Simultaneous Rapid Detection of Multiple Physicochemical Properties of Jet Fuel Using Near-Infrared Spectroscopy.

Jet fuel is the primary fuel used in the aviation industry, and its quality has a direct impact on the safety and operational efficiency of aircraft. The accurate quantitative detection and analysis of various physicochemical property indicators are important for improving and ensuring the quality of jet fuel in the domestic market. This study used near-infrared (NIR) spectroscopy to establish a suitable model for the simultaneous and rapid detection of multiple physicochemical properties in jet fuel. Using more than 40 different sources of jet fuel, a rapid detection model was established by optimizing the spectral processing methods. The measurement models were separately built using the partial least-squares (PLS) and orthogonal PLS algorithms, and the model parameters were optimized. The results show that after the Savitzky-Golay second derivative preprocessing, the PLS model built using the feature spectra selected by the uninformative variable elimination wavelength algorithm achieved the best measurement performance. Compared with the PLS model without preprocessing, the range of the resulting accuracy improvement was at least 15.01%. Under the optimal model parameters, the calibration set regression coefficient (Rc2) of the 11 jet fuel property index models ranged from 0.9102 to 0.9763, with the root-mean-square error of calibration values up to 0.8468 °C (for flash points). The regression coefficient (Rp2) of the validation set ranged from 0.8239 to 0.9557, with the root-mean-square error of prediction values up to 1.1354 °C (for flash points). The ratios of prediction to deviation (RPD) values were all in the range of 1.9-3.0, indicating high accuracy and reliability of the model. The rapid NIR analysis method established in this study enables the simultaneous and rapid detection of multiple physicochemical properties of jet fuel, thereby providing effective technical support for ensuring the quality of jet fuel in the market.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information.

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.

Gut Microbiota Alternation in Disease Progression of Neurosyphilis.

Background: The gut microbiota plays an important role in the development of neurological disorders such as Parkinson's disease and Alzheimer's disease. However, studies on the gut microbiota of patients with neurosyphilis (NS) were rarely reported. Methods: In this study, we collected fecal samples from 62 syphilis patients, including 39 with NS and 23 with non-NS. Among the NS patients, 18 were general paresis (GP). The white blood cell counts, protein concentrations, and Venereal Disease Research Laboratory test positive rates of cerebrospinal fluid from patients in NS or GP group were significantly higher than those from patients in non-NS group. 16S ribosomal RNA sequencing results revealed that the alpha and beta diversities of the gut microbiota were similar between NS and non-NS patients or GP and non-NS patients. Results: Linear discriminant analysis with effect size (LEfSe) analysis showed that some taxa, such as Coprobacter, were increased in both NS group and GP group, compared with non-NS group. Besides, the clade of Akkermansia was also overrepresented in GP Patients. Meanwhile, some taxa such as Clostridia_UCG-014 and SC-I-84 were underrepresented in NS patients. The abundances of class Bacilli and genus Alloprevotella were decreased in GP patients. Among them, the abundances of some taxa such as Coprobacter and Akkermansia have been reported to be associated with other neuropsychiatric disorders. Conclusion: Our findings suggest that the alternation of the gut microbiota in NS patients may contribute to the course of NS, which will deepen our understanding of NS.

Platelet Indices Are the Promising Biomarkers in Monitoring Disease Activities in Patients with Syphilis.

OBJECTIVES: To uncover the role of the platelet indices in patients with syphilis. METHODS: A total of 2061 patients with syphilis and 528 healthy controls were enrolled in this retrospective cohort study. The data of platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and indicators of syphilis activities were collected. The correlations between the platelet indices and disease activities were analyzed. RESULTS: A total of 425 (20.6%) of the 2061 patients were of primary and secondary syphilis, 433 (21.0%) latent, 463 (22.5%) serofast, 350 (17.0%) asymptomatic neurosyphilis, and 390 (18.9%) symptomatic neurosyphilis. Compared with the healthy controls, PLT was significantly increased in the primary and secondary syphilis group; whereas, MPV and PDW were significantly decreased in all stages of syphilis. These changes of platelet indices were reversed after anti-treponemal therapy. Further correlation analysis showed that PLT was positively associated with the syphilis activity indicators [rapid plasma reagin (RPR) titer, cerebrospinal fluid white blood cell (CSF-WBC), CSF-protein, and CSF-VDRL (venereal disease research laboratory)] and inflammatory markers [WBC, C-reaction protein (CRP), and erythrocyte sedimentation rate (ESR)]. Conversely, PDW was negatively correlated with all of these parameters. MPV had an inverse relationship with RPR, ESR, and CRP. CONCLUSIONS: Platelet indices are associated with syphilis activities.