No abstract

Tumor necrosis factor-α (TNF-α) is a critical proinflammatory cytokine regulating neuroinflammation. Elevated levels of TNF-α have been associated with various neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. However, the signaling events that lead to TNF-α-initiated neurotoxicity are still unclear. Here, we report that RIP3-mediated necroptosis, a form of regulated necrosis, is activated in the mouse hippocampus after intracerebroventricular injection of TNF-α. RIP3 deficiency attenuates TNF-α-initiated loss of hippocampal neurons. Furthermore, we characterized the molecular mechanism of TNF-α-induced neurotoxicity in HT-22 hippocampal neuronal cells. HT-22 cells are sensitive to TNF-α only upon caspase blockage and subsequently undergo necrosis. The cell death is suppressed by knockdown of CYLD or RIP1 or RIP3 or MLKL, suggesting that this necrosis is necroptosis and mediated by CYLD-RIP1-RIP3-MLKL signaling pathway. TNF-α-induced necroptosis of HT-22 cells is largely independent of both ROS accumulation and calcium influx although these events have been shown to be critical for necroptosis in certain cell lines. Taken together, these data not only provide the first in vivo evidence for a role of RIP3 in TNF-α-induced toxicity of hippocampal neurons, but also demonstrate that TNF-α promotes CYLD-RIP1-RIP3-MLKL-mediated necroptosis of hippocampal neurons largely bypassing ROS accumulation and calcium influx.

HighlightsWe have found that PD can be characterized by unique spatial microstate different from healthy controls, which may be related to the brain dysfunction in PD.The drug-free patients with PD show abnormal brain dynamics revealed by the regular changes of temporal microstate features in early PD and such temporal dynamics in microstates are correlated with motor function and cognition of the subjects.The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

Self-attention model have shown its flexibility in parallel computation and the effectiveness on modeling both longand short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which have proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculates the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantity how the context vectors participate in the self-attention model.

Surface-enhanced Raman scattering (SERS) is well-recognized as a powerful analytical tool for ultrahighly sensitive detection of analytes. In this article, we present a kind of silicon-based SERS sensing platform made of a hairpin DNA-modified silver nanoparticles decorated silicon wafer (AgNPs@Si). In particular, the AgNPs@Si with a high enhancement factor (EF) value of ~4.5 × 10(7) is first achieved under optimum reaction conditions (i.e., pH = 12, reaction time = 20 min) based on systematic investigation. Such resultant AgNPs@Si is then employed for construction of a silicon-based SERS sensing platform through surface modification of hairpin DNA, which is superbly suitable for highly reproducible, multiplexed, and ultrasensitive DNA detection. A detection limit of 1 fM is readily achieved in a very reproducible manner along with high specificity. Most significantly, for the first time, we demonstrate that the silicon-based SERS platform is highly efficacious for discriminating deafness-causing mutations in a real system at the femtomolar level (500 fM), which is about 3-4 orders of magnitude lower than that (~5 nM) ever reported by conventional detection methods. Our results raise the exciting potential of practical SERS applications in biology and biomedicine.