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Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods.

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process.

Quantitative and mechanistic understanding of learning and long-term memory at the level of single neurons in living brains require highly demanding techniques. A specific need is to precisely label one cell whose firing output property is pinpointed amidst a functionally characterized large population of neurons through the learning process and then investigate the distribution and properties of dendritic inputs. Here, we disseminate an integrated method of daily two-photon neuronal population Ca2+ imaging through an auditory associative learning course, followed by targeted single-cell loose-patch recording and electroporation of plasmid for enhanced chronic Ca2+ imaging of dendritic spines in the targeted cell. Our method provides a unique solution to the demand, opening a solid path toward the hard-cores of how learning and long-term memory are physiologically carried out at the level of single neurons and synapses.

NK group 2, member D (NKG2D) is one of the most critical activating receptors expressed by natural killer (NK) cells. There is growing evidence that acute myeloid leukemia (AML) cells may evade NK cell-mediated cell lysis by expressing low or no ligands for NKG2D (NKG2D-Ls). We hypothesized that CCAAT/enhancer-binding protein α (C/EBPα), one of the most studied lineage-specific transcription factors in hematopoiesis, might influence the expression of NKG2D-Ls. To test this hypothesis, we first examined the endogenous expression of wild-type C/EBPα (C/EBPα-p42) in human AML cell lines and demonstrated that its expression level was highly relevant to the sensitivity of AML cells to NK cell cytotoxicity. Induction of C/EBPα-p42 in the low endogenous CEBPA-expressing AML cell line increased the sensitivity to NK-induced lysis. Moreover, decreased expression of C/EBPα-p42 by RNA interference in AML cells abrogated NK-mediated cytotoxicity. We further showed that the increase in NK susceptibility caused by C/EBPα-p42 occurred through up-regulation of the NKG2D-Ls ULBP2/5/6 in AML cells. More importantly, chromatin immunoprecipitation (ChIP) coupled with high-throughput sequencing captured C/EBPα motif signatures at the enhancer regions of the ULBP 2/5/6 genes. Whilst, the AML-associated C/EBPα C-terminal mutant and N-terminal truncated mutant (C/EBPα-p30) diminished ULBP2/5/6 transcription. Finally, we identified that histone demethylase lysine-specific demethylase 1 (LSD1) inhibition can restore the expression of ULBPs via induction of CEBPA expression in AML cells, which may represent a novel therapeutic strategy for CEBPA-mutated AML. Abbreviations: C/EBPα: CCAAT/enhancer-binding protein α; TF: Transcription factor; AML: Acute myeloid leukemia; TAD: Transactivation domain; FS: Frameshift; NK: Natural killer; NKG2D: NK group 2, member D; NKG2D-Ls: Ligands for NKG2D; MHC: Major histocompatibility complex; MICA: MHC class I-related chain A; ULBP: UL16-binding protein; STAT3: Signal transducer and activator of transcription 3; LSD1: Lysine-specific demethylase 1; Ab: Antibody; PBMC: Peripheral blood mononuclear cell; PBS: Phosphate-buffered saline; CFSE: Carboxyfluorescein diacetate succinimidyl ester; PI: Propidium iodide; shRNA: Short hairpin RNA; ChIP: Chromatin immunoprecipitation; BM: Binding motif; HCNE: Highly conserved noncoding element; TSS: Transcription start site; HMA: Hypomethylating agent; AZA: Azacitidine/5-azacytidine; DAC: Decitabine/5-aza-29-deoxycytidine; 2-PCPA: Tranylcypromine; RBP: RNA-binding protein; MSI2: MUSASHI-2; HDACi: Inhibitor of histone deacetylases; VPA: Valproate; DNMTi: DNA methyl transferase inhibitor; SCLC: Small cell lung cancer