A secretory kinase complex regulates extracellular protein phosphorylation.

Although numerous extracellular phosphoproteins have been identified, the protein kinases within the secretory pathway have only recently been discovered, and their regulation is virtually unexplored. Fam20C is the physiological Golgi casein kinase, which phosphorylates many secreted proteins and is critical for proper biomineralization. Fam20A, a Fam20C paralog, is essential for enamel formation, but the biochemical function of Fam20A is unknown. Here we show that Fam20A potentiates Fam20C kinase activity and promotes the phosphorylation of enamel matrix proteins in vitro and in cells. Mechanistically, Fam20A is a pseudokinase that forms a functional complex with Fam20C, and this complex enhances extracellular protein phosphorylation within the secretory pathway. Our findings shed light on the molecular mechanism by which Fam20C and Fam20A collaborate to control enamel formation, and provide the first insight into the regulation of secretory pathway phosphorylation

A Unified Framework Integrating Parent-of-Origin Effects for Association Study

Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting is related to several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we generalize the natural and orthogonal interactions (NOIA) framework to allow for estimation of both main allelic effects and POEs. We develop a statistical (Stat-POE) model that has the orthogonal estimates of parameters including the POEs. We conducted simulation studies for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat- POE and Func-POE models under HWE for quantitative traits

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

Curcumin exhibits therapeutic effect against spinal cord injury via inhibition of neuronal inflammation and apoptosis

Purpose: To investigate the effect of curcumin on spinal cord injury (SCI) in a rat model. Methods: SCI was induced in the rats using mid thoracic spinal cord compression, after which curcumin was injected intraperitoneally. Western blotting was used for assay of expressions of apoptotic proteins, viz, IL-1β, NF-κB p65, TLR4, TNF-α, LC3, Bax and Bcl-2. Malondialdehyde (MDA) and myeloperoxidase were measured using standard methods. Neuronal loss in spinal cord tissues was determined with TUNEL staining and NeuN labelling. Results: Curcumin treatment significantly (p < 0.05) suppressed SCI-mediated upregulation of myeloperoxidase activity and increase in MDA level in rat spinal cord. The reduction of glutathione (GSH) and superoxide dismutase (SOD) activities in the spinal cord of SCI rats were suppressed by curcumin treatment. Curcumin treatment also led to a significant (p < 0.02) increase in the proportion of NeuN positive cells and marked reduction in TUNEL positive cells, but it decreased caspase-3 in the spinal cord tissues of SCI rats. Moreover, curcumin reversed the effect of SCI on protein expressions of Bax and Bcl 2 in a dose-based manner. There was marked curcumin-induced decline in CD11b and GFAP levels in the spinal cord tissues of the SCI rats. Conclusion: These results demonstrate that curcumin protects rats against SCI via inhibition of oxidative stress-mediated neuronal apoptosis. Therefore, curcumin may be useful for the development of an effective treatment for spinal cord injury

Shaft orbit identification for rotating machinery based on statistical fuzzy vector chain code and support vector machine

Shaft orbit is a significant diagnosis criterion, and its identification plays an important role in the fault diagnosis of large rotating machinery. The main difficulty of shaft orbit identification is how to extract the shape features automatically and effectively. Therefore, in this paper, a novel method named statistical fuzzy vector chain code (SFVCC) is proposed for the feature extraction of shaft orbit, which has such advantages as invariance, simple calculation and high separability. Furthermore, taking the extracted feature vectors as input, support vector machine (SVM) is utilized to identify various kinds of shaft orbits for rotating machinery. Comparative experiments are implemented, the results reveal that, compared with previous methods, the proposed method can identify the shaft orbit more effectively and efficiently with satisfactory accuracy

Shaft orbit identification for rotating machinery based on statistical fuzzy vector chain code and support vector machine

Shaft orbit is a significant diagnosis criterion, and its identification plays an important role in the fault diagnosis of large rotating machinery. The main difficulty of shaft orbit identification is how to extract the shape features automatically and effectively. Therefore, in this paper, a novel method named statistical fuzzy vector chain code (SFVCC) is proposed for the feature extraction of shaft orbit, which has such advantages as invariance, simple calculation and high separability. Furthermore, taking the extracted feature vectors as input, support vector machine (SVM) is utilized to identify various kinds of shaft orbits for rotating machinery. Comparative experiments are implemented, the results reveal that, compared with previous methods, the proposed method can identify the shaft orbit more effectively and efficiently with satisfactory accuracy