Decreased Filamin b expression regulates trophoblastic cells invasion through ERK/MMP-9 pathway in pre-eclampsia

Objectives: The purpose of this study was to investigate the expression of Filamin b in the placental placenta of patients with early or late onset pre-eclampsia (PE) and its potential effects on the pathophysiology of the disease. Methods and methods: Immunohistochemistry staining, western blot assays and real time PCR were used to detect the expression level of FLN-b. The expression levels of MMP-2, MMP-9 and ERK1/2 proteins from control and FLN-b-silenced JEG-3 cells were also detected by western blot and JEG-3 cell invasion. Results: Compared with normal term pregnancies placentas, the FLN-b expression was significantly lower than that of women with PE, its level in late-onset PE is lower than in early-onset PE. In FLN-b-silenced JEG-3 cells, the protein levels of MMP-2, MMP-9 and phosphorylated ERK1/2 decreased markedly and the number of cells penetrating through the transwell chamber membrane is also greatly reduced. Conclusions: Down-regulation of FLN-b inhibits the ERK/MMP-2 and MMP-9 pathways, leading to trophoblastic invasion disorders in the PE placenta.

Parallel Multistage Wide Neural Network

Deep learning networks have achieved great success in many areas such as in large scale image processing. They usually need large computing resources and time, and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gc forest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with RBF networks and knowledge transfer (FDRK). In this paper, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances, and be trained fast in one epoch using subsampling and least squares (LS). Secondly, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data is acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of (1) fast training, (2) optimized computing resources, (3) incremental learning, and (4) parallel testing with stages. The experimental results with the MNIST, a number of large hyperspectral remote sensing data, CVL single digits, SVHN datasets, and audio signal datasets show that the WRBF and PMWNN have the competitive accuracy compared to learning models such as stacked auto encoders, deep belief nets, SVM, MLP, LeNet-5, RBF network, recently proposed CDL, broad learning, gc forest etc. In fact, the PMWNN has often the best classification performance

Controlled formation of gold nanoparticles with tunable plasmonic properties in tellurite glass

Silicate glasses with metallic nanoparticles (NPs) have been of intense interest in art, science and technology as the plasmonic properties of these NPs equip glass with light modulation capability. The so-called striking technique has enabled precise control of the in situ formation of metallic NPs in silicate glasses for applications from coloured glasses to photonic devices. Since tellurite glasses exhibit the unique combination of comparably easy fabrication, low phonon energy, wide transmission window and high solubility of luminescent rare earth ions, there has been a significant amount of work over the past two decades to adapt the striking technique to form gold or silver NPs in tellurite glasses. Despite this effort, the striking technique has remained insufficient for tellurite glasses to form metal NPs suitable for photonic applications. Here, we first uncover the challenges of the traditional striking technique to create gold NPs in tellurite glass. Then, we demonstrate precise control of the size and concentration of gold NPs in tellurite glass by developing new approaches to both steps of the striking technique: a controlled gold crucible corrosion technique to incorporate gold ions in tellurite glass and a glass powder reheating technique to subsequently transform the gold ions to gold NPs. Using the Mie theory, the size, size distribution and concentration of the gold NPs formed in tellurite glass are determined from the plasmonic properties of the NPs. This fundamental research provides guidance for designing and manipulating the plasmonic properties in tellurite glass for photonics research and applications

m6A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade

Melanoma is one of the most deadly and therapy-resistant cancers. Here we show that N6-methyladenosine (m6A) mRNA demethylation by fat mass and obesity-associated protein (FTO) increases melanoma growth and decreases response to anti-PD-1 blockade immunotherapy. FTO level is increased in human melanoma and enhances melanoma tumorigenesis in mice. FTO is induced by metabolic starvation stress through the autophagy and NF-κB pathway. Knockdown of FTO increases m6A methylation in the critical protumorigenic melanoma cell-intrinsic genes including PD-1 (PDCD1), CXCR4, and SOX10, leading to increased RNA decay through the m6A reader YTHDF2. Knockdown of FTO sensitizes melanoma cells to interferon gamma (IFNγ) and sensitizes melanoma to anti-PD-1 treatment in mice, depending on adaptive immunity. Our findings demonstrate a crucial role of FTO as an m6A demethylase in promoting melanoma tumorigenesis and anti-PD-1 resistance, and suggest that the combination of FTO inhibition with anti-PD-1 blockade may reduce the resistance to immunotherapy in melanoma. © 2019, The Author(s)

Numerical study on landslide dynamic process and its impact damage prediction to brick-concrete buildings, a case from Fenghuang street landslide in Shaanxi, China

The study of landslide dynamic process and impact damage has important theoretical and practical significance for landslide risk quantitative assessment. Taking Fenghuang Street landslide in Ningqiang County, Shaanxi Province, China as an example, the dynamic process of landslide and its damage to brick-concrete structure buildings are predicted by using discrete element method. Firstly, a three-dimensional numerical landslide model is established by means of the particle flow code system (PFC3D), which is based on landslide investigation, surveying, engineering exploration and geotechnical testing. Secondly, the whole process of landslide deformation, failure, movement and impact damage was simulated, and the velocity, displacement and impact force of the landslide in the motion process were quantitatively studied. Thirdly, the building model (brick-concrete structure) located at the foot of the landslide was constructed by PFC3D and finite element software (Midas/gen), respectively. The characteristics of deformation and displacement of the buildings after the landslide impact are analyzed, and the impact damage of the landslide is predicted. The results show that the rear edge of Fenghuang Street landslide first deforms and fails, and the leading edge is gradually pushed out. After the locking section of the front edge is broken, the landslide begins to slide as a whole, which is a typical push landslide. The main sliding time of the landslide is about 30 s, the maximum average velocity is 3.2 m/s, and the maximum displacement is about 40 m. After the landslide hits the building, the building is displaced in the moving direction of the landslide, and the wall of the building impacted by the landslide is destroyed, resulting in an collapse evident. The relevant research methodologies and findings in this paper can provide a reference for the risk assessment of the same type of landslides, especially the quantitative assessment of the vulnerability for the brick-concrete buildings at risk

Green Synthesis and Characterization of Silver Nanoparticles Using Ginkgo Biloba Laf Extract

A facile, effective and green method using Ginkgo Biloba leaf extract was applied and optimized for the preparation of well dispersed silver nanoparticles. In the method, Ginkgo Biloba leaf extract was employed as both stabilizing and reducing agent without the addition of a toxic agent. 0.1 % silver nitrate solution (w/v) was used silver source. The synthesized silver nanoparticles were investigated and examined by UV-vis absorption spectroscopy (UV-vis), Scanning electron microscope (SEM), Transmission electron microscopy (TEM), powder X-ray diffraction (XRD) and Dynamic light scattering (DLS). The formation of silver nanoparticles was found by a change of color from light yellow to red, which was further proved by absorbance peak at 456 nm in UV-vis spectroscopy. The prepared nanoparticles are global in shape, highly crystalline in nature with a narrow distribution from 10 nm to 40 nm. The silver nanoparticles were capped with extracts, which prevented them from agglomeration and oxidation. Different parameters affecting the generation performance of silver nanoparticles, such as time, amount of silver nitrate and extract were investigated. The results demonstrate that these reaction parameters play important roles in the synthesis of silver nanoparticles

Early bruising detection of ‘Korla’ pears by low-cost visible-LED structured-illumination reflectance imaging and feature-based classification models

IntroductionNondestructive detection of thin-skinned fruit bruising is one of the main challenges in the automated grading of post-harvest fruit. The structured-illumination reflectance imaging (SIRI) is an emerging optical technique with the potential for detection of bruises.MethodsThis study presented the pioneering application of low-cost visible-LED SIRI for detecting early subcutaneous bruises in ‘Korla’ pears. Three types of bruising degrees (mild, moderate and severe) and ten sets of spatial frequencies (50, 100, 150, 200, 250, 300, 350, 400, 450 and 500 cycles m-1) were analyzed. By evaluation of contrast index (CI) values, 150 cycles m-1 was determined as the optimal spatial frequency. The sinusoidal pattern images were demodulated to get the DC, AC, and RT images without any stripe information. Based on AC and RT images, texture features were extracted and the LS-SVM, PLS-DA and KNN classification models combined the optimized features were developed for the detection of ‘Korla’ pears with varying degrees of bruising.Results and discussionIt was found that RT images consistently outperformed AC images regardless of type of model, and LS-SVM model exhibited the highest detection accuracy and stability. Across mild, moderate, severe and mixed bruises, the LS-SVM model with RT images achieved classification accuracies of 98.6%, 98.9%, 98.5%, and 98.8%, respectively. This study showed that visible-LED SIRI technique could effectively detect early bruising of ‘Korla’ pears, providing a valuable reference for using low-cost visible LED SIRI to detect fruit damage