CUMS and dexamethasone induce depression- like phenotypes in mice by differentially altering gut microbiota and triggering macroglia activation

Background Although the link between gut microbiota and depression has been suggested,changes of gut microbiota vary largely among individuals with depression.Aims Explore the heterogeneity of microbiota-gut-brain axis and new pathogenic characteristics in murine models of depression.Methods Adolescent female mice were randomly divided into control(CON)group(n=10),chronic unexpected mild stress(CUMS)group(n=15)and dexamethasone(DEX)group(n=15).Mice in the DEX group were gavaged twice a day with 0.2 mg/kg of DEX for 5 weeks,whereas CON mice were given the same amount of solvent.Mice in the CUMS group were exposed to stressors.After behavioural evaluations,all mice were sacrificed for harvesting tissues and blood samples.Enzyme-linked immunosorbent assay(ELISA)was conducted for measuring levels of corticosterone(CORT)and interleukin-1β(IL-1β)in sera,whereas levels of protein expression in colon and hippocampal tissues were examined by western blot.Faecal microbial communities were analysed by sequencing 16S rDNAs.Results Mice in CUMS and DEX groups exhibited severe depression-like behaviours.Compared with CON mice,CUMS-exposed mice showed a significant increase in bothαandβdiversity.Prevotellaceae and Desulfovibrio were enriched,whereas Bacilli were decreased in the faeces of mice in the CUMS group.DEX-treated mice had a decrease in the abundance of Clostridium XVIII.Levels of occludin in colon tissue of DEX-treated mice were reduced.Relative to mice in the CON and CUMS groups,DEX-treated mice contained higher serum levels of CORT and IL-1β.Compared with CON mice,mice in the DEX and CUMS groups had higher levels of IL-1βin sera and lower levels of glial fibrillary acidic protein(GFAP),Nestin,Synapsin-1 and P2Y12 receptor in the hippocampus.Conclusions Changes of gut microbiota diversity,intestinal integrity and neuroinflammation in the brain contribute to CUMS-induced depression,whereas pathobionts and excessive immunosuppression with damaged neuronal synapses is a basis of the DEX-induced depression.

Finite element modeling and analysis of planetary gear transmission based on transient meshing properties

Planetary gear trains are widely applied in various transmission units.Whether strengths of all gears are accurately calculated or not can affect reliability of the entire system significantly.Strength calculation method for planetary gear trains usually follows the method for cylindrical gears,in which the worst meshing positions for both contact stress and bending stress cannot be determined precisely,and calculation results tend to be conservative.To overcome these shortcomings,a kinematics analysis for a planetary gear train is firstly performed,in which the influence of relative speed is investigated.Then the finite element strength analysis of a planetary gear train based on its transient meshing properties is carried out in ANSYS.Time–history curves of contact and bending stresses of sun gear,planetary gears and ring gear are respectively obtained.Also the accurate moment and its corresponding position of the maximum stress are precisely determined.Finally,calculation results of finite element method(FEM)and traditional method are compared in order to verify the effectiveness.Simulation and comparison show the stability of the proposed method in this paper.Researches in this paper establish the foundations for fatigue analysis and optimization for a planetary gear train.

Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

In aerospace industry,gears are the most common parts of a mechanical transmission system.Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration.In this paper,a novel method named augmented deep sparse autoencoder(ADSAE)is proposed.The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data.This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear.The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions.The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy.This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults.The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods.This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.

Fault diagnosis of bearings based on deep separable convolutional neural network and spatial dropout

Bearing pitting,one of the common faults in mechanical systems,is a research hotspot in both academia and industry.Traditional fault diagnosis methods for bearings are based on manual experience with low diagnostic efficiency.This study proposes a novel bearing fault diagnosis method based on deep separable convolution and spatial dropout regularization.Deep separable convolution extracts features from the raw bearing vibration signals,during which a 3×1 convolutional kernel with a one-step size selects effective features by adjusting its weights.The similarity pruning process of the channel convolution and point convolution can reduce the number of parameters and calculation quantities by evaluating the size of the weights and removing the feature maps of smaller weights.The spatial dropout regularization method focuses on bearing signal fault features,improving the independence between the bearing signal features and enhancing the robustness of the model.A batch normalization algorithm is added to the convolutional layer for gradient explosion control and network stability improvement.To validate the effectiveness of the proposed method,we collect raw vibration signals from bearings in eight different health states.The experimental results show that the proposed method can effectively distinguish different pitting faults in the bearings with a better accuracy than that of other typical deep learning methods.