Preeclampsia impedes foetal kidney development by delivering placenta-derived exosomes to glomerular endothelial cells.

BACKGROUND: Foetal renal dysplasia is still the main cause of adult renal disease. Placenta-derived exosomes are an important communication tool, and they may play an important role in placental (both foetal and maternal) function. We hypothesize that in women with preeclampsia, foetal renal dysplasia is impeded by delivering placenta-derived exosomes to glomerular endothelial cells. METHODS: In the present study, we established a PE trophoblast oxidative stress model to isolate exosomes from supernatants by ultracentrifugation (NO-exo and H/R-exo) and collected normal and PE umbilical cord blood plasma to isolate exosomes by ultracentrifugation combined with sucrose density gradient centrifugation (N-exo and PE-exo), then we investigated their effects on foetal kidney development by in vitro, ex vivo and in vivo models. RESULTS: The PE trophoblast oxidative stress model was established successfully. After that, in in vitro studies, we found that H/R-exo and PE-exo could adversely affect glomerular endothelial cell proliferation, tubular formation, migration, and barrier functions. In ex vivo studies, H/R-exo and PE-exo both inhibited the growth and branch formation of kidney explants, along with the decrease of VE-cadherin and Occludin. In in vivo studies, we also found that H/R-exo and PE-exo could result in renal dysplasia, reduced glomerular number, and reduced barrier function in foetal mice. CONCLUSIONS: In conclusion, we demonstrated that PE placenta-derived exosomes could lead to foetal renal dysplasia by delivering placenta-derived exosomes to foetal glomerular endothelial cells, which provides a novel understanding of the pathogenesis of foetal renal dysplasia. Video Abstract.

MVDR-LSTM Distance Estimation Model Based on Diagonal Double Rectangular Array.

Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.

Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network.

At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed. The convolution neural network adopts a simple structure of three-layer convolution. The average pooling layer is used to replace the common maximum pooling layer, and the global average pooling layer is used to replace the full connection layer. The BN layer is used to optimize the model. The collected multi-class signals are used as the input of the model, and the improved convolution neural network is used for fault identification and classification of the input signals. The experimental data of XJTU-SY and Paderborn University show that the method proposed in this paper has a good effect on the multi-classification of bearing faults.

Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement.

A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance.

Gestational Diabetes Mellitus Impedes Fetal Lung Development Through Exosome-Dependent Crosstalk Between Trophoblasts and Lung Epithelial Cells.

Background: Fetal lung underdevelopment (FLUD) is associated with neonatal and childhood severe respiratory diseases, among which gestational diabetes mellitus (GDM) play crucial roles as revealed by recent prevalence studies, yet mechanism underlying GDM-induced FLUD, especially the role of trophoblasts, is not all known. Methods: From the perspective of trophoblast-derived exosomes, we established in vitro, ex vivo, in vivo and GDM trophoblast models. Utilizing placenta-derived exosomes (NUB-exos and GDMUB-exos) isolated from normal and GDM umbilical cord blood plasma and trophoblast-derived exosomes (NC-exos and HG-exos) isolated from HTR8/SVneo trophoblasts medium with/without high glucose treatment, we examined their effects on fetal lung development and biological functions. Results: We found that, compared with the NUB-exos group, the exosome concentration increased in GDMUB-exos group, and the content of exosomes also changed evidenced by 61 dysregulated miRNAs. After applying these exosomes to A549 alveolar type II epithelial cells, the proliferation and biological functions were suppressed while the proportion of apoptotic cells was increased as compared to the control. In ex vivo studies, we found that GDMUB-exos showed significant suppression on the growth of the fetal lung explants, where the number of terminal buds and the area of explant surface decreased and shrank. Besides, the expression of Fgf10, Vegfa, Flt-1, Kdr and surfactant proteins A, B, C, and D was downregulated in GDMUB-exos group, whilst Sox9 was upregulated. For in vivo studies, we found significant suppression of fetal lung development in GDMUB-exos group. Importantly, we found consistent alterations when we used NC-exos and HG-exos, suggesting a dominant role of trophoblasts in placenta-derived exosome-induced FLUD. Conclusion: In conclusion, GDM can adversely affect trophoblasts and alter exosome contents, causing crosstalk disorder between trophoblasts and fetal lung epithelial cells and finally leading to FLUD. Findings of this study will shine insight into the theoretical explanation for the pathogenesis of FLUD.

Fetal Lung-Derived Exosomes in Term Labor Amniotic Fluid Induce Amniotic Membrane Senescence.

