advantages. Here, we present a neuromorphic global-local synergic learning model by introducing a brain-inspired meta-learning paradigm and a differentiable spiking model incorporating neuronal dynamics and synaptic plasticity. It can meta-learn local plasticity and receive top-down supervision information for multiscale learning. We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors. It achieves significantly higher performance than single-learning methods. We further implement the model in the Tianjic neuromorphic platform by exploiting algorithm-hardware co-designs and prove that the model can fully utilize neuromorphic many-core architecture to develop hybrid computation paradigm.

No abstract