Emergence of brain-inspired small-world spiking neural network through neuroevolution.

Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.

Dihydrophenanthropyrans derived from the pseudobulbs of Pholidota chinensis alleviates neutrophilic inflammation by inhibiting MAPKs and calcium.

Five dihydrophenanthropyrans (1-5) were isolated from the pseudobulbs of Pholidota chinensis, among which 1,3-di(4'-hydroxybenzy)-imbricatin (3) was isolated from the nature for the first time. Their structures were elucidated and established through various spectroscopic methods. These compounds exhibited a potent inhibition effect on both N-formyl-methionyl-leucyl-phenylalanine (fMLF)-induced superoxide anion generation and elastase release with IC50 values ranging from 0.23 to 7.63 µM. Furthermore, dihydrophenanthropyrans (1-3) also demonstrated a dose-dependent reactive oxygen species (ROS) scavenging effect. In addition, dihydrophenanthropyrans (2-3) exhibited a dose-dependent reduction in the intracellular Ca2+ concentration ([Ca2+]i) in fMLF-activated human neutrophils. Moreover, dihydrophenanthropyrans (1-3) selectively inhibited the phosphorylation of c-Jun N-terminal kinases (JNKs) and p38, while only dihydrophenanthropyran (1) inhibited the phosphorylation of extracellular signal-regulated kinases (ERKs) in fMLF-activated human neutrophils. Notably, dihydrophenanthropyrans (1-3) did not affect protein kinase B (AKT) activity in these cells. These findings highlight the potent anti-inflammatory capabilities of dihydrophenanthropyrans, manifested through their ability to inhibit superoxide anion generation, suppress elastase release, and selectively modulate key signaling pathways in human neutrophils. This suggests that dihydrophenanthropyrans hold significant promise as therapeutic agents for conditions associated with neutrophil-mediated inflammation.

Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.

The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks.

The Metabolic Chemical Reporter Ac46AzGal Could Incorporate Intracellular Protein Modification in the Form of UDP-6AzGlc Mediated by OGT and Enzymes in the Leloir Pathway.

Galactose is a naturally occurring monosaccharide used to build complex glycans that has not been targeted for labeling as a metabolic reporter. Here, we characterize the cellular modification of proteins by using Ac46AzGal in a dose- and time-dependent manner. It is noted that a vast majority of this labeling of Ac46AzGal occurs intracellularly in a range of mammalian cells. We also provided evidence that this labeling is dependent on not only the enzymes of OGT responsible for O-GlcNAcylation but also the enzymes of GALT and GALE in the Leloir pathway. Notably, we discover that Ac46AzGal is not the direct substrate of OGT, and the labeling results may attribute to UDP-6AzGlc after epimerization of UDP-6AzGal via GALE. Together, these discoveries support the conclusion that Ac46AzGal as an analogue of galactose could metabolically label intracellular O-glycosylation modification, raising the possibility of characterization with impaired functions of the galactose metabolism in the Leloir pathway under certain conditions, such as galactosemias.