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Left ventricular noncompaction (LVNC) is a heterogeneous disorder with unclear genetic causes and an unknown mechanism. eIF3a, an important member of the Eukaryotic translation initiation factor 3 (eIF3) family, is involved in multiple biological processes, including cell proliferation and migration during myocardial development, suggesting it could play a role in LVNC development. To investigate the association between a novel variant (c.1145 A- > G) in eIF3a and LVNC, and explore potential mechanisms that could lead to the development of LVNC. A novel eIF3a variant, c.1145 A- > G, was identified by whole-exome sequencing in a familial pedigree with LVNC. Adenovirus vectors containing wild-type eIF3a and the mutated version were constructed and co-infected into H9C2 cells. Cell proliferation, apoptosis, cell migration, and differentiation, as well as phosphorylation of ERK1/2 were studied and were measured by proliferation assays, flow cytometry, real-time PCR and Western blot, respectively. The eIF3a mutation inhibited the proliferation of H9C2 cells, induced apoptosis, promoted cell migration, and inhibited the differentiation of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). The effect of the eIF3a mutation may be attributed to a decrease in expression of p-ERK1/2. A novel eIF3a gene mutation disrupted the p-ERK1/2 pathway and caused decreased myocardium proliferation, differentiation, accelerated migration.This finding may provide some insight into the mechanism involved in LVNC development.

The quality of sinter ore is a key part in ironmaking industry, and the content of FeO of sinter ore is one of its significant quality indexes. In order to predict the content of FeO of ore agglomerate, a data-driven prediction scheme based on multi-source information and long short-term memory (Long Short-Term Memory, LSTM) network is proposed. In the proposed method, the multi-source information is firstly extracted and then they are input to the LSTM, whose target output is the corresponding Reference-FeO. The multi-source information used in this paper includes image, vibration and temperature information. And for the feature extraction of the images, a method is developed to preprocess and extract features from images. Besides, the information of vibration and temperature are selected on account of the correlation analysis and mechanism of sintering. Moreover, the corresponding Reference-FeO at the end of the sintering pallets is obtained as the target output of the LSTM network. Finally, a LSTM network is utilized to predict the target output. The experimental results show that the data-driven prediction scheme using multi-source features can reach a good performance with absolute error within 0.05 and can meet the needs of practical engineering.

Background: The MTUS1 gene encodes a microtubule-associated protein involved in multiple processes including cell polarity and microtubule balance during myocardial development. Aims: To investigate the association between a de novo c. 2617A->C mutation in MTUS1 (NM_001001924.2) and non-compaction of ventricular myocardium (NVM) and explore the potential mechanisms. Methods: A de novo mutation in MTUS1 was identified for a familial pedigree with NVM. Lentiviral vectors containing MTUS1 wild type or the mutation MTUS1 were constructed and co-infected into HEK-293 cells. MTUS1, Rac1/Cdc42, α-tubulin, α/β-tubulin, polarity protein (PAR6), and the morphology of daughter cells were measured by real-time PCR, Western blot, and immunofluorescence assays, respectively. Results: The lentiviral vectors were constructed successfully. Immunofluorescence assays revealed the fluorescence intensity of α-tubulin to be decreased and α/β-tubulin to be increased in the mutation MTUS1 group. The fluorescence intensity of PAR6 was higher and morphology of the daughter cells in the mutation group was different from the wild type group. The phosphorylation of Rac1/Cdc42 in the mutation group was significantly lower than in the wild type group. Conclusions: A de novo mutation in MTUS1 decreased the stability of microtubules and increased cell polarity via the Rac1/Cdc42 pathway, which may partly elucidate the mechanism underlying cellular protection in NVM.

Fusing the pertinence of natural scene statistics-based methods and the ubiquity of convolutional neural network-based methods, a no-reference image quality assessment (IQA) method fusing deep learning and statistical visual features for no-reference image quality assessment (FDSVIQA) is proposed. For the statistical visual features, a local normalized luminance map and a local normalized local binary pattern (LBP) map of the image are constructed, and the local normalized luminance features and the gradient-weighted local normalized LBP features are extracted on the two maps, respectively. These two kinds of features are concatenated to build the image statistical visual features. For deep learning, the local normalized luminance block and the localized normalized LBP block are input into a double-path deep learning network, and the statistical visual features are input into the deep learning network to be integrated with the depth features. After learning and training, IQA is achieved. The performance of the proposed FDSVIQA algorithm is tested on the Laboratory for Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality Database, and Multiply Distortion Optics Remote Sensing Image databases. Experimental results show that the FDSVIQA algorithm has excellent subjective and objective consistency and good robustness for both distorted natural images and distorted remote sensing images. In addition, the FDSVIQA has database independence.

Plasma sprayed hydroxyapatite (HA) coatings are widely used on bone implants due to excellent osteoconductivity. However, the coating also provides an appropriate environment for biofilm formation. To prevent bacteria adhesion, this study tried to modify the HA coating with silver (Ag) by hydrothermal sterilization in dilute silver nitrate (AgNO3) solution at 121 °C for 20 min. Surface morphology, phase and chemical composition and antibacterial ability of the sterilized coating were investigated. After treatment, Ag was successfully incorporated onto the HA coating surface as in situ growing silver phosphate (Ag3PO4) compound and the distribution of Ag element was homogeneously. Inhibition zone test showed that, the sterilized HA coating had an excellent antibacterial ability against Staphylococcus aureus (S. aureus). This simple and feasible method shows promising potential for clinical applications.

Titanium nitride (TiN) coating is a promising to be used on metal implant and surface oxidation by hydrothermal treatment has been proved to effectively enhance its osteoconductivity. In this study, the physicochemical properties and corrosion resistance of the TiN coating after hydrothermal treatment in distilled water were investigated. The results showed that, Anatase-type titanium oxide (TiO2) nanoparticles formed on the TiN surface once the temperature was over 150 °C and that they formed a compact layer when the temperature increased to over 200 °C. The treatment significantly improved the wettability of TiN coating. Although surface hardness of TiN coating decreased slightly after formation of oxide layer, it could still provide enough protection for substrate. Electrochemical experiments showed that corrosion resistance of TiN was greatly improved in present of fluoride ions (F−). Immersion test for 5 weeks showed that numerous corrosion pits appeared on untreated TiN coating, whereas, the TiO2 layer on treated TiN coating remained intact and provided good protection for the underlying TiN. This work showed that hydrothermal treatment at 200–250 °C could comprehensively improve the properties of TiN coating necessary for biological applications and it also provide a possible solution to improve corrosion resistance of hard TiN coating on metal substrates.

Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a no-reference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics is proposed. A 36-dimensional image feature vector is constructed by extracting the local normalized luminance features and the gradient-weighted local binary pattern features of local normalized luminance map in three scales. First, a support vector machine classifier is obtained by learning the relationship between image features and distortion types. Then based on the support vector machine classifier, the support vector regression scorer is obtained by learning the relationship between image features and image quality scores. A series of comparative experiments were carried out in the optics remote sensing image database, the LIVE database, the LIVEMD database, and the TID2013 database, respectively. Experimental results show the high accuracy of distinguishing distortion types, the high consistency with subjective scores, and the high robustness of the method for remote sensing images. In addition, experiments also show the independence for the database and the relatively high operation efficiency of this method.

Considering the relatively poor robustness of quality scores for different types of distortion and the lack of mechanism for determining distortion types, a no-reference image quality assessment (NR-IQA) method based on the AdaBoost BP Neural Network in Wavelet domain (WABNN) is proposed. A 36-dimensional image feature vector is constructed by extracting natural scene statistics (NSS) features and local information entropy features of the distorted image wavelet sub-band coefficients in three scales. The ABNN classifier is obtained by learning the relationship between image features and distortion types. The ABNN scorer is obtained by learning the relationship between image features and image quality scores. A series of contrast experiments are carried out in the LIVE database and TID2013 database. Experimental results show the high accuracy of the distinguishing distortion type, the high consistency with subjective scores and the high robustness of the method for distorted images. Experiment results also show the independence for the database and the relatively high operation efficiency of this method.