The mechanism of parturition is still unclear. Evidence has shown that delivery is associated with cellular senescence of the amniotic membrane. We isolated fetal lung-associated exosomes from the amniotic fluid from term labor (TL-exos) and verified that the exosomes can cause primary human amniotic epithelial cell (hAEC) senescence and apoptosis and can release higher levels of senescence-associated secretory phenotype (SASP)-related molecules and proinflammatory damage-associated molecular patterns (DAMPs) than exosomes isolated from the amniotic fluid from term not in labor (TNIL-exos). The human lung carcinoma cell lines (A549) can be used as an alternative to alveolar type 2 epithelial cells producing pulmonary surfactant. Therefore, we isolated A549 cell-derived exosomes (A549-exos) and found that they can trigger hAEC to undergo the same aging process. Finally, the animal experiments suggested that A549-exos induced vaginal bleeding and preterm labor in pregnant mice. Therefore, we conclude that exosomes derived from fetal lungs in term labor amniotic fluid induce amniotic membrane senescence, which may provide new insight into the mechanism of delivery.

Protection by hydroxychloroquine prevents placental injury in obstetric antiphospholipid syndrome.

Obstetric antiphospholipid syndrome (OAPS) is mediated by antiphospholipid antibodies (aPLs, and anti-ß2 glycoprotein I antibody is the main pathogenic antibody), and recurrent abortion, preeclampsia, foetal growth restriction and other placental diseases are the main clinical characteristics of placental pathological pregnancy. It is a disease that seriously threatens the health of pregnant women. Hydroxychloroquine (HCQ) was originally used as an anti-malaria drug and has now shown benefit in refractory OAPS where conventional treatment has failed, with the expectation of providing protective clinical benefits for both the mother and foetus. However, its efficacy and mechanism of action are still unclear. After clinical data were collected to determine the therapeutic effect, human trophoblast cells in early pregnancy were prepared and treated with aPL. After the addition of HCQ, the proliferation, invasion, migration and tubule formation of the trophoblast cells were observed so that the therapeutic mechanism of HCQ on trophoblast cells could be determined. By establishing an obstetric APS mouse model similar to the clinical situation, we were able to detect the therapeutic effect of HCQ on pathological pregnancy. The normal function of trophoblast cells is affected by aPL. Antibodies reduce the ability of trophoblast cells to invade and migrate and can impair tubule formation, which are closely related to placental insufficiency. HCQ can partially reverse these side effects. In the OAPS mouse model, we found that HCQ prevented foetal death and reduced the incidence of pathological pregnancy. Therefore, HCQ can improve pregnancy outcomes and reverse the aPL inhibition of trophoblast disease. In OAPS, the use of HCQ needs to be seriously considered.

Distinguishing placenta accreta from placenta previa via maternal plasma levels of sFlt-1 and PLGF and the sFlt-1/PLGF ratio.

INTRODUCTION: Our study aimed to distinguish patients with placenta accreta (crete, increta, and percreta) from those with placenta previa using maternal plasma levels of soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PLGF) and the sFlt-1/PLGF ratio. METHODS: We obtained maternal plasma from 185 women in late pregnancy and sorted them into three groups: 72 women with normal placental imaging results (control group), 50 women with placenta previa alone (PP group), and 63 women with placenta previa and placenta accreta (PAS group). The concentrations of sFlt-1 and PLGF in the maternal plasma were measured using ELISA kits and the sFlt-1/PLGF ratio was calculated. RESULT: The median (min-max) sFlt-1 levels and the sFlt-1/PLGF ratio in the PAS group (12.8 ng/ml, 3.8-34.2 ng/ml) (133, 14-361) were lower than in the PP group (28.7 ng/ml, 13.1-60.3 ng/ml) (621, 156-2013) (p < 0.0001 and P < 0.0001, respectively). The median (min-max) PLGF levels in the PAS group (108 pg/ml, 38-679 pg/ml) was higher than that in the PP group (43 pg/ml, 12-111 pg/ml) (p < 0.0001 and p < 0.0001, respectively). The area under the ROC of the sFlt-1 levels, PLGF levels, and sFlt-1/PLGF ratio were 0.91, 0.90, and 0.99, respectively; the cut-off values were 18.9 ng/ml, 75.9 pg/ml, and 229.5, respectively. The concentration of sFlt-1 and sFlt-1/PLGF ratio were associated with the volume of blood loss (-.288*, -.301*). DISCUSSION: The concentrations of sFlt-1 and PLGF and ratio of plasma sFlt-1/PLGF may distinguish patients with placenta accreta from those with placenta previa.

Influence of placental exosomes from early onset preeclampsia women umbilical cord plasma on human umbilical vein endothelial cells.

Background: Early onset preeclampsia (EOSP, PE) is characterized by hypertension, proteinuria, and endothelial dysfunction. Oxidative stress-induced trophoblast dysfunction is a major pathology in PE. Placental exosomes are extracellular vesicles that are involved in "mother-placenta-foetal communication" and can regulate the biological functions of endothelial cells. Our study was designed to evaluate placental exosomes effects on endothelial cells. Methods: Umbilical cord blood from normal pregnant women and patients with PE were collected. A hypoxia/reoxygenation (H/R) model in human first trimester extravillous trophoblast cell (HTR8/SVneo) line to simulate the PE model of oxidative stress in vitro. Then, placental exosomes (i.e., NO-exo, H/R-exo, N-exo, and PE-exo) were extracted and identified. Finally, the effects of placental exosomes on the biological functions of human umbilical vein endothelial cells (HUVECs) were further evaluated by performing a series of experiments. Results: Placental exosomes had a double-membrane cup structure with diameters of 30-150 nm, and there was no obvious difference in placental exosomes. Compared with NO-exo and N-exo, H/R-exo and PE-exo inhibited HUVECs proliferation, tube formation and migration, increased permeability and apoptosis in vitro. Conclusion: We hypothesize that H/R-exo and PE-exo impair vessel development by disrupted biological functions in endothelial cells, which may result in vascular disorders in offspring.

Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis.

Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k -Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

Intelligent fault diagnosis of high-voltage circuit breakers using triangular global alignment kernel extreme learning machine.

In recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling asynchrony caused by the fluctuation of the control voltage. To address this problem, an improved kernel extreme learning machine (K-ELM) called triangular global alignment kernel (TGAK) extreme learning machine (TGAK-ELM) was presented in this study, which was developed by introducing TGAK into K-ELM. The TGAK is an elastic kernel which was designed by considering all the possible alignments between samples. Therefore, it provides a flexible similarity measure for samples, resulting in the improvement of the diagnostic performance. Experiments on the 35kV HVCB verified the effectiveness of the proposed method. Compared to other state-of-the-art machine learning methods, the proposed TGAK-ELM produced better diagnostic results. And further experiments on eight datasets picked from UCR repository suggested the applicability of TGAK-ELM in other fields.

An Integrated Approach Based on Swarm Decomposition, Morphology Envelope Dispersion Entropy, and Random Forest for Multi-Fault Recognition of Rolling Bearing.

Aiming at the problem that the weak faults of rolling bearing are difficult to recognize accurately, an approach on the basis of swarm decomposition (SWD), morphology envelope dispersion entropy (MEDE), and random forest (RF) is proposed to realize effective detection and intelligent recognition of weak faults in rolling bearings. The proposed approach is based on the idea of signal denoising, feature extraction and pattern classification. Firstly, the raw signal is divided into a group of oscillatory components through SWD algorithm. The first component has the richest fault information and perceived as the principal oscillatory component (POC). Secondly, the MEDE value of the POC is calculated and used to describe the characteristics of signal. Ultimately, the obtained MEDE values of various states are trained and recognized by being input as the feature vectors into the RF classifier to achieve the automatic identification of rolling bearing fault under different operation states. The dataset of Case Western Reserve University is conducted, the proposed approach achieves recognition accuracy rate of 100%. In summary, the proposed approach is efficient and robust, which can be used as a supplement to the rolling bearing fault diagnosis methods.

An Early Fault Diagnosis Method of Rolling Bearings on the Basis of Adaptive Frequency Window and Sparse Coding Shrinkage.

Early fault information of rolling bearings is weak and often submerged by background noise, easily leading to misdiagnosis or missed diagnosis. In order to solve this issue, the present paper puts forward a fault diagnosis method on the basis of adaptive frequency window (AFW) and sparse coding shrinkage (SCS). The proposed method is based on the idea of determining the resonance frequency band, extracting the narrowband signal, and envelope demodulating the extracted signal. Firstly, the paper introduces frequency window, which can slip on the frequency axis and extract the frequency band. Secondly, the double time domain feature entropy is proposed to evaluate the strength of periodic components in signal. The location of the optimal frequency window covering the resonance band caused by bearing fault is determined adaptively by this entropy index and the shifting/expanding frequency window. Thirdly, the signal corresponding to the optimal frequency window is reconstructed, and it is further filtered by the sparse coding shrinkage algorithm to highlight the impact feature and reduce the residue noise. Fourthly, the de-noised signal is demodulated by envelope operation, and the corresponding envelope spectrum is calculated. Finally, the bearing failure type can be judged by comparing the frequency corresponding to the spectral lines with larger amplitude in the envelope spectrum and the fault characteristic frequency. Two bearing vibration signals are applied to validate the proposed method. The analysis results illustrate that this method can extract more failure information and highlight the early failure feature. The data files of Case Western Reserve University for different operation conditions are used, and the proposed approach achieves a diagnostic success rate of 83.3%, superior to that of the AFW method, SCS method, and Fast Kurtogram method. The method presented in this paper can be used as a supplement to the early fault diagnosis method of rolling bearings.

Shannon Entropy of Binary Wavelet Packet Subbands and Its Application in Bearing Fault Extraction.

The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature via the envelope demodulation method. However, the FSK method has some limitations due to its susceptibility to noise and random knocks. To overcome this shortage, a new method is proposed in this paper. Firstly, we use the binary wavelet packet transform (BWPT) instead of the finite impulse response (FIR) filter bank as the frequency band segmentation method. Following this, the Shannon entropy of each frequency band is calculated. The appropriate center frequency and bandwidth are chosen for filtering by using the inverse of the Shannon entropy as the index. Finally, the envelope spectrum of the filtered signal is analyzed and the faulty feature information is obtained from the envelope spectrum. Through simulation and experimental verification, we found that Shannon entropy is-to some extent-better than kurtosis as a frequency-selective index, and that the Shannon entropy of the binary wavelet packet transform method is more accurate for fault feature extraction.

Application of Multiscale Entropy in Mechanical Fault Diagnosis of High Voltage Circuit Breaker.

Mechanical fault diagnosis of a circuit breaker can help improve the reliability of power systems. Therefore, a new method based on multiscale entropy (MSE) and the support vector machine (SVM) is proposed to diagnose the fault in high voltage circuit breakers. First, Variational Mode Decomposition (VMD) is used to process the high voltage circuit breaker's vibration signals, and the reconstructed signal can eliminate the effect of noise. Second, the multiscale entropy of the reconstructed signal is calculated and selected as a feature vector. Finally, based on the feature vector, the fault identification and classification are realized by SVM. The feature vector constructed by multiscale entropy is compared with other feature vectors to illustrate the superiority of the proposed method. Through experimentation on a 35 kV SF6 circuit breaker, the feasibility and applicability of the proposed method for fault diagnosis are verified.

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis.

Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram) is proposed. The TEER algorithm takes the wavelet packet transform (WPT) as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER) is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR) filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.

Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier.

As high-voltage circuit breakers (HVCBs) are directly related to the safety and the stability of a power grid, it is of great significance to carry out fault diagnoses of HVCBs. To accurately identify operating states of HVCBs, a novel mechanical fault diagnosis method of HVCBs based on multi-feature entropy fusion (MFEF) and a hybrid classifier is proposed. MFEF involves the decomposition of vibration signals of HVCBs into several intrinsic mode functions using variational mode decomposition (VMD) and the calculation of multi-feature entropy by the integration of three Shannon entropies. Principle component analysis (PCA) is then used to reduce the dimension of the multi-feature entropy to achieve an effective fusion of features for selecting the feature vector. The detection of an unknown fault in HVCBs is achieved using support vector data description (SVDD) trained by normal-state samples and specific fault samples. On this basis, the identification and classification of the known states are realized by the support vector machine (SVM). Three faults (i.e., closing spring force decrease fault, buffer spring invalid fault, opening spring force decrease fault) are simulated on a real SF6 HVCB to test the feasibility of the proposed method. The detection accuracies of the unknown fault are 100%, 87.5%, and 100% respectively when each of the three faults is assumed to be the unknown fault. The comparative experiments show that SVM has no ability to detect the unknown fault, and that one-class support vector machine (OCSVM) has a weaker ability to detect the unknown fault than SVDD. For known-state classification, the adoption of the MFEF method achieved an accuracy of 100%, while the use of a single-feature method only achieved an accuracy of 75%. These results indicate that the proposed method combining MFEF with hybrid classifier is thus more efficient and robust than traditional methods.

A graph-based ant colony optimization approach for process planning.

The complex process planning problem is modeled as a combinatorial optimization problem with constraints in this paper. An ant colony optimization (ACO) approach has been developed to deal with process planning problem by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the optimal process plan. A weighted directed graph is conducted to describe the operations, precedence constraints between operations, and the possible visited path between operation nodes. A representation of process plan is described based on the weighted directed graph. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPC). Two cases have been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been conducted to demonstrate the feasibility and efficiency of the proposed approach.