Plin4-Dependent Lipid Droplets Hamper Neuronal Mitophagy in the MPTP/p-Induced Mouse Model of Parkinson’s Disease

Epidemiological studies have shown that both lipid metabolism disorder and mitochondrial dysfunction are correlated with the pathogenesis of neurodegenerative diseases (NDDs), including Parkinson’s disease (PD). Emerging evidence suggests that deposition of intracellular lipid droplets (LDs) participates in lipotoxicity and precedes neurodegeneration. Perilipin family members were recognized to facilitate LD movement and cellular signaling interactions. However, the direct interaction between Perilipin-regulated LD deposition and mitochondrial dysfunction in dopaminergic (DA) neurons remains obscure. Here, we demonstrate a novel type of lipid dysregulation involved in PD progression as evidenced by upregulated expression of Plin4 (a coating protein and regulator of LDs), and increased intracellular LD deposition that correlated with the loss of TH-ir (Tyrosine hydroxylase-immunoreactive) neurons in the MPTP/p-induced PD model mouse mesencephalon. Further, in vitro experiments showed that inhibition of LD storage by downregulating Plin4 promoted survival of SH-SY5Y cells. Mechanistically, reduced LD storage restored autophagy, leading to alleviation of mitochondrial damage, which in turn promoted cell survival. Moreover, the parkin-poly-Ub-p62 pathway was involved in this Plin4/LD-induced inhibition of mitophagy. These findings were further confirmed in primary cultures of DA-nergic neurons, in which autophagy inhibitor treatment significantly countermanded the ameliorations conferred by Plin4 silencing. Collectively, these experiments demonstrate that a dysfunctional Plin4/LD/mitophagy axis is involved in PD pathology and suggest Plin4-LDs as a potential biomarker as well as therapeutic strategy for PD

MicroRNA-212-5p Prevents Dopaminergic Neuron Death by Inhibiting SIRT2 in MPTP-Induced Mouse Model of Parkinson’s Disease

Recently, emerging evidences show that sirtuins (SIRTs) modulate aging progress and affect neurodegenerative diseases. For example, inhibition of SIRT2 has been recognized to exert neuroprotective effects in Parkinson’s disease (PD). However, current SIRT2 inhibitors are lack of selective property distinguished from its homolog. In this study, we found that SIRT2 protein level was highly increased in PD model, which was negatively regulated by miR-212-5p. In detail, miR-212-5p transfection reduced SIRT2 expression and inhibited SIRT2 activity. In vivo study, miR-212-5p treatment prevented dopaminergic neuron loss and DAT reduction by targeting SIRT2, which means miR-212-5p shows neuroprotective effect in PD. Mechanismly, we found nuclear acetylated p53 was up-regulation according to p53 is a major deacetylation substrate of SIRT2. Furthermore, decreased cytoplasmic p53 promoted autophagy in PD model, which was showed as autophagosomes, autophagic flux, LC3 B and p62 expression. Meanwhile, we also found miR-212-5p treatment somehow alleviated apoptosis in PD model, which might have some underlying mechanisms. In conclusions, our study provides a direct link between miR-212-5p and SIRT2-mediated p53-dependent programmed cell death in the pathogenesis of PD. These findings will give us an insight into the development of highly specifically SIRT2 inhibitor of opening up novel therapeutic avenues for PD

Towards Cyber Security for Low-Carbon Transportation: Overview, Challenges and Future Directions

In recent years, low-carbon transportation has become an indispensable part as sustainable development strategies of various countries, and plays a very important responsibility in promoting low-carbon cities. However, the security of low-carbon transportation has been threatened from various ways. For example, denial of service attacks pose a great threat to the electric vehicles and vehicle-to-grid networks. To minimize these threats, several methods have been proposed to defense against them. Yet, these methods are only for certain types of scenarios or attacks. Therefore, this review addresses security aspect from holistic view, provides the overview, challenges and future directions of cyber security technologies in low-carbon transportation. Firstly, based on the concept and importance of low-carbon transportation, this review positions the low-carbon transportation services. Then, with the perspective of network architecture and communication mode, this review classifies its typical attack risks. The corresponding defense technologies and relevant security suggestions are further reviewed from perspective of data security, network management security and network application security. Finally, in view of the long term development of low-carbon transportation, future research directions have been concerned.Comment: 34 pages, 6 figures, accepted by journal Renewable and Sustainable Energy Review

in KKAy mice

and mechanisms of resveratrol on the amelioration of oxidative stress and hepatic steatosi

A Trimodel SAR Semisupervised Recognition Method Based on Attention-Augmented Convolutional Networks

Semisupervised learning in synthetic aperture radars (SARs) is one of the research hotspots in the field of radar image automatic target recognition. It can efficiently deal with challenging environments where there are insufficient labeled samples and large unlabeled samples in the SAR dataset. In recent years, consistency regularization methods in semisupervised learning have shown considerable improvement in recognition accuracy and efficiency. Current consistency regularization approaches suffer from two main shortcomings: first, extracting all of the relevant information in the image target is difficult owing to the inability of conventional convolutional neural networks to capture global relational information; second, the standard teacher–student regularization methodology causes confirmation biases due to the high coupling between teacher and student models. This article adopts an innovative trimodel semisupervised method based on attention-augmented convolutional networks to address the aforementioned obstacles. Specifically, we develop an attention mechanism incorporating a novel positional embedding method based on recurrent neural networks and integrate this with a standard convolutional network as a feature extractor, to improve the network's ability to extract global feature information from images. Furthermore, we address the confirmation bias problem by introducing a classmate model to the standard teacher–student structure and utilize the model to impose a weak consistency constraint designed on the student to weaken the strong coupling between the teacher and the student. Comparative experiments on the Moving and Stationary Target Acquisition and Recognition dataset show that our method outperforms state-of-the-art semisupervised methods in terms of recognition accuracy, demonstrating its potential as a new benchmark approach for the deep learning and SAR research community

Intention Understanding in Human-Robot Interaction Based on Visual-NLP Semantics

With the rapid development of robotic and AI technology in recent years, human-robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human-robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures

Say What You Are Looking At: An Attention-Based Interactive System for Autistic Children

Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications.</jats:p

SARS-CoV-2 N protein induced acute kidney injury in diabetic db/db mice is associated with a Mincle-dependent M1 macrophage activation

“Cytokine storm” is common in critically ill COVID-19 patients, however, mechanisms remain largely unknown. Here, we reported that overexpression of SARS-CoV-2 N protein in diabetic db/db mice significantly increased tubular death and the release of HMGB1, one of the damage-associated molecular patterns (DAMPs), to trigger M1 proinflammatory macrophage activation and production of IL-6, TNF-α, and MCP-1 via a Mincle-Syk/NF-κB-dependent mechanism. This was further confirmed in vitro that overexpression of SARS-CoV-2 N protein caused the release of HMGB1 from injured tubular cells under high AGE conditions, which resulted in M1 macrophage activation and production of proinflammatory cytokines via a Mincle-Syk/NF-κB-dependent mechanism. This was further evidenced by specifically silencing macrophage Mincle to block HMGB1-induced M1 macrophage activation and production of IL-6, TNF-α, and MCP-1 in vitro. Importantly, we also uncovered that treatment with quercetin largely improved SARS-CoV-2 N protein-induced AKI in db/db mice. Mechanistically, we found that quercetin treatment significantly inhibited the release of a DAMP molecule HMGB1 and inactivated M1 pro-inflammatory macrophage while promoting reparative M2 macrophage responses by suppressing Mincle-Syk/NF-κB signaling in vivo and in vitro. In conclusion, SARS-CoV-2 N protein-induced AKI in db/db mice is associated with Mincle-dependent M1 macrophage activation. Inhibition of this pathway may be a mechanism through which quercetin inhibits COVID-19-associated AKI

Curcumin inhibits ovarian cancer progression by regulating circ-PLEKHM3/miR-320a/SMG1 axis

Abstract Background Curcumin has a potential therapeutic role in ovarian cancer. However, whether curcumin plays anti-cancer role in ovarian cancer by mediating the circular RNA (circRNA)/microRNA (miRNA)/mRNA network is still unclear. Methods The expression of circ-PLEKHM3, miR-320a, and suppressor of morphogenesis in genitalia 1 (SMG1) was detected via qRT-PCR. Cell viability, colony-formation ability and apoptosis were analyzed via cell counting kit-8 assay, colony formation analysis, and flow cytometry. Protein expression was measured using western blot. The in vivo experiments were performed using a xenograft model. Target association was evaluated via dual-luciferase reporter analysis and RIP assay. Results Curcumin suppressed ovarian cancer cell proliferation and promoted apoptosis. Circ-PLEKHM3 was downregulated in ovarian cancer, and its expression could be promoted by curcumin treatment. Circ-PLEKHM3 overexpression exacerbated the effect of curcumin on ovarian cancer cell proliferation and apoptosis, as well as anti-tumor effect. MiR-320a was targeted by circ-PLEKHM3. The inhibition effect of circ-PLEKHM3 overexpression on cell proliferation and the enhancing effect on cell apoptosis could be reversed by miR-320a mimic. SMG1 was targeted by miR-320a, and its knockdown also reversed the regulation of miR-320a inhibitor on the proliferation and apoptosis of ovarian cancer cells. In addition, circ-PLEKHM3 could upregulate SMG1 expression via sponging miR-320a. Conclusion Curcumin restrained proliferation and facilitated apoptosis in ovarian cancer by regulating the circ-PLEKHM3/miR-320a/SMG1 axis