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Objective To explore the relationship between degree of differentiation and time of differentiation of nucleus pulposus-like urine-derived stem cells (NP-USC) and the effects of human urine-derived stem cell exosomes (USC-EXO) on the proliferation and differentiation of NP-USC. Methods Nucleus pulposus cells (NPC) and urine-derived stem cells (USC) were extracted, identified, and cultured. According to the co-cultivation time, the cells were divided into group A (after 7 d of co-culture, the NP-USC were cultured in exosome-free USC medium for 14 d), group B (after 14 d of co-culture, the NP-USC were cultured in exosome-free USC medium for 7 d), group C (USC were cultured alone for 21 d), and NPC group (NPC were cultured alone for 21 d). Quantitative real-time PCR (RT-qPCR) and Western blot were used to measure the mRNA and protein expression of hypoxia-inducible factor 1α (HIF-1α), glucose transporter 1 (GLUT1), type Ⅱ collagen (COL2), and proteoglycan (ACAN) in the five groups. The cells in the five groups were cultured in exosome-free USC medium for 28 d, and CCK-8 was used to detect the cell proliferation on day 7, 14, 21, and 28. Results RT-qPCR and Western blot results showed that the relative mRNA and protein expression levels of HIF-1α, GLUT1, COL2, and ACAN increased sequentially in groups USC, A, B, and C (t=53.36-371.86,P0.05). There was no significant difference in the relative expression levels of the four genes in the cells of the five groups after adding USC-EXO (P>0.05). The results of CCK-8 showed that the cell viability decreased sequentially in groups USC, A, B, and C at each time point (F=3.77-103.58,P0.05). In each of the five groups, the cell viability gradually increased with the prolongation of culture time (F=9.96-121.68,P

To more accurately and effectively solve the problem of comprehensive reliability of multi-failure mode with the thermal creep, dual response surface method（DRSM） was adopted to implement the reliability of aero-engine turbine blisk. The numerical characteristics of the material parameters were obtained through the creep test data. The gas temperature, material density, elastic modulus and working rotation speed of the blisk structure were select as input variables. By constructing the finite element model of the blisk and the thermal-structural coupling analysis method, the stress and strain of turbine blisk were obtained. The mathematical model of dual response surface for stress and strain of turbine blisk was established by using the Latin hypercube small sampling technique. The comprehensive reliability analysis of the blisk was completed by using Monte Carlo method with linkage sampling of dual response surface model. The results indicate that the comprehensive reliability of blisk with thermal creep is 0.985 6. The comparison of method shows that dual response surface method can significantly improve computational efficiency without the loss of accuracy.

In response to the problem of poor robustness of traditional visual odometers that only rely on point features, a monocular visual inertial odometry that utilize point features, line features and structural features are deduced. The structural feature is an orthonormal basis that satisfy the Manhattan world hypothesis. This feature contains the parallel and orthogonal information of the structural object. The introduction of structural feature provide an overall constrain for visual inertial odometry and can reduce the accumulative error of rotation effectively. Meanwhile, to ensure the real-time performance of the system after the introduction of structural features, a robust and efficient algorithm is proposed to extract structural feature, which is based on the matching relationship of line features and the direction of gravity vector. The experimental results demonstrate that, compared with the existing open source systems, the proposed system can effectively reduce the accumulated rotation error while maintaining the real-time performance. [ABSTRACT FROM AUTHOR]

In this study, cucurbits powdery mildew in the Zhoukou region of Henan province were identified and analysed, the results provided the basis for pathogen de-fence and systematic evolution. In order to identify the pathogen, the characteristics of histological morphological, molecular identification of ribosomal DNA internal tran-scribed spacer (ITS) and phylogenetic trees were conducted. The results indicated that the cucurbits powdery mildew in Zhoukou belonged to Sphaerotheca fuliginea. Phyloge-netic tree revealed that the differences between cucurbits powdery mildew and legumes, verbena hybrida, medusagyne oppositifolia powdery mildew were small, meanwhile the pathogen of Zhoukou was highly conserved with USA, Japan, Australia and UK. We speculated that the evolution of cucurbits powdery mildew may not be directly related to areas but to its own physiological race. [ABSTRACT FROM AUTHOR]

A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.

Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.