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Objective To screen out the biomarkers with diagnostic value in lung cancer by biochip technology. Methods We screened four pairs of differentially-expressed circRNAs in lung cancer by circRNA expression profiling chip, and then verified the screened differential circRNA hsa_circ_0044569 by qRT-PCR, and collected clinical case information of patients at the same time. The independent sample t test and ROC curve were used to analyze the relation between the clinical data of lung cancer patients and the expression of circRNA hsa_circ_0044569 on clinical samples. Results The expression level of hsa_circ_0044569 in lung cancer tissues was higher than that of its paired adjacent tissues. The cutoff value of hsa_circ_0044569 for the diagnosis of lung cancer was 9.62, AUC was 0.758, P < 0.05, sensitivity was 0.712, and specificity was 0.712. The expression of hsa_circ_0044569 was significantly related with the pathological type of lung cancer patients (P < 0.05). Conclusion hsa_circ_0044569 is highly expressed in lung cancer and related to the pathological type, suggesting that it may become a potential biomarker in the early diagnosis of lung cancer.

The morphology, classitication and mechanism of burrs in mcro cutting were summarized. The research progresses of rncro burr simulation analysis were demonstrated. In the expetimental research progresses, the influences of processing parameters, geometry of the cutting tool, cutting method and processes on micro burrs were discussed. The research status of burr processing for some common composite mateiiaals was introduced. According to the forming material and processing technology of rrncro burrs, the deburting equipment and technology were summarized and the performance requirements of the deburing equipment were proposed. Finally, the deficiencies of the micro burr research were summaried, and the research directions in the future were pointed out. [ABSTRACT FROM AUTHOR]

Little reports focus on estimating soils with organic matter content smaller than 2% using hyperspectral data. However, it is important for desert soils where most soil organic matter is low. Due to low organic matter content (<2%) in deserts soil, it is difficult to identify spectral features of soil organic matter and to determine sensitive bands. Therefore, this study aimed to establish a method for organic matter content of desert soils using Hyperspectral data. Thirty-two soil samples were collected from the Eastern Junggar Basin, China. The soil organic matter of these samples was determined. Meanwhile, the reflectance of samples was measured. The correlation between soil spectrum and its organic matter content was analyzed. Twenty-four of 32 samples was used for establishing hyperspectral models of estimating deserts soil organic matter content (SOMC) and the other 8 samples was for model verification. The results showed all the soil samples had the organic matter content <2%, in agreement with the study requirement here. Through comparing with response bands of soil organic matter with its content of 2% above, we found that spectral sensitive region 640-790 nm for deserts soil organic matter <2% was most sensitive. The correlations between reflectance spectra, inverse-log spectra (log (1/A)) and SOMC were low. Their correlations were improved after conducting the first-order differential and second-order differential transformation on soil spectra data. The correlation of partial spectrum bands by F test was significant (p<0.01). Thus it could be used to estimate deserts SOMC. The accuracy of linear regression model was rather low (R2<0.60) and not suitable to estimate deserts SOMC. Compared to the linear regression model, the R2 of second-order differential model and inverse-log second-order differential model based on multiple stepwise regressions was increased by 0.22 and 0.21, respectively, and their RMSE were decreased by 0.66 and 0.88, respectively. As such, the estimation accuracy of multiple stepwise regression models was higher than that from the linear regression model. Compared with the multiple stepwise regression models, the R2 of first-order differential model and second-order differential model based on partial least square regression was increased by 0.07 and 0.04 but the RMSE was reduced by 0.11. Therefore, the second-order differential model based on partial least square regression was the optimal model among the 12 estimation models established in this paper. For both multiple stepwise regressions and partial least square regression, the second-order differential estimation model was best in all the estimation models established. Therefore, the estimation of deserts SOMC using multiple stepwise regressions was feasible. The results here provide valuable information for hyperspectral remote sensing analysis on deserts soil organic matter, realize timeliness and accuracy of monitoring deserts soil organic matter, and helps with regional ecological environment restoration. [ABSTRACT FROM AUTHOR]

Equol, a microbial metabolite of daidzein, has been hypothesized as clue to the effectiveness of soy and its isoflavones but only 33% - 50% of human population can excrete equol. Recently studies indicated that hydrogen gas playsan important role in equol formation. However, researches focus on this area are just started. In this paper, the corresponding research results of previous studies have been reviewed, including the underlying mechanism of equol formation and bioavailability, factors influencing equol production and the role of hydrogen gas on microbial equol formation.