An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

Fucoxanthin attenuates LPS-induced acute lung injury via inhibition of the TLR4/MYD88 signaling axis

Acute lung injury (ALI) is a critical clinical condition with a high mortality rate. It is believed that the inflammatory storm is a critical contributor to the occurrence of ALI. Fucoxanthin is a natural extract from marine seaweed with remarkable biological properties, including antioxidant, anti-tumor, and anti-obesity. However, the anti-inflammatory activity of Fucoxanthin has not been extensively studied. The current study aimed to elucidate the effects and the molecular mechanism of Fucoxanthin on lipopolysaccharide-induced acute lung injury. In this study, Fucoxanthin efficiently reduced the mRNA expression of pro-inflammatory factors, including IL-10, IL-6, iNOS, and Cox-2, and down-regulated the NF-kappaB signaling pathway in Raw264.7 macrophages. Furthermore, based on the network pharmacological analysis, our results showed that anti-inflammation signaling pathways were screened as fundamental action mechanisms of Fucoxanthin on ALI. Fucoxanthin also significantly ameliorated the inflammatory responses in LPS-induced ALI mice. Interestingly, our results revealed that Fucoxanthin prevented the expression of TLR4/MyD88 in Raw264.7 macrophages. We further validated Fucoxanthin binds to the TLR4 pocket using molecular docking simulations. Altogether, these results suggest that Fucoxanthin suppresses the TLR4/MyD88 signaling axis by targeting TLR4, which inhibits LPS-induced ALI, and fucoxanthin inhibition may provide a novel strategy for controlling the initiation and progression of ALI

Information Recovery-Driven Deep Incomplete Multiview Clustering Network

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.Comment: Accepted by TNNLS 2023. Please contact me if you have any questions: [email protected]. The code is available at: https://github.com/justsmart/RecForme

Factor Analysis Model Based on the Theory of the TOPSIS in the Application Research

In view of the existing literature panel data factor analysis model in practical application of the deficiency, this paper established the model of factor analysis based on TOPSIS method, which is applied to the analysis of the panel data factor in practice. Compared with the generalized dynamic factor analysis model, the model does not need to satisfy the 4 assumptions of the generalized dynamic factor analysis model at the same time. The model is calculated with regard to every year's cross section data factor composite scores the highest and lowest, respectively, for the best and worst vector. By TOPSIS theory, the optimal factor scheme approach degree of each research object is obtained. Take the development of China's service industry as an example; use the optimal factor scheme proximity of model degree to depict the eastern, central, and western development of service industry. The study found that the development of service industry in eastern provinces and in central and western regions differs greatly. In total, China's service industry has a great development space

15,16-Dihydrotanshinone I inhibits the proliferation of MV4-11 by means of apoptosis via antagonizing FLT3-ITD/STAT5/Mcl-1 pathway

Till now, the medicines approved for acute myeloid leukemia with internal tandem duplication mutation of FMS-like tyrosine kinase 3 (FLT3-ITD) display not ideal efficacy. This study aimed to evaluate the effects of 15,16-dihydrotanshinone I on FLT3-ITD acute myeloid leukemia cells. The inhibitory effect of this compound against MV4-11 was determined using CCK-8 assay. Western blotting detecting caspase-3, PARP, and annexin V-APC/7-AAD was carried out. Activation of FLT3, STAT5, and Mcl-1 expression was analyzed by western blotting. The results showed that MV4-11 was sensitive toward dihydrotanshinone I in a dose-dependent manner (p<0.05). MV4-11 apoptosis was induced notably after dihydrotanshinone I treatment. Western blotting revealed suppressed activation of FLT3, STAT5 and decreased Mcl-1 (p<0.05). This study suggests that dihydrotanshinone I inhibits MV4-11 proliferation by apoptosis via antagonizing FLT3-ITD/STAT5/Mcl-1 path-way, which might provide a novel therapy for acute myeloid leukemia