The Autism-Related lncRNA MSNP1AS Regulates Moesin Protein to Influence the RhoA, Rac1, and PI3K/Akt Pathways and Regulate the Structure and Survival of Neurons.

Autism spectrum disorder (ASD) is a complex disease involving multiple genes and multiple sites, and it is closely related to environmental factors. It has been gradually revealed that long noncoding RNAs (lncRNAs) may regulate the pathogenesis of ASD at the epigenetic level. In neuronal cells, the lncRNA moesin pseudogene 1 antisense (MSNP1AS) forms a double-stranded RNA with moesin (MSN) to suppress moesin protein expression. MSNP1AS overexpression can activate the RhoA pathway and inhibit the Rac1 and PI3K/Akt pathways; however, the regulation of Rac1 by MSNP1AS is not associated with MSN, and the effect on the RhoA pathway may also be associated with other factors. MSNP1AS can decrease the number and length of neurites, inhibit neuronal cell viability and migration, and promote apoptosis. Downregulation of MSN expression functions similarly to MSNP1AS, and its overexpression can block the above functions of MSNP1AS. In addition, in vivo experiments show that MSN improves social interactions and reduces repetitive behaviors in BTBR mice, decreases the activity of RhoA and restores the activity of PI3K/Akt pathway. Therefore, the abnormal expression of MSNP1AS in ASD patients might influence the structure and survival of neuronal cells through the regulation of moesin protein expression to facilitate the development and progression of ASD. These findings provide new evidence for studying the mechanisms of lncRNAs in ASD. LAY SUMMARY: Autism spectrum disorder (ASD) is a common neurodevelopmental disease and its neurodevelopmental mechanisms have not been elucidated. More and more studies have found that long noncoding RNAs (lncRNAs) can regulate the development of central nervous system in many ways and affect the pathogenic process of ASD. Moesin pseudogene 1 antisense (MSNP1AS) is an up-regulated lncRNA in ASD patients. In-depth functional experiments showed that MSNP1AS inhibited moesin protein expression and regulated the activation of multiple signaling pathways, thus decreasing the number and length of neurites, inhibiting neuronal cell viability and migration, and promoting apoptosis. Therefore, MSNP1AS is an important lncRNA related to ASD and can regulate the biological function of neurons.

[Research on Spectroscopy Spectrum De-Noising of Mineral Oil Based on Lifting Scheme Wavelet Transform].

The use of the mineral oil is an important cause of air pollution such as fog. The effectiveness and rapidity of the de-noising processing in mineral oil fluorescence spectroscopy detection system is a hot issue of the online real-time monitoring system. The de-noising method of the lifting wavelet transform (LWT) in the application of mineral oil fluorescence spectrum is proposed. Compared with traditional discrete wavelet transform (DWT), this wavelet transform method decomposes the existing wavelet filter module into the basic construction modules and steps to complete the transform with simplicity and a fast speed. There are characteristics of low computational complexity, in situ operation and the easy implement in the denoising process of mineral oil fluorescence spectra. The LWT can effectively solve the problems in these respects. The three methods of LWT, DWT and EMD are applied to the fluorescence spectra of 0# diesel oil, 97# gasoline and kerosene. The indicators evaluating de-noising effect such as the Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE) and Normalied Correlation Coefficient (NCC) of the three kinds of mineral oil in the fluorescence spectra denoising prove the effectiveness of the lifting scheme wavelet transform in the application of mineral oil fluorescence spectrum. Meanwhile, the lifting scheme transform can improve the flexibility of structure and operation simplicity that makes the de-noising time reduced by 62%, validating the speediness of the de-noising method of the LWT in the application of mineral oil fluorescence spectrum and it is suitable for mineral oil fast de-noising processing system in real time.

[The Fluorescence Detection of Oil Pollutants Based on Self-Weighted Alternating Trilinear Decomposition].

The oil pollutants detector is designed in this paper. The pulse xenon lamp is used as light source; the step type multi-mode pure silica fiber is chosen to transmit the excitation and emission light. The asymmetric Czemy-Turner light path of high precision grating monochromator is adopted. The detector is applied to determine the fluorescence spectrum of diesel, gasoline and kerosene. The optimal excitation /emission wavelengths are: 290/330 nm (diesel)，270/300 nm (gasoline) and 280/330 nm (kerosene). The detection limits are: diesel (0.025 mg·L-1), gasoline (0.042 mg·L-1) and kerosene(0.054 mg·L-1). The relative errors are: diesel(2.55%), gasoline(2.06%) and kerosene(1.71%). Experiment results show that the designed detector has high accuracy of measurement. The different concentration of diesel, gasoline and kerosene mixed solution is configured, and three dimensional fluorescence spectra being measured. The self-weighted alternating trilinear decomposition is adopted to decompose the spectrum data. The predicted concentration and recovery rate show that self-weighted alternating trilinear decomposition has high resolution for mixed oil substance.

[Research on the Medicinal Effective Component and Characterization of Virus-Free Breeding and Sulfur Smoked Qi Chrysanthemum Based on FTIR and FT-Raman].

Based on Fourier transform infrared (FTIR) and Raman spectroscopy (FT - Raman), the effective medicinal composition and its content change of Qi chrysanthemum are directly and quickly determined among the virus-free breeding and sulfur smoked samples of three different groups. FTIR and FT-Raman spectra of three groups of Qi chrysanthemum sample are compared and analyzed. The results show that the intensities of multiple infrared absorption peaks are obvious different within the range of 1 800~500 cm-1 and the characteristic peak shapes are slightly different in the FTIR spectra of the three groups with obvious differences of characteristic peak shapes in FT - Raman spectrum have. FTIR and FT - Raman spectrum directly reflect that the stem tip virus-free breeding will make the volatile oil, flavonoids and other medicinal component content increase in Qi Chrysanthemum, but the sulfur smoked reduce. The FTIR, FT Raman spectroscopy for detection of effective medicinal composition changes in Qi Chrysanthemum caused by virus-free breeding or sulfur smoked establishes a scientific basis, also an effective method to test their component content.

[Study on Refined Oil Identification and Measurement Based on the Extension Neural Network Pattern Recognition].

There are four major problems related to fuel consumption, "large consumption", "low quality", "lack of front-end clean" and "lack of end emission control", which needs to address urgently for our country. More than 60 percent of the air pollution is due to the burning of coal and oil in our country, so the haze problem depends on how much we can deal with energy issues. We should achieve the identification and measurement of gasoline, diesel, kerosene and other refined oil products rapidly and accurately, which is important for the implementation of air pollution monitoring and controlling. in order to characterize the type information of the refined oil accurately and to improve the efficiency of the network model identification, it is effective to use principal component analysis method which could achieve the data dimension reductionwhile reducing the complexity of the problem. With principal component analysis of the most commonly used three-dimensional fluorescence spectra based on excitation-emission matrix (Excitation-Emission Matrix, EEM) data, we could obtain finer, deeper characteristic parameters. During the process of classification, it could avoid the "over-fitting" phenomenon because of the application of the cross-validation method, A neural network capable of both qualitative and quantitative analysis is designed. The neural network pattern recognition result becomes feedback to the input of the concentration network, together with the relative slope, the comprehensive background parameters, and the relative fluorescence intensity, we could achieve the measurement of the concentration of the corresponding types, then use the extension neural network pattern recognition technology to achieve identification and measurement of kerosene, diesel, gasoline and other refined oil products. The results of the study show that the average recognition rate reaches 0.99, the average recovery rate of concentration reaches 0.95, the average time of pattern recognition is 2.5 seconds and this time is 48.5% of the time used by PARAFAC model analysis method. The method significantly improves the operation speed with ideal application effect . It should be pointed out that, in order to ensure the accuracy and precision of the analysis, we should make corresponding calibration samples for specific analytes in terms of the analysis of complex mixtures such as refined oil, pesticides, tea, etc.