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Background Due to its excellent physicochemical properties and wide applications in consumer goods, titanium dioxide nanoparticles (TiO 2 NPs) have been increasingly exposed to the environment and the public. However, the health effects of oral exposure of TiO 2 NPs are still controversial. This study aimed to illustrate the hepatotoxicity induced by TiO 2 NPs and the underlying mechanisms. Rats were administered with TiO 2 NPs (29 nm) orally at exposure doses of 0, 2, 10, 50 mg/kg daily for 90 days. Changes in the gut microbiota and hepatic metabolomics were analyzed to explore the role of the gut-liver axis in the hepatotoxicity induced by TiO 2 NPs. Results TiO 2 NPs caused slight hepatotoxicity, including clear mitochondrial swelling, after subchronic oral exposure at 50 mg/kg. Liver metabolomics analysis showed that 29 metabolites and two metabolic pathways changed significantly in exposed rats. Glutamate, glutamine, and glutathione were the key metabolites leading the generation of energy-related metabolic disorders and imbalance of oxidation/antioxidation. 16S rDNA sequencing analysis showed that the diversity of gut microbiota in rats increased in a dose-dependent manner. The abundance of Lactobacillus_reuteri increased and the abundance of Romboutsia decreased significantly in feces of TiO 2 NPs-exposed rats, leading to changes of metabolic function of gut microbiota. Lipopolysaccharides (LPS) produced by gut microbiota increased significantly, which may be a key factor in the subsequent liver effects. Conclusions TiO 2 NPs could induce slight hepatotoxicity at dose of 50 mg/kg after long-term oral exposure. The indirect pathway of the gut-liver axis, linking liver metabolism and gut microbiota, played an important role in the underlying mechanisms.

RNA–protein interactions underlie a wide range of cellular processes. Improved methods are needed to systematically map RNA–protein interactions in living cells in an unbiased manner. We used two approaches to target the engineered peroxidase APEX2 to specific cellular RNAs for RNA-centered proximity biotinylation of protein interaction partners. Both an MS2-MCP system and an engineered CRISPR-Cas13 system were used to deliver APEX2 to the human telomerase RNA hTR with high specificity. One-minute proximity biotinylation captured candidate binding partners for hTR, including more than a dozen proteins not previously linked to hTR. We validated the interaction between hTR and the N⁶-methyladenosine (m⁶A) demethylase ALKBH5 and showed that ALKBH5 is able to erase the m⁶A modification on endogenous hTR. ALKBH5 also modulates telomerase complex assembly and activity. MS2- and Cas13-targeted APEX2 may facilitate the discovery of novel RNA–protein interactions in living cells.

The cerebellum plays a central role in sensory input, voluntary motor action, and many neuropsychological functions and is involved in many brain diseases and neurological disorders. Cerebellar parcellation from magnetic resonance images provides a way to study regional cerebellar atrophy and also provides an anatomical map for functional imaging. In a recent comparison, a multi-atlas approach proved to be superior to other parcellation methods including some based on convolutional neural networks (CNNs) which have a considerable speed advantage. In this work, we developed an alternative CNN design for cerebellar parcellation, yielding a method that achieves the leading performance to date. The proposed method was evaluated on multiple data sets to show its broad applicability, and a Singularity container has been made publicly available. •State-of-the-art cerebellum parcellation based on convolutional neural networks.•We include a positive comparison to the winner of the only cerebellum parcellation challenge.•The proposed method has been evaluated on multiple data sets including healthy controls and patients with cerebellum ataxia, Alzheimer’s disease, and autism.•A Singularity container of the proposed methods has been made publicly available.

The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health assessment. In this paper, a rolling bearing-rotor test rig with forced lubrication is set up and the nonferrous contaminants with higher hardness were introduced artificially to accelerate the occurrence of pitting and spalling. The early failure and abnormal wear of rolling bearings cannot be effectively detected only through the vibration signal; the temperature and oil debris monitoring data are also collected synchronously. Two features regarding the ferrous particle size distribution are extracted and fused with vibration based-features to form a feature set. The sensitive features are extracted from the features set using the Neighborhood Component Analysis method to avoid feature redundancy. Finally, the importance of the oil debris based-features for the diagnosis of abnormal bearing wear is analyzed with different machine learning algorithms. Taking SVM classifier as an example, the experiment results show that the introduction of oil debris based-features increases the diagnostic accuracy by 15.7%.

Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple linear regression and BP neural network respectively. Firstly, the main parameters affecting the bridge pavement temperature were determined by Pearson partial correlation analysis based on the three-year winter meteorological data of the traffic meteorological monitoring station on the bridge in Shandong province. Secondly, the bridge pavement temperature is selected as the dependent variable, while air temperature, wind speed, relative humidity, dew point temperature, wet bulb temperature and wind cold temperature were selected as independent variables, and the bridge pavement temperature prediction models of linear regression and 5-layer hidden layer classical BP neural network regression were established respectively based on whether the variables are linear or not. Finally, the prediction accuracy of the above models was compared by using the measured data. The results show that the linear regression model could be established only with air temperature, relative humidity and wind speed, owing to collinearity problem. Compared with multiple linear regression model, the predicted value of the BP neural network has a higher degree of fitting with the measured data, and the coefficient of determination reaches 0.7929. Using multiple linear regression and BP neural network, the critical meteorological conditions of bridge deck icing in winter can be effectively predicted even when the sample size is insufficient.

Myocardial infarction (MI) results in dysfunction and irreversible loss of cardiomyocytes and is among the most serious health threats today. Bone marrow mesenchymal stem cells (BMSCs), with their capacity for multidirectional differentiation, low immunogenicity, and high portability, can serve as ideal seed cells in cardiovascular disease therapy. In this review, we examine recent literature concerning the application of BMSCs for the treatment of MI and consider the following aspects: activity of transplanted cells, migration and homing of BMSCs, immunomodulatory and anti-inflammatory effects of BMSCs, anti-fibrotic activity of BMSCs, the role of BMSCs in angiogenesis, and differentiation of BMSCs into cardiomyocyte-like cells and endothelial cells. Each aspect is complementary to the others and together they promote the repair of cardiomyocytes by BMSCs after MI. Although transplantation of BMSCs has enabled new options for MI treatment, the critical issue we must now address is the reduced viability of transplanted BMSCs due to inadequate blood supply, poor nourishment of cells, and generation of free radicals. More clinical trials are needed to prove the therapeutic potential of BMSCs in MI.

Gut microorganisms modulate host phenotypes and are associated with numerous health effects in humans, ranging from host responses to cancer immunotherapy to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microorganisms has hindered efforts to define mechanistic connections between individual microbial strains and host phenotypes. One key way in which the gut microbiome influences host physiology is through the production of small molecules 1 – 3 , yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect the products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites in diverse sample types. We report the metabolic profiles of 178 gut microorganism strains using our library of 833 metabolites. Using this metabolomics resource, we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover a previously undescribed type of metabolism in Bacteroides , and reveal candidate biochemical pathways using comparative genomics. Microbiota-dependent metabolites can be detected in diverse biological fluids from gnotobiotic and conventionally colonized mice and traced back to the corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microorganisms and interactions between microorganisms and their host. A microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for the characterization of gut-resident microorganisms and the interactions between microorganisms and their host.

Adhesion G protein-coupled receptors (aGPCRs) are essential for a variety of physiological processes such as immune responses, organ development, cellular communication, proliferation and homeostasis 1 – 7 . An intrinsic manner of activation that involves a tethered agonist in the N-terminal region of the receptor has been proposed for the aGPCRs 8 , 9 , but its molecular mechanism remains elusive. Here we report the G protein-bound structures of ADGRD1 and ADGRF1, which exhibit many unique features with regard to the tethered agonism. The stalk region that proceeds the first transmembrane helix acts as the tethered agonist by forming extensive interactions with the transmembrane domain; these interactions are mostly conserved in ADGRD1 and ADGRF1, suggesting that a common stalk–transmembrane domain interaction pattern is shared by members of the aGPCR family. A similar stalk binding mode is observed in the structure of autoproteolysis-deficient ADGRF1, supporting a cleavage-independent manner of receptor activation. The stalk-induced activation is facilitated by a cascade of inter-helix interaction cores that are conserved in positions but show sequence variability in these two aGPCRs. Furthermore, the intracellular region of ADGRF1 contains a specific lipid-binding site, which proves to be functionally important and may serve as the recognition site for the previously discovered endogenous ADGRF1 ligand synaptamide. These findings highlight the diversity and complexity of the signal transduction mechanisms of the aGPCRs. Cryo-electron microscopy structures of the adhesion G protein-coupled receptors ADGRD1 and ADGRF1 provide insight into how these receptors are activated in an intrinsic manner through a ‘stalk’ region that acts as a tethered agonist.

Background Silica nanoparticles (SiNPs) have been demonstrated to have beneficial effects on plant growth and development, especially under biotic and abiotic stresses. However, the mechanisms of SiNPs-mediated plant growth strengthening are still unclear, especially under field condition. In this study, we evaluated the effect of SiNPs on the growth and sugar and hormone metabolisms of wheat in the field. Results SiNPs increased tillers and elongated internodes by 66.7% and 27.4%, respectively, resulting in a larger biomass. SiNPs can increase the net photosynthetic rate by increasing total chlorophyll contents. We speculated that SiNPs can regulate the growth of leaves and stems, partly by regulating the metabolisms of plant hormones and soluble sugar. Specifically, SiNPs can increase auxin (IAA) and fructose contents, which can promote wheat growth directly or indirectly. Furthermore, SiNPs increased the expression levels of key pathway genes related to soluble sugars ( SPS , SUS , and α- glucosidase ), chlorophyll ( CHLH , CAO , and POR ), IAA ( TIR1 ), and abscisic acid (ABA) ( PYR / PYL , PP2C , SnRK2 , and ABF ), whereas the expression levels of genes related to CTKs ( IPT ) was decreased after SiNPs treatment. Conclusions This study shows that SiNPs can promote wheat growth and provides a theoretical foundation for the application of SiNPs in field conditions